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Hyper-heuristic Bibliography



626 publications

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2017 (34 publications)

  • A Hybrid Approach of Genetic Algorithm and Multi Objective PSO Task Scheduling in Cloud Computing, by Kumari, K Raja and Sengottuvelan, P and Shanthini, J, Asian Journal of Research in Social Sciences and Humanities, 7(3), Asian Research Consortium, 2017 [PDF] [ABSTRACT]

    The genetic algorithm is an evolutionary optimization algorithm based upon Initial population, crossover, mutation and Evaluation. On the other side, Multi Objective particle swarm optimization (MOPSO) is a swarm intelligence algorithm functioning by means of inertia weight, learning factors and the mutation probability. In high-performance hyper-heuristic algorithm is used to find better scheduling solutions in cloud computing. To improve the scheduling results in terms of makespan, throughput, cost. Hyper-heuristic algorithm finds better scheduling solutions for cloud computing systems and to further improve the scheduling results in terms of make span. A novel Multi objective particle swarm optimization and Genetic Algorithm based hyper-heuristic resource scheduling algorithm has been designed as the hybrid algorithm. Performance of the proposed algorithm has also been evaluated through the Cloud Sim toolkit. We have compared our hybrid scheduling algorithm with existing common heuristic-based scheduling algorithms. The results thus obtained have shown a better performance by our algorithm than the existing algorithms, in terms of giving reduce cost and improve makespan. The proposed model shows the improved resource utilization, makespan, throughput.

  • A Multi-Objective and Evolutionary Hyper-Heuristic Applied to the Integration and Test Order Problem, by Guizzo, Giovani and Vergilio, Silvia R and Pozo, Aurora TR and Fritsche, Gian M, Applied Soft Computing, 56, Elsevier, 2017 [PDF] [ABSTRACT]

    The field of Search-Based Software Engineering (SBSE) has widely utilized Multi-Objective Evolutionary Algorithms (MOEAs) to solve complex software engineering problems. However, the use of such algorithms can be a hard task for the software engineer, mainly due to the significant range of parameter and algorithm choices. To help in this task, the use of Hyper-heuristics is recommended. Hyper-heuristics can select or generate low-level heuristics while optimization algorithms are executed, and thus can be generically applied. Despite their benefits, we find only a few works using hyper-heuristics in the SBSE field. Considering this fact, we describe HITO, a Hyper-heuristic for the Integration and Test Order Problem, to adaptively select search operators while MOEAs are executed using one of the selection methods: Choice Function and Multi-Armed Bandit. The experimental results show that HITO can outperform the traditional MOEAs NSGA-II and MOEA/DD. HITO is also a generic algorithm, since the user does not need to select crossover and mutation operators, nor adjust their parameters.

  • A methodology for determining an effective subset of heuristics in selection hyper-heuristics, by Soria-Alcaraz, Jorge A and Ochoa, Gabriela and Sotelo-Figeroa, Marco A and Burke, Edmund K, European Journal of Operational Research, 260(3), Elsevier, 2017 [PDF] [ABSTRACT]

    We address the important step of determining an effective subset of heuristics in selection hyper-heuristics. Little attention has been devoted to this in the literature, and the decision is left at the discretion of the investigator. The performance of a hyper-heuristic depends on the quality and size of the heuristic pool. Using more than one heuristic is generally advantageous, however, an unnecessary large pool can decrease the performance of adaptive approaches. Our goal is to bring methodological rigour to this step. The proposed methodology uses non-parametric statistics and fitness landscape measurements from an available set of heuristics and benchmark instances, in order to produce a compact subset of effective heuristics for the underlying problem. We also propose a new iterated local search hyper-heuristic using multi-armed bandits coupled with a change detection mechanism. The methodology is tested on two real-world optimization problems: course timetabling and vehicle routing. The proposed hyper-heuristic with a compact heuristic pool, outperforms state-of-the-art hyper-heuristics and competes with problem-specific methods in course timetabling, even producing new best-known solutions in 5 out of the 24 studied instances.

  • An Improved Hyper-Heuristic Clustering Algorithm for Wireless Sensor Networks, by Tsai, Chun-Wei and Chang, Wei-Lun and Hu, Kai-Cheng and Chiang, Ming-Chao, Mobile Networks and Applications, Springer, 2017 [PDF] [ABSTRACT]

    Clustering is one of the most famous open problems of wireless sensor network (WSN) that has been studied for years because all the sensors in a WSN have only a limited amount of energy. As such, the so-called low-energy adaptive clustering hierarchy (LEACH) was presented to prolong the lifetime of a WSN. Although the original idea of LEACH is to keep each sensor in a WSN from being chosen as a cluster head (CH) too frequently so that the loading of the sensors will be balanced, thus avoiding particular sensors from running out of their energy quickly and particular regions from failing to work, it is far from perfect because LEACH may select an unsuitable set of sensors as the cluster heads. In this paper, a high-performance hyper-heuristic algorithm will be presented to enhance the clustering results of WSN called hyper-heuristic clustering algorithm (HHCA). The proposed algorithm is designed to reduce the energy consumption of a WSN, by using a high-performance metaheuristic algorithm to find a better solution to balance the residual energy of all the sensors so that the number of alive sensor nodes will be maximized. To evaluate the performance of the proposed algorithm, it is compared with LEACH, LEACH with genetic algorithm, and hyper-heuristic algorithm alone in this study. Experimental results show that HHCA is able to provide a better result than all the other clustering algorithms compared in this paper, in terms of the energy consumed.

  • An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation, by Zamli, Kamal Z and Din, Fakhrud and Kendall, Graham and Ahmed, Bestoun S, Information Sciences, 399, Elsevier, 2017 [PDF] [ABSTRACT]

    Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t-way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, meta-heuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyper-heuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and/or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS), using the t-way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance.

  • Drone Squadron Optimization: a novel self-adaptive algorithm for global numerical optimization, by de Melo, Vinicius Veloso and Banzhaf, Wolfgang, Neural Computing and Applications, Springer, 2017 [PDF] [ABSTRACT]

    This paper proposes Drone Squadron Optimization (DSO), a new self-adaptive metaheuristic for global numerical optimization which is updated online by a hyper-heuristic. DSO is an artifact-inspired technique, as opposed to many nature-inspired algorithms used today. DSO is very flexible because it is not related to natural behaviors or phenomena. DSO has two core parts: the semiautonomous drones that fly over a landscape to explore, and the command center that processes the retrieved data and updates the drones' firmware whenever necessary. The self-adaptive aspect of DSO in this work is the perturbation/movement scheme, which is the procedure used to generate target coordinates. This procedure is evolved by the command center during the global optimization process in order to adapt DSO to the search landscape. We evaluated DSO on a set of widely employed single-objective benchmark functions. The statistical analysis of the results shows that the proposed method is competitive with the other methods, but we plan several future improvements to make it more powerful and robust.

  • Dynamic optimisation of preventative and corrective maintenance schedules for a large scale urban drainage system, by Chen, Yujie and Cowling, Peter and Polack, Fiona and Remde, Stephen and Mourdjis, Philip, European Journal of Operational Research, 257(2), Elsevier, 2017 [PDF] [ABSTRACT]

    Gully pots or storm drains are located at the side of roads to provide drainage for surface water. We consider gully pot maintenance as a risk-driven maintenance problem. We explore policies for preventative and corrective maintenance actions, and build optimised routes for maintenance vehicles. Our solutions take the risk impact of gully pot failure and its failure behaviour into account, in the presence of factors such as location, season and current status. The aim is to determine a maintenance policy that can automatically adjust its scheduling strategy in line with changes in the local environment, to minimise the surface flooding risk due to clogged gully pots. We introduce a rolling planning strategy, solved by a hyper-heuristic method. Results show the behaviour and strength of the automated adjustment in a range of real-world scenarios.

  • Enhancing agents with genetic programming: an evaluation of hyper-heuristics in dynamic real-time logistics, by van Lon, Rinde RS and Branke, Jurgen and Holvoet, Tom, Genetic Programming and Evolvable Machines, Springer, 2017 [PDF] [ABSTRACT]

    Dynamic pickup and delivery problems (PDPs) require online algorithms for managing a fleet of vehicles. Generally, vehicles can be managed either centrally or decentrally. A common way to coordinate agents decentrally is to use the contract-net protocol (CNET) that uses auctions to allocate tasks among agents. To participate in an auction, agents require a method that estimates the value of a task. Typically, this method involves an optimization algorithm, e.g. to calculate the cost to insert a customer. Recently, hyper-heuristics have been proposed for automated design of heuristics. Two properties of automatically designed heuristics are particularly promising: (1) a generated heuristic computes quickly, it is expected therefore that hyper-heuristics perform especially well for urgent problems, and (2) by using simulation-based evaluation, hyper-heuristics can create a 'rule of thumb' that anticipates situations in the future. In the present paper we empirically evaluate whether hyper-heuristics, more specifically genetic programming (GP), can be used to improve agents decentrally coordinated via CNET. We compare several GP settings and compare the resulting heuristic with existing centralized and decentralized algorithms based on the OptaPlanner optimization library. The tests are conducted in real-time on a dynamic PDP dataset with varying levels of dynamism, urgency, and scale. The results indicate that the evolved heuristic always outperforms the optimization algorithm in the decentralized multi-agent system (MAS) and often outperforms the centralized optimization algorithm. Our paper demonstrates that designing MASs using genetic programming is an effective way to obtain competitive performance compared to traditional operational research approaches. These results strengthen the relevance of decentralized agent based approaches in dynamic logistics.

  • Evolutionary hyper-heuristics for tackling bi-objective 2D bin packing problems, by Gomez, Juan Carlos and Terashima-Mar\in, Hugo, Genetic Programming and Evolvable Machines, Springer, 2017 [PDF] [ABSTRACT]

    In this article, a multi-objective evolutionary framework to build selection hyper-heuristics for solving instances of the 2D bin packing problem is presented. The approach consists of a multi-objective evolutionary learning process, using specific tailored genetic operators, to produce sets of variable length rules representing hyper-heuristics. Each hyper-heuristic builds a solution to a given problem instance by sensing the state of the instance, and deciding which single heuristic to apply at each decision point. The hyper-heuristics consider the minimization of two conflicting objectives when building a solution: the number of bins used to accommodate the pieces and the total time required to do the job. The proposed framework integrates three well-studied multi-objective evolutionary algorithms to produce sets of Pareto-approximated hyper-heuristics: the Non-dominated Sorting Genetic Algorithm-II, the Strength Pareto Evolutionary Algorithm 2, and the Generalized Differential Evolution Algorithm 3. We conduct an extensive experimental analysis using a large set of 2D bin packing problem instances containing convex and non-convex irregular pieces, under many conditions, settings and using several performance metrics. The analysis assesses the robustness and flexibility of the proposed approach, providing encouraging results when compared against a set of well-known baseline single heuristics.

  • Evolvability metric estimation by a parallel perceptron for on-line selection hyper-heuristics, by Soria-Alcaraz, Jorge A and Espinal, Andres and Sotelo-Figueroa, Marco A, IEEE Access, IEEE, 2017 [PDF] [ABSTRACT]

    On-line Hyper-heuristic Selection is a novel and powerful approach to solving complex problems. This approach dynamically selects, based on the state of a given solution, the most promising operator (from a pool of operators) to continue the search process. The dynamic selection is usually based on the analysis of the latest applications of a given operator during actual execution, estimating the potential success of the operator at the current solution state. The estimation can be made by Evolvability Metrics. Calculating an Evolvability metric is computationally expensive since it requires the generation and evaluation of a neighborhood of solutions. This paper aims to estimate the potential success of an operator for a given solution state by using a pre-trained neural network; known as a parallel perceptron. The proposal accelerates the on-line selection process, allowing us to achieve better performance than hyper-heuristic models which directly use evolvability functions.

  • Expert System and Heuristics Algorithm for Cloud Resource Scheduling, by Mamatha, E and Sasritha, S and Reddy, CS and others, Romanian Statistical Review, 65(1), Romanian Statistical Review, 2017 [PDF] [ABSTRACT]

    Rule-based scheduling algorithms have been widely used on cloud computing systems and there is still plenty of room to improve their performance. This paper proposes to develop an expert system to allocate resources in cloud by using Rule based Algorithm, thereby measuring the performance of the system by letting the system adapt new rules based on the feedback. Here performance of the action helps to make better allocation of the resources to improve quality of services, scalability and flexibility. The performance measure is based on how the allocation of the resources is dynamically optimized and how the resources are utilized properly. It aims to maximize the utilization of the resources. The data and resource are given to the algorithm which allocates the data to resources and an output is obtained based on the action occurred. Once the action is completed, the performance of every action is measured that contains how the resources are allocated and how efficiently it worked. In addition to performance, resource allocation in cloud environment is also considered.

  • Genetic programming for production scheduling: a survey with a unified framework, by Nguyen, Su and Mei, Yi and Zhang, Mengjie, Complex & Intelligent Systems, Springer, 2017 [PDF] [ABSTRACT]

    Genetic programming has been a powerful technique for automated design of production scheduling heuristics. Many studies have shown that heuristics evolved by genetic programming can outperform many existing heuristics manually designed in the literature. The flexibility of genetic programming also allows it to discover very sophisticated heuristics to deal with complex and dynamic production environments. However, as compared to other applications of genetic programming or scheduling applications of other evolutionary computation techniques, the configurations and requirements of genetic programming for production scheduling are more complicated. In this paper, a unified framework for automated design of production scheduling heuristics with genetic programming is developed. The goal of the framework is to provide the researchers with the overall picture of how genetic programming can be applied for this task and the key components. The framework is also used to facilitate our discussions and analyses of existing studies in the field. Finally, this paper shows how knowledge from machine learning and operations research can be employed and how the current challenges can be addressed.

  • Markov Chain methods for the bipartite Boolean quadratic programming problem, by Karapetyan, Daniel and Punnen, Abraham P and Parkes, Andrew J, European Journal of Operational Research, Elsevier, 2017 [PDF] [ABSTRACT]

    We study the Bipartite Boolean Quadratic Programming Problem (BBQP) which is an extension of the well known Boolean Quadratic Programming Problem (BQP). Applications of the BBQP include mining discrete patterns from binary data, approximating matrices by rank-one binary matrices, computing the cut-norm of a matrix, and solving optimisation problems such as maximum weight biclique, bipartite maximum weight cut, maximum weight induced sub-graph of a bipartite graph, etc. For the BBQP, we first present several algorithmic components, specifically, hill climbers and mutations, and then show how to combine them in a high-performance metaheuristic. Instead of hand-tuning a standard metaheuristic to test the efficiency of the hybrid of the components, we chose to use an automated generation of a multi-component metaheuristic to save human time, and also improve objectivity in the analysis and comparisons of components. For this we designed a new metaheuristic schema which we call Conditional Markov Chain Search (CMCS). We show that CMCS is flexible enough to model several standard metaheuristics; this flexibility is controlled by multiple numeric parameters, and so is convenient for automated generation. We study the configurations revealed by our approach and show that the best of them outperforms the previous state-of-the-art BBQP algorithm by several orders of magnitude. In our experiments we use benchmark instances introduced in the preliminary version of this paper and described here, which have already become the de facto standard in the BBQP literature.

  • Multi-objective Evolutionary Algorithms and Hyper-heuristics for Wind Farm Layout Optimisation, by W. Li and E. Ozcan and R. John, Renewable Energy, 105, Elsevier, 2017 [PDF] [ABSTRACT]

    Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maximisation of energy production. However, due to the complexity of handling multiple objectives simultaneously, many approaches proposed in the literature often focus on the optimisation of a single objective when deciding the locations for a set of wind turbines spread across a given region. In this study, we tackle a multi-objective wind farm layout optimisation problem. Different from the previously proposed approaches, we are applying a high-level search method, known as selection hyper-heuristic to solve this problem. Selection hyper-heuristics mix and control a predefined set of low-level (meta)heuristics which operate on solutions. We test nine different selection hyper-heuristics including an online learning hyper-heuristic on a multi-objective wind farm layout optimisation problem. Our hyper-heuristic approaches manage three well-known multi-objective evolutionary algorithms as low-level metaheuristics. The empirical results indicate the success and potential of selection hyper-heuristics for solving this computationally difficult problem. We additionally explore other objectives in wind farm layout optimisation problems to gain a better understanding of the conflicting nature of those objectives.

  • Optimizing agents with genetic programming: an evaluation of hyper-heuristics in dynamic real-time logistics, by van Lon, Rinde RS and Branke, Juergen and Holvoet, Tom, Genetic Programming and Evolvable Machines, Springer, 2017 [PDF] [ABSTRACT]

    Dynamic pickup and delivery problems (PDPs) require online algorithms for managing a fleet of vehicles. Generally, vehicles can be managed either centrally or decentrally. A common way to coordinate agents decentrally is to use the contract-net protocol (CNET) that uses auctions to allocate tasks among agents. To participate in an auction, agents require a method that estimates the value of a task. Typically, this method involves an optimization algorithm, e.g. to calculate the cost to insert a customer. Recently, hyper-heuristics have been proposed for automated design of heuristics. Two properties of automatically designed heuristics are particularly promising: (1) a generated heuristic computes quickly, it is expected therefore that hyper-heuristics perform especially well for urgent problems, and (2) by using simulation-based evaluation, hyper-heuristics can create a 'rule of thumb' that anticipates situations in the future. In the present paper we empirically evaluate whether hyper-heuristics, more specifically genetic programming (GP), can be used to improve agents decentrally coordinated via CNET. We compare several GP settings and compare the resulting heuristic with existing centralized and decentralized algorithms based on the OptaPlanner optimization library. The tests are conducted in real-time on a dynamic PDP dataset with varying levels of dynamism, urgency, and scale. The results indicate that the evolved heuristic always outperforms the optimization algorithm in the decentralized multi-agent system (MAS) and often outperforms the centralized optimization algorithm. Our paper demonstrates that designing MASs using genetic programming is an effective way to obtain competitive performance compared to traditional operational research approaches. These results strengthen the relevance of decentralized agent based approaches in dynamic logistics.

  • Supplementary Material for the Information Sciences Paper: An Experimental Study of Hyper-Heuristic Selection and Acceptance Mechanism for Combinatorial t-way Test Suite Generation, by Zamli, Kamal Z and Din, Fakhrud and Kendall, Graham and Ahmed, Bestoun S, arXiv preprint arXiv:1702.04501, 2017 [PDF] [ABSTRACT]

    Software testing relates to the process of accessing the functionality of a program against some defined specifications. To ensure conformance, test engineers often generate a set of test cases to validate against the user requirements. Owing to the growing complexity of software and its increasing diffusion into various application domains, it is no longer unusual for a software project to have testing teams in more than one location or even distributed over many continents. Owing to the intertwined dependencies of many software development activities and their geographical and temporal issues, there are potentially many overlapping test cases which can cause unwarranted redundancies across the shared modules (i.e. a test for one requirement may be covered by more than one test). In this paper, we explore the application of our newly developed hyperheuristic, called Fuzzy Inference Selection (FIS), for addressing test redundancy reduction problem. This paper presents the supplementary results for the paper : An Experimental Study of Hyper-Heuristic Selection and Acceptance Mechanism for Combinatorial t way Test Suite Generation published in Information Sciences.

  • A Hyper-Heuristic of Scalarizing Functions, by Raquel Hernandez Gomez and Carlos Coello Coello, the 18th Annual Conference on Genetic and Evolutionary Computation (GECCO), Berlin, Germany, 2017
  • A PSO-based Reference Point Adaption Method for Genetic Programming Hyper-heuristic in Many-Objective Job Shop Scheduling, by Masood, Atiya and Mei, Yi and Chen, Gang and Zhang, Mengjie, Australasian Conference on Artificial Life and Computational Intelligence (ACALCI), Melbourne, Australia, 2017 [PDF] [ABSTRACT]

    Job Shop Scheduling is an important combinatorial optimisation problem in practice. It usually contains many (four or more) potentially conflicting objectives such as makespan and mean weighted tardiness. On the other hand, evolving dispatching rules using genetic programming has demonstrated to be a promising approach to solving job shop scheduling due to its flexibility and scalability. In this paper, we aim to solve many-objective job shop scheduling with genetic programming and NSGA-III. However, NSGA-III is originally designed to work with uniformly distributed reference points which do not match well with the discrete and non-uniform Pareto front in job shop scheduling problems, resulting in many useless points during evolution. These useless points can significantly affect the performance of NSGA-III and genetic programming. To address this issue and inspired by particle swarm optimisation, a new reference point adaptation mechanism has been proposed in this paper. Experiment results on many-objective benchmark job shop scheduling instances clearly show that prominent improvement in performance can be achieved upon using our reference point adaptation mechanism in NSGA-III and genetic programming.

  • A re-characterization of hyper-heuristics, by Swan, Jerry and De Causmaecker, Patrick and Martin, Simon and Ozcan, Ender, Recent Developments of Metaheuristics, Springer, 2017 [PDF] [ABSTRACT]

    Hyper-heuristics are an optimization methodology which 'search the space of heuristics' rather than directly searching the space of the underlying candidate-solution representation. Hyper-heuristic search has traditionally been divided into two layers: a lower problem-domain layer (where domain-specific heuristics are applied) and an upper hyper-heuristic layer, where heuristics are selected or generated. The interface between the two layers is commonly termed the "domain barrier". Historically this interface has been defined to be highly restrictive, in the belief that this is required for generality. We argue that this prevailing conception of domain barrier is so limiting as to defeat the original motivation for hyper-heuristics. We show how it is possible to make use of domain knowledge without loss of generality and describe generalized hyper-heuristics which can incorporate arbitrary domain knowledge.

  • Applying Automatic Heuristic-Filtering to Improve Hyper-heuristic Performance, by Andres Eduardo Gutierrez Rodriguez and Jose Carlos Ortiz Bayliss and Alejandro Rosales Perez and Ivan Mauricio Amaya Contreras and Santiago Enrique Conant Pablos and Hugo Terashima Marin and Carlos Artemio Coello Coello, IEEE Congress on Evolutionary Computation (CEC), San Sebastian, Spain, 2017 [ABSTRACT]

    Hyper-heuristics have emerged as an important strategy for combining the strengths of different heuristics into a single method. Although hyper- heuristics have been found to be successful in many scenarios, little attention has been paid to the subsets of heuristics that these methods manage and apply. In several cases, heuristics can interfere with each other and can be harmful for the search. Thus, obtaining information about the differences among heuristics, and how they contribute to the search process is very important. The main contribution of this paper is an automatic heuristic-filtering process that allows hyper-heuristics to exclude heuristics that do not contribute to improving the solution. Based on some previous works in feature selection, two methods are proposed that rank heuristics and sequentially select only suitable heuristics in a hyper- heuristic framework. Our experiments over a set of Constraint Satisfaction Problem instances show that a hyper-heuristic with only selected heuristics obtains significantly better results than a hyper-heuristic containing all heuristics, in terms of running times. In addition, the success rate of solving such instances is better for the hyper-heuristic with the suitable heuristics than for the hyper-heuristic without our proposed filtering process.

  • Automated Heuristic Design Using Genetic Programming Hyper-Heuristic for Uncertain Capacitated Arc Routing Problem, by Yuxin Liu and Yi Mei and Mengjie Zhang and Zili Zhang, the 18th Annual Conference on Genetic and Evolutionary Computation (GECCO), Berlin, Germany, 2017 [PDF] [ABSTRACT]

    Uncertain Capacitated Arc Routing Problem (UCARP) is a variant of the well-known CARP. It considers a variety of stochastic factors to reeect the reality where the exact information such as the actual task demand and accessibilities of edges are unknown in advance. Existing works focus on obtaining a robust solution beforehand. However, it is also important to design eeective heuris-tics to adjust the solution in real time. In this paper, we develop a new Genetic Programming-based Hyper-Heuristic (GPHH) for automated heuristic design for UCARP. A novel eeective meta-algorithm is designed carefully to address the failures caused by the environment change. In addition, it employs domain knowledge to lter some infeasible candidate tasks for the heuristic function. e experimental results show that the proposed GPHH signiicantly outperforms the existing GPHH methods and manually designed heuristics. Moreover, we nd that eliminating the infeasible and distant tasks in advance can reduce much noise and improve the eecacy of the evolved heuristics. In addition, it is found that simply adding a slack factor to the expected task demand may not improve the performance of the GPHH.

  • Automatic Generation of Neural Networks with Structured Grammatical Evolution, by Assunccao, Filipe and Lourencco, Nuno and Machado, Penousal and Ribeiro, Bernardete, IEEE Congress on Evolutionary Computation (CEC), San Sebastian, Spain, 2017 [PDF] [ABSTRACT]

    The effectiveness of Artificial Neural Networks (ANNs) depends on a non-trivial manual crafting of their topology and parameters. Typically, practitioners resort to a time consuming methodology of trial-and-error to find and/or adjust the models to solve specific tasks. To minimise this burden one might resort to algorithms for the automatic selection of the most appropriate properties of a given ANN. A remarkable example of such methodologies is Grammar-based Genetic Programming. This work analyses and compares the use of two grammar-based methods, Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE), to automatically design and configure ANNs. The evolved networks are used to tackle several classification datasets. Experimental results show that SGE is able to automatically build better models than GE, and that are competitive with the state of the art, outperforming hand-designed ANNs in all the used benchmarks.

  • Dynamic Job Shop Scheduling Under Uncertainty Using Genetic Programming, by Karunakaran, Deepak and Mei, Yi and Chen, Gang and Zhang, Mengjie, the 20th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES), Canberra, Australia, Springer, 2017 [PDF] [ABSTRACT]

    Job shop scheduling (JSS) is a hard problem with most of the research focused on scenarios with the assumption that the shop parameters such as processing times, due dates are constant. But in the real world uncertainty in such parameters is a major issue. In this work, we investigate a genetic programming based hyper-heuristic approach to evolving dispatching rules suitable for dynamic job shop scheduling under uncertainty. We consider uncertainty in processing times and consider multiple job types pertaining to different levels of uncertainty. In particular, we propose an approach to use exponential moving average of the deviations of the processing times in the dispatching rules. We test the performance of the proposed approach under different uncertain scenarios. Our results show that the proposed method performs significantly better for a wide range of uncertain scenarios.

  • EvoHyp - A Java Toolkit for Evolutionary Algorithm Hyper-Heuristics, by Pillay, N. and Beckedahl, D., IEEE Congress on Evolutionary Computation (CEC), San Sebastian, Spain, 2017 [PDF] [ABSTRACT]

    Hyper-heuristics is an emergent technology that has proven to be effective at solving real-world problems. The two main categories of hyper-heuristics are selection and generation. Selection hyper-heuristics select existing low-level heuristics while generation hyper-heuristics create new heuristics. At the inception of the field single point searches were essentially employed by selection hyper-heuristics, however as the field progressed evolutionary algorithms are becoming more prominent. Evolutionary algorithms, namely, genetic programming, have chiefly been used for generation hyper-heuristics. Implementing evolutionary algorithm hyper-heuristics can be quite a time-consuming task which is daunting for first time researchers and practitioners who want to rather focus on the application domain the hyper-heuristic will be applied to which can be quite complex. This paper presents a Java toolkit for the implementation of evolutionary algorithm hyper-heuristics, namely, EvoHyp. EvoHyp includes libraries for a genetic algorithm selection hyper-heuristic (GenAlg), a genetic programming generation hyper-heuristic (GenProg), a distributed version of GenAlg (DistrGenAlg) and a distributed version of GenProg (DistrGenProg). The paper describes the libraries and illustrates how they can be used. The ultimate aim is to provide a toolkit which a non-expert in evolutionary algorithm hyper-heuristics can use. The paper concludes with an overview of future extensions of the toolkit.

  • Evolutionary Multilabel Hyper-Heuristic Design, by Alejandro Rosales-Perez and Andres E. Gutierrez-Rodriguez and Jose C. Ortiz-Bayliss and Hugo Terashima-Marin and Carlos A. Coello Coello, IEEE Congress on Evolutionary Computation (CEC), San Sebastian, Spain, 2017 [ABSTRACT]

    Nowadays, heuristics represent a commonly used alternative to solve complex optimization problems. This, however, has given rise to the problem of choosing the most effective heuristic for a given problem. In recent years, one of the most used strategies for this task are the hyper-heuristics, which aim at selecting/generating heuristics to solve a wide range of optimization problems. Most of the existing selection hyper-heuristics attempt to recommend only one heuristic for a given instance. However, for some classes of problems, more than one heuristic can be suitable. With this premise, in this paper, we address this issue through an evolutionary multilabel learning approach for building hyper- heuristics. Unlike traditional approaches, in the multilabel formulation, the result could not be a single recommendation, but a set of potential heuristics. Due to the fact that cooperative coevolutionary algorithms allow us to divide the problem into several subproblems, it results in a natural approach for dealing with multilabel classification. The proposed cooperative coevolutionary multilabel approach aims at choosing the most relevant patterns for each heuristic. For the experimental study included in this paper, we have used a set of constraint satisfaction problems as our study case. Our experimental results suggest that the proposed method is able to generate accurate hyper-heuristics that outperform reference methods.

  • Evolving Heuristics for Dynamic Vehicle Routing with Time Windows Using Genetic Programming, by Josiah Jacobsen-Grocott, Yi Mei, Gang Chen, Mengjie Zhang, IEEE Congress on Evolutionary Computation (CEC), San Sebastian, Spain, 2017 [PDF] [ABSTRACT]

    Dynamic vehicle routing problem with time windows is an important combinatorial optimisation problem in many real-world applications. The most challenging part of the problem is to make real-time decisions (i.e. whether to accept the newly arrived service requests or not) during the execution of the routes. It is hardly applicable to use the optimisation methods such as mathematical programming and evolutionary algorithms that are competitive for static problems, since they are usually time consuming, and cannot give real-time responses. In this paper, we consider solving this problem using heuristics. A heuristic gradually builds a solution by adding the requests to the end of the route one by one. This way, it can take advantage of the latest information when making the next decision, and give immediate response. In this paper, we propose a meta-algorithm to generate a solution given any heuristic. The meta-algorithm maintains a set of routes throughout the scheduling horizon. Whenever a new request arrives, it tries to re-generate new routes to include the new request by the heuristic. It accepts the new request if successful, and reject otherwise. Then we manually designed several heuristics, and proposed a genetic programming-based hyper-heuristic to automatically evolve heuristics. The results showed that the heuristics evolved by genetic programming significantly outperformed the manually designed heuristics.

  • Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization, by Sotelo-Figueroa, Marco Aurelio and Soberanes, Hector Jose Puga and Carpio, Juan Mart\in and Huacuja, Hector J Fraire and Reyes, Laura Cruz and Alcaraz, Jorge Alberto Soria and Espinal, Andres, Nature-Inspired Design of Hybrid Intelligent Systems, Springer, 2017 [PDF] [ABSTRACT]

    In the recent years, Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP). GE can use a diversity of search strategies including Swarm Intelligence (SI). Bee Swarm Optimization (BSO) is part of SI and it tries to solve the main problems of the Particle Swarm Optimization (PSO): the premature convergence and the poor diversity. In this paper we propose using BSO as part of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP). A comparison between BSO, PSO, and BPP heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is to propose a way to implement different algorithms as search strategy in GE. In this paper, it is proposed that the BSO obtains better results than the ones obtained by PSO, also there is a grammar proposed to generate online and offline heuristics to improve the heuristics generated by other grammars and humans.

  • Improving Hyper-heuristic Performance Through Feature Transformation, by Ivan Amaya and Jose Carlos Ortiz-Bayliss and Andres Eduardo Gutirrez-Rodriguez and Hugo Terashima-Marin and Carlos A. Coello Coello, IEEE Congress on Evolutionary Computation (CEC), San Sebastian, Spain, 2017 [ABSTRACT]

    Hyper-heuristics are powerful search methodologies that can adapt to different kinds of problems. One element of paramount importance, however, is the selection module that they incorporate. Traditional approaches define a set of features for characterizing a problem and, thus, define how to best solve it. However, some features may vary nonlinearly as the solver progresses, requiring higher resolution in specific areas of the feature domain. This work focuses on assessing the advantage of using feature transformations to improve the given resolution and, as a consequence, to improve the overall performance of a hyper-heuristic. We provide evidence that using feature transformations may result in a better discrimination of the problem instance and, as consequence, a better performance of the hyper- heuristics. The feature transformation strategy was applied to an evolutionary-based hyper-heuristic model taken from the literature and tested on constraint satisfaction problems. The proposed strategy increased the median success rate of hyper-heuristics by more than 13% and reduced its standard deviation in about 7%, while reducing the median number of adjusted consistency checks by almost 30%.

  • Investigating the Generality of Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling with Machine Breakdown, by Park, John and Mei, Yi and Nguyen, Su and Chen, Gang and Zhang, Mengjie, Australasian Conference on Artificial Life and Computational Intelligence (ACALCI), Melbourne, Australia, 2017 [PDF] [ABSTRACT]

    Dynamic job shop scheduling (DJSS) problems are combinatorial optimisation problems that have been extensively studied in the literature due to their difficulty and their applicability to real-world manufacturing systems, e.g., car manufacturing systems. In a DJSS problem instance, jobs arrive on the shop floor to be processed on specific sequences of machines on the shop floor and unforeseen events such as dynamic job arrivals and machine breakdown occur that affect the properties of the shop floor. Many researchers have proposed genetic programming based hyper-heuristic (GP-HH) approaches to evolve high quality dispatching rules for DJSS problems with dynamic job arrivals, outperforming good man-made rules for the problems. However, no GP-HH approaches have been proposed for DJSS problems with dynamic job arrivals and machine breakdowns, and it is not known how well GP generalises over both DJSS problem instances with no machine breakdown to problem instances with machine breakdown. Therefore, this paper investigates the generality of GP for DJSS problem with dynamic job arrivals and machine breakdowns. To do this, a machine breakdown specific DJSS dataset is proposed, and an analysis procedure is used to observe the differences in the structures of the GP rules when evolved under different machine breakdown scenarios. The results show that performance and the distributions of the terminals for the evolved rules is sensitive to the frequency of machine breakdowns in the training instances used to evolve the rules

  • Iterated VND Versus Hyper-heuristics: Effective and General Approaches to Course Timetabling, by Soria-Alcaraz, Jorge A and Ochoa, Gabriela and Sotelo-Figueroa, Marco A and Carpio, Martin and Puga, Hector, Nature-Inspired Design of Hybrid Intelligent Systems, Springer, 2017 [PDF] [ABSTRACT]

    The course timetabling problem is one of the most difficult combinatorial problems, it requires the assignment of a fixed number of subjects into a number of time slots minimizing the number of student conflicts. This article presents a comparison between state-of-the-art hyper-heuristics and a newly proposed iterated variable neighborhood descent algorithm when solving the course timetabling problem. Our formulation can be seen as an adaptive iterated local search algorithm that combines several move operators in the improvement stage. Our improvement stage not only uses several neighborhoods, but it also incorporates state-of-the-art reinforcement learning mechanisms to adaptively select them on the fly. Our approach substitutes the adaptive improvement stage by a variable neighborhood descent (VND) algorithm. VND is an ingredient of the more general variable neighborhood search (VNS), a powerful metaheuristic that systematically exploits the idea of neighborhood change. This leads to a more effective search process according course timetabling benchmark results.

  • Learning heuristic selection using a time delay neural network for open vehicle routing, by Tyasnurita, Raras and Ozcan, Ender and John, Robert, IEEE Congress on Evolutionary Computation (CEC), San Sebastian, Spain, 2017 [PDF] [ABSTRACT]

    A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heuristic for Open Vehicle Routing Problem (OVRP). As a chosen 'expert' hyper-heuristic is run on a small set of training problem instances, data is collected to learn from the expert regarding how to decide which low-level heuristic to select and apply to the solution in hand during the search process. In this study, a Time Delay Neural Network (TDNN) is used to extract hidden patterns within the collected data in the form of a classifier ,i.e an 'apprentice' hyper-heuristic, which is then used to solve the 'unseen' problem instances. Firstly, the parameters of TDNN are tuned using Taguchi orthogonal array as a design of experiments method. Then the influence of extending and enriching the information collected from the expert and fed into TDNN is explored on the behaviour of the generated apprentice hyper-heuristic. The empirical results show that the use of distance between solutions as an additional information collected from the expert generates an apprentice which outperforms the expert algorithm on a benchmark of OVRP instances.

  • RECIPE: A Grammar-Based Framework for Automatically Evolving Classification Pipelines, by Pappa, Gisele L, Proceedings of the 20th European Conference on Genetic Programming (EuroGP), LNCS, 10196, Springer, 2017 [PDF] [ABSTRACT]

    Automatic Machine Learning is a growing area of machine learning that has a similar objective to the area of hyper-heuristics: to automatically recommend optimized pipelines, algorithms or appropriate parameters to specific tasks without much dependency on user knowledge. The background knowledge required to solve the task at hand is actually embedded into a search mechanism that builds personalized solutions to the task. Following this idea, this paper proposes RECIPE (REsilient ClassifIcation Pipeline Evolution), a framework based on grammar-based genetic programming that builds customized classification pipelines. The framework is flexible enough to receive different grammars and can be easily extended to other machine learning tasks. RECIPE overcomes the drawbacks of previous evolutionary-based frameworks, such as generating invalid individuals, and organizes a high number of possible suitable data pre-processing and classification methods into a grammar. Results of f-measure obtained by RECIPE are compared to those two state-of-the-art methods, and shown to be as good as or better than those previously reported in the literature. RECIPE represents a first step towards a complete framework for dealing with different machine learning tasks with the minimum required human intervention.

  • Sparse, continuous policy representations for uniform online bin packing via regression of interpolants, by Drake, John H and Swan, Jerry and Neumann, Geoff and Ozcan, Ender, European Conference on Evolutionary Computation in Combinatorial Optimization, Springer, 2017 [PDF] [ABSTRACT]

    Online bin packing is a classic optimisation problem, widely tackled by heuristic methods. In addition to human-designed heuristic packing policies (e.g. first- or best- fit), there has been interest over the last decade in the automatic generation of policies. One of the main limitations of some previously-used policy representations is the trade-off between locality and granularity in the associated search space. In this article, we adopt an interpolation-based representation which has the jointly-desirable properties of being sparse and continuous (i.e. exhibits good genotype-to-phenotype locality). In contrast to previous approaches, the policy space is searchable via real-valued optimization methods. Packing policies using five different interpolation methods are comprehensively compared against a range of existing methods from the literature, and it is determined that the proposed method scales to larger instances than those in the literature.

  • Toward Evolving Dispatching Rules for Dynamic Job Shop Scheduling Under Uncertainty, by Deepak Karunakaran, Yi Mei, Gang Chen and Mengjie Zhang, the 18th Annual Conference on Genetic and Evolutionary Computation (GECCO), Berlin, Germany, 2017 [PDF] [ABSTRACT]

    Dynamic job shop scheduling (DJSS) is a complex problem which is an important aspect of manufacturing systems. Even though the manufacturing environment is uncertain, most of the existing research works consider merely deterministic problems where the time required for processing any job is known in advance and never changes. However many DJSS problems in practice involve high level of uncertainty that must be explicitly addressed. In this work, we consider DJSS problems with varied uncertainty configurations of machines in terms of processing times. We find that with the varying levels of uncertainty, more and more machines cannot fulfill their duties as scheduled and will become bottlenecks of the job shop. To cope with uncertainties, it is therefore essential to identify these bottleneck machines and schedule the jobs to be performed by them carefully. Driven by this idea, we develop a new effective method to evolve pairs of dispatching rules each for a different bottleneck level on the machines. A clustering approach to classify the bottleneck level of the machines arising in the system due to uncertain processing times is proposed. Then, a cooperative co-evolution technique to evolve pairs of dispatching rules which generalizes well across different uncertainty configurations is presented. We perform empirical analysis to show its generalization characteristic over the different uncertainty configurations and show that the proposed method outperforms the current approaches.

2016 (108 publications)

  • A Hidden Markov Model Approach to the Problem of Heuristic Selection in Hyper-heuristics with a Case Study in High School Timetabling Problems, by Kheiri, Ahmed and Keedwell, Ed, Evolutionary Computation, MIT Press, 2016 [PDF] [ABSTRACT]

    Operations research is a well established field that uses computational systems to support decisions in business and public life. Good solutions to operations research problems can make a large difference to the efficient running of businesses and organisations and so the field often searches for new methods to improve these solutions. The high school timetabling problem is an example of an operations research problem and is a challenging task which requires assigning events and resources to time slots subject to a set of constraints. In this paper a new sequence-based selection hyper-heuristic is presented that produces excellent results on a suite of high school timetabling problems. In this study, we present an easy-to-implement, easy-to-maintain and effective sequence-based selection hyper-heuristic to solve high school timetabling problems using a benchmark of unified real-world instances collected from different countries. We show that with sequence-based methods, it is possible to discover new best known solutions for a number of the problems in the timetabling domain. Through this investigation, the usefulness of sequence-based selection hyper-heuristics has been demonstrated and the capability of these methods has been shown to exceed the state-of-the-art.

  • A Hybrid Evolutionary Hyper-Heuristic Approach for Intercell Scheduling Considering Transportation Capacity, by Li, Dongni and Zhan, Rongxin and Zheng, Dan and Li, Miao and Kaku, Ikou, IEEE Transactions on Automation Science and Engineering, 13(2), IEEE, 2016 [PDF] [ABSTRACT]

    The problem ofintercell scheduling considering transportation capacity with the objective of minimizing total weighted tardiness is addressed in this paper, which in nature is the coordination of production and transportation. Since it is a practical decision-making problem with high complexity and large problem instances, a hybrid evolutionary hyper-heuristic (HEH) approach, which combines heuristic generation and heuristic selection, is developed in this paper. In order to increase the diversity and effectiveness of heuristic rules, genetic programming is used to automatically generate new rules based on the attributes of parts, machines, and vehicles. The new rules are added to the candidate rule set, and a rule selection genetic algorithm is developed to choose appropriate rules for machines and vehicles. Finally, scheduling solutions are obtained using the selected rules. A comparative evaluation is conducted, with some state-of-the-art hyper-heuristic approaches which lack some of the strategies proposed in HEH, with a meta-heuristic approach that is suitable for large scale scheduling problems, and with adaptations of some well-known heuristic rules. Computational results show that the new rules generated in HEH have similarities to the best-performing human-made rules, but are more effective due to the evolutionary processes in HEH. Moreover, the HEH approach has advantages over other approaches in both computational efficiency and solution quality, and is especially suitable for problems with large instance sizes. Note to Practitioners-Our survey of the equipment manufacturing industry in China indicates that, for complex products like synthetic transmission devices, intercell transfers occur in the processing routes of more than 51% of parts. More than 47% of tardy parts are caused by inefficient intercell cooperation. Therefore, intercell transfers are inevitable and it is worth an effort to find out an effective approach to intercell scheduling. To solv- intercell scheduling problems, two characteristics in industrial environments of complex products cannot be neglected. The first one is the large problem sizes, which involve up to hundreds of parts and thousands of operations; and the second one is the importance of transportation to intercell scheduling, which involves allocation and utilization of vehicles. However, sufficient transportation capacity is taken as a common assumption in most of research with respect to intercell scheduling, which shields the transportation dimension and hinders the application of these intercell scheduling approaches. Therefore, intercell scheduling with limited transportation capacity is considered, and a hybrid evolutionary hyper-heuristic is proposed in this paper. The advantages of this approach lie in that, (i) as a hyper-heuristic, it provides high computational efficiency, which is suitable for industrial environments with large problem sizes; and (ii) genetic programming is employed to generate problem-specific heuristic rules, which enhances the learning and searching ability of the approach. We compare the proposed approach with the man-made heuristic rules that are widely used in practice. Experimental results indicate that, for hundreds of parts and thousands of operations, given the same running time, our approach outperforms man-made rules with an average gap of 60.6% in minimizing total weighted tardiness. Therefore, our approach is advantageous in both computational efficiency and solution quality, and is especially suitable for the intercell scheduling problems in practice.

  • A Hyper-Heuristic Based On An Adapter Layer For Transportation Combinatorial Problems, by Urra, Enrique and Cubillos, Claudio and Paniagua, Daniel Cabrera, IEEE Latin America Transactions, 14(6), IEEE, 2016 [PDF] [ABSTRACT]

    Hyper-heuristics are optimization techniques for solving hard combinatorial problems. Their main feature is that their design involves an important decoupling of the search components from the problem domain ones. This allows them to extend their applicability to different problem domains without major redesign, unlike traditional methods such as metaheuristics. In this work, a hyper-heuristic is evaluated for a transportation problem. The implemented hyper-heuristic uses a greedy operator, and it implements an adapter layer that would allow it to be used in other similar problems. Experimental results shows balanced solution quality and CPU time performance, regarding other metaheuristics in literature.

  • A Hyper-Heuristic Ensemble Method for Static Job-shop Scheduling, by Hart, Emma and Sim, Kevin, Evolutionary computation, MIT Press, 2016 [PDF] [ABSTRACT]

    We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyperheuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances.

  • A Model Selection Framework for Pricing Options, by Orbay, Berk and Gullu, Refik and Hormann, Wolfgang, SSRN 2812392, 2016 [PDF] [ABSTRACT]

    Empirical studies show that even the best performing option pricing models cannot sustain their performance for all contracts. It can also be added that each model can give the best price estimate for at least a set of contracts. Our aim is to detect which model (and parametrization) is the best price estimate for each individual contract and delta hedging. A model selection framework is proposed to achieve this aim. Both model selection and individual models are benchmarked with different error metrics and underlying assets. Results indicate that model selection is a good and consistent way of pricing option contracts.

  • A Multilayered Heuristic for Solving Curricula Scheduling Problems, by Ahmed, Aftab and Atif, Muhammad and Ahmad, Jamil, Journal of Applied and Emerging Sciences, 5(1), 2016 [PDF] [ABSTRACT]

    Curricula Scheduling problem is recognized essentially on account of its vital significance in academia. The problem is echoed as tough resources placement job against troublesome constraints. The problem has been investigated by research community for several decades because of its inevitable importance and association with Non-deterministic Polynomialtime hard (NP-Hard) complexity. This research article investigates a novel and contemporary approach of using Memetic Algorithms (MA) centered Hyper Heuristic model to scrutinize the performance. The dynamic parameters of higher heuristic are get corrected and improvised with each iteration on the basis of performance measure. The signs learned from the experiments conclude the study-work steps forward in scheduling research and the scope of prospective and significant research direction are noticeable and remain open in the future. The work concluded with implementation of prototype coded in python language.

  • A Neuro-evolutionary Hyper-heuristic Approach for Constraint Satisfaction Problems, by Ortiz-Bayliss, Jose Carlos and Terashima-Marin, Hugo and Conant-Pablos, Santiago Enrique, Cognitive Computation, 8(3), Springer, 2016 [PDF] [ABSTRACT]

    Constraint satisfaction problems represent an important topic of research due to their multiple applications in various areas of study. The most common way to solve this problem involves the use of heuristics that guide the search into promising areas of the space. In this article, we present a novel way to combine the strengths of distinct heuristics to produce solution methods that perform better than such heuristics on a wider range of instances. The methodology proposed produces neural networks that represent hyper-heuristics for variable ordering in constraint satisfaction problems. These neural networks are generated and trained by running a genetic algorithm that has the task of evolving the topology of the networks and some of their learning parameters. The results obtained suggest that the produced neural networks represent a feasible alternative for coding hyper-heuristics that control the use of different heuristics in such a way that the cost of the search is minimized.

  • A Tabu Search hyper-heuristic strategy for t-way test suite generation, by Zamli, Kamal Z and Alkazemi, Basem Y and Kendall, Graham, Applied Soft Computing, 44, Elsevier, 2016 [PDF] [ABSTRACT]

    This paper proposes a novel hybrid t-way test generation strategy (where t indicates interaction strength), called High Level Hyper-Heuristic (HHH). HHH adopts Tabu Search as its high level meta-heuristic and leverages on the strength of four low level meta-heuristics, comprising of Teaching Learning based Optimization, Global Neighborhood Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm. HHH is able to capitalize on the strengths and limit the deficiencies of each individual algorithm in a collective and synergistic manner. Unlike existing hyper-heuristics, HHH relies on three defined operators, based on improvement, intensification and diversification, to adaptively select the most suitable meta-heuristic at any particular time. Our results are promising as HHH manages to outperform existing t-way strategies on many of the benchmarks.

  • A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional knapsack problem, by Drake, John H and Ozcan, Ender and Burke, Edmund K, Evolutionary computation, 24(1), MIT Press, 2016 [PDF] [ABSTRACT]

    Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain.

  • A learning and optimizing system for order acceptance and scheduling, by Nguyen, Su, The International Journal of Advanced Manufacturing Technology, Springer, 2016 [PDF] [ABSTRACT]

    Order acceptance and scheduling is an interesting scheduling problem when scheduling and acceptance decisions need to be handled simultaneously. The complexity of the problem causes difficulty for many solution methods. In this paper, we proposed a learning and optimizing system to deal with the order acceptance and scheduling problem with a single-machine and dependent setup times. The aim of this system is to combine the advantages of the hyper-heuristic for learning useful scheduling rules and the meta-heuristic for further refining the solutions from the obtained rules. The experiments show that the proposed system is very effective as compared to other heuristics proposed in the literature. The analyses also show the benefits of scheduling rules obtained by the hyper-heuristic, especially for large-scale problem instances.

  • A novel multistart hyper-heuristic algorithm on the grid for the quadratic assignment problem, by Dokeroglu, Tansel and Cosar, Ahmet, Engineering Applications of Artificial Intelligence, 52, Elsevier, 2016 [PDF] [ABSTRACT]

    There is a growing interest towards the design of reusable general purpose search methods that are applicable to different problems instead of tailored solutions to a single particular problem. Hyper-heuristics have emerged as such high level methods that explore the space formed by a set of heuristics (move operators) or heuristic components for solving computationally hard problems. A selection hyper-heuristic mixes and controls a predefined set of low level heuristics with the goal of improving an initially generated solution by choosing and applying an appropriate heuristic to a solution in hand and deciding whether to accept or reject the new solution at each step under an iterative framework. Designing an adaptive control mechanism for the heuristic selection and combining it with a suitable acceptance method is a major challenge, because both components can influence the overall performance of a selection hyper-heuristic. In this study, we describe a novel iterated multi-stage hyper-heuristic approach which cycles through two interacting hyper-heuristics and operates based on the principle that not all low level heuristics for a problem domain would be useful at any point of the search process. The empirical results on a hyper-heuristic benchmark indicate the success of the proposed selection hyper-heuristic across six problem domains beating the state-of-the-art approach.

  • A review of hyper-heuristics for educational timetabling, by Pillay, Nelishia, Annals of Operations Research, 239(1), Springer, 2016 [PDF] [ABSTRACT]

    Educational timetabling problems, namely, university examination timetabling, university course timetabling and school timetabling, are combinatorial optimization problems requiring the allocation of resources so as to satisfy a specified set of constraints. Hyper-heuristics have been successfully applied to a variety of combinatorial optimization problems. This is a rapidly growing field which aims at providing generalized solutions to combinatorial optimization problems by exploring a heuristic space instead of a solution space. From the research conducted thus far it is evident that hyper-heuristics are effective at solving educational timetabling problems and have the potential of advancing this field by providing a generalized solution to educational timetabling as a whole. Given this, the paper provides an overview and critical analysis of hyper-heuristics for educational timetabling and proposes future research directions, focusing on using hyper-heuristics to provide a generalized solution to educational timetabling.

  • A selection hyper-heuristic with online learning for control of genetic algorithm ensemble, by Sopov, Evgenii, International Journal of Hybrid Intelligent Systems, 13(2), IOS Press, 2016 [PDF] [ABSTRACT]

    Evolutionary algorithms (EAs), in general, and genetic algorithms (GAs), in particular, are popular and efficient search metaheuristics, which have been applied for many complex optimization problems. At the same time, the performance of EAs depends on appropriate choice of the EA's structure and parameters. One of the ways to automate the EA design is to apply a hyper-heuristic approach. The hyper-heuristic is a high-level approach that can select and apply an appropriate low-level heuristic at each decision point. In this paper, we present a selection hyper-heuristic with online learning that is used to design and adaptively control an ensemble of many different genetic algorithms. The proposed approach combines concepts of the island model and cooperative and competitive coevolutions. The general method and some particular applications are discussed. The experimental results for a wide range of optimization problems are presented. The experiments show that the proposed approach outperforms its component metaheuristics on average. It also outperforms some state-of-the-art techniques. The main advantage of the approach is that it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.

  • A stochastic local search algorithm with adaptive acceptance for high-school timetabling, by Kheiri, Ahmed and Ozcan, Ender and Parkes, Andrew J., Annals of Operations Research, 239(1), 2016 [PDF] [ABSTRACT]

    Automating high school timetabling is a challenging task. This problem is a well known hard computational problem which has been of interest to practitioners as well as researchers. High schools need to timetable their regular activities once per year, or even more frequently. The exact solvers might fail to find a solution for a given instance of the problem. A selection hyper-heuristic can be defined as an easy-to-implement, easy-to-maintain and effective `heuristic to choose heuristics' to solve such computationally hard problems. This paper describes the approach of the team hyper-heuristic search strategies and timetabling (HySST) to high school timetabling which competed in all three rounds of the third international timetabling competition. HySST generated the best new solutions for three given instances in Round 1 and gained the second place in Rounds 2 and 3. It achieved this by using a fairly standard stochastic search method but significantly enhanced by a selection hyper-heuristic with an adaptive acceptance mechanism.

  • A tensor based hyper-heuristic for nurse rostering, by Asta, Shahriar and Ozcan, Ender and Curtois, Tim, Knowledge-Based Systems, 98, Elsevier, 2016 [PDF] [ABSTRACT]

    Nurse rostering is a well-known highly constrained scheduling problem requiring assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to produce high quality solutions, hence (meta)heuristics are commonly preferred as solution methods which are often designed and tuned for specific (group of) problem instances. Hyper-heuristics have emerged as general search methodologies that mix and manage a predefined set of low level heuristics while solving computationally hard problems. In this study, we describe an online learning hyper-heuristic employing a data science technique which is capable of self-improvement via tensor analysis for nurse rostering. The proposed approach is evaluated on a well-known nurse rostering benchmark consisting of a diverse collection of instances obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four of the instances.

  • An Online Chronic Diseases Consulting System: A Hyper Heuristic Algorithm Using Random and Greedy Strategy for Complex Scheduling Problems, by Wen, Tingxi and Wang, Huirong and Hsieh, Ming-Fa and Xie, Lingwei and Wang, Daoyuan and Luo, Weizhen and Dong, Huailin, Journal of Medical Imaging and Health Informatics, 6(1), American Scientific Publishers, 2016 [PDF] [ABSTRACT]

    This study attempts to develop an online chronic diseases consulting system by using a customized heuristic algorithm for complex scheduling of medical experts to consult patients in a major hospital. Methods: We proved this problem is NP-complete problem and used heuristic algorithms to solve it. When the data set is small, most existing algorithms can reach the optimal solution using linear programming. However, traditional greedy algorithm and off-trap strategy fail to give reasonable results in large data set. In this study, we used the algorithm with appropriate oblivion strategy for efficient convergence and optimal solution. Results: To compare different algorithms, synthetic data sets of different size and a year's clinical data set provided by the hospital were used. The outcome of our algorithm was closely matched to the optimal solution from linear programming for sixty synthetic data sets. In addition, our algorithm is more efficient than that of linear programming when clinical data set was used. Meanwhile we found that the outcome is an approximate optimal solution and the algorithm is able to save a lot of cost for the hospital in practice. Conclusions: In this paper, we analyzed the results obtained from the algorithms of data set of different size and found that the algorithm can handle large volumes of data efficiently and reduce cost of hospitals.

  • An iterated multi-stage selection hyper-heuristic, by Kheiri, Ahmed and Ozcan, Ender, European Journal of Operational Research, 250(1), Elsevier, 2016 [PDF] [ABSTRACT]

    There is a growing interest towards the design of reusable general purpose search methods that are applicable to different problems instead of tailored solutions to a single particular problem. Hyper-heuristics have emerged as such high level methods that explore the space formed by a set of heuristics (move operators) or heuristic components for solving computationally hard problems. A selection hyper-heuristic mixes and controls a predefined set of low level heuristics with the goal of improving an initially generated solution by choosing and applying an appropriate heuristic to a solution in hand and deciding whether to accept or reject the new solution at each step under an iterative framework. Designing an adaptive control mechanism for the heuristic selection and combining it with a suitable acceptance method is a major challenge, because both components can influence the overall performance of a selection hyper-heuristic. In this study, we describe a novel iterated multi-stage hyper-heuristic approach which cycles through two interacting hyper-heuristics and operates based on the principle that not all low level heuristics for a problem domain would be useful at any point of the search process. The empirical results on a hyper-heuristic benchmark indicate the success of the proposed selection hyper-heuristic across six problem domains beating the state-of-the-art approach.

  • Automated design of production scheduling heuristics: a review, by Branke, Juergen and Nguyen, Su and Pickardt, Christoph W and Zhang, Mengjie, IEEE Transactions on Evolutionary Computation, 20(1), IEEE, 2016 [PDF] [ABSTRACT]

    Hyper-heuristics have recently emerged as a powerful approach to automate the design of heuristics for a number of different problems. Production scheduling is a particularly popular application area for which a number of different hyper-heuristics have been developed and are shown to be effective, efficient, easy to implement, and reusable in different shop conditions. In particular, they seem to be a promising way to tackle highly dynamic and stochastic scheduling problems, an aspect that is specifically emphasized in this survey. Despite their success and the substantial number of papers in this area, there is currently no systematic discussion of the design choices and critical issues involved in the process of developing such approaches. This paper strives to fill this gap by summarizing the state-of-the-art approaches, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling. This paper also identifies challenges and open questions and highlights various directions for future work.

  • Automatic Workflow Scheduling Tuning for Distributed Processing Systems, by Visheratin, Alexander A and Melnik, Mikhail and Nasonov, Denis, Procedia Computer Science, 101, Elsevier, 2016 [PDF] [ABSTRACT]

    Modern scientific applications are composed of various methods, techniques and models to solve complicated problems. Such composite applications commonly are represented as workflows. Workflow scheduling is a well-known optimization problem, for which there is a great amount of solutions. Most of the algorithms contain parameters, which affect the result of a method. Thus, for the efficient scheduling it is important to tune parameters of the algorithms. Moreover, performance models, which are used for the estimation of obtained solutions, are crucial parts of workflow scheduling. In this work we present a combined approach for automatic parameters tuning and performance models construction in the background of the WMS lifecycle. Algorithms tuning is provided by hyper-heuristic genetic algorithm, whereas models construction is performed via symbolic regression methods. Developed algorithm was evaluated using CLAVIRE platform and is applicable for any distributed computing systems to optimize the execution of composite applications.

  • Automatic design of scheduling rules for complex manufacturing systems by multi-objective simulation-based optimization, by Freitag, Michael and Hildebrandt, Torsten, CIRP Annals-Manufacturing Technology, Elsevier, 2016 [PDF] [ABSTRACT]

    Complex manufacturing systems pose challenges for production planning and control. Amongst other objectives, orders have to be finished according to their due-dates. However, avoiding both earliness and tardiness requires a high level of process control. This article describes the use of simulation-based multi-objective optimization (multi-objective Genetic Programming) as a hyper-heuristic to automatically develop improved dispatching rules specifically for this control problem. Using a complex manufacturing scenario from semiconductor manufacturing as an example, it is shown that the resulting rules significantly outperform state-of-the-art dispatching rules from literature.

  • Automatically Produced Algorithms for the Generalized Minimum Spanning Tree Problem, by Contreras-Bolton, Carlos and Rey, Carlos and Ramos-Cossio, Sergio and Rodr\iguez, Claudio and Gatica, Felipe and Parada, Victor, Scientific Programming, 2016, Hindawi Publishing Corporation, 2016 [PDF] [ABSTRACT]

    The generalized minimum spanning tree problem consists of finding a minimum cost spanning tree in an undirected graph for which the vertices are divided into clusters. Such spanning tree includes only one vertex from each cluster. Despite the diverse practical applications for this problem, the NP-hardness continues to be a computational challenge. Good quality solutions for some instances of the problem have been found by combining specific heuristics or by including them within a metaheuristic. However studied combinations correspond to a subset of all possible combinations. In this study a technique based on a genotype-phenotype genetic algorithm to automatically construct new algorithms for the problem, which contain combinations of heuristics, is presented. The produced algorithms are competitive in terms of the quality of the solution obtained. This emerges from the comparison of the performance with problem-specific heuristics and with metaheuristic approaches.

  • CHAMP: Creating heuristics via many parameters for online bin packing, by Asta, Shahriar and Ozcan, Ender and Parkes, Andrew J, Expert Systems with Applications, 63, Elsevier, 2016 [PDF] [ABSTRACT]

    The online bin packing problem is a well-known bin packing variant and which requires immediate decisions to be made for the placement of a lengthy sequence of arriving items of various sizes one at a time into fixed capacity bins without any overflow. The overall goal is maximising the average bin fullness. We investigate a 'policy matrix' representation, which assigns a score for each decision option independently and the option with the highest value is chosen, for one-dimensional online bin packing. A policy matrix might also be considered as a heuristic with many parameters, where each parameter value is a score. We hence effectively investigate a framework which can be used for creating heuristics via many parameters. The proposed framework combines a Genetic Algorithm optimiser, which searches the space of heuristics in policy matrix form, and an online bin packing simulator, which acts as the evaluation function. The empirical results indicate the success of the proposed approach, providing the best solutions for almost all item sequence generators used during the experiments. We also present a novel fitness landscape analysis on the search space of policies. This study hence gives evidence of the potential for automated discovery by intelligent systems of powerful heuristics for online problems; reducing the need for expensive use of human expertise.

  • Characterization of neighborhood behaviours in a multi-neighborhood local search algorithm, by Dang, Nguyen Thi Thanh and De Causmaecker, Patrick, arXiv preprint arXiv:1603.06459, 2016 [PDF] [ABSTRACT]

    We consider a multi-neighborhood local search algorithm with a large number of possible neighborhoods. Each neighborhood is accompanied by a weight value which represents the probability of being chosen at each iteration. These weights are fixed before the algorithm runs, and are considered as parameters of the algorithm. Given a set of instances, off-line tuning of the algorithm's parameters can be done by automated algorithm configuration tools (e.g., SMAC). However, the large number of neighborhoods can make the tuning expensive and difficult even when the number of parameters has been reduced by some intuition. In this work, we propose a systematic method to characterize each neighborhood's behaviours, representing them as a feature vector, and using cluster analysis to form similar groups of neighborhoods. The novelty of our characterization method is the ability of reflecting changes of behaviours according to hardness of different solution quality regions. We show that using neighborhood clusters instead of individual neighborhoods helps to reduce the parameter configuration space without misleading the search of the tuning procedure. Moreover, this method is problem-independent and potentially can be applied in similar contexts.

  • Combine and conquer: an evolutionary hyper-heuristic approach for solving constraint satisfaction problems, by Ortiz-Bayliss, Jose Carlos and Terashima-Marin, Hugo and Conant-Pablos, Santiago Enrique, Artificial Intelligence Review, Springer, 2016 [PDF] [ABSTRACT]

    Selection hyper-heuristics are a technology for optimization in which a high-level mechanism controls low-level heuristics, so as to be capable of solving a wide range of problem instances efficiently. Hyper-heuristics are used to generate a solution process rather than producing an immediate solution to a given problem. This process is a re-usable mechanism that can be applied both to seen and unseen problem instances. In this paper, we propose a selection hyper-heuristic process with the intention to rise the level of generality and solve consistently well a wide range of constraint satisfaction problems. The hyper-heuristic technique is based on a messy genetic algorithm that generates high-level heuristics formed by rules (condition -> heuristic). The high-level heuristics produced are seen to be good at solving instances from certain parts of the parameterized space of problems, producing results using effort comparable to the best single heuristic per instance. This is beneficial, as the choice of best heuristic varies from instance to instance, so the high-level heuristics are definitely preferable to selecting any one low-level heuristic for all instances. The results confirm the robustness of the proposed approach and how high-level heuristics trained for some specific classes of instances can also be applied to unseen classes without significant lost of efficiency. This paper contributes to the understanding of heuristics and the way they can be used in a collaborative way to benefit from their combined strengths.

  • Community Detection from Biological and Social Networks: A Comparative Analysis of Metaheuristic Algorithms, by Atay, Yilmaz and Koc, Ismail and Babaoglu, Ismail and Kodaz, Halife, Applied Soft Computing, Elsevier, 2016 [PDF] [ABSTRACT]

    In order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big Bang-Big Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks - five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested.

  • Cooperative coevolutionary approach for integrated vehicle routing and scheduling using cross-dock buffering, by Yin, Peng-Yeng and Lyu, Sin-Ru and Chuang, Ya-Lan, Engineering Applications of Artificial Intelligence, 52, Elsevier, 2016 [PDF] [ABSTRACT]

    Cross-docking technology transships products from incoming vehicles directly to outgoing vehicles by using the warehouse as a temporary buffer instead of a place for storage and retrieval. The supply chain management (SCM) with cross-docks is both effective and efficient where no storage is facilitated at the cross-dock and the order-picking is replaced by fast consolidation. However, cross-docking involves interrelated operations such as vehicle routing and vehicle scheduling which require proper planning and synchronization. Traditional cross-docking methods treat the operations separately and overlook the potential advantage of cooperative planning. This paper proposes a bi-objective mathematical formulation for the cross-docking with the noted new challenges. As the addressed problem is highly constrained, we develop a cooperative coevolution approach consisting of Hyper-heuristics and Hybrid-heuristics for achieving continuous improvement in alternating objectives. The performance of our approach is illustrated with real geographical data and is compared with existing models. Statistical tests based on intensive simulations, including the convergence 95% confidence analysis and the worst-case analysis, are conducted to provide reliable performance guarantee.

  • Deriving products for variability test of Feature Models with a hyper-heuristic approach, by Strickler, Andrei and Lima, Jackson A Prado and Vergilio, Silvia R and Pozo, Aurora TR, Applied Soft Computing, 49, Elsevier, 2016 [PDF] [ABSTRACT]

    Deriving products from a Feature Model (FM) for testing Software Product Lines (SPLs) is a hard task. It is important to select a minimum number of products but, at the same time, to consider the coverage of testing criteria such as pairwise, among other factors. To solve such problems Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully applied. However, to design a solution for this and other software engineering problems can be very difficult, because it is necessary to choose among different search operators and parameters. Hyper-heuristics can help in this task, and have raised interest in the Search-Based Software Engineering (SBSE) field. Considering the growing adoption of SPL in the industry and crescent demand for SPL testing approaches, this paper introduces a hyper-heuristic approach to automatically derive products to variability testing of SPLs. The approach works with MOEAs and two selection methods, random and based on FRR-MAB (Fitness Rate Rank based Multi-Armed Bandit). It was evaluated with real FMs and the results show that the proposed approach outperforms the traditional algorithms used in the literature, and that both selection methods present similar performance.

  • Developing a context-aware ubiquitous learning system based on a hyper-heuristic approach by taking real-world constraints into account, by Yin, Peng-Yeng and Chuang, Kuo-Hsien and Hwang, Gwo-Jen, Universal Access in the Information Society, 15(3), 2016 [PDF] [ABSTRACT]

    In a context-aware ubiquitous learning environment, learning systems are aware of students' locations and learning status in the real world via the use of sensing technologies which provide personalized guidance or support. In such a learning environment that guides students to observe and learn from real-world targets, various physical world constraints need to be taken into account when planning learning paths for individuals. In this study, an optimization problem is formulated by taking the relevance of real-world learning targets and the environmental constraints into account when determining personalized learning paths in the real world to maximize students' learning efficacy. Moreover, a hyper-heuristic approach is proposed to efficiently find quality learning paths for individual students. To evaluate the performance of the proposed approach, the teachers' feedback was collected and analyzed based on the learning activities conducted in an elementary school natural science course; in addition, the performances of the proposed algorithm and other approaches were compared based on a set of test data.

  • Evolution of new algorithms for the binary knapsack problem, by Parada, Lucas and Herrera, Carlos and Sepulveda, Mauricio and Parada, Victor, Natural Computing, 15(1), Springer, 2016 [PDF] [ABSTRACT]

    Due to its NP-hard nature, it is still difficult to find an optimal solution for instances of the binary knapsack problem as small as 100 variables. In this paper, we developed a three-level hyper-heuristic framework to generate algorithms for the problem. From elementary components and multiple sets of problem instances, algorithms are generated. The best algorithms are selected to go through a second step process, where they are evaluated with problem instances that differ in size and difficulty. The problem instances are generated according to methods that are found in the literature. In all of the larger problem instances, the generated algorithms have less than 1 % error with respect to the optimal solution. Additionally, generated algorithms are efficient, taking on average fractions of a second to find a solution for any instance, with a standard deviation of 1 s. In terms of structure, hyper-heuristic algorithms are compact in size compared with those in the literature, allowing an in-depth analysis of their structure and their presentation to the scientific world.

  • Evolving a Nelder-Mead Algorithm for Optimization with Genetic Programming, by Fajfar, Iztok and Puhan, Janez and BHurmen, Arpad, Evolutionary Computation, MIT Press, 2016 [PDF] [ABSTRACT]

    We used genetic programming to evolve a direct search optimization algorithm, similar to that of the standard downhill simplex optimization method proposed by Nelder and Mead (1965). In the training process, we used several ten-dimensional quadratic functions with randomly displaced parameters and different randomly generated starting simplices. The genetically obtained optimization algorithm showed overall better performance than the original Nelder-Mead method on a standard set of test functions. We observed that many parts of the genetically produced algorithm were seldom or never executed, which allowed us to greatly simplify the algorithm by removing the redundant parts. The resulting algorithm turns out to be considerably simpler than the original Nelder-Mead method while still performing better than the original method.

  • Gezgin Satici Problemi Icin Merkezden Kenarlara Hipersezgisel Yontem, by Nuriyeva, Fidan and Kizilatecs, Gozde, Suleyman Demirel Universitesi Fen Bilimleri Enstitusu Dergisi, 20, 2016 [PDF] [ABSTRACT]

    TURKISH: Bu makalede Gezgin Satici Problemi icin yeni bir hipersezgisel algoritma onerilmistir. Bu yontemde once N adet sehir icerisinden merkez sehir ve 4 uc sehir secilip, sonra ise merkez ile ikiser-ikiser uc sehirlerin orta noktalari belirlenerek merkez sehirden baslanarak bu 9 sehirden gecen bir devre olusturulmustur. Daha sonra "en kisa yol" ve "ekleme sezgiseli" algoritmalari kullanilarak bulunan devre tum sehirlerden gececek sekilde genisletilmistir. Onerilen algoritmalar ile kutuphane problemleri uzerinde hesaplama denemeleri yapilmis, elde edilen sonuclar "en yakin komsu" algoritmasindan elde edilen sonuclar ile karsilastirilmistir. Hesaplama denemeleri onerilen algoritmanin verimli oldugunu gostermektedir.

  • Grammar-based generation of variable-selection heuristics for constraint satisfaction problems, by Sosa-Ascencio, Alejandro and Ochoa, Gabriela and Terashima-Marin, Hugo and Conant-Pablos, Santiago Enrique, Genetic Programming and Evolvable Machines, 17(2), Springer, 2016 [PDF] [ABSTRACT]

    We propose a grammar-based genetic programming framework that generates variable-selection heuristics for solving constraint satisfaction problems. This approach can be considered as a generation hyper-heuristic. A grammar to express heuristics is extracted from successful human-designed variable-selection heuristics. The search is performed on the derivation sequences of this grammar using a strongly typed genetic programming framework. The approach brings two innovations to grammar-based hyper-heuristics in this domain: the incorporation of if-then-else rules to the function set, and the implementation of overloaded functions capable of handling different input dimensionality. Moreover, the heuristic search space is explored using not only evolutionary search, but also two alternative simpler strategies, namely, iterated local search and parallel hill climbing. We tested our approach on synthetic and real-world instances. The newly generated heuristics have an improved performance when compared against human-designed heuristics. Our results suggest that the constrained search space imposed by the proposed grammar is the main factor in the generation of good heuristics. However, to generate more general heuristics, the composition of the training set and the search methodology played an important role. We found that increasing the variability of the training set improved the generality of the evolved heuristics, and the evolutionary search strategy produced slightly better results.

  • HHFS: Hyper-heuristic feature selection, by Montazeri, Mitra, Intelligent Data Analysis, 20(4), IOS Press, 2016 [PDF] [ABSTRACT]

    Feature selection is an important machine learning field which can provide a key role for the challenging problem of classifying high-dimensional data. This problem is finding effective features among the set of all features in such that the final feature set can improve accuracy and reduce complexity. Since feature selection is an NP-Hard problem, many heuristic algorithms have been studied so far to solve this problem. In this paper, we propose a novel method based on hyper-heuristic approach to find an efficient proper feature subset which is named Hyper-Heuristic Feature Selection (HHFS). In the proposed method, Low level heuristics are categorized into two groups: the first group contains exploiters which cause to exploit the search space efficiently by improving the quality of the candidate solution at hand; the second one includes explorer heuristics which explore the solution space by dwelling on random perturbations. Since each region of the solution space can have its own characteristics, an appropriate low level heuristic should be selected and applied to the current solution. We propose Genetic Algorithm to select among the set of low level heuristic and balance between exploitation and exploration. It chooses the low level heuristic based on the existing functional history of low level heuristic. We aim to investigate the role of cooperation between low level heuristics within a hyper-heuristic framework to find the best feature subset. Since different low level heuristics have different strengths and weaknesses, we believe that cooperation can allow the strengths of one low level heuristic to compensate for the weaknesses of another. In this study, we also propose Adaptive Hyper-Heuristic Feature Selection (AHHFS) which is an extension of HHFS. Empirical study of the proposed method on several commonly used data sets from UCI repository indicates that it outperforms recent methods in the literature for feature selection.

  • Hyper-heuristic approach for multi-objective software module clustering, by Kumari, A Charan and Srinivas, K, Journal of Systems and Software, 117, Elsevier, 2016 [PDF] [ABSTRACT]

    In the software maintenance phase of software development life cycle, one of the main concerns of software engineers is to group the modules into clusters with maximum cohesion and minimum coupling. To analyze the efficacy of Multi-objective Hyper-heuristic Evolutionary Algorithm (MHypEA) in solving real-world clustering problems and to compare the results with the reported results in the literature for single as well as multi-objective formulations of the problem and also to present a CASE tool that assists software engineers in software module clustering process. The paper reports on empirical evaluation of the performance of MHypEA with the reported results in the literature. The comparison is mainly based on two factors - quality of the obtained solutions and the computational effort. On all the attempted problems, MHypEA reported good results in comparison to all the studies that were reported on multi-objective formulation of the problem, with a computational effort of nearly one-twentieth of the computational effort required by the other multi-objective algorithms. The hyper-heuristic approach is able to produce high quality clustered systems with less computational effort.

  • Improved Hyper-Heuristic Scheduling with Load-Balancing and RASA for Cloud Computing Systems, by Geetinder kaur and Sarabjit kaur, International Journal of Grid and Distributed Computing, 9(1), IJGDC, 2016 [PDF] [ABSTRACT]

    Nowadays cloud computing has turned into a key innovation and has become a great solution for indulging a flexible utility oriented, online allocation and storage of computing resources and client's information in lower expense, on- interest and dynamically scalable framework on pay per use premise. This technology is a new pattern emerging in IT environment with immense necessities of framework and resources. Job Scheduling Problem is an essential issue. For efficient usage and managing resources, administrations, scheduling plays a critical role. This paper apportion the performance enhancement of Hyper- Heuristic Scheduling Approach to schedule cloudlets and resources, by taking account of both , computation time and transmission cost with two detection operators. Load Balancing and RASA concept is applied for efficient Load Scheduling, resource utilization and thereby enhancing the overall performance of cloud computing environment. The numerical investigations of HHSA were performed on CloudSim. Experimental results generated via simulation shows that enhanced heuristic scheduling approach is much better than individual heuristic approach in terms of minimizing makespan time.

  • Improving local-search metaheuristics through look-ahead policies, by Meignan, David and Schwarze, Silvia and Voss, Stefan, Annals of Mathematics and Artificial Intelligence, 76(1-2), Springer, 2016 [PDF] [ABSTRACT]

    As a basic principle, look-ahead approaches investigate the outcomes of potential future steps to evaluate the quality of alternative search directions. Different policies exist to set up look-ahead methods differing in the object of inspection and in the extensiveness of the search. In this work, two original look-ahead strategies are developed and tested through numerical experiments. The first method introduces a look-ahead mechanism that acts as a hyper-heuristic for comparing and selecting local-search operators. The second method uses a look-ahead strategy on a lower level in order to guide a local-search metaheuristic. The proposed approaches are implemented using a hyper-heuristic framework. They are tested against alternative methods using two different competition benchmarks, including a comparison with results given in literature. Furthermore, in a second set of experiments, a detailed investigation regarding the influence of particular parameter values is executed for one method. The experiments reveal that the inclusion of a simple look-ahead principle into an iterated local-search procedure significantly improves the outcome regarding the considered benchmarks.

  • Iterated local search using an add and delete hyper-heuristic for university course timetabling, by Soria-Alcaraz, Jorge A and Ozcan, Ender and Swan, Jerry and Kendall, Graham and Carpio, Martin, Applied Soft Computing, 40, Elsevier, 2016 [PDF] [ABSTRACT]

    Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach.

  • Joint optimization models for shelf display and inventory control considering the impact of spatial relationship on demand, by Ju Zhao and Yong-Wu Zhou and M.I.M. Wahab, European Journal of Operational Research, 255(3), 2016 [PDF] [ABSTRACT]

    This research investigates joint optimization models for shelf space allocation and display location with multi-item replenishment. The demand for each item is considered to be dependent not only on its and other items' allocated shelf space and displayed locations, but also on spatial relationships between items. Joint optimization models are developed for two different scenarios: (a) each item is replenished individually; and (b) multiple items are replenished jointly. A multi-stage simulated annealing (SA) based hyper-heuristic algorithm is proposed to solve both joint optimization models. These models are then evaluated numerically for different problem sizes. The results demonstrate that: (1) the proposed SA based hyper-heuristic algorithm is robust and efficient for both joint optimization models; and (2) the model for the joint replenishment policy leads to a higher profit than that of the model for the individual replenishment policy. Hence, the joint optimization model with joint replenishment policy will be helpful for retailers making decisions about shelf display arrangement and inventory control for multiple items.

  • Machine reassignment problem: the ROADEF/EURO challenge 2012, by Afsar, H Murat and Artigues, Christian and Bourreau, Eric and Kedad-Sidhoum, Safia, Annals of Operations Research, 242(1), Springer, 2016 [PDF] [ABSTRACT]

    The ROADEF/EURO challenge is a contest jointly organized by the French Operational Research and Decision Aid society (ROADEF) and the European Operational Research society (EURO). The contest appears on a regular basis since 1999 and always concerns an industrial optimization problem proposed by an industrial partner. Google proposed a subject for the ROADEF/EURO challenge 2012 (http://challenge.roadef.org/2012/en/), presenting a complex and large-scale machine reassignment problem, where a set of processes assigned to a set of machines have to be reassigned (or moved) while balancing machine usage improvement and moving costs, under resource (more precisely CPU, RAM, disk) and operational constraints. The 2012 challenge edition has been an unprecedented success with 82 registered teams, 48 teams that actually sent a program for qualification, 30 qualified teams and 27 teams that sent a program for the final evaluation. This paper aims at introducing the Annals of Operations Research special issue by presenting the ROADEF/EURO challenge 2012 subject, as well as the methods of the finalist teams and their results.

  • Multi-component approach to the bipartite Boolean quadratic programming problem, by Daniel Karapetyan and Abraham P. Punnen and Andrew J. Parkes, CoRR, abs/1605.02038, 2016 [PDF] [ABSTRACT]

    We study the Bipartite Boolean Quadratic Programming Problem (BBQP) which is an extension of the well known Boolean Quadratic Programming Problem (BQP). Applications of the BBQP include mining discrete patterns from binary data, approximating matrices by rank-one binary matrices, computing the cut-norm of a matrix, and solving optimisation problems such as maximum weight biclique, bipartite maximum weight cut, maximum weight induced sub-graph of a bipartite graph, etc. For the BBQP, we first present several algorithmic components, specifically, hillclimbers and mutations, and then show how to combine them in a high-performance metaheuristic. Instead of hand-tuning a standard metaheuristic to test the efficiency of the hybrid of the components, we chose to use an automated generation of a multi-component metaheuristic to save human time, and also improve objectivity in the analysis and comparisons of components. For this we designed a new metaheuristic schema which we call Conditional Markov Chain Search (CMCS). We show that CMCS is flexible enough to model several standard metaheuristics; this flexibility is controlled by multiple numeric parameters, and so is convenient for automated generation. We study the configurations revealed by our approach and show that the best of them outperforms the previous state-of-the-art BBQP algorithm by several orders of magnitude. In our experiments we use benchmark instances introduced in the preliminary version of this paper and described here, which have already become the de facto standard in the BBQP literature.

  • On the Development of Hyper Heuristics Based Framework for Scheduling Problems in Textile Industry, by Nugraheni, Cecilia E and Abednego, Luciana, International Journal of Modeling and Optimization, 6(5), IACSIT Press, 2016 [PDF] [ABSTRACT]

    Textile industry, which is one of the most prominent industries in Indonesia, faces a problem caused by the condition of machine productions. This situation leads to a need of good machine scheduling system. Generally, production processes in textile industry belong to the flow shop scheduling problems (FSSP). Many approaches/heuristics have been proposed for solving FSSP. Two of them are Palmer's algorithm and Gupta's algorithm. This paper investigates a method, called genetic algorithm hyper-heuristic, for combining those heuristics in order to obtain some new better heuristics. This method is then implemented in a framework.

  • Parallel multi-core hyper-heuristic GRASP to solve permutation flow-shop problem, by Alekseeva, Ekaterina and Mezmaz, Mohand and Tuyttens, Daniel and Melab, Nouredine, Concurrency and Computation: Practice and Experience, Wiley Online Library, 2016 [PDF] [ABSTRACT]

    In this paper, we aim to propose a parallel multi-core hyper-heuristic based on greedy randomized adaptive search procedure (GRASP) for the permutation flow-shop problem with the makespan criterion. The GRASP is a well-known two-phase metaheuristic. First, a construction phase builds a complete solution iteratively, component by component, by a greedy randomized algorithm. After that, a local search phase improves this solution. The choice of a component and the order in which it is added in a solution mostly depend on its incremental cost. Thus, a basic GRASP configuration is defined by a cost function, a probabilistic parameter of greediness and a neighbourhood structure. We consider five cost functions and seven well-known neighbourhood structures. In this paper a cost function based on a bounding operator is integrated in GRASP for the first time. Mechanisms that investigate automatically algorithm configurations refer to hyper-heuristics. Our hyper-heuristic investigates 315 GRASP configurations and reports which one produces better results. Parallel multi-core computing is used as a way to efficiently implement the hyper-heuristic. Taillard's benchmark instances are used to test the hyper-heuristic for the permutation flow-shop problem.

  • Quantum-inspired Hyper-heuristics for Energy-aware Scheduling on Heterogeneous Computing Systems, by Chen, Shaomiao and Li, Zhiyong and Yang, Bo and Rudolph, Gunter, IEEE Transactions on Parallel and Distributed Systems, 27(6), IEEE, 2016 [PDF] [ABSTRACT]

    Power and performance tradeoff optimization is one of the most significant issues on heterogeneous multiprocessor or multicomputer systems (HMCSs) with dynamically variable voltage. In this paper, the problem is defined as energy-constrained performance optimization and performance-constrained energy optimization. Task scheduling for precedence-constrained parallel applications represented by a directed acyclic graph (DAG) in HMCSs is an NP-HARD problem. Over the last three decades, several task scheduling techniques have been developed for energy-aware scheduling. However, it is impossible for a single task scheduling technique to outperform all other techniques for all types of applications and situations. Motivated by these observations, hyperheuristic framework is introduced. Moreover, a quantum-inspired high-level learning strategy is proposed to improve the performance of this framework. Meanwhile, a fast solution evaluation technique is designed to reduce the computational burden for each iteration step. Experimental results show that the fast solution evaluation technique can improve average algorithm search speed by 38 percent and that the proposed algorithm generally exhibits outstanding convergence performance.

  • Rotated neighbor learning-based auto-configured evolutionary algorithm, by Laili, Yuanjun and Zhang, Lin and Tao, Fei and Ma, Pingchuan, Science China Information Sciences, Springer, 2016 [PDF] [ABSTRACT]

    More and more evolutionary operators have been integrated and manually configured together to solve wider range of problems. Considering the very limited progress made on the automatic configuration of evolutionary algorithms (EAs), a rotated neighbor learning-based auto-configured evolutionary algorithm (RNLACEA) is presented. In this framework, multiple EAs are combined as candidates and automatically screened for different scenarios with a rotated neighbor structure. According to a ranking record and a group of constraints, the algorithms can be better scheduled to improve the searching efficiency and accelerate the searching pace. Experimental studies based on 14 classical EAs and 22 typical benchmark problems demonstrate that RNLACEA outperforms other six representative auto-adaptive EAs and has high scalability and robustness in solving different kinds of numerical optimization problems.

  • Rule based scheduling Algorithm for Scheduling Mechanism in Large Scale Data Center, by Asha, MEM and Vivekanandan, P, Asian Journal of Research in Social Sciences and Humanities, 6(12), Asian Research Consortium, 2016 [PDF] [ABSTRACT]

    Rule Based Scheduling Algorithm have been widely used in the cloud computing as it is simple and easy to implement the Scheduling criteria in terms of energy efficiency and less delay, In this paper, we propose Improved Hyper Heuristic Scheduling which is used to find the candidate solution (low level heuristic) form Scheduling Solutions (heuristics algorithms) from the simulated annealing and genetic algorithm in dynamic large scale Cloud Computing system with diversity operator as sequence dependent and sequence independent scheduling. Specifically, Resources and workloads characterised using the simulated annealing and improved genetic algorithm with n point crossover. Hyper heuristic algorithm is used select best possible solution to the dynamic workload to candidate solutions. The Simulation results on cloudsim proves that proposed system outperforms existing state of approaches in terms of reduced make span and flow time for the task scheduling and resource management

  • Selecting Efficient Features via a Hyper-Heuristic Approach, by Montazeri, Mitra and Baghshah, Mahdieh Soleymani and Niknafs, Aliakbar, arXiv preprint arXiv:1601.05409, 2016 [PDF] [ABSTRACT]

    By Emerging huge databases and the need to efficient learning algorithms on these datasets, new problems have appeared and some methods have been proposed to solve these problems by selecting efficient features. Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. One way to solve this problem is to evaluate all possible feature subsets. However, evaluating all possible feature subsets is an exhaustive search and thus it has high computational complexity. Until now many heuristic algorithms have been studied for solving this problem. Hyper-heuristic is a new heuristic approach which can search the solution space effectively by applying local searches appropriately. Each local search is a neighborhood searching algorithm. Since each region of the solution space can have its own characteristics, it should be chosen an appropriate local search and apply it to current solution. This task is tackled to a supervisor. The supervisor chooses a local search based on the functional history of local searches. By doing this task, it can trade of between exploitation and exploration. Since the existing heuristic cannot trade of between exploration and exploitation appropriately, the solution space has not been searched appropriately in these methods and thus they have low convergence rate. For the first time, in this paper use a hyper-heuristic approach to find an efficient feature subset. In the proposed method, genetic algorithm is used as a supervisor and 16 heuristic algorithms are used as local searches. Empirical study of the proposed method on several commonly used data sets from UCI data sets indicates that it outperforms recent existing methods in the literature for feature selection.

  • Self-Adaptive Differential Evolution Hyper-Heuristic with Applications in Process Design, by Peraza-Vazquez, Hernan and Torres-Huerta, Aide M and Flores-Vela, Abelardo, Computacion y Sistemas, 20(2), 2016 [PDF] [ABSTRACT]

    The paper presents a differential evolution (DE)-based hyper-heuristic algorithm suitable for the optimization of mixed-integer non-linear programming (MINLP) problems. The hyper-heuristic framework includes self-adaptive parameters, an epsilon-constrained method for handling constraints, and 18 DE variants as low-level heuristics. Using the proposed approach, we solved a set of classical test problems on process synthesis and design and compared the results with those of several state-of-the-art evolutionary algorithms. To verify the consistency of the proposed approach, the above-mentioned comparison was made with respect to the percentage of convergences to the global optimum (NRC) and the average number of objective function evaluations (NFE) over several trials. Thus, we found that the proposed methodology significantly improves performance in terms of NRC and NFE.

  • Stochastic Scheduling Algorithm for Distributed Cloud Networks using Heuristic Approach, by Mamatha, E and Saritha, S and Reddy, CS, International Journal of Advanced Networking and Applications, 8(1), Eswar Publications, 2016 [PDF] [ABSTRACT]

    Rule based heuristic scheduling algorithms in real time and cloud computing Systems employ for resource or task scheduling since they are suitable to implement for NP-complete problems. However, they are simple but there is much room to improve these algorithms. This study presents a heuristic scheduling algorithm, called High performance hyper-heuristic scheduling algorithm (HHSA) using detection operator, to find better scheduling solutions for real and cloud computing systems. The two operators - diversity detection and improvement detection operators - are employed in this algorithm to determine the timing to determine the heuristic algorithm. These two are employed to dynamically determine a low level heuristic that can be used to find better solution. To evaluate the performance of this method, authors examined the above method with several scheduling algorithms and results prove that Hyper Heuristic Scheduling Algorithm can significantly decrease the makespan of task scheduling when compared with all other scheduling algorithms. A novel high-performance hyper-heuristic algorithm is proposed for scheduling on cloud computing systems to reduce the makespan. This algorithm can be applied to both sequence dependent and sequence independent scheduling problems.

  • What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO, by Dunning, Iain and Gupta, Swati and Silberholz, John, Optimization Online e-Prints, 2016 [PDF] [ABSTRACT]

    Though empirical testing is broadly used to evaluate heuristics, there are shortcomings with how it is often applied in practice. In a systematic review of Max-Cut and Quadratic Unconstrained Binary Optimization (QUBO) heuristics papers, we found only 4% publish source code, only 14% compare heuristics with identical termination criteria, and most experiments are performed with an artificial, homogeneous set of problem instances. To address these limitations, we implement and release as open-source a code-base of 10 MaxCut and 27 QUBO heuristics. We perform heuristic evaluation using cloud computing across a library of 3,296 instances. This large-scale evaluation provides insight into the types of problem instances for which each heuristic performs well or poorly. Because no single heuristic outperforms all others across all problem instances, we use machine learning to predict which heuristic will work best on a previously unseen problem instance, a key question facing practitioners.

  • A Combined Generative and Selective Hyper-heuristic for the Vehicle Routing Problem, by Sim, Kevin and Hart, Emma, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, ACM, 2016 [PDF] [ABSTRACT]

    Hyper-heuristic methods for solving vehicle routing problems (VRP) have proved promising on a range of data. The vast majority of approaches apply selective hyper-heuristic methods that iteratively choose appropriate heuristics from a fixed set of pre-defined low-level heuristics to either build or perturb a candidate solution. We propose a novel hyper-heuristic called GP-MHH that operates in two stages. The first stage uses a novel Genetic Programming (GP) approach to evolve high quality constructive heuristics; these can be used with any existing method that relies on a candidate solution(s) as its starting point. In the second stage, a perturbative hyper-heuristic is applied to candidate solutions created from the new heuristics. The new constructive heuristics are shown to outperform existing low-level heuristics. When combined with a naive perturbative hyper-heuristic they provide results which are both competitive with known optimal values and outperform a recent method that also designs new heuristics on some standard benchmarks. Finally, we provide results on a set of rich VRPs, showing the generality of the approach.

  • A Generative Hyper-Heuristic for Deriving Heuristics for Classical Artificial Intelligence Problems, by Pillay, Nelishia, Advances in Nature and Biologically Inspired Computing, Springer, 2016 [PDF] [ABSTRACT]

    A recent direction of hyper-heuristics is the automated design of intelligent systems with the aim of reducing the man hours needed to implement such systems. One of the design decisions that often has to be made when developing intelligent systems is the low-level construction heuristic to use. These are usually rules of thumb derived based on human intuition. Generally a heuristic is derived for a particular domain. However, according to the no free lunch theorem different low-level heuristics will be effective for different problem instances. Deriving low-level heuristics for problem instances will be time consuming and hence we examine the automatic induction of low-level heuristics using hyper-heuristics. We investigate this for classical artificial intelligence. At the inception of the field of artificial intelligence search methods to solve problems were generally uninformed, such as the depth first and breadth first searches, and did not take any domain specific knowledge into consideration. As the field matured domain specific knowledge in the form of heuristics were used to guide the search, thereby reducing the search space. Search methods using heuristics to guide the search became known as informed searches, such as the best-first search, hill-climbing and the A* algorithm. Heuristics used by these searches are problem specific rules of thumb created by humans. This study investigates the use of a generative hyper-heuristic to derive these heuristics. The hyper-heuristic employs genetic programming to evolve the heuristics. The approach was tested on two classical artificial intelligence problems, namely, the 8-puzzle problem and Towers of Hanoi. The genetic programming system was able to evolve heuristics that produced solutions for 20 8-puzzle problems and 5 instances of Towers of Hanoi. Furthermore, the heuristics induced were able to produce solutions to the instances of the 8-puzzle problem which could not be solved using the A* algorithm with the number of tiles out of place heuristic and at least one admissible heuristic was evolved for all 25 problems.

  • A Hyper-Heuristic Approach to Solving the Ski-Lodge Problem, by Hassan, Ahmed and Pillay, Nelishia, Advances in Nature and Biologically Inspired Computing, Springer, 2016 [PDF] [ABSTRACT]

    Hyper-heuristics seek solution methods instead of solutions and thus provides a higher level of generality compared to bespoke metaheuristics and traditional heuristic approaches. In this paper, a hyper-heuristic is proposed to solve the ski-lodge problem which involves allocating shared-time apartments to customers during a skiing season in a way that achieves a certain objective while respecting the constraints of the problem. Prior approaches to the problem include simulated annealing and genetic algorithm. To the best of our knowledge, this is the first time the ski-lodge problem is approached from a hyper-heuristic perspective. Although the aim of hyper-heuristics is to provide good results over problem sets rather than producing best results for certain problem instances, for completeness and to get an idea of the quality of solutions, the results of the proposed hyper-heuristic are compared to that of genetic algorithm and simulated annealing. The hyper-heuristic was found to perform better than simulated annealing and comparatively to the genetic algorithm, producing better results for some of the instances. Furthermore, the hyper-heuristic has better overall performance over the problem set being considered.

  • A Hyper-Heuristic Framework for Agent-Based Crowd Modeling and Simulation, by Zhong, Jinghui and Cai, Wentong, Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 2016 [PDF] [ABSTRACT]

    This paper proposes a hyper-heuristic crowd modeling framework to generate realistic crowd dynamics that can match video data. In the proposed framework, motions of agents are driven by a high-level heuristic (HH) which intelligently selects way-points for agents based on the current situations. Three low-level heuristics are defined and used as building blocks of the HH. Based on the newly defined building blocks and fitness evaluation function, the Self-Learning Gene Expression Programming (SL-GEP) is utilized to automatically evolve a suitable HH. To test its effectiveness, the proposed framework is applied to learn suitable HHs based on real video data. The best HH learned is then applied to generate crowd simulations and the simulation results demonstrate that the proposed method is effective to generate realistic crowd dynamics.

  • A Hyperheuristic Approach to Leveraging Domain Knowledge in Multi-Objective Evolutionary Algorithms, by Hitomi, Nozomi and Selva, Daniel, ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers, 2016 [PDF] [ABSTRACT]

    Evolutionary algorithms have shown much success in solving real-world design problems, but they are considered computationally inefficient because they rely on many objective-function evaluations instead of leveraging domain knowledge to guide the optimization. An evolutionary algorithm's performance can be improved by utilizing operators called domain-specific heuristics that incorporate domain knowledge, but existing knowledge-intensive algorithms utilize one or two domain-specific heuristics, which limits the amount of incorporated knowledge or treats all knowledge as equally effective. We propose a hyperheuristic approach that efficiently utilizes multiple domain-specific heuristics that incorporate knowledge from different sources by allocating computational resources to the effective ones. Furthermore, a hyperheuristic allows the simultaneous use of conventional evolutionary operators that assist in escaping local optima. This paper empirically demonstrates the efficacy of the proposed hyperheuristic approach on a multi-objective design problem for an Earth observation satellite system. Results show that the hyperheuristic approach significantly improves the search performance compared to an evolutionary algorithm that does not use any domain knowledge.

  • A Parameterized Scheme of Metaheuristics to Solve NP-Hard Problems in Data Envelopment Analysis, by Aparicio, Juan and Gonzalez, Martin and Lopez-Espin, Jose J and Pastor, Jesus T, Advances in Efficiency and Productivity, Springer, 2016 [PDF] [ABSTRACT]

    Data Envelopment Analysis (DEA) is a well-known methodology for estimating technical efficiency from a set of inputs and outputs of Decision Making Units (DMUs). This paper is devoted to computational aspects of DEA models when the determination of the least distance to the Pareto-efficient frontier is the goal. Commonly, these models have been addressed in the literature by applying unsatisfactory techniques, based essentially on combinatorial NP-hard problems. Recently, some heuristics have been introduced to solve these situations. This work improves on previous heuristics for the generation of valid solutions. More valid solutions are generated and with lower execution time. A parameterized scheme of metaheuristics is developed to improve the solutions obtained through heuristics. A hyper-heuristic is used over the parameterized scheme. The hyper-heuristic searches in a space of metaheuristics and generates metaheuristics that provide solutions close to the optimum. The method is competitive versus exact methods, and has a lower execution time.

  • A Sequence-based Selection Hyper-heuristic - A Case Study in Nurse Rostering, by Kheiri, Ahmed and Ozcan, Ender and Lewis, Rhyd and Thompson, Jonathan, the 11th International Confenference on Practice and Theory of Automated Timetabling (PATAT), 2016 [PDF] [ABSTRACT]

    The nurse rostering problem has been of interest to practitioners and researchers in the fields of operational research and artificial intelligence. This problem is known to be NP-hard [1]. We have joined the second international nurse rostering competition (INRC-II1) to solve an extended version of the problem, referred to as the multi-stage nurse rostering problem, using a sequence-based selection hyper-heuristic method. The full description of the problem can be found at the competition website. We present our solution method in this study.

  • A dynamic truck dispatching problem in marine container terminal, by Chen, Jianjun and Bai, Ruibin and Dong, Haibo and Qu, Rong and Kendall, Graham, IEEE Symposium on Computational Intelligence in Scheduling and Network Design (SSCI), 2016 [PDF] [ABSTRACT]

    In this paper, a dynamic truck dispatching problem of a marine container terminal is described and discussed. In this problem, a few containers, encoded as work instructions, need to be transferred between yard blocks and vesselby a fleet of trucks. Both the yard blocks and the quay are equipped with cranes to support loading/unloading operations. In order to service more vessels, any unnecessary idle time between quay crane (QC) operations need to be minimised to speed up the container transfer process. Due to the unpredictable port situations that can affect routing plans and the short calculation time allowed to generate one, static solution methods are not suitable for this problem. In this paper, we introduce a new mathematical model that minimises both the QC makespan and the truck travelling time. Three dynamic heuristics are proposed and a genetic algorithm hyperheuristic (GAHH) under development is also described. Experiment results show promising capabilities the GAHH may offer.

  • Adaptive Thompson Sampling for hyper-heuristics, by Alanazi, Fawaz, IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2016 [PDF] [ABSTRACT]

    There is an interest in search algorithms capable of learning and adapting their behaviour while solving a given problem. A hyper-heuristic operates on a set of predefined heuristics and applies a machine learning technique to predict which heuristic is the most effective to apply at a given point in time. Thompson Sampling is a machine learning mechanism interacting with the search environment to adapt its behaviour through trial-and-error. Despite the fact that it originated in the 1930s, the work on Thompson Sampling in the literature on search heuristics is limited. This paper is the first study investigating the Thompson Sampling approach in the field of hyper-heuristics. I propose an adaptive Thompson Sampling mechanism for hyper-heuristics and extensively evaluate its performance on a wide range of test models and combinatorial optimisation problems. The proposed algorithm is tested and compared with a large number of hyper-heuristics within a well-known competition for hyper-heuristics called CHeSC 2011. The results reveal that the proposed hyper-heuristic outperforms all the competing hyper-heuristics, including the state-of-the-art algorithm, on three combinatorial optimisation problems: (1) Personnel Scheduling; (2) Permutation Flow-shop, and (3) the Travelling Salesman problem.

  • An Analysis of the Taguchi Method for Tuning a Memetic Algorithm with Reduced Computational Time Budget, by Gumus, Duriye Betul and Ozcan, Ender and Atkin, Jason, Proceedings of the 31st International Symposium on Computer and Information Sciences (ISCIS), 2016 [PDF] [ABSTRACT]

    Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a predefined set of low level heuristics for solving computationally hard combinatorial optimisation problems. Being reusable methods, they are expected to be applicable to multiple problem domains, hence performing well in cross-domain search. HyFlex is a general purpose heuristic search API which separates the high level search control from the domain details enabling rapid development and performance comparison of heuristic search methods, particularly hyper-heuristics. In this study, the performance of six previously proposed selection hyper-heuristics are evaluated on three recently introduced extended HyFlex problem domains, namely 0-1 Knapsack, Quadratic Assignment and Max-Cut. The empirical results indicate the strong generalising capability of two adaptive selection hyper-heuristics which perform well across the 'unseen' problems in addition to the six standard HyFlex problem domains.

  • An Evolutionary Hyper-heuristic for the Software Project Scheduling Problem, by Xiuli Wu and Pietro Consoli and Leandro Minku and Gabriela Ochoa and Xin Yao, 14th International Conference on Parallel Problem Solving from Nature (PPSN), LNCS, 9921, Edinburgh, UK, Springer, 2016 [PDF] [ABSTRACT]

    Software project scheduling plays an important role in reducing the cost and duration of software projects. It is an NP-hard combinatorial optimization problem that has been addressed based on single and multi-objective algorithms. However, such algorithms have always used fixed genetic operators, and it is unclear which operators would be more appropriate across the search process. In this paper, we propose an evolutionary hyper-heuristic to solve the software project scheduling problem. Our novelties include the following: (1) this is the first work to adopt an evolutionary hyper-heuristic for the software project scheduling problem; (2) this is the first work for adaptive selection of both crossover and mutation operators; (3) we design different credit assignment methods for mutation and crossover; and (4) we use a sliding multi-armed bandit strategy to adaptively choose both crossover and mutation operators. The experimental results show that the proposed algorithm can solve the software project scheduling problem effectively.

  • An Investigation of Tuning a Memetic Algorithm for Cross-domain Search, by Gumus, Duriye Betul and Ozcan, Ender and Atkin, Jason, IEEE Congress on Evolutionary Computation (CEC), IEEE, 2016 [PDF] [ABSTRACT]

    Memetic algorithms, which hybridise evolutionary algorithms with local search, are well-known metaheuristics for solving combinatorial optimisation problems. A common issue with the application of a memetic algorithm is determining the best initial setting for the algorithmic parameters, but these can greatly influence its overall performance. Unlike traditional studies where parameters are tuned for a particular problem domain, in this study we do tuning that is applicable to cross-domain search. We extend previous work by tuning the parameters of a steady state memetic algorithm via a 'design of experiments' approach and provide surprising empirical results across nine problem domains, using a cross-domain heuristic search tool, namely HyFlex. The parameter tuning results show that tuning has value for cross-domain search. As a side gain, the results suggest that the crossover operators should not be used and, more interestingly, that single point based search should be preferred over a population based search, turning the overall approach into an iterated local search algorithm. The use of the improved parameter settings greatly enhanced the crossdomain performance of the algorithm, converting it from a poor performer in previous work to one of the stronger competitors.

  • An Iterated Variable Neighborhood Descent Hyperheuristic for the Quadratic Multiple Knapsack Problem, by Tlili, Takwa and Yahyaoui, Hiba and Krichen, Saoussen, Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2015, Springer, 2016 [PDF] [ABSTRACT]

    The Quadratic Multiple Knapsack Problem (QMKP) is a variant of the well-known NP-hard knapsack problem that assign profits not only to individual items but also to pairs of items. QMKP aims to maximize a quadratic objective function subject to a linear capacity constraint. In this paper, we focus on proposing a hyper-heuristic approach based in the iterated variable neighborhood descent algorithm for solving the QMKP. Numerical investigations based on well-known benchmark instances are conducted. The results clearly demonstrate the good performance of the proposed algorithm in solving the QMKP.

  • Automatic parameter configuration for an elite solution hyper-heuristic applied to the Multidimensional Knapsack Problem, by Urra, Enrique and Cubillos, Claudio and Cabrera-Paniagua, Daniel and Lefranc, Gaston, 2016 6th International Conference on Computers Communications and Control (ICCCC), IEEE, 2016 [PDF] [ABSTRACT]

    Hyper-heuristics are methods for problem solving that decouple the search mechanisms from the domain features, providing a reusable approach across different problems. Even when they make a difference regarding metaheuristics under this perspective, proposals in literature commonly expose parameters for controlling their behavior such as metaheuristics does. Several internal mechanisms for automatically adapt those parameters can be implemented, but they require extra design effort and their validation no necessarily is generalizable to multiple domains. Such effort is prohibitive for their practical application on decision-support systems. Rather than implementing internal adapting mechanisms, the exploration of automatic parameter configuration through external tools is performed in this work. A new hyper-heuristic implementation based on a elite set of solutions was implemented and automatically configured with SMAC (Sequential Model-Based Algorithm Configuration), a state-of-art tool for automatic parameter configuration. Experiments with and without automated configuration are performed over the Multidimensional Knapsack Problem (MKP). Comparative results demonstrate the effectiveness of the tool for improving the algorithm performance. Additionally, results provided insights that configurations applied over subsets of instances could provide better improvements in the algorithm performance.

  • Automatically Designing More General Mutation Operators of Evolutionary Programming for Groups of Function Classes Using a Hyper-Heuristic, by Hong, Libin and Drake, John H and Woodward, John R and Ozcan, Ender, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, ACM, 2016 [PDF] [ABSTRACT]

    In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation operator for Evolutionary Programming. This is done using the Gaussian and uniform distributions as the terminal set, and arithmetic operators as the function set. The mutation operators are automatically designed for a specific function class. The contribution of this paper is to show that a GP can not only automatically design a mutation operator for Evolutionary Programming (EP) on functions generated from a specific function class, but also can design more general mutation operators on functions generated from groups of function classes. In addition, the automatically designed mutation operators also show good performance on new functions generated from a specific function class or a group of function classes.

  • Case study: An analysis of accidental complexity in a state-of-the-art hyper-heuristic for HyFlex, by Adriaensen, Steven and Nowe, Ann, IEEE Congress on Evolutionary Computation (CEC), IEEE, 2016 [PDF] [ABSTRACT]

    While simplicity is an important factor affecting algorithm re-usability, it is often overlooked in algorithm design, which has a tendency to produce overly complex methods. In this paper we demonstrate Accidental Complexity Analysis (ACA), a research practice targeted at detecting and eliminating accidental complexity, without loss of performance (c.f. refactoring in software engineering), using it to analyze the presence of accidental complexity in GIHH, a state-of-the-art selection hyper-heuristic for HyFlex. We identify various algorithmic sub-mechanisms contributing little to GIHH's overall performance, and validate many other. As an outcome we present Lean-GIHH, a simplified, re-implementation of GIHH.

  • Characterization of neighborhood behaviours in a multi-neighborhood local search algorithm, by Dang, Nguyen Thi Thanh and De Causmaecker, Patrick, Proceedings of the 10th Learning and Intelligent OptimizatioN Conference (LION), LNCS, 10079, Naples, Italy, 2016 [PDF] [ABSTRACT]

    We consider a multi-neighborhood local search framework with a large number of possible neighborhoods. Each neighborhood is accompanied by a weight value which represents the probability of being chosen at each iteration. These weights are fixed before the algorithm runs, and can be tuned by off-the-shelf off-line automated algorithm configuration tools (e.g., SMAC). However, the large number of parameters might deteriorate the tuning tool's efficiency, especially in our case where each run of the algorithm is not computationally cheap, even when the number of parameters has been reduced by some intuition. In this work, we propose a systematic method to characterize each neighborhood's behaviours, representing them as a feature vector, and using cluster analysis to form similar groups of neighborhoods. The novelty of our characterization method is the ability of reflecting changes of behaviours according to hardness of different solution quality regions based on simple statistics collected during any algorithm runs. We show that using neighborhood clusters instead of individual neighborhoods helps to reduce the parameter configuration space without misleading the search of the tuning procedure. Moreover, this method is problem-independent and potentially can be applied in similar contexts.

  • Connecting Automatic Parameter Tuning, Genetic Programming as a Hyper-heuristic, and Genetic Improvement Programming, by Woodward, John R and Johnson, Colin G and Brownlee, Alexander EI, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, ACM, 2016 [PDF] [ABSTRACT]

    Automatically designing algorithms has long been a dream of computer scientists. Early attempts which generate computer programs from scratch, have failed to meet this goal. However, in recent years there have been a number of different technologies with an alternative goal of taking existing programs and attempting to improving them.These methods form a range of methodologies, from the limited ability to change (for example only the parameters) to the complete ability to change the whole program. These include; automatic parameter tuning (APT), using GP as a hyper-heuristic (GPHH), and GI, which we will now briefly review. Part of research is building links between existing work, and the aim of this paper is to bring together these currently separate approaches.

  • Design of QOS based Web Service Selection/Composition Hyper-Heuristic Model, by Muthuraman, Sangeetha and Venkatesan, V Prasanna, Proceedings of the International Conference on Informatics and Analytics, ACM, 2016 [PDF] [ABSTRACT]

    A web service selection/composition problem is a NP-complete problem that cannot be solved in polynomial time. An efficient solution is essential to solve this problem. This solution may be attained by following hyper-heuristic strategies. As a first step in addressing the problem, this paper presents a new web services selection/composition model which enables such a hyper-heuristic notion. Various parts of this proposed model can be implemented by using different algorithms thus enabling many hybrid implementations. In this paper the proposed model has been implemented by using a reference score and trust based service selection algorithm and a strategic tree based service composition algorithm. To realize this implementation agent based architecture has been proposed. A well defined QOS model has been used to accurately receive customer's request and update service specific quality values. The algorithms implemented are efficient as the computational complexities of these algorithms have been greatly reduced and also a fault tolerant approach has been adopted. The experimental results illustrate that the proposed model and algorithms have effectively solved the web services selection/composition problem.

  • Designing and Comparing Multiple Portfolios of Parameter Configurations for Online Algorithm Selection, by Gunawan, Aldy and Lau, Hoong Chuin and Misir, Mustafa, Proceedings of the 10th Learning and Intelligent OptimizatioN Conference (LION), LNCS, 10079, Naples, Italy, 2016 [PDF] [ABSTRACT]

    Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using different parameter configurations. In this paper, we employ a Design of Experiments (DOE) approach to determine a promising range of values for each parameter of an algorithm. These ranges are further processed to determine a portfolio of parameter configurations, which would be used within two online Algorithm Selection approaches for solving different instances of a given combinatorial optimization problem effectively. We apply our approach on a Simulated Annealing-Tabu Search (SA-TS) hybrid algorithm for solving the Quadratic Assignment Problem (QAP) as well as an Iterated Local Search (ILS) on the Travelling Salesman Problem (TSP). We also generate a portfolio of parameter configurations using best-of-breed parameter tuning approaches directly for the comparison purpose. Experimental results show that our approach lead to improvements over best-of-breed parameter tuning approaches.

  • Ensemble Move Acceptance in Selection Hyper-heuristics, by Kheiri, Ahmed and Misir, Mustafa and Ozcan, Ender, Proceedings of the 31st International Symposium on Computer and Information Sciences (ISCIS), 2016 [PDF] [ABSTRACT]

    Selection hyper-heuristics are high level search methodologies which control a set of low level heuristics while solving a given problem. Move acceptance is a crucial component of selection hyper-heuristics, deciding whether to accept or reject a new solution at each step during the search process. This study investigates group decision making strategies as ensemble methods exploiting the strengths of multiple move acceptance methods for improved performance. The empirical results indicate the success of the proposed methods across six combinatorial optimisation problems from a benchmark as well as an examination timetabling problem.

  • Evaluating Hyperheuristics and Local Search Operators for Periodic Routing Problems, by Chen, Yujie and Mourdjis, Philip and Polack, Fiona and Cowling, Peter and Remde, Stephen, European Conference on Evolutionary Computation in Combinatorial Optimization, Springer, 2016 [PDF] [ABSTRACT]

    Meta-heuristics and hybrid heuristic approaches have been successfully applied to Periodic Vehicle Routing Problems (PVRPs). However, to be competitive, these methods require careful design of specific search strategies for each problem. By contrast, hyperheuristics use the performance of low level heuristics to automatically select and tailor search strategies. Hyperheuristics have been successfully applied to problem domains such as timetabling and production scheduling. In this study, we present a comprehensive analysis of hyperheuristic approaches to solving PVRPs. The performance of hyperheuristics is compared to published performance of state-of-the-art meta-heuristics.

  • Evolving construction heuristics for the curriculum based university course timetabling problem, by Pillay, Nelishia, IEEE Congress on Evolutionary Computation (CEC), IEEE, 2016 [PDF] [ABSTRACT]

    In solving combinatorial optimization problems construction heuristics are generally used to create an initial solution which is improved using optimization techniques like genetic algorithms. These construction heuristics are usually derived by humans and this is usually quite a time consuming task. Furthermore, according to the no free lunch theorem different heuristics are effective for different problem instances. Ideally we would like to derive construction heuristics for different problem instances or classes of problems. However, due to the time it takes to manually derive construction heuristics it is generally not feasible to induce problem instance specific heuristics. The research presented in the paper forms part of the initiative aimed at automating the derivation of construction heuristics. Genetic programming is used to evolve construction heuristics for the curriculum based university course timetabling (CB-CTT) problem. Each heuristic is a hierarchical combination of problem characteristics and a period selection heuristic. The paper firstly presents and analyses the performance of known construction heuristics for CB-CTT. The analysis has shown that different heuristics are effective for different problem instances. The paper then presents the genetic programming approach for the automated induction of construction heuristics for the CB-CTT problem and evaluates the approach on the ITC 2007 problem instances for the second international timetabling competition. The evolved heuristics performed better than the known construction heuristics, producing timetables with lower soft constraint costs.

  • Evolving random graph generators: A case for increased algorithmic primitive granularity, by Pope, Aaron S and Tauritz, Daniel R and Kent, Alexander D, IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2016 [PDF] [ABSTRACT]

    Random graph generation techniques provide an invaluable tool for studying graph related concepts. Unfortunately, traditional random graph models tend to produce artificial representations of real-world phenomenon. Manually developing customized random graph models for every application would require an unreasonable amount of time and effort. In this work, a platform is developed to automate the production of random graph generators that are tailored to specific applications. Elements of existing random graph generation techniques are used to create a set of graph-based primitive operations. A hyper-heuristic approach is employed that uses genetic programming to automatically construct random graph generators from this set of operations. This work improves upon similar research by increasing the level of algorithmic sophistication possible with evolved solutions, allowing more accurate modeling of subtle graph characteristics. The versatility of this approach is tested against existing methods and experimental results demonstrate the potential to outperform conventional and state of the art techniques for specific applications.

  • Fitness landscape analysis of hyper-heuristic transforms for the vertex cover problem, by Trunda, Otakar and Brunetto, Robert, the 16th ITAT Conference Information Technologies - Applications and Theory - the 4th international workshop on Computational Intelligence and Data Mining, 1649, 2016 [PDF] [ABSTRACT]

    Hyper-heuristics have recently proved efficient in several areas of combinatorial search and optimization, especially scheduling. The basic idea of hyper-heuristics is based on searching for search-strategy. Instead of traversing the solution-space, the hyper-heuristic traverses the space of algorithms to find or construct an algorithm best suited for the given problem instance. The observed efficiency of hyper-heuristics is not yet fully explained on the theoretical level. The leading hypothesis suggests that the fitness landscape of the algorithm-space is more favorable to local search techniques than the original space. In this paper, we analyse properties of fitness landscapes of the problem of minimal vertex cover. We focus on properties that are related to efficiency of metaheuristics such as locality and fitness-distance correlation. We compare properties of the original space and the algorithm space trying to verify the hypothesis explaining hyper-heuristics performance. Our analysis shows that the hyper-heuristic space really has some more favorable properties than the original space.

  • Genetic Programming Based Hyper-heuristics for Dynamic Job Shop Scheduling: Cooperative Coevolutionary Approaches, by Park, John and Mei, Yi and Nguyen, Su and Chen, Gang and Johnston, Mark and Zhang, Mengjie, European Conference on Genetic Programming, Springer, 2016 [PDF] [ABSTRACT]

    Job shop scheduling (JSS) problems are optimisation problems that have been studied extensively due to their computational complexity and application in manufacturing systems. This paper focuses on a dynamic JSS problem to minimise the total weighted tardiness. In dynamic JSS, attributes of a job are only revealed after it arrives at the shop floor. Dispatching rule heuristics are prominent approaches to dynamic JSS problems, and Genetic Programming based Hyper-heuristic (GP-HH) approaches have been proposed to automatically generate effective dispatching rules for dynamic JSS problems. Research on static JSS problems shows that high quality ensembles of dispatching rules can be evolved by a GP-HH that uses cooperative coevolution. Therefore, we compare two coevolutionary GP approaches to evolve ensembles of dispatching rules for dynamic JSS problems. First, we adapt the Multilevel Genetic Programming (MLGP) approach, which has never been applied to JSS problems. Second, we extend an existing approach for a static JSS problem, called Ensemble Genetic Programming for Job Shop Scheduling (EGP-JSS), by adding "less-myopic" terminals that take job and machine attributes outside of the scope of the attributes commonly used in the literature. The results show that MLGP for JSS evolves ensembles that are significantly better than single "less-myopic" rules evolved using GP with only little difference in computation time. In addition, the rules evolved using EGP-JSS perform better than the MLGP-JSS rules, but MLGP-JSS evolves rules significantly faster than EGP-JSS.

  • Grammar-based Selection Hyper-heuristics for Solving Irregular Bin Packing Problems, by Sosa-Ascencio, Alejandro and Terashima-Marin, Hugo and Ortiz-Bayliss, Jose C and Conant-Pablos, Santiago E, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, ACM, 2016 [PDF] [ABSTRACT]

    This article describes a grammar-based hyper-heuristic model for selecting heuristics to solve the two-dimensional bin packing problem (2D-PBB) with irregular pieces and regular objects. We propose to use a genetic programming approach to generate rules for selecting one suitable heuristic according to the features that characterize the problem state. The experiments confirm the idea that the results produced by the proposed approach are able to rival those obtained by some heuristics described in the literature.

  • Grammatical Evolution for the Multi-Objective Integration and Test Order Problem, by Mariani, Thaina and Guizzo, Giovani and Vergilio, Silvia R and Pozo, Aurora TR, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, ACM, 2016 [PDF] [ABSTRACT]

    Search techniques have been successfully applied for solving different software testing problems. However, choosing, implementing and configuring a search technique can be hard tasks. To reduce efforts spent in such tasks, this paper presents an offline hyper-heuristic named GEMOITO, based on Grammatical Evolution (GE). The goal is to automatically generate a Multi-Objective Evolutionary Algorithm (MOEA) to solve the Integration and Test Order (ITO) problem. The MOEAs are distinguished by components and parameters values, described by a grammar. The proposed hyper-heuristic is compared to conventional MOEAs and to a selection hyper-heuristic used in related work. Results show that GEMOITO can generate MOEAs that are statistically better or equivalent to the compared algorithms.

  • Heuristic optimization for the resource constrained Project Scheduling Problem: A systematic mapping, by Ciupe, Aurelia and Meza, Serban and Orza, Bogdan, Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE, 2016 [PDF] [ABSTRACT]

    Context: Heuristic optimization has been of strong focus in the recent modeling of the Resource Constrained Project Scheduling Problem (RCPSP), but lack of evidence exists in systematic assessments. New solution methods arise from random evaluation of existing studies. Objective: The current work conducts a secondary study, aiming to systemize existing primary studies in heuristic optimization techniques applied to solving classes of RCPSPs. Method: The systemizing framework consists of performing a systematic mapping study (SM), following a 3-steped protocol. Results: 371 primary studies have been depicted from the multi-stage search and filtering process, to which inclusion and exclusion criteria have been applied. Results have been visually mapped in several distributions. Conclusions: Specific RCPSP classes have been grounded and therefore a rigorous classification is required before performing a systematic mapping. Focusing on recent developments of the RCPSP (2010-2015, a strong interest has been acknowledged on solution methods incorporating AI techniques in meta- and hyper-heuristic algorithms.

  • Heuristics methods for solving the block packing problem, by Kureychik, Viktor M and Kureychik, Vladimir Vl and Potarusov, Roman and Kureychik, Liliya, Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016), Atlantis Press, 2016 [PDF] [ABSTRACT]

    In the given paper one-dimensional Bin Packing Problem which plays an important role for the optimization of transportations and production activities is considered. The Hybrid Genetic Algorithm for one-dimensional Bin Packing Problem is proposed. For this purpose two evolution models (de Vries' evolution model and Lamarck's evolution model) have been adapted. Besides, new problem-oriented genetic operators are developed. The main advantage of the suggested approach is that it never decreases the quality of solution so it allows obtaining valid Bin Packing Problem solutions. Two effective local search algorithms allowing to improve of Bin Packing Problem solutions by getting quasi-optimal and optimal packings are proposed. Computational experiments show that a new hybrid approach based on genetic algorithm intended for solving one-dimensional BPP provides approximation and optimal solutions for all benchmarks in-stances in a tolerable computational time as well as demonstrate the robustness of the proposed approach.

  • Hybridisation of Evolutionary Algorithms through hyper-heuristics for global continuous optimisation., by Segredo, Eduardo and Lalla-Ruiz, Eduardo and Hart, Emma and Paechter, Ben and Voss, Stefan, Proceedings of the 10th Learning and Intelligent OptimizatioN Conference (LION), LNCS, 10079, Naples, Italy, 2016 [PDF] [ABSTRACT]

    Choosing the correct algorithm to solve a problem still remains an issue 40 years after the Algorithm Selection Problem was first posed. Here we propose a hyper-heuristic which can apply one of two meta-heuristics at the current stage of the search. A scoring function is used to select the most appropriate algorithm based on an estimate of the improvement that might be made by applying each algorithm. We use a differential evolution algorithm and a genetic algorithm as the two meta-heuristics and assess performance on a suite of 18 functions provided by the Generalization-based Contest in Global Optimization (genopt). The experimental evaluation shows that the hybridisation is able to provide an improvement with respect to the results obtained by both the differential evolution scheme and the genetic algorithm when they are executed independently. In addition, the high performance of our hybrid approach allowed two out of the three prizes available at genopt to be obtained.

  • Hyper-heuristic General Video Game Playing, by Mendes, Andre and Togelius, Julian and Nealen, Andy, Proceedings of IEEE Computational Intelligence and Games, IEEE, 2016 [PDF] [ABSTRACT]

    In general video game playing, the challenge is to create agents that play unseen games proficiently. Stochastic tree search algorithms, like Monte Carlo Tree Search, perform relatively well on this task. However, performance is nontransitive: different agents perform best in different games, which means that there is not a single agent that is the best in all the games. Rather, some types of games are dominated by a few agents whereas other different agents dominate other types of games. Thus, it should be possible to construct a hyper-agent that selects from a portfolio, in which constituent sub-agents will play a new game best. Since there is no knowledge about the games, the agent needs to use available features to predict the most suitable algorithm. This work constructs such a hyper-agent using the General Video Game Playing Framework (GVGAI). The proposed method achieves promising results that show the applicability of hyper-heuristics in general video game playing and related tasks.

  • Hyper-heuristics for the Flexible Job Shop Scheduling Problem with Additional Constraints, by Grobler, Jacomine and Engelbrecht, Andries P, International Conference in Swarm Intelligence, Springer, 2016 [PDF] [ABSTRACT]

    This paper investigates a highly relevant real world scheduling problem, namely the multi-objective flexible job shop scheduling problem (FJSP) with sequence-dependent set-up times, auxiliary resources and machine down time. A hyper-heuristic algorithm is presented which makes use of a set of meta-heuristic algorithms which are self-adaptively selected at different stages of the optimization process to optimize a set of candidate solutions. This meta-hyper-heuristic algorithm was tested on a number of real world production scheduling data sets and was also benchmarked against the previous state-of-the-art job shop scheduling algorithms applied to this specific problem. In addition to the competitive results obtained, the self-adaptive nature of the algorithm avoids the resource intensive process of developing a meta-heuristic algorithm for one specific problem instance.

  • Iterative Cartesian Genetic Programming: Creating General Algorithms for Solving Travelling Salesman Problems, by Ryser-Welch, Patricia and Miller, Julian F and Swan, Jerry and Trefzer, Martin A, European Conference on Genetic Programming, Springer, 2016 [PDF] [ABSTRACT]

    Evolutionary algorithms have been widely used to optimise or design search algorithms, however, very few have considered evolving iterative algorithms. In this paper, we introduce a novel extension to Cartesian Genetic Programming that allows it to encode iterative algorithms. We apply this technique to the Traveling Salesman Problem to produce human-readable solvers which can be then be independently implemented. Our experimental results demonstrate that the evolved solvers scale well to much larger TSP instances than those used for training.

  • Learning Heuristics for Mining RNA Sequence-Structure Motifs, by Elyasaf, Achiya and Vaks, Pavel and Milo, Nimrod and Sipper, Moshe and Ziv-Ukelson, Michal, Genetic Programming Theory and Practice XIII, Springer, 2016 [PDF] [ABSTRACT]

    The computational identification of conserved motifs in RNA molecules is a major-yet largely unsolved-problem. Structural conservation serves as strong evidence for important RNA functionality. Thus, comparative structure analysis is the gold standard for the discovery and interpretation of functional RNAs.In this paper we focus on one of the functional RNA motif types, sequence-structure motifs in RNA molecules, which marks the molecule as targets to be recognized by other molecules.We present a new approach for the detection of RNA structure (including pseudoknots), which is conserved among a set of unaligned RNA sequences. Our method extends previous approaches for this problem, which were based on first identifying conserved stems and then assembling them into complex structural motifs. The novelty of our approach is in simultaneously preforming both the identification and the assembly of these stems. We believe this novel unified approach offers a more informative model for deciphering the evolution of functional RNAs, where the sets of stems comprising a conserved motif co-evolve as a correlated functional unit.Since the task of mining RNA sequence-structure motifs can be addressed by solving the maximum weighted clique problem in an n-partite graph, we translate the maximum weighted clique problem into a state graph. Then, we gather and define domain knowledge and low-level heuristics for this domain. Finally, we learn hyper-heuristics for this domain, which can be used with heuristic search algorithms (e.g., A*, IDA*) for the mining task.The hyper-heuristics are evolved using HH-Evolver, a tool for domain-specific, hyper-heuristic evolution. Our approach is designed to overcome the computational limitations of current algorithms, and to remove the necessity of previous assumptions that were used for sparsifying the graph.This is still work in progress and as yet we have no results to report. However, given the interest in the methodology and its previous success in other domains we are hopeful that these shall be forthcoming soon.

  • Limits to Learning in Reinforcement Learning Hyper-heuristics, by Alanazi, Fawaz and Lehre, Per Kristian, European Conference on Evolutionary Computation in Combinatorial Optimization, Springer, 2016 [PDF] [ABSTRACT]

    Learning mechanisms in selection hyper-heuristics are used to identify the most appropriate subset of heuristics when solving a given problem. Several experimental studies have used additive reinforcement learning mechanisms, however, these are inconclusive with regard to the performance of selection hyper-heuristics with these learning mechanisms. This paper points out limitations to learning with additive reinforcement learning mechanisms. Our theoretical results show that if the probability of improving the candidate solution in each point of the search process is less than 1 / 2 which is a mild assumption, then additive reinforcement learning mechanisms perform asymptotically similar to the simple random mechanism which chooses heuristics uniformly at random. In addition, frequently used adaptation schemes can affect the memory of reinforcement learning mechanisms negatively. We also conducted experiments on two well-known combinatorial optimisation problems, bin-packing and flow-shop, and the obtained results confirm the theoretical findings. This study suggests that alternatives to the additive updates in reinforcement learning mechanisms should be considered.

  • Metaheuristic Design Pattern: Visitor for Genetic Operators, by Guizzo, Giovani and Vergilio, Silvia R, the 5th Brazilian Conference on Intelligent System (BRACIS), 2016 [PDF] [ABSTRACT]

    Metaheuristics, such as Genetic Algorithms (GAs), and hyper-heuristics have been widely studied and applied in the literature. This led to the development of several frameworks to aid the execution and development of such algorithms. Consequently, the reusability, scalability and maintainability became fundamental points to be attacked by developers. Such points can be improved using Design Patterns, but despite their advantages, few works have explored their usage with metaheuristics and hyper-heuristics. In order to contribute to this research topic, we present a solution based on the Visitor pattern used to design genetic operators. A case study is presented with the Hyper-heuristic for the Integration and Test Order problem (HITO). This case study shows that the proposed solution can increase the reusability of the implemented operators, and also enable easy addition of new genetic operators and representations.

  • Multi Agent Hyper-Heuristics based framework for production scheduling problem, by Nugraheni, Cecilia E and Abednego, Luciana, International Conference on Informatics and Computing (ICIC), IEEE, 2016 [PDF] [ABSTRACT]

    This paper investigates the potential use of hyper-heuristics and multi agent approach for solution of the real single machine production scheduling problem. A framework consisting of six agents is proposed. The agents are Problem Agent, Trainer Agent, Training Dataset Agent, Heuristic Pool Agent, Algorithm Agent, Advisor Agent, and Solver Agent. Three Algorithm Agents are proposed to solve the problem, i.e. Genetic Programming Hyper-Heuristics (GPHH) agent, Genetic Algorithm Hyper-Heuristic (GAHH) agent, and Simulated Annealing Hyper-Heuristics (SAHH) agent. Experimental results show that the performance of GAHH is comparable with SAHH. While GPHH agent outperforms GAHH algorithm agent and SAHH algorithm agent, and also six other benchmark heuristics including MRT, SPT, LPT, EDD, LDD, and MON rules with respect to minimum tardiness and minimum flow time objectives.

  • Multi-objective Optimisation of a Water Distribution Network with a Sequence-based Selection Hyper-heuristic, by Walker, DJ and Keedwell, E and Savic, D, the 14th International Conference on Computing and Control for the Water Industry, 2016 [PDF] [ABSTRACT]

    Multi-objective hyper-heuristics are fast becoming an efficient way of optimising complex problems. The water distribution network design problem is an example of such a problem, and this work employs a recent hyper-heuristic that generates sequences of low-level heuristics to solve the multi-objective water distribution design problem. The results presented are comparable to those generated by state-of-the-art metaheuristics, as well as a single-objective version of the algorithm from the literature. The information revealed from analysing the sequences generated to solve the problem reveal important information about the nature of the problem space that is not available from the metaheuristics, and the entire Pareto front can be explored in a single run as opposed to the multiple runs needed with the original single-objective algorithm.

  • Multi-objective Optimisation with a Sequence-based Selection Hyper-heuristic, by Walker, David J and Keedwell, Ed, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, ACM, 2016 [PDF] [ABSTRACT]

    Hyper-heuristics have been used widely to solve optimisation problems, often single-objective and discrete in nature. Herein, we extend a recently-proposed selection hyper-heuristic to the multi-objective domain and with it optimise continuous problems. The MOSSHH algorithm operates as a hidden Markov model, using transition probabilities to determine which low-level heuristic or sequence of heuristics should be applied next. By incorporating dominance into the transition probability update rule, and an elite archive of solutions, MOSSHH generates solutions to multi-objective problems that are competitive with bespoke multi-objective algorithms. When applied to test problems, it is able to find good approximations to the true Pareto front, and yields information about the type of low-level heuristics that it uses to solve the problem.

  • Niching Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling: An Investigation into Distance Metrics, by Park, John and Mei, Yi and Chen, Gang and Zhang, Mengjie, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, ACM, 2016 [PDF] [ABSTRACT]

    This paper investigates the application of fitness sharing to a coevolutionary genetic programming based hyper-heuristic (GP-HH) approach to a dynamic job shop scheduling (DJSS) problem that evolves an ensemble of dispatching rules. Evolving ensembles using GP-HH for DJSS problem is a relatively unexplored area, and has been shown to outperform standard GP-HH procedures that evolve single rules. As a fitness sharing algorithm has not been applied to the specific GP-HH approach, we investigate four different phenotypic distance measures as part of a fitness sharing algorithm. The fitness sharing algorithm may potentially improve the diversity of the constituent members of the ensemble and improve the quality of the ensembles. The results show that the niched coevolutionary GP approaches evolve smaller sized rules than the base coevolutionary GP approaches, but have similar performances.

  • Online Hyper-Evolution of Controllers in Multirobot Systems, by Silva, Fernando and Correia, Lu\is and Christensen, Anders Lyhne, Self-Adaptive and Self-Organizing Systems (SASO), 2016 IEEE 10th International Conference on, IEEE, 2016 [PDF] [ABSTRACT]

    In this paper, we introduce online hyper-evolution (OHE) to accelerate and increase the performance of online evolution of robotic controllers. Robots executing OHE use the different sources of feedback information traditionally associated with controller evaluation to find effective evolutionary algorithms and controllers online during task execution. We present two approaches: OHE-fitness, which uses the fitness score of controllers as the criterion to select promising algorithms over time, and OHE-diversity, which relies on the behavioural diversity of controllers for algorithm selection. Both OHE-fitness and OHE-diversity are distributed across groups of robots that evolve in parallel. We assess the performance of OHE-fitness and of OHE-diversity in two foraging tasks with differing complexity, and in five configurations of a dynamic phototaxis task with varying evolutionary pressures. Results show that our OHE approaches: (i) outperform multiple state-of-the-art algorithms as they facilitate controllers with superior performance and faster evolution of solutions, and (ii) can increase effectiveness at different stages of evolution by combining the benefits of multiple algorithms over time. Overall, our study shows that OHE is an effective new paradigm to the synthesis of controllers for robots.

  • Optimizing Metaheuristics and Hyperheuristics through Multi-level Parallelism on a Many-Core System, by Lozano, Jose Mat\ias Cutillas and Gimenez, Domingo and Garc\ia, Luis Pedro, 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), IEEE, 2016 [PDF] [ABSTRACT]

    Hyperheuristics based on parameterized metaheuristic schemas are computationally demanding. To reduce execution times, a shared-memory schema of hyperheuristics is used, with four levels of parallelism, with two being selected for the hyperheuristic and two for the metaheuristics. The parallel schema is executed in a many-core system in "native mode", and the four-level parallelism allows us to take full advantage of the massive parallelism offered by this architecture. An auto-tuning methodology is used to select the number of threads used at each level. A theoretical model of the execution time of the parameterized metaheuristic schema is developed, and the model is adapted to a particular metaheuristic by experimentation. The massive parallelism in a many-core system can help to obtain satisfactory fitness and an important reduction in execution times, for which the four-levels parallelism schema is useful, and the auto-tuning engine facilitates the optimum selection of the number of threads at each level. The best results are obtained with a relatively low number of threads distributed among the four levels of parallelism between the hyper and metaheuristics.

  • Parallel Multi-objective Job Shop Scheduling Using Genetic Programming, by Karunakaran, Deepak and Chen, Gang and Zhang, Mengjie, Australasian Conference on Artificial Life and Computational Intelligence, Springer, 2016 [PDF] [ABSTRACT]

    In recent years, multi-objective optimization for job shop scheduling has become an increasingly important research problem for a wide range of practical applications. Aimed at effectively addressing this problem, the usefulness of an evolutionary hyper-heuristic approach based on both genetic programming and island models will be thoroughly studied in this paper. We focus particularly on evolving energy-aware dispatching rules in the form of genetic programs that can schedule jobs for the purpose of minimizing total energy consumption, makespan and total tardiness in a job shop. To improve the opportunity of identifying desirable dispatching rules, we have also explored several alternative topologies of the island model. Our experimental results clearly showed that, with the help of the island models, our evolutionary algorithm could outperform some general-purpose multi-objective optimization methods, including NSGA-II and SPEA-2.

  • Performance of Selection Hyper-heuristics on the Extended HyFlex Domains, by Almutairi, Alhanof and Ozcan, Ender and Kheiri, Ahmed and Jackson, Warren G, Proceedings of the 31st International Symposium on Computer and Information Sciences (ISCIS), 2016 [PDF] [ABSTRACT]

    Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a predefined set of low level heuristics for solving computationally hard combinatorial optimisation problems. Being reusable methods, they are expected to be applicable to multiple problem domains, hence performing well in cross-domain search. HyFlex is a general purpose heuristic search API which separates the high level search control from the domain details enabling rapid development and performance comparison of heuristic search methods, particularly hyper-heuristics. In this study, the performance of six previously proposed selection hyper-heuristics are evaluated on three recently introduced extended HyFlex problem domains, namely 0-1 Knapsack, Quadratic Assignment and Max-Cut. The empirical results indicate the strong generalising capability of two adaptive selection hyper-heuristics which perform well across the 'unseen' problems in addition to the six standard HyFlex problem domains.

  • Selection Hyper-heuristics Can Provably Be Helpful in Evolutionary Multi-objective Optimization, by Qian, Chao and Tang, Ke and Zhou, Zhi-Hua, International Conference on Parallel Problem Solving from Nature (PPSN), Springer, 2016 [PDF] [ABSTRACT]

    Selection hyper-heuristics are automated methodologies for selecting existing low-level heuristics to solve hard computational problems. They have been found very useful for evolutionary algorithms when solving both single and multi-objective real-world optimization problems. Previous work mainly focuses on empirical study, while theoretical study, particularly in multi-objective optimization, is largely insufficient. In this paper, we use three main components of multi-objective evolutionary algorithms (selection mechanisms, mutation operators, acceptance strategies) as low-level heuristics, respectively, and prove that using heuristic selection (i.e., mixing low-level heuristics) can be exponentially faster than using only one low-level heuristic. Our result provides theoretical support for multi-objective selection hyper-heuristics, and might be helpful for designing efficient heuristic selection methods in practice.

  • Selection and Generation Hyper-heuristics for Solving the Vehicle Routing Problem with Time Windows, by Espinoza-Nevarez, David and Ortiz-Bayliss, Jose Carlos and Terashima-Marin, Hugo and Gatica, Gustavo, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, ACM, 2016 [PDF] [ABSTRACT]

    The vehicle routing problem is a classic optimization problem with many variants. One of the variants is given by the inclusion of the time windows constraint which requires the clients to be served within a delimited time frame. Because of its complexity, vehicle routing problems are usually solved by using heuristics without optimality guarantee. This paper describes two hyper-heuristics capable of producing results comparable to the ones obtained by the best-performing heuristics on different sets of benchmark instances.

  • Towards many-objective optimisation with hyper-heuristics: Identifying good heuristics with indicators, by Walker, DJ and Keedwell, EK, Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN), LNCS, Springer, 2016 [PDF] [ABSTRACT]

    The use of hyper-heuristics is increasing in the multi-objective optimisation domain, and the next logical advance in such methods is to use them in the solution of many-objective problems. Such problems comprise four or more objectives and are known to present a significant challenge to standard dominance-based evolutionary algorithms. We in- corporate three comparison operators as alternatives to dominance and investigate their potential to optimise many-objective problems with a hyper-heuristic from the literature. We discover that the best results are obtained using either the favour relation or hypervolume, but conclude that changing the comparison operator alone will not allow for the generation of estimated Pareto fronts that are both close to and fully cover the true Pareto front.

  • Two Frameworks for Cross-Domain Heuristic and Parameter Selection Using Harmony Search, by Dempster, Paul and Drake, John H, Harmony Search Algorithm, Springer, 2016 [PDF] [ABSTRACT]

    Harmony Search is a metaheuristic technique for optimizing problems involving sets of continuous or discrete variables, inspired by musicians searching for harmony between instruments in a performance. Here we investigate two frameworks, using Harmony Search to select a mixture of continuous and discrete variables forming the components of a Memetic Algorithm for cross-domain heuristic search. The first is a single-point based framework which maintains a single solution, updating the harmony memory based on performance from a fixed starting position. The second is a population-based method which co-evolves a set of solutions to a problem alongside a set of harmony vectors. This work examines the behaviour of each framework over thirty problem instances taken from six different, real-world problem domains. The results suggest that population co-evolution performs better in a time-constrained scenario, however both approaches are ultimately constrained by the underlying metaphors.

  • Variable Neighbourhood Descent with Memory: A Hybrid Metaheuristic for Supermarket Resupply, by Mourdjis, Philip and Chen, Yujie and Polack, Fiona and Cowling, Peter and Robinson, Martin, International Workshop on Hybrid Metaheuristics, Springer, 2016 [PDF] [ABSTRACT]

    Supermarket supply chains represent an area in which optimisation of vehicle routes and scheduling can lead to huge cost and environmental savings. As just-in-time ordering practices become more common, traditionally fixed resupply routes and schedules are increasingly unable to meet the demands of the supermarkets. Instead, we model this as a dynamic pickup and delivery problem with soft time windows (PDPSTW). We present the variable neighbourhood descent with memory (VNDM) hybrid metaheuristic (HM) and compare its performance against Q-learning (QL), binary exponential back off (BEBO) and random descent (RD) hyperheuristics on published benchmark and real-world instances of the PDPSTW. We find that VNDM consistently generates the highest quality solutions, with the fewest routes or shortest distances, amongst the methods tested. It is capable of finding the best known solutions to 55 of 176 published benchmarks as well as producing the best results on our real-world data set, supplied by Transfaction Ltd.

  • Why Asynchronous Parallel Evolution is the Future of Hyper-heuristics: A CDCL SAT Solver Case Study, by Bertels, Alex R and Tauritz, Daniel R, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, ACM, 2016 [PDF] [ABSTRACT]

    Evolutionary Algorithms (EAs) are inherently parallel due to their ability to simultaneously evaluate the fitness of individuals. Synchronous Parallel EAs (SPEAs) leverage this with the intent to gain significant speed-ups when executed on multiple processors. However, many important problem classes lead to large variations in fitness evaluation times, such as is often the case in hyper-heuristics where the time complexity of executing one individual may differ greatly from that of another. Asynchronous Parallel EAs (APEAs) omit the generational synchronization step of traditional EAs which work in well-defined cycles. They can provide scalability improvements proportional to the variation in fitness evaluation times of the evolved individuals, and therefore should be considered for use in hyper-heuristics. This paper provides an empirical analysis of the improvements obtained by applying APEAs, compared to SPEAs, on a case study involving the evolution of conflict-driven clause learning Boolean satisfiability solvers, demonstrating that APEAs are the future of hyper-heuristics.

  • CMA--VNS2: An efficient hyper-heuristic algorithm for combinatorial black-box optimization, by Xue, Fan and Shen, Geoffrey QP, 2016 [PDF] [ABSTRACT]

    The CMA-VNS2 (Covariance Matrix Adaptation Variable Neighborhood Search, version 2016) solver is a hyper-heuristic entry for the second Combinatorial Black-Box Optimization Competition (CBBOC 2016).

  • A cross-domain multi-armed bandit hyper-heuristic, by Ferreira, Alexandre Silvestre, Master Thesis, University of Parana, 2016 [PDF] [ABSTRACT]

    Many real word optimization problems are very complex with many variables and constraints, and cannot be solved by exact methods in a reasonable computational time. As an alternative, meta-heuristics emerged as an efficient way to solve this type of problems even though they cannot ensure optimal values. The main issue of meta-heuristics is that they are built using domain-specific knowledge, therefore they require a great effort to be used in a new domain. In order to solve this problem, the concept of Hyper-heuristics were proposed. Hyper-heuristics are search methods that aim to solve optimization problems by selecting or generating heuristics. Selection hyper-heuristics choose from a pool of heuristics a good one to be applied at the current stage of the optimization process. The selection mechanism is the main part of a selection hyper-heuristic and has a great impact on its performance. Although there are several works focused on selection hyperheuristics, there is no unanimity about which is the best way to define a selection strategy. In this dissertation, a deterministic selection strategy based on the concepts of the MultiArmed Bandit (MAB) problem is proposed to cross-domain optimization. Multi-armed bandit approaches define a selection function with two components, the first is based on the performance of an operator and the second based on the number of times that the operator was used. These approaches had showed a promising performance over the Adaptive Operator Selection context. However, there are few works on literature that aim the hyper-heuristic context, as proposed here. The proposed approach is integrated into the HyFlex framework, that was developed to facilitate the implementation and comparison of hyper-heuristics. An empirical parameter configuration was performed and the best setup was compared to the top ten CHeSC 2011 algorithms using the same methodology adopted during the competition. The results obtained were good comparable to those attained by the literature. Moreover, it was concluded that the behavior of MAB selection is heavily affected by its parameters. As this is not a desirable behavior to hyper-heuristics, future research will investigate ways to better deal with the parameter setting.

  • Automated design of boolean satisfiability solvers employing evolutionary computation, by Bertels, Alex Raymond, Master Thesis, Department of Computer Science, Missouri University of Science and Technology, 2016 [PDF] [ABSTRACT]

    Modern society gives rise to complex problems which sometimes lend themselves to being transformed into Boolean satisfiability (SAT) decision problems; this thesis presents an example from the program understanding domain. Current conflict-driven clause learning (CDCL) SAT solvers employ all-purpose heuristics for making decisions when finding truth assignments for arbitrary logical expressions called SAT instances. The instances derived from a particular problem class exhibit a unique underlying structure which impacts a solver's effectiveness. Thus, tailoring the solver heuristics to a particular problem class can significantly enhance the solver's performance; however, manual specialization is very labor intensive. Automated development may apply hyper-heuristics to search program space by utilizing problem-derived building blocks. This thesis demonstrates the potential for genetic programming (GP) powered hyper-heuristic driven automated design of algorithms to create tailored CDCL solvers, in this case through custom variable scoring and learnt clause scoring heuristics, with significantly better performance on targeted classes of SAT problem instances. As the run-time of GP is often dominated by fitness evaluation, evaluating multiple offspring in parallel typically reduces the time incurred by fitness evaluation proportional to the number of parallel processing units. The naive synchronous approach requires an entire generation to be evaluated before progressing to the next generation; as such, heterogeneity in the evaluation times will degrade the performance gain, as parallel processing units will have to idle until the longest evaluation has completed. This thesis shows empirical evidence justifying the employment of an asynchronous parallel model for GP powered hyper-heuristics applied to SAT solver space, rather than the generational synchronous alternative, for gaining speed-ups in evolution time. Additionally, this thesis explores the use of a multi-objective GP to reveal the trade-off surface between multiple CDCL attributes.

  • Evaluation of School Timetabling Algorithms, by Lindberg, Viktor, Master Thesis, Department of Computing Science, Umea University, 2016 [PDF] [ABSTRACT]

    Most schools have the problem that they need to organise the meetings between students and teachers in lectures and place these lectures in a timetable. Four different algorithms that can be used to solve this problem will be evaluated in this thesis. The algorithms are Simulated Annealing, Particle Swarm Optimisation, Hyper-Heuristic Genetic Algorithm and Iterated Local Search. In this thesis a description of the algorithms will be given and then evaluated by running them on a set of different known timetabling problems and have their results compared with each other to find out which algorithm is best suited for use in a potential end-user application. Simulated Annealing combined with Iterated Local Search gave the best resultsin this thesis.

  • Evolutionary algorithms with mixed strategy, by Shen, Liang, PhD Thesis, Department of Computer Science, Aberystwyth University, 2016 [PDF] [ABSTRACT]

    During the last several decades, many kinds of population based Evolutionary Algorithms have been developed and considerable work has been devoted to computational methods which are inspired by biological evolution and natural selection, such as Evolutionary Programming and Clonal Selection Algorithm. The objective of these algorithms is not only to find suitable adjustments to the current population and hence the solution, but also to perform the process efficiently. However, a parameter setting that was optimal at the beginning of the algorithm may become unsuitable during the evolutionary process. Thus, it is preferable to automatically modify the control parameters during the runtime process. The approach required could have a bias on the distribution towards appropriate directions of the search space, thereby maintaining sufficient diversity among individuals in order to enable further ability of evolution. This thesis has offered an initial approach to developing this idea. The work starts from a clear understanding of the literature that is of direct relevance to the aforementioned motivations. The development of this approach has been built upon the basis of the fundamental and generic concepts of evolutionary algorithms. The work has exploited and benefited from a range of representative evolutionary computational mechanisms. In particular, essential issues in evolutionary algorithms such as parameter control, including the general aspects of parameter tuning and typical means for implementing parameter control have been investigated. Both the hyperheuristic algorithm and the memetic algorithm have set up a comparative work for the present development. This work has developed several novel techniques that contribute towards the advancement of evolutionary computation and optimization. One such novel approach is to construct a mixed strategy based on the concept of local fitness landscape. It exploits the concepts of fitness landscape and local fitness landscape. Both theoretical description and experimental investigation of this local fitness landscape-based mixed strategy have been provided, and systematic comparisons with alternative approaches carried out. Another contribution of this thesis is the innovative application of mixed strategy. This is facilitated by encompassing two mutation operators into the mixed strategy, which are borrowed from classical differential evolution techniques. Such an improved method has been shown to be simple and easy for implementation. The work has been utilised to deal with the problem of protein folding in bioinformatics. It is demonstrated that the proposed algorithm possesses an appropriate balance between exploration and exploitation. The use of this improved algorithm is less likely to fall into local optimal, entailing a faster and better convergence in resolving challenging realistic application problems.

  • Genetic Programming Hyper-heuristics for Job Shop Scheduling, by Hunt, Rachel, PhD Thesis, School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, 2016 [PDF] [ABSTRACT]

    Scheduling problems arise whenever there is a choice of order in which a number of tasks should be performed; they arise commonly, daily and everywhere. A job shop is a common manufacturing environment in which a schedule for processing a set of jobs through a set of machines needs to be constructed. Job shop scheduling (JSS) has been called a fascinating challenge as it is computationally hard and prevalent in the real-world. Developing more effective ways of scheduling jobs could increase profitability through increasing throughput and decreasing costs. Dispatching rules (DRs) are one of the most popular scheduling heuristics. DRs are easy to implement, have low computational cost, and cope well with the dynamic nature of real-world manufacturing environments. However, the manual development of DRs is time consuming and requires expert knowledge of the scheduling environment. Genetic programming (GP) is an evolutionary computation method which is ideal for automatically discovering DRs. This is a hyper-heuristic approach, as GP is searching the search space of heuristic (DR) solutions rather than constructing a schedule directly. The overall goal of this thesis is to develop GP based hyper-heuristics for the efficient evolution (automatic generation) of robust, reusable and effective scheduling heuristics for JSS environments, with greater interpretability. Firstly, this thesis investigates using GP to evolve optimal DRs for the static two-machine JSS problem with makespan objective function. The results show that some evolved DRs were equivalent to an optimal scheduling algorithm. This validates both the GP based hyper-heuristic approach for generating DRs for JSS and the representation used. Secondly, this thesis investigates developing ``less-myopic'' DRs through the use of wider-looking terminals and local search to provide additional fitness information. The results show that incorporating features of the state of the wider shop improves the mean performance of the best evolved DRs, and that the inclusion of local search in evaluation evolves DRs which make better decisions over the local time horizon, and attain lower total weighted tardiness. Thirdly, this thesis proposes using strongly typed GP (STGP) to address the challenging issue of interpretability of DRs evolved by GP. Several grammars are investigated and the results show that the DRs evolved in the semantically constrained search space of STGP do not have (on average) performance that is as good as unconstrained. However, the interpretability of evolved rules is substantially improved. Fourthly, this thesis investigates using multiobjective GP to encourage evolution of DRs which are more readily interpretable by human operators. This approach evolves DRs with similar performance but smaller size. Fragment analysis identifies popular combinations of terminals which are then used as high level terminals; the inclusion of these terminals improved the mean performance of the best evolved DRs. Through this thesis the following major contributions have been made: (1) the first use of GP to evolve optimal DRs for the static two-machine job shop with makespan objective function; (2) an approach to developing less-myopic DRs through the inclusion of wider looking terminals and the use of local search to provide additional fitness information over an extended decision horizon; (3) the first use of STGP for the automatic discovery of DRs with better interpretability and semantic validity for increased trust; and (4) the first multiobjective GP approach that considers multiple objectives investigating the trade-off between scheduling behaviour and interpretability. This is also the first work that uses analysis of evolved GP individuals to perform feature selection and construction for JSS.

  • Heuristic algorithms for static and dynamic frequency assignment problems, by Alrajhi, Khaled, PhD Thesis, School of Mathematics, Cardiff University, 2016 [PDF] [ABSTRACT]

    This thesis considers the frequency assignment problem (FAP), which is a real world problem of assigning frequencies to wireless communication connections (also known as requests) while satisfying a set of constraints in order to prevent a loss of signal quality. This problem has many different applications such as mobile phones, TV broadcasting, radio and military operations. In this thesis, two variants of the FAP are considered, namely the static and the dynamic FAPs. The static FAP does not change over time, while the dynamic FAP changes over time as new requests gradually be-come known and frequencies need to be assigned to those requests effectively and promptly. The dynamic FAP has received little attention so far in the literature com-pared with the static FAP. This thesis consists of two parts: the first part discusses and develops three heuristic algorithms, namely tabu search (TS), ant colony optimization (ACO) and hyper heuris-tic (HH), to solve the static FAP. These heuristic algorithms are chosen to represent different characteristics of heuristic algorithms in order to identify an appropriate solu-tion method for this problem. Several novel and existing techniques have been used to improve the performance of these heuristic algorithms. In terms of TS, one of the nov-el techniques aims to determine a lower bound on the number of frequencies that are required from each domain for a feasible solution to exist, based on the underlying graph colouring model. These lower bounds are used to ensure that we never waste time trying to find a feasible solution with a set of frequencies that do not satisfy the lower bounds, since there is no feasible solution in this search area. Another novel technique hybridises TS with multiple neighbourhood structures, one of which is used as a diversification technique. In terms of ACO, the concept of a well-known graph colouring algorithm, namely recursive largest first, is used. Moreover, some of the key factors in producing a high quality ACO implementation are examined such as differ-ent definitions of visibility and trail, and optimization of numerous parameters. In terms of HH, simple and advanced low level heuristics each with an associated inde-pendent tabu list are applied in this study. The lower bound on the number of fre-quencies that are required from each domain for a feasible solution to exist is also used. Based on the experimental results, it is found that the best performing heuristic algo-rithm is TS, with HH also being competitive, whereas ACO achieves poor perfor-mance. Additionally, TS shows competitive performance compared with other algo-rithms in the literature. In the second part of this thesis, various approaches are designed to solve the dynamic FAP. The best heuristic algorithms considered in the first part of this thesis are used to construct these approaches. It is interesting to investigate whether heuristic algorithms which work well on the static FAP also prove efficient on the dynamic FAP. Addi-tionally, several techniques are applied to improve the performance of these approach-es. One of these, called the Gap technique, is novel. This technique aims to identify a good frequency to be assigned to a given request. Based on the experimental results, it is found that the best approach for the dynamic FAP shows competitive results com-pared with other approaches in the literature. Finally, this thesis proposes a novel ap-proach to solve the static FAP by modelling it as a dynamic FAP through dividing this problem into smaller sub-problems, which are then solved in turn in a dynamic process. The lower bound on the number of frequencies that are required from each domain for a feasible solution to exist, based on the underlying graph colouring model, and the Gap technique are also used. The proposed approach shows the ability to improve the results which have been found by the heuristic algorithms in the first part of this thesis (which solve the static FAP as a whole). Moreover, it shows competitive results com-pared with other algorithms in the literature.

  • Hyper-heuristic based particle swarm optimization for many-objective problems, by Fritsche, Gian Mauricio, Master Thesis, University of Parana, 2016 [PDF] [ABSTRACT]

    Multi-objective Particle Swarm Optimization (MOPSO) is a promising meta-heuristic to solve Many-Objective Problems (MaOPs), however, its performance decreases as the number of objective functions increases. Selecting a good combination of leader and archiving methods helps the algorithm to deal with the challenges caused by this increase in the number of objectives, but finding the most appropriate combination for a given problem is a hard task. To deal with this issue, previous works proposed the use of a simple hyper-heuristic to select dynamically a good combination of leader and archiving methods and achieved promising results. In this work, we hypothesize that by using more advanced heuristic selection methods we could further improve the performance of the algorithm. To investigate this hypothesis we conducted experimental studies comparing four heuristic selection methods. After selecting the best performing variant from this study, we conducted a second empirical study to compare this variant to a state-of-the- art optimizer, where the resulting algorithm outperformed it in most of the problems investigated.

2015 (58 publications)

  • A Column Generation Based Hyper-Heuristic to the Bus Driver Scheduling Problem, by Hong Li and Ying Wang and Shi Li and Sujian Li, Discrete Dynamics in Nature and Society, 2015, Hindawi, 2015 [PDF]
  • A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems, by Nasser R. Sabar and Masri Ayob and Graham Kendall and Rong Qu, IEEE Transactions on Cybernetics, 45(2), IEEE, 2015 [PDF] [ABSTRACT]

    Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite.

  • A Fuzzy Logic Controller Applied to a Diversity-based Multi-objective Evolutionary Algorithm for Single-objective Optimisation, by Eduardo Segredo and Carlos Segura and Coromoto Leon and Emma Hart, Soft Computing, 19(10), Springer, 2015 [PDF]
  • A Grouping Hyper-heuristic Framework: Application on Graph Colouring, by Anas Elhag and Ender Ozcan, Expert Systems with Applications, 42(13), Elsevier, 2015 [PDF] [ABSTRACT]

    Grouping problems are hard to solve combinatorial optimisation problems which require partitioning of objects into a minimum number of subsets while a given objective is simultaneously optimised. Selection hyper-heuristics are high level general purpose search methodologies that operate on a space formed by a set of low level heuristics rather than solutions. Most of the recently proposed selection hyper-heuristics are iterative and make use of two key methods which are employed successively; heuristic selection and move acceptance. In this study, we present a novel generic selection hyper-heuristic framework containing a fixed set of reusable grouping low level heuristics and an unconventional move acceptance mechanism for solving grouping problems. This framework deals with one solution at a time at any given decision point during the search process. Also, a set of high quality solutions, capturing the trade-off between the number of groups and the additional objective for the given grouping problem, is maintained. The move acceptance mechanism embeds a local search approach which is capable of progressing improvements on those trade-off solutions. The performance of different selection hyper-heuristics with various components under the proposed framework is investigated on graph colouring as a representative grouping problem. Then, the top performing hyper-heuristics are applied to a benchmark of examination timetabling instances. The empirical results indicate the effectiveness and generality of the proposed framework enabling grouping hyper-heuristics to achieve high quality solutions in both domains.

  • A Hyper-heuristic Approach for Resource Provisioning-based Scheduling in Grid Environment, by Rajni Aron and Inderveer Chana and Ajith Abraham, The Journal of Supercomputing, 71(4), Springer, 2015 [PDF] [ABSTRACT]

    Grid computing being immensely based on the concept of resource sharing has always been closely associated with a lot many challenges. Growth of Resource provisioning-based scheduling in large-scale distributed environments like Grid computing brings in new requirement challenges that are not being considered in traditional distributed computing environments. Resources being the backbone of the system, their efficient management plays quite an important role in its execution environment. Many constraints such as heterogeneity and dynamic nature of resources need to be taken care as steps toward managing Grid resources efficiently. The most important challenge in Grids being the job-resource mapping as per the users' requirement in the most secure way. The mapping of the jobs to appropriate resources for execution of the applications in Grid computing is found to be an NP-complete problem. Novel algorithm is required to schedule the jobs on the resources to provide reduced execution time, increased security and reliability. The main aim of this paper is to present an efficient strategy for secure scheduling of jobs on appropriate resources. A novel particle swarm optimization-based hyper-heuristic resource scheduling algorithm has been designed and used to schedule jobs effectively on available resources without violating any of the security norms. Performance of the proposed algorithm has also been evaluated through the GridSim toolkit. We have compared our resource scheduling algorithm with existing common heuristic-based scheduling algorithms experimentally. The results thus obtained have shown a better performance by our algorithm than the existing algorithms, in terms of giving more reduced cost and makespan of user's application being submitted to the Grids.

  • A Hyperheuristic Approach for Intercell Scheduling With Single Processing Machines and Batch Processing Machines, by Dongni Li and Miao Li and Xianwen Meng and Yunna Tian, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(2), IEEE, 2015 [PDF] [ABSTRACT]

    Intercell transfers in cellular manufacturing systems disrupt the philosophy of creating independent cells, but are essential for enterprises to reduce production costs. The problem of intercell scheduling with single processing machines and batch processing machines is considered in this paper, which involves an assignment subproblem, a sequencing subproblem, and a batch formation subproblem. An ant colony optimization (ACO)-based hyperheuristic (ABH) is developed in this paper, searching assignment rules for parts, sequencing rules for single processing machines, and batch formation rules for batch processing machines, simultaneously, and then using the obtained combinatorial rules to generate scheduling solutions. Computational results show that ABH is an effective and significantly efficient approach to provide near-optimum solutions even when CPLEX shows poor performance, and as compared to genetic algorithm that is widely used in hyperheuristics, ABH has better performance with respect to the problem addressed in this paper.

  • A Lifelong Learning Hyper-heuristic Method for Bin Packing, by Kevin Sim and Emma Hart and Ben Paechter, Evolutionary Computation, 23(1), MIT, 2015 [PDF] [ABSTRACT]

    We describe a novel hyper-heuristic system that continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; and representative problems and heuristics are incorporated into a self-sustaining network of interacting entities inspired by methods in artificial immune systems. The network is plastic in both its structure and content, leading to the following properties: it exploits existing knowledge captured in the network to rapidly produce solutions; it can adapt to new problems with widely differing characteristics; and it is capable of generalising over the problem space. The system is tested on a large corpus of 3,968 new instances of 1D bin-packing problems as well as on 1,370 existing problems from the literature; it shows excellent performance in terms of the quality of solutions obtained across the datasets and in adapting to dynamically changing sets of problem instances compared to previous approaches. As the network self-adapts to sustain a minimal repertoire of both problems and heuristics that form a representative map of the problem space, the system is further shown to be computationally efficient and therefore scalable.

  • A Memetic Algorithm based on Hyper-heuristics for Examination Timetabling Problems, by Yu Lei and Maoguo Gong and Licheng Jiao and Yi Zuo, International Journal of Intelligent Computing and Cybernetics, 8(2), Emerald Insight, 2015 [PDF] [ABSTRACT]

    Purpose - The examination timetabling problem is an NP-hard problem. A large number of approaches for this problem are developed to find more appropriate search strategies. Hyper-heuristic is a kind of representative methods. In hyper-heuristic, the high-level search is executed to construct heuristic lists by traditional methods (such as Tabu search, variable neighborhoods and so on). The purpose of this paper is to apply the evolutionary strategy instead of traditional methods for high-level search to improve the capability of global search. Design/methodology/approach - This paper combines hyper-heuristic with evolutionary strategy to solve examination timetabling problems. First, four graph coloring heuristics are employed to construct heuristic lists. Within the evolutionary algorithm framework, the iterative initialization is utilized to improve the number of feasible solutions in the population; meanwhile, the crossover and mutation operators are applied to find potential heuristic lists in the heuristic space (high-level search). At last, two local search methods are combined to optimize the feasible solutions in the solution space (low-level search). Findings - Experimental results demonstrate that the proposed approach obtains competitive results and outperforms the compared approaches on some benchmark instances. Originality/value - The contribution of this paper is the development of a framework which combines evolutionary algorithm and hyper-heuristic for examination timetabling problems.

  • A Survey of Evolutionary Heuristic Algorithm for Job Scheduling in Grid Computing, by Singh, Kanwerjit and Chhabra, Amit and GNDU, Amritsar, International Journal of Computer Science and Mobile Computing, 4, 2015 [PDF] [ABSTRACT]

    An efficient management of the resources in Grid computing crucially depends upon the efficient mapping of the jobs to resources according to the user's requirements. Grid resources scheduling has become a challenge in the computational Grid. The mapping of the jobs to appropriate resources for execution of the application in Grid computing is an NP-Complete problem. So there is no best solution for all grid computing system. Job and resource scheduling in grid environment is one of the key research area in grid environment. The comparison of the heuristic has been shown and experimental result shows that the hyper-heuristics can be of significance importance in Grid scheduling. Over the time, heuristics and meta-heuristics have proved to provide an optimum solution for the combinatorial optimization problems. In this paper, a survey of scheduling algorithms and heuristic approaches is done.

  • A Tensor-based Selection Hyper-heuristic for Cross-domain Heuristic Search, by Shahriar Asta and Ender Ozcan, Information Sciences, 299, Elsevier, 2015 [PDF] [ABSTRACT]

    Hyper-heuristics have emerged as automated high level search methodologies that manage a set of low level heuristics for solving computationally hard problems. A generic selection hyper-heuristic combines heuristic selection and move acceptance methods under an iterative single point-based search framework. At each step, the solution in hand is modified after applying a selected heuristic and a decision is made whether the new solution is accepted or not. In this study, we represent the trail of a hyper-heuristic as a third order tensor. Factorization of such a tensor reveals the latent relationships between the low level heuristics and the hyper-heuristic itself. The proposed learning approach partitions the set of low level heuristics into two subsets where heuristics in each subset are associated with a separate move acceptance method. Then a multi-stage hyper-heuristic is formed and while solving a given problem instance, heuristics are allowed to operate only in conjunction with the associated acceptance method at each stage. To the best of our knowledge, this is the first time tensor analysis of the space of heuristics is used as a data science approach to improve the performance of a hyper-heuristic in the prescribed manner. The empirical results across six different problem domains from a benchmark indeed indicate the success of the proposed approach.

  • A hyperheuristic for the dial-a-ride problem with time windows, by Urra, Enrique and Cubillos, Claudio and Cabrera-Paniagua, Daniel, Mathematical Problems in Engineering, 2015, Hindawi Publishing Corporation, 2015 [PDF] [ABSTRACT]

    The dial-a-ride problem with time windows (DARPTW) is a combinatorial optimization problem related to transportation, in which a set of customers must be picked up from an origin location and they have to be delivered to a destination location. A transportation schedule must be constructed for a set of available vehicles, and several constraints have to be considered, particularly time windows, which define an upper and lower time bound for each customer request in which a vehicle must arrive to perform the service. Because of the complexity of DARPTW, a number of algorithms have been proposed for solving the problem, mainly based on metaheuristics such as Genetic Algorithms and Simulated Annealing. In this work, a different approach for solving DARPTW is proposed, designed, and evaluated: hyperheuristics, which are alternative heuristic methods that operate at a higher abstraction level than metaheuristics, because rather than searching in the problem space directly, they search in a space of low-level heuristics to find the best strategy through which good solutions can be found. Although the proposed hyperheuristic uses simple and easy-to-implement operators, the experimental results demonstrate efficient and competitive performance on DARPTW when compared to other metaheuristics from the literature.

  • An Analysis of Generalised Heuristics for Vehicle Routing and Personnel Rostering Problems, by Mustafa Misir and Pieter Smet and Greet Vanden Berghe, Journal of the Operational Research Society, 66(5), Palgrave Macmillan, 2015 [PDF]
  • An Enhanced Hyper-Heuristics Task Scheduling In Cloud Computing, by R. Priyanka and M. Nakkeeran, International Journal of Computer Science and Mobile Computing, 4(2), IJCSMC, 2015 [PDF]
  • An ant colony based hyper-heuristic approach for the set covering problem, by Ferreira, Alexandre Silvestre and Pozo, Aurora and Gonccalves, Richard Aderbal, Ediciones Universidad de Salamanca (Espa~na), 2015 [PDF] [ABSTRACT]

    The Set Covering Problem (SCP) is a NP-hard combinatorial optimization problem that is challenging for meta-heuristic algorithms. In the optimization literature, several approaches using meta-heuristics have been developed to tackle the SCP and the quality of the results provided by these approaches highly depends on customized operators that demands high effort from researchers and practitioners. In order to alleviate the complexity of designing metaheuristics, a methodology called hyper-heuristic has emerged as a possible solution. A hyper-heuristic is capable of dynamically selecting simple low-level heuristics accordingly to their performance, alleviating the design complexity of the problem solver and obtaining satisfactory results at the same time. In a previous study, we proposed a hyper-heuristic approach based on Ant Colony Optimization (ACO-HH) for solving the SCP. This paper extends our previous efforts, presenting better results and a deeper analysis of ACO-HH parameters and behavior, specially about the selection of low-level heuristics. The paper also presents a comparison with an ACO meta-heuristic customized for the SCP.

  • Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems, by Nasser R. Sabar and Masri Ayob and Graham Kendall and Rong Qu, IEEE Transactions on Evolutionary Computation, 19(3), IEEE, 2015 [PDF] [ABSTRACT]

    Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high-level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems, or even instances, have different landscape structures and complexity, the design of efficient high-level heuristics can have a dramatic impact on hyper-heuristic performance. In this paper, instead of using human knowledge to design the high-level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance-solving process, the high-level heuristic of the hyper-heuristic framework. The generated heuristic takes information (such as the quality of the generated solution and the improvement made) from the current problem state as input and decides which low-level heuristic should be selected and the acceptance or rejection of the resultant solution. The benefit of this framework is the ability to generate, for each instance, different high-level heuristics during the problem-solving process. Furthermore, in order to maintain solution diversity, we utilize a memory mechanism that contains a population of both high-quality and diverse solutions that is updated during the problem-solving process. The generality of the proposed hyper-heuristic is validated against six well-known combinatorial optimization problems, with very different landscapes, provided by the HyFlex software. Empirical results, comparing the proposed hyper-heuristic with state-of-the-art hyper-heuristics, conclude that the proposed hyper-heuristic generalizes well across all domains and achieves competitive, if not superior, results for several instances on all domains.

  • Choice Function based Hyper-heuristics for Multi-objective Optimization, by Mashael Maashi and Graham Kendall and Ender Ozcan, Applied Soft Computing, 28, Elsevier, 2015 [PDF]
  • Combining monte-carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem, by Asta, Shahriar and Karapetyan, Daniel and Kheiri, Ahmed and Ozcan, Ender and Parkes, Andrew J, arXiv preprint arXiv:1511.04387, 2015 [PDF] [ABSTRACT]

    Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. This problem has been addressed by a competition, and associated set of benchmark instances, as a part of the MISTA 2013 conference. A critical aspect of the benchmarks is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of good solutions and discuss the effects this has on the algorithm design. This paper presents the resulting competition winning approach; it is a carefully designed hybrid of Monte-Carlo tree search, novel neighbourhood moves, memetic algorithms, and hyper-heuristic methods. The implementation is also engineered to increase the speed with which iterations are performed, and to exploit the computing power of multicore machines. The resulting information-sharing multi-component algorithm significantly outperformed the other approaches during the competition, producing the best solution for 17 out of the 20 test instances and performing the best in around 90% of all the trials.

  • Emergency Railway Transportation Planning Using a Hyper-Heuristic Approach, by Yu-Jun Zheng and Min-Xia Zhang and Hai-Feng Ling and Sheng-Yong Chen, IEEE Transactions on Intelligent Transportation Systems, 16(1), IEEE, 2015 [PDF]
  • Fault Tolerant based Hyper-heuristic Algorithm for Task Scheduling in Cloud, by R. Priyanka and P. Priyadharsini and M. Nakkeeran, Journal of Engineering and Applied Sciences, 10(7), ARPN, 2015 [PDF]
  • Graph-based hybrid hyper-heuristic channel scheduling algorithm in multicell networks, by Dong, Bei and Jiao, Licheng and Wu, Jianshe, Transactions on Emerging Telecommunications Technologies, Wiley Online Library, 2015 [PDF] [ABSTRACT]

    In this paper, we consider the scheduling problem that minimises the number of required channel without violation of traffic demand by considering the intercell interference and intracell interference simultaneously. This concerned problem is proved to be a non-deterministic polynomial-time hard problem. We propose a graph-based hyper-heuristic method composed of two level heuristics: the high level heuristic and a set of low level heuristics. A sequence of graph-based low level heuristics is generated to guide the channel assignment process, and then searching in the heuristic space by the high level heuristic obtains the best channel scheduling scheme. The performance is tested on 20 benchmark problems, which show that the proposed graph-based hyper-heuristic algorithm is effective and outperforms the existing method.

  • Heuristic Space Diversity Control for Improved Meta-hyper-heuristic Performance, by Jacomine Grobler and Andries P. Engelbrecht and Graham Kendall and V.S.S. Yadavalli, Information Sciences, 300, Elsevier, 2015 [PDF] [ABSTRACT]

    This paper expands on the concept of heuristic space diversity and investigates various strategies for the management of heuristic space diversity within the context of a meta-hyper-heuristic algorithm in search of greater performance benefits. Evaluation of various strategies on a diverse set of floating-point benchmark problems shows that heuristic space diversity has a significant impact on hyper-heuristic performance. An exponentially increasing strategy (EIHH) obtained the best results. The value of a priori information about constituent algorithm performance on the benchmark set in question was also evaluated. Finally, EIHH demonstrated good performance when compared to a popular population based algorithm portfolio algorithm and the best performing constituent algorithm.

  • Hybridising Heuristics within an Estimation Distribution Algorithm for Examination Timetabling, by Rong Qu and Nam Pham and Ruibin Bai and Graham Kendall, Applied Intelligence, 42(4), Springer, 2015 [PDF]
  • Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease, by Montazeri, Mitra and Baghshah, Mahdieh Soleymani and Enhesari, Ahmad, arXiv preprint arXiv:1512.04652, 2015 [PDF] [ABSTRACT]

    Background: Lung cancer was known as primary cancers and the survival rate of cancer is about 15%. Early detection of lung cancer is the leading factor in survival rate. All symptoms (features) of lung cancer do not appear until the cancer spreads to other areas. It needs an accurate early detection of lung cancer, for increasing the survival rate. For accurate detection, it need characterizes efficient features and delete redundancy features among all features. Feature selection is the problem of selecting informative features among all features. Materials and Methods: Lung cancer database consist of 32 patient records with 57 features. This database collected by Hong and Youngand indexed in the University of California Irvine repository. Experimental contents include the extracted from the clinical data and X-ray data, etc. The data described 3 types of pathological lung cancers and all features are taking an integer value 0-3. In our study, new method is proposed for identify efficient features of lung cancer. It is based on Hyper-Heuristic. Results: We obtained an accuracy of 80.63% using reduced 11 feature set. The proposed method compare to the accuracy of 5 machine learning feature selections. The accuracy of these 5 methods are 60.94, 57.81, 68.75, 60.94 and 68.75. Conclusions: The proposed method has better performance with the highest level of accuracy. Therefore, the proposed model is recommended for identifying an efficient symptom of Disease. These finding are very important in health research, particularly in allocation of medical resources for patients who predicted as high-risks.

  • Hyper-heuristic evolution of dispatching rules: A comparison of rule representations, by Branke, Jurgen and Hildebrandt, Torsten and Scholz-Reiter, Bernd, Evolutionary Computation, 23(2), MIT Press, 2015 [PDF] [ABSTRACT]

    Dispatching rules are frequently used for real-time, online scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed to automate the design process. There are several possibilities to represent rules for this hyper-heuristic search. Because the representation determines the search neighborhood and the complexity of the rules that can be evolved, a suitable choice of representation is key for a successful evolutionary algorithm. In this paper we empirically compare three different representations, both numeric and symbolic, for automated rule design: A linear combination of attributes, a representation based on artificial neural networks, and a tree representation. Using appropriate evolutionary algorithms (CMA-ES for the neural network and linear representations, genetic programming for the tree representation), we empirically investigate the suitability of each representation in a dynamic stochastic job shop scenario. We also examine the robustness of the evolved dispatching rules against variations in the underlying job shop scenario, and visualize what the rules do, in order to get an intuitive understanding of their inner workings. Results indicate that the tree representation using an improved version of genetic programming gives the best results if many candidate rules can be evaluated, closely followed by the neural network representation that already leads to good results for small to moderate computational budgets. The linear representation is found to be competitive only for extremely small computational budgets.

  • Investigating Fitness Functions for a Hyper-heuristic Evolutionary Algorithm in the Context of Balanced and Imbalanced Data Classification, by Rodrigo C. Barros and Marcio P. Basgalupp and Andre C.P.L.F. de Carvalho, Genetic Programming and Evolvable Machines, 16(3), Springer, 2015 [PDF] [ABSTRACT]

    In this paper, we analyse in detail the impact of different strategies to be used as fitness function during the evolutionary cycle of a hyper-heuristic evolutionary algorithm that automatically designs decision-tree induction algorithms (HEAD-DT). We divide the experimental scheme into two distinct scenarios: (1) evolving a decision-tree induction algorithm from multiple balanced data sets; and (2) evolving a decision-tree induction algorithm from multiple imbalanced data sets. In each of these scenarios, we analyse the difference in performance of well-known classification performance measures such as accuracy, F-Measure, AUC, recall, and also a lesser-known criterion, namely the relative accuracy improvement. In addition, we analyse different schemes of aggregation, such as simple average, median, and harmonic mean. Finally, we verify whether the best-performing fitness functions are capable of providing HEAD-DT with algorithms more effective than traditional decision-tree induction algorithms like C4.5, CART, and REPTree. Experimental results indicate that HEAD-DT is a good option for generating algorithms tailored to (im)balanced data, since it outperforms state-of-the-art decision-tree induction algorithms with statistical significance.

  • Population based Monte Carlo Tree Search Hyper-heuristic for Combinatorial Optimization Problems, by Nasser R. Sabar and Graham Kendall, Information Sciences, 314, Elsevier, 2015 [PDF] [ABSTRACT]

    Hyper-heuristics aim to automate the heuristic selection process in order to operate well across different problem instances, or even across different problem domains. A traditional hyper-heuristic framework has two levels, a high level strategy and a set of low level heuristics. The role of the high level strategy is to decide which low level heuristic should be executed at the current decision point. This paper proposes a Monte Carlo tree search hyper-heuristic framework. We model the search space of the low level heuristics as a tree and use Monte Carlo tree search to search through the tree in order to identify the best sequence of low level heuristics to be applied to the current state. To improve the effectiveness of the proposed framework, we couple it with a memory mechanism which contains a population of solutions, utilizing different population updating rules. The generality of the proposed framework is demonstrated using the six domains of the hyper-heuristic competition (CHeSC) test suite (boolean satisfiability (MAX-SAT), one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing with time windows). The results demonstrate that the proposed hyper-heuristic generalizes well over all six domains and obtains competitive, if not better results, when compared to the best known results that have previously been presented in the scientific literature.

  • Robust hyper-heuristic algorithms for the offline oriented/non-oriented 2D bin packing problems, by Beyaz, Muhammed and Dokeroglu, Tansel and Cosar, Ahmet, Applied Soft Computing, 36, Elsevier, 2015 [PDF] [ABSTRACT]

    The offline 2D bin packing problem (2DBPP) is an NP-hard combinatorial optimization problem in which objects with various width and length sizes are packed into minimized number of 2D bins. Various versions of this well-known industrial engineering problem can be faced frequently. Several heuristics have been proposed for the solution of 2DBPP but it has not been possible to find the exact solutions for large problem instances. Next fit, first fit, best fit, unified tabu search, genetic and memetic algorithms are some of the state-of-the-art methods successfully applied to this important problem. In this study, we propose a set of novel hyper-heuristic algorithms that select/combine the state-of-the-art heuristics and local search techniques for minimizing the number of 2D bins. The proposed algorithms introduce new crossover and mutation operators for the selection of the heuristics. Through the results of exhaustive experiments on a set of offline 2DBPP benchmark problem instances, we conclude that the proposed algorithms are robust with their ability to obtain high percentage of the optimal solutions.

  • Sequence analysis-based hyper-heuristics for water distribution network optimisation, by Kheiri, Ahmed and Keedwell, Edward and Gibson, Michael J and Savic, Dragan, Procedia Engineering, 119, Elsevier, 2015 [PDF] [ABSTRACT]

    Hyper-heuristics operate at the level above traditional (meta-)heuristics that 'optimise the optimiser'. These algorithms can combine low level heuristics to create bespoke algorithms for particular classes of problems. The lowlevel heuristics can be mutation operators or hill climbing algorithms and can include industry expertise. This paper investigates the use of a new hyper-heuristic basedon sequence analysis in the biosciences, to develop new optimisers that can outperform conventional evolutionary approaches. It demonstrates that the new algorithms develop high quality solutions on benchmark water distribution network optimisation problems efficiently, and can yield important information about the problem search space.

  • Solution Methods for Scheduling of Heterogeneous Parallel Machines Applied to the Workover Rig Problem, by Rahimeh Neamatian Monemi and Kassem Danach and Wissam Khalil and Shahin Gelareh and Francisco C. Lima Jr. and Dario Jose Aloise, Expert Systems with Applications, 42(9), Elsevier, 2015 [PDF]
  • Solving High School Timetabling Problems Worldwide using Selection Hyper-heuristics, by Leena N. Ahmed and Ender Ozcan and Ahmed Kheiri, Expert Systems with Applications, 42(13), Elsevier, 2015 [PDF] [ABSTRACT]

    High school timetabling is one of those recurring NP-hard real-world combinatorial optimisation problems that has to be dealt with by many educational institutions periodically, and so has been of interest to practitioners and researchers. Solving a high school timetabling problem requires scheduling of resources and events into time slots subject to a set of constraints. Recently, an international competition, referred to as ITC 2011 was organised to determine the state-of-the-art approach for high school timetabling. The problem instances, obtained from eight different countries across the world used in this competition became a benchmark for further research in the field. Selection hyper-heuristics are general-purpose improvement methodologies that control/mix a given set of low level heuristics during the search process. In this study, we evaluate the performance of a range of selection hyper-heuristics combining different reusable components for high school timetabling. The empirical results show the success of the approach which embeds an adaptive great-deluge move acceptance method on the ITC 2011 benchmark instances. This selection hyper-heuristic ranks the second among the previously proposed approaches including the ones competed at ITC 2011.

  • A Comparison of Crossover Control Mechanisms within Single-point Selection Hyper-heuristics using HyFlex, by John H. Drake and Ender Ozcan and Edmund K. Burke, the IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan, 2015
  • A Comparison of Genetic Programming Variants for Hyper-Heuristics, by Harris, Sean and Bueter, Travis and Tauritz, Daniel R, Proceedings of the 17th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO), ACM, 2015 [PDF] [ABSTRACT]

    General-purpose optimization algorithms are often not well suited for real-world scenarios where many instances of the same problem class need to be repeatedly and efficiently solved. Hyper-heuristics automate the design of algorithms for a particular scenario, making them a good match for real-world problem solving. For instance, hardware model checking induced Boolean Satisfiability Problem (SAT) instances have a very specific distribution which general SAT solvers are not necessarily well targeted to. Hyper-heuristics can automate the design of a SAT solver customized to a specific distribution of SAT instances. The first step in employing a hyper-heuristic is creating a set of algorithmic primitives appropriate for tackling a specific problem class. The second step is searching the associated algorithmic primitive space. Hyper-heuristics have typically employed Genetic Programming (GP) to execute the second step, but even in GP there are many alternatives. This paper reports on an investigation of the relationship between the choice of GP type and the performance obtained by a hyper-heuristic employing it. Results are presented on SAT, demonstrating the existence of problems for which there is a statistically significant performance differential between the use of different GP types.

  • A Hyper-Heuristic for the Multi-Objective Integration and Test Order Problem, by Giovani Guizzo and Gian Mauricio Fritsche and Silvia Regina Vergilio and Aurora Trinidad Ramirez Pozo, the 17th Annual Conference on Genetic and Evolutionary Computation (GECCO), Madrid, Spain, 2015 [PDF] [ABSTRACT]

    Multi-objective evolutionary algorithms (MOEAs) have been efficiently applied to Search-Based Software Engineering (SBSE) problems. However, skilled software engineers waste significant effort designing such algorithms for a particular problem, adapting them, selecting operators and configuring parameters. Hyper-heuristics can help in these tasks by dynamically selecting or creating heuristics. Despite of such advantages, we observe a lack of works regarding this subject in the SBSE field. Considering this fact, this work introduces HITO, a Hyper-heuristic for the Integration and Test Order Problem. It includes a set of well-defined steps and is based on two selection functions (Choice Function and Multi-armed Bandit) to select the best low-level heuristic (combination of mutation and crossover operators) in each mating. To perform the selection, a quality measure is proposed to assess the performance of low-level heuristics throughout the evolutionary process. HITO was implemented using NSGA-II and evaluated to solve the integration and test order problem in seven systems. The introduced hyper-heuristic obtained the best results for all systems, when compared to a traditional algorithm.

  • A Modified Choice Function Hyper-heuristic Controlling Unary and Binary Operators, by John H. Drake and Ender Ozcan and Edmund K. Burke, the IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan, 2015 [PDF] [ABSTRACT]

    Hyper-heuristics are a class of high-level search methodologies which operate on a search space of low-level heuristics or components, rather than on solutions directly. Traditional iterative selection hyper-heuristics rely on two key components, a heuristic selection method and a move acceptance criterion. Choice Function heuristic selection scores heuristics based on a combination of three measures, selecting the heuristic with the highest score. Modified Choice Function heuristic selection is a variant of the Choice Function which emphasises intensification over diversification within the heuristic search process. Previous work has shown that improved results are possible in some problem domains when using Modified Choice Function heuristic selection over the classic Choice Function, however in most of these cases crossover low-level heuristics (operators) are omitted. In this paper, we introduce crossover low-level heuristics into a Modified Choice Function selection hyper-heuristic and present results over six problem domains. It is observed that although on average there is an increase in performance when using crossover low-level heuristics, the benefit of using crossover can vary on a per-domain or per-instance basis.

  • A Sequence-based Selection Hyper-heuristic Utilising a Hidden Markov Model, by Ahmed Kheiri and Ed Keedwell, the 17th Annual Conference on Genetic and Evolutionary Computation (GECCO), Madrid, Spain, 2015 [PDF] [ABSTRACT]

    Selection hyper-heuristics are optimisation methods that operate at the level above traditional (meta-)heuristics. Their task is to evaluate low level heuristics and determine which of these to apply at a given point in the optimisation process. Traditionally this has been accomplished through the evaluation of individual or paired heuristics. In this work, we propose a hidden Markov model based method to analyse the performance of, and construct, longer sequences of low level heuristics to solve difficult problems. The proposed method is tested on the well known hyper-heuristic benchmark problems within the CHeSC 2011 competition and compared with a large number of algorithms in this domain. The empirical results show that the proposed hyper-heuristic is able to outperform the current best-in-class hyper-heuristic on these problems with minimal parameter tuning and so points the way to a new field of sequence-based selection hyper-heuristics.

  • A Software Interface for Supporting the Application of Data Science to Optimisation, by Parkes, Andrew J and Ozcan, Ender and Karapetyan, Daniel, International Conference on Learning and Intelligent Optimization, LNCS, 8994, Springer, 2015 [PDF] [ABSTRACT]

    Many real world problems can be solved effectively by metaheuristics in combination with neighbourhood search. However, implementing neighbourhood search for a particular problem domain can be time consuming and so it is important to get the most value from it. Hyper-heuristics aim to get such value by using a specific API such as 'HyFlex' to cleanly separate the search control structure from the details of the domain. Here, we discuss various longer-term additions to the HyFlex interface that will allow much richer information exchange, and so enhance learning via data science techniques, but without losing domain independence of the search control.

  • A benchmark set extension and comparative study for the HyFlex framework, by Adriaensen, Steven and Ochoa, Gabriela and Nowe, Ann, IEEE Congress on Evolutionary Computation (CEC), IEEE, 2015 [PDF] [ABSTRACT]

    In this work we conduct a comparative study of several publicly available, state-of-the-art hyper-heuristics for HyFlex in order to assess their generality across domains. To this purpose we extend the HyFlex benchmark set with 3 new problem domains: The 0-1 Knap Sack, Quadratic Assignment and Max-Cut Problem. To our knowledge, this is the first public extension of the benchmark since the CHeSC 2011 competition. In addition, this is the first study testing the Fair-Share Iterated Local Search (FS-ILS) method, designed in prior research, using a semi-automated design approach, on new unseen problem domains. We show that, of the methods compared, Adap-HH (CHeSC 2011 winner) clearly perfoms the most consistently, overall. In addition, we identify a weakness of, as well as a way to further simplify the FS-ILS method. Finally, we found that, overall, the state-of-the-art methods compared, generalized much better than a naive baseline.

  • A math-hyper-heuristic approach for large-scale vehicle routing problems with time windows, by Sabar, Nasser R and Zhang, Xiuzhen Jenny and Song, Andy, 2015 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2015 [PDF] [ABSTRACT]

    Vehicle routing is known as the most challenging but an important problem in the transportation and logistics filed. The task is to optimise a set of vehicle routes to serve a group of customers with minimal delivery cost while respecting the problem constraints such as arriving within given time windows. This study presented a math-hyper-heuristic approach to tackle this problem more effectively and more efficiently. The proposed approach consists of two phases: a math phase and a hyper-heuristic phase. In the math phase, the problem is decomposed into sub-problems which are solved independently using the column generation algorithm. The solutions for these sub-problems are combined and then improved by the hyper-heuristic phase. Benchmark instances of large-scale vehicle routing problems with time windows were used for evaluation. The results show the effectiveness of the math phase. More importantly the proposed method achieved better solutions in comparison with two state of the art methods on all instances. The computational cost of the proposed method is also lower than that of other methods.

  • Designing a Portfolio of Parameter Configurations for Online Algorithm Selection, by Aldy Gunawan and Mustafa Misir and Hoong Chuin Lau, the 29th AAAI Conference on Artificial Intelligence: Workshop on Algorithm Configuration (AlgoConf), Austin/Texas, USA, 2015 [PDF] [ABSTRACT]

    Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using different parameter configurations. In this paper, we employ a Design of Experiments (DOE) approach to determine a promising range of values for each parameter of an algorithm. These ranges are further processed to determine a portfolio of parameter configurations, which would be used within two online Algorithm Selection approaches for solving different instances of a given combinatorial optimization problem effectively. We apply our approach on a Simulated Annealing-Tabu Search (SA-TS) hybrid algorithm for solving the Quadratic Assignment Problem (QAP) as well as an Iterated Local Search (ILS) on the Travelling Salesman Problem (TSP). We also generate a portfolio of parameter configurations using best-of-breed parameter tuning approaches directly for the comparison purpose. Experimental results show that our approach lead to improvements over best-of-breed parameter tuning approaches.

  • Evaluating a Multi-objective Hyper-Heuristic for the Integration and Test Order Problem, by Guizzo, Giovani and Vergilio, Silvia R and Pozo, Aurora TR, 2015 Brazilian Conference on Intelligent Systems (BRACIS), IEEE, 2015 [PDF] [ABSTRACT]

    Multi-objective evolutionary algorithms (MOEAs) have been successfully applied for solving different software engineering problems. However, adapting and configuring these algorithms for a specific problem can demand significant effort from software engineers. Therefore, to help in this task, a hyper-heuristic, named HITO (Hyper-heuristic for the Integration and Test Order problem) was proposed to adaptively select search operators during the optimization process. HITO was successfully applied using NSGA-II for solving the integration and test order problem. HITO can use two hyper-heuristic selection methods: Choice Function and Multi-armed Bandit. However, a hypotheses behind this study is that HITO does not depend of NSGA-II and can be used with other MOEAs. To this aim, this paper presents results from evaluation experiments comparing the performance of HITO using two different MOEAs: NSGA-II and SPEA2. The results show that HITO is able to outperform both MOEAs.

  • Evolutionary Cross-domain Hyper-Heuristics, by Ryser-Welch, Patricia and Miller, Julian F and Asta, Shariar, Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO), ACM, 2015 [PDF] [ABSTRACT]

    Hyper-heuristis searches the space of heuristics and meta-heuristics, so that it can generate high-quality algorithms for a problem; it is a fast growing area of interest in the research community. Algorithms have been constructed iteratively using "templates of operations" based on well-known heuristic and meta-heuristic methods (i.e. Iterated Local Search and Memetic algorithms). Problem-specific heuristics are chosen iteratively during the search to find better solutions in the problem search space. These "adaptive algorithms" have solved several well-established combinatorial problems, with a high level of generality. However, the evolved sequences of heuristic operations are often very long, not re-usable and defy human comprehensibility. We focus on evolving a fixed sequence of operators inside the loop of a Memetic Algorithm, using an innovative automatic algorithm creation method. We have extracted and hard-coded these evolved algorithms in new independent solvers. These have found good solutions to the Travelling Salesman Problem. These have been and discuss the potential of this type of hyper-heuristic technique to produce effective human-readable algorithms.

  • Evolving Decision-Tree Induction Algorithms with a Multi-Objective Hyper-Heuristic, by Marcio Basgalupp and Rodrigo Barrosand Vili Podgorelec, the 30th Annual ACM Symposium on Applied Computing (SAC), Salamanca, Spain, 2015
  • Generating human-readable algorithms for the travelling salesman problem using hyper-heuristics, by Ryser-Welch, Patricia and Miller, Julian F and Asta, Shahriar, Proceedings of the 17th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO), ACM, 2015 [PDF] [ABSTRACT]

    Hyper-heuristics search the space of heuristics and metaheuristics, so that it can generate high-quality algorithms. It is a growing area of interest in the research community. Algorithms have been constructed iteratively using "templates of operations" based on well-known heuristic and metaheuristic methods (i.e. Iterated Local Search and Memetic algorithms). These hyper-heuristic algorithms choose sequences of problem-specific heuristics that can find good solutions in the problem domain. Such "adaptive algorithms" have solved several well-established combinatorial problems, with a high level of generality. However, the evolved sequences of heuristic operations are often very long and defy human comprehension. In this paper, we focus on evolving a fixed sequence of operators inside the loop of a metaheuristic, using an innovative automatic algorithm creation method. We have extracted and hard-coded these evolved algorithms in new independent solvers for Travelling Salesman Problems.

  • Hyper-Heuristics: A Study On Increasing Primitive-Space, by Martin, Matthew A and Tauritz, Daniel R, Proceedings of the 17th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO), ACM, 2015 [PDF] [ABSTRACT]

    Practitioners often need to solve real world problems for which no custom search algorithms exist. In these cases they tend to use general-purpose solvers that have no guarantee to perform well on their specific problem. The relatively new field of hyper-heuristics provides an alternative to the potential pit-falls of general-purpose solvers, by allowing practitioners to generate a custom algorithm optimized for their problem of interest. Hyper-heuristics are meta-heuristics operating on algorithm space employing targeted primitives to compose algorithms. This paper explores the advantages and disadvantages of expanding a hyper-heuristic's primitive-space with additional primitives. This should allow for an increase in quality of evolved algorithms. However, increasing the search space of a meta-heuristic almost always results in longer time to convergence and lower quality results for the same amount of computational time, but also all too often lower quality results at convergence, potentially making a problem impractical to solve for a practitioner. This paper explores the scalability of hyper-heuristics as the primitive-space is increased, demonstrating significantly increased quality solutions at convergence with a corresponding increase in convergence time. Additionally, this paper explores the impact that the nature of the added primitives have on the performance of the hyper-heuristic.

  • Hyper-heuristic Operator Selection and Acceptance Criteria, by Richard Marshall and Mark Johnston and Mengjie Zhang, the 15th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP15), Copenhagen, Denmark, 2015 [PDF] [ABSTRACT]

    Earlier research has shown that an adaptive hyper-heuristic can be a successful approach to solving combinatorial optimisation problems. By using a pairing of an operator (low-level heuristic) selection vector and a solution acceptance criterion, an adaptive hyper-heuristic can manage development of a "good" solution within an unseen low-level problem domain in a commercially realistic computational time. However not all selection vectors and solution acceptance criteria pairings deliver competitive results when faced with differing problem instance features and computational time limits. We evaluate pairings of six different operator selection vectors and eight solution acceptance criteria, and monitor the performance of the adaptive hyper-heuristic when applying each pairing to a set of Capacitated Vehicle Routing Problem instances of the same size but with different features. The results show that a few pairings of operator selection vector and acceptance criterion perform consistently well, while others require a longer computational time to deliver competitive results. We also investigate some of the features of a problem instance that may influence the performance of the selection vector and acceptance criterion pairings.

  • Hyperheuristic search for SBST, by Jia, Yue, Proceedings of the Eighth International Workshop on Search-Based Software Testing, IEEE Press, 2015 [PDF] [ABSTRACT]

    This paper argues that incorporating hyperheuristic techniques into existing SBST approaches could help to increase their applicability and generality. We propose a general two layer selective hyperheuristic approach for SBST and provide an example of its use for Combinatorial Interaction Testing (CIT).

  • Learning a Hidden Markov Model-based Hyper-heuristic, by Willem Van Onsem and Bart Demoen and Patrick De Causmaecker, the 9th Learning and Intelligent OptimizatioN Conference (LION), LNCS, 8994, Lille, France, 2015 [PDF] [ABSTRACT]

    A simple model shows how a reasonable update scheme for the probability vector by which a hyper-heuristic chooses the next heuristic leads to neglecting useful mutation heuristics. Empirical evidence supports this on the MaxSat, TravelingSalesman, PermutationFlowshop and VehicleRoutingProblem problems. A new approach to hyper-heuristics is proposed that addresses this problem by modeling and learning hyper-heuristics by means of a hidden Markov Model. Experiments show that this is a feasible and promising approach.

  • Learning combinatorial interaction test generation strategies using hyperheuristic search, by Jia, Yue and Cohen, Myra B and Harman, Mark and Petke, Justyna, Proceedings of the 37th International Conference on Software Engineering-Volume 1, IEEE, 2015 [PDF] [ABSTRACT]

    The surge of search based software engineering research has been hampered by the need to develop customized search algorithms for different classes of the same problem. For instance, two decades of bespoke Combinatorial Interaction Testing (CIT) algorithm development, our exemplar problem, has left software engineers with a bewildering choice of CIT techniques, each specialized for a particular task. This paper proposes the use of a single hyperheuristic algorithm that learns search strategies across a broad range of problem instances, providing a single generalist approach. We have developed a Hyperheuristic algorithm for CIT, and report experiments that show that our algorithm competes with known best solutions across constrained and unconstrained problems: For all 26 real-world subjects, it equals or outperforms the best result previously reported in the literature. We also present evidence that our algorithm's strong generic performance results from its unsupervised learning. Hyperheuristic search is thus a promising way to relocate CIT design intelligence from human to machine.

  • MOEA/D-HH: A Hyper-Heuristic for Multi-objective Problems, by Richard A. Goncalves and Josiel N. Kuk and Carolina P. Almeida and Sandra M. Venske, the 8th International Conference on Evolutionary Multi-Criterion Optimization (EMO), Guimaraes, Portugal, 2015 [PDF] [ABSTRACT]

    Hyper-Heuristics is a high-level methodology for selection or automatic generation of heuristics for solving complex problems. Despite the hyper-heuristics success, there is still only a few multi-objective hyper-heuristics. Our approach, MOEA/D-HH, is a multi-objective selection hyper-heuristic that expands the MOEA/D framework. It uses an innovative adaptive choice function proposed in this work to determine the low level heuristic (Differential Evolution mutation strategy) that should be applied to each individual during a MOEA/D execution. We tested MOEA/D-HH in a well established set of 10 instances from the CEC 2009 MOEA Competition. MOEA/D-HH is compared with some important multi-objective optimization algorithms and the resultsobtained are promising.

  • Multiple Strings Planing Problem in Maritime Service Network: Hyper-heuristic Approach, by Kassem Danach and Wissam Khalil and Shahin Gelareh, the 3rd International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), Beirut, Lebanon, 2015 [PDF]
  • OSCAR: Online Selection of Algorithm Portfolios with Case Study on Memetic Algorithms, by Mustafa Misir and Daniel Handoko and Hoong Chuin Lau, the 9th Learning and Intelligent OptimizatioN Conference (LION), LNCS, 8994, Lille, France, 2015 [PDF] [ABSTRACT]

    This paper introduces an automated approach called OSCAR that combines algorithm portfolios and online algorithm selection. The goal of algorithm portfolios is to construct a subset of algorithms with diverse problem solving capabilities. The portfolio is then used to select algorithms from for solving a particular (set of) instance(s). Traditionally, algorithm selection is usually performed in an offline manner and requires the need of domain knowledge about the target problem; while online algorithm selection techniques tend not to pay much attention to a careful construction of algorithm portfolios. By combining algorithm portfolios and online selection, our hope is to design a problem-independent hybrid strategy with diverse problem solving capability. We apply OSCAR to design a portfolio of memetic operator combinations, each including one crossover, one mutation and one local search rather than single operator selection. An empirical analysis is performed on the Quadratic Assignment and Flowshop Scheduling problems to verify the feasibility, efficacy, and robustness of our proposed approach.

  • Routing Heterogeneous Mobile Hospital with Different Patients Priorities: Hyper-heuristic Approach, by Kassem Danach and Jomana Al-Haj Hassan and Wissam Khalil and Shahin Gelareh, the 5th International Conference on Digital Information and Communication Technology and its Applications (DICTAP), Beirut, Lebanon, 2015 [PDF]
  • Templar--a framework for template-method hyper-heuristics, by Swan, Jerry and Burles, Nathan, European Conference on Genetic Programming, Springer, 2015 [PDF] [ABSTRACT]

    In this work we introduce Templar, a software framework for customising algorithms via the generative technique of template-method hyper-heuristics. We first discuss the need for such an approach, presenting Quicksort as an example. We provide a functional definition of template-method hyper-heuristics, describe how this is implemented by Templar, and show how Templar may be invoked using simple client-code. Finally, we describe experiments using Templar to define a 'hyper-quicksort' with the aim of reducing power consumption-the results demonstrate that the generated algorithm has significantly improved performance on the test set.

  • Unsupervised land-cover classification through hyper-heuristic-based Harmony Search, by Papa, J and Papa, L and Pisani, R and Pereira, D, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, 2015 [PDF] [ABSTRACT]

    Unsupervised land-cover classification aims at learning intrinsic properties of spectral and spatial features for the task of area coverage in urban and rural areas. In this paper, we propose to model the problem of optimizing the well-known k-means algorithm by combining different variations of the Harmony Search technique using Genetic Programming (GP). We have shown GP can improve the recognition rates when using one optimization technique only, but it still deserves a deeper study when we have a very good individual technique to be combined.

  • Using Hyper-Heuristic to Select Leader and Archiving Methods for Many-Objective Problems, by Olacir R. Castro Jr. and Aurora Pozo, the 8th International Conference on Evolutionary Multi-Criterion Optimization (EMO), Guimaraes, Portugal, 2015 [PDF]
  • Modified Choice Function Heuristic Selection for the Multidimensional Knapsack Problem, by John H. Drake and Ender Ozcan and Edmund Burke, Genetic and Evolutionary Computing, Advances in Intelligent Systems and Computing, 2015 [PDF] [ABSTRACT]

    Hyper-heuristics are a class of high-level search methods used to solve computationally difficult problems, which operate on a search space of low-level heuristics rather than solutions directly. Previous work has shown that selection hyper-heuristics are able to solve many combinatorial optimisation problems, including the multidimensional 0-1 knapsack problem (MKP). The traditional framework for iterative selection hyper-heuristics relies on two key components, a heuristic selection method and a move acceptance criterion. Existing work has shown that a hyper-heuristic using Modified Choice Function heuristic selection can be effective at solving problems in multiple problem domains. Late Acceptance Strategy is a hill climbing metaheuristic strategy often used as a move acceptance criteria in selection hyper-heuristics. This work compares a Modified Choice Function - Late Acceptance Strategy hyper-heuristic to an existing selection hyper-heuristic method from the literature which has previously performed well on standard MKP benchmarks.

  • Adapting a Hyper-heuristic to Respond to Scalability Issues in Combinatorial Optimisation, by Richard J. Marshall, MSc Thesis, School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, 2015 [PDF]
  • Enhancing Grammar-Based Approaches for the Automatic Design of Algorithms, by Lourencco, Nuno Antonio Marques, PhD Thesis, Department of Informatics Engineering, Faculty of Sciences and Technology, University of Coimbra, 2015 [PDF] [ABSTRACT]

    Evolutionary Algorithms (EA) are stochastic computational methods loosely inspired by the principles of natural selection and genetics. They have been successfully used to solve complex problems in the domains of learning, design and optimization. When using an EA practitioners have to define its main components such as the variation operators, the selection and replacement mechanisms. The performance of an EA can be greatly enhanced if the components are tailored to the specific situation being addressed. These modifications are usually done manually and require a reasonable degree of expertise. In order to ease the use of EAs some researchers have developed methods to automatically design this type of algorithms. Usually, these methods rely on an (meta-) algorithm that combine components and parameters, in order to learn the one that is most suited for the problem being addressed. The area of Hyper-Heuristics (HH) emerges in this context focusing on the development of efficient meta-algorithms. Genetic Programming (GP), specifically the grammar based variants, are commonly used as HH. In this work, we study and analyze the conditions in which Grammatical Evolution (GE) can be enhanced to automatically design EAs. The main contributions can be divided in three aspects. Firstly, we propose an HH framework that relies on GE as the search algorithm. The proposed framework is divided in two complementary phases: Learning and Validation. In Learning the GE engine is used to combine low level components that are specified in a Context Free Grammar. In the second phase, Validation, the best algorithms learned are selected to be applied to scenarios different from the learning, in order to evaluate their generalization capacity. Secondly we study the impact that the learning conditions have in the final structure of the algorithms that are being learned. Moreover, we analyze the relationship between the quality exhibited by the algorithms during learning and their effective optimization ability when used in unseen scenarios. In concrete we analyze if the best strategies discover in learning still have the same good behavior in validation. Our final contribution addresses some of the limitations exhibited by Grammatical Evolution. The result is a novel representation with an enhanced performance.

2014 (68 publications)

  • A Combined Meta-Heuristic with Hyper-Heuristic Approach to Single Machine Production Scheduling Problem, by C. E. Nugraheni and L. Abednego, International Journal of Computer, Information, WASET, 2014 [PDF]
  • A Hyper-Heuristic Scheduling Algorithm for Cloud, by Chun-Wei Tsai and W Huang and M Chiang and C Yang, IEEE Transactions on Cloud Computing, 2(2), IEEE, 2014 [PDF]
  • A Hyper-heuristic based Framework for Dynamic Optimization Problems, by Haluk Rahmi Topcuoglu and Abdulvahid Ucar and Lokman Altin, Applied Soft Computing, 19, Elsevier, 2014 [PDF]
  • A Multi-objective Hyper-heuristic based on Choice Function, by Mashael Maashi and Ender Ozcan and Graham Kendall, Expert Systems with Applications, 41(9), Elsevier, 2014 [PDF]
  • A Parallel Hyper-heuristic Approach for the Two-dimensional Rectangular Strip-packing Problem, by Istvan Borgulya, Journal of Computing and Information Technology, 22(4), SRCE, 2014 [PDF]
  • A Particle Swarm Optimization based Hyper-heuristic Algorithm for the Classic Resource Constrained Project Scheduling Problem, by Georgios Koulinas and Lazaros Kotsikas and Konstantinos Anagnostopoulos, Information Sciences, 277, Elsevier, 2014 [PDF]
  • A Unified Hyper-heuristic Framework for Solving Bin Packing Problems, by Eunice Lopez-Camacho and Hugo Terashima-Marin and Peter Ross and Gabriela Ochoa, Expert Systems with Applications, 41(15), Elsevier, 2014 [PDF]
  • A genetic programming hyper-heuristic for the multidimensional knapsack problem, by Drake, John H and Hyde, Matthew and Ibrahim, Khaled Z and Ozcan, Ender, Kybernetes, 43(9/10), 2014 [PDF] [ABSTRACT]

    Purpose - Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem Design/methodology/approach - Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. Findings - The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results. Originality/value - In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort.

  • Adaptive Selection of Heuristics for Improving Exam Timetables, by Edmund Burke and Rong Qu and Amr Soghier, Annals of Operations Research, 218(1), Springer, 2014 [PDF]
  • An Evolutionary-based Hyper-heuristic Approach for the Jawbreaker Puzzle, by S. Salcedo-Sanz and J. M. Matias-Roman and S. Jimenez-Fernandez and A. Portilla-Figueras and L. Cuadra, Applied Intelligence, 40(3), Springer, 2014 [PDF]
  • Automated Construction of Evolutionary Algorithm Operators for the Bi-objective Water Distribution Network Design Problem using a Genetic Programming based Hyper-heuristic Approach, by Kent McClymont and Edward C. Keedwell and Dragan Savic and Mark Randall-Smith, Journal of Hydroinformatics, 16(2), IWA Publishing, 2014 [PDF]
  • Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming, by Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan, IEEE Transactions on Evolutionary Computation, 18(2), IEEE, 2014 [PDF]
  • Constructing Constrained-Version of Magic Squares Using Selection Hyper-heuristics, by Ahmed Kheiri and Ender Ozcan, the Computer Journal, 57(3), Oxford Journals, 2014 [PDF]
  • Contrasting Meta-learning and Hyper-heuristic Research: the Role of Evolutionary Algorithms, by Gisele L. Pappa and Gabriela Ochoa and Matthew Hyde and Alex Freitas and John Woodward and Jerry Swan, Genetic Programming and Evolvable Machines, 15(1), Springer, 2014 [PDF]
  • Design of Efficient Packing System using Genetic Algorithm based on Hyper heuristic Approach, by Jaya Thomas and Narendra S. Chaudhari, Advances in Engineering Software, 73, Elsevier, 2014 [PDF]
  • Dynamic Hyper-Heuristic Based on Scatter Search for the Aircraft Landing Scheduling Problem, by Wen Shi and Xueyan Song and Jizhou Sun, IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences, IEICE, 2014 [PDF]
  • Effective learning hyper-heuristics for the course timetabling problem, by Jorge A. Soria-Alcaraz and Gabriela Ochoa and Jerry Swan and Martin Carpio and Hector Puga and Edmund K. Burke, European Journal of Operational Research, 238(1), Elsevier, 2014 [PDF]
  • Evolving an Improved Algorithm for Envelope Reduction Using a Hyper-Heuristic Approach, by Behrooz Koohestani and Riccardo Poli, IEEE Transactions on Evolutionary Computation, 18(4), IEEE, 2014 [PDF]
  • Memetic Algorithms and Hyperheuristics Applied to a Multiobjectivised Two-dimensional Packing Problem, by Eduardo Segredo and Carlos Segura and Coromoto Leon, Journal of Global Optimization, 58(4), Springer, 2014 [PDF]
  • New Insights Into Diversification of Hyper-Heuristics, by Zhilei Ren and He Jiang and Jifeng Xuan and Yan Hu and Zhongxuan Luo, IEEE Transactions on Cybernetics, 44(10), IEEE, 2014 [PDF]
  • Optimizing Shared-memory Hyperheuristics on Top of Parameterized Metaheuristics, by Jose-Matias Cutillas-Lozano and Domingo Gimenez, Procedia Computer Science, 29, Elsevier, 2014 [PDF]
  • Searching the Hyper-heuristic Design Space, by Jerry Swan and John Woodward and Ender Ozcan and Graham Kendall and Edmund Burke, Cognitive Computation, 6(1), Springer, 2014 [PDF]
  • Tailoring Hyper-heuristics to Specific Instances of a Scheduling Problem Using Affinity and Competence Functions, by Abdellah Salhi and Jose Antonio Vazquez Rodriguez, Memetic Computing, 6(2), Springer, 2014 [PDF]
  • The Effect of Pheromone in Ant-Based Hyper-Heuristic, by Abd Aziz Zalilah, Advanced Research in Material Science and Mechanical Engineering, 446-447(2014), Trans Tech, 2014 [PDF]
  • Unified Encoding for Hyper-heuristics with Application to Bioinformatics, by Aleksandra Swiercz and Edmund K. Burke and Mateusz Cichenski and Grzegorz Pawlak and Sanja Petrovic and Tomasz Zurkowski and Jacek Blazewicz, Central European Journal of Operations Research, 22(3), Springer, 2014 [PDF]
  • A Comparison between Two Evolutionary Hyper-heuristics for Combinatorial Optimisation, by Richard Marshall and Mark Johnston and Mengjie Zhang, the 10th International Conference on Simulated Evolution and Learning (SEAL14), Dunedin, New Zealand, 2014 [PDF]
  • A Genetic Programming-based Hyper-heuristic Approach for Storage Location Assignment Problem, by J. Xie and Y. Mei and A. Ernst and X. Li and A. Song, the IEEE Congress of Evolutionary Computation (CEC14), Beijing, China, 2014 [PDF]
  • A Grammatical Evolution Based Hyper-Heuristic for the Automatic Design of Split Criteria, by Marcio Porto Basgalupp and Rodrigo Coelho Barros and Tiago Barabasz, the 16th Annual Conference on Genetic and Evolutionary Computation (GECCO14), Voncouver/BC, Canada, 2014
  • A Hyper-Heuristic Approach to Solve the Multi-Objective Container Loading Problem, by Yanira Gonzalez Gonzalez and Coromoto Leon Hernandez and Gara Miranda Valladares, the International Conference on Metaheuristics and Nature Inspired Computing (META14), Marrakech, Morocco, 2014 [PDF]
  • A Hyper-Heuristic method for MAX-SAT, by Mourad Lassouaoui and Dalila Boughaci and Belaid Benhamou, the International Conference on Metaheuristics and Nature Inspired Computing (META14), Marrakech, Morocco, 2014 [PDF]
  • A Hyper-heuristic Evolutionary Algorithm for Learning Bayesian Network Classifiers, by Alex de Sa and Gisele Pappa, the the 14th Ibero-American Conference on Artificial Intelligence (IBERAMIA14), Santiago, Chile, 2014
  • A Lifelong Learning Hyper-Heuristic Method for Bin Packing, by Kevin Sim and Emma Hart, the 16th Annual Conference on Genetic and Evolutionary Computation (GECCO14), Voncouver/BC, Canada, 2014
  • A MOPSO based on Hyper-heuristic to Optimize Many-objective Problems, by Olacir Castro Jr. and Aurora Pozo, the IEEE Symposium Series on Computational Intelligence (SSCI14), Orlando/Florida, USA, 2014
  • A Problem Configuration Study of the Robustness of a Black-Box Search Algorithm Hyper-Heuristic, by Matthew A. Martin and Daniel R. Tauritz, the 4th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA) - the 16th Annual Conference on Genetic and Evolutionary Computation (GECCO14), Voncouver/BC, Canada, 2014
  • A Separability Prototype for Automatic Memes with Adaptive Operator Selection, by Michael G. Epitropakis and Fabio Caraffini and Ferrante Neri and Edmund K. Burke, the IEEE Symposium Series on Computational Intelligence (SSCI14), Orlando/Florida, USA, 2014
  • A Tensor-based Approach to Nurse Rostering, by Shahriar Asta and Ender Ozcan, the 10th International Conference on the Practice and Theory of Automated Timetabling (PATAT14), York, UK, 2014
  • A review of hyper-heuristic frameworks, by Ryser-Welch, Patricia and Miller, Julian F, Proceedings of the Evo20 Workshop, AISB, 2014 [PDF] [ABSTRACT]

    Hyper-heuristic frameworks have emerged out of the shadows of meta-heuristic techniques. In this very active field, new frameworks are developed all the time. Shared common features that help to classify them in different types of hyper-heuristic. Similarly to an iceberg, this large subfield of artificial intelligence hide a substantial amount of bio-inspired solvers and many research communities. In this paper, the tip of the iceberg is reviewed; recent hyper-heuristic frameworks are surveyed and the overall context of the field is presented. We believe its content complements recent reviews and offers another perspective of this important and developing field to the research community. Some hyper-heuristic frameworks tend to be largely constrained and prevent the state-of-the-art algorithms being obtained. We suggest in addition to relaxing constraints together with analysis of the evolved algorithms may lead to human-competitive results.

  • An Apprenticeship Learning Hyper-Heuristic for Vehicle Routing in HyFlex, by Shahriar Asta and Ender Ozcan, the IEEE Symposium Series on Computational Intelligence (SSCI14), Orlando/Florida, USA, 2014 [PDF]
  • An Improved Immune Inspired Hyper-Heuristic for Combinatorial Optimisation Problems, by Kevin Sim and Emma Hart, the 16th Annual Conference on Genetic and Evolutionary Computation (GECCO14), Voncouver/BC, Canada, 2014
  • Analysis of Hyper-heuristic Performance in Different Dynamic Environments, by Stefan van der Stockt and Andries Engelbrecht, the IEEE Symposium Series on Computational Intelligence (SSCI14), Orlando/Florida, USA, 2014
  • Developing a Hyper-Heuristic Using Grammatical Evolution and the Capacitated Vehicle Routing Problem, by Richard Marshall and Mark Johnston and Mengjie Zhang, the 10th International Conference on Simulated Evolution and Learning (SEAL14), Dunedin, New Zealand, 2014 [PDF] [ABSTRACT]

    A common problem when applying heuristics is that they often perform well on some problem instances, but poorly on others. We work towards developing a hyper-heuristic that manages delivery of good quality solutions to Vehicle Routing Problem instances with only limited prior knowledge of the problem domain to be solved. This paper develops a hyper-heuristic, using Grammatical Evolution, to generate and apply heuristics that develop good solutions. Through a series of experiments we expand and refine the technique, achieving good quality results on 40 well known Capacitated Vehicle Routing Problem instances.

  • Development on Harmony Search Hyper-heuristic Framework for Examination Timetabling Problem, by Khairul Anwar and Ahamad Tajudin Khader and Mohammed Azmi Al-Betar and Mohammed A. Awadallah, the 5th International Conference on Swarm Intelligence (ICSI14), Hefei/Anhui, China, 2014 [PDF]
  • Diversity-Oriented Bi-Objective Hyper-heuristics for Patrol Scheduling, by Mustafa Misir and Hoong Chuin Lau, the 10th International Conference on the Practice and Theory of Automated Timetabling (PATAT14), York, UK, 2014
  • Fair-Share ILS: A Simple State-of-the-Art Iterated Local Search Hyperheuristic, by Steven Adriaensen and Tim Brys and Ann Nowe, the 16th Annual Conference on Genetic and Evolutionary Computation (GECCO14), Voncouver/BC, Canada, 2014
  • Fuzzy Adaptive Parameter Control of a Late Acceptance Hyper-heuristic, by Warren G. Jackson and Ender Ozcan and Robert I. John, the 13th Annual Workshop on Computational Intelligence (UKCI14), Bradford, UK, 2014 [PDF]
  • Heuristic Generation via Parameter Tuning for Online Bin Packing, by Ahmet Yarimcam and Shahriar Asta and Ender Ozcan and Andrew J. Parkes, the IEEE Symposium Series on Computational Intelligence (SSCI14), Orlando/Florida, USA, 2014 [PDF] [ABSTRACT]

    Online bin packing requires immediate decisions to be made for placing an incoming item one at a time into bins of fixed capacity without causing any overflow. The goal is to maximise the average bin fullness after placement of a long stream of items. A recent work describes an approach for solving this problem based on a 'policy matrix' representation in which each decision option is independently given a value and the highest value option is selected. A policy matrix can also be viewed as a heuristic with many parameters and then the search for a good policy matrix can be treated as a parameter tuning process. In this study, we show that the Irace parameter tuning algorithm produces heuristics which outperform the standard human designed heuristics for various instances of the online bin packing problem.

  • Hyper-Heuristic Genetic Algorithm for Solving Frequency Assignment Problem in TD-SCDMA, by Chao Yang and Shuming Peng and Bin Jiang and Lei Wang and Renfa Li, the Workshop on Problem Understanding and Real-World Optimisation (PURO), the 16th Annual Conference on Genetic and Evolutionary Computation (GECCO14), Voncouver/BC, Canada, 2014
  • Hyper-Heuristics for Online UAV Path Planning Under Imperfect Information, by Engin Akar and Haluk Rahmi Topcuoglu and Murat Ermis, the 17th European Conference - Applications of Evolutionary Computation (EvoApplications14), Granada, Spain, 2014 [PDF]
  • Hyper-heuristic Approach for Solving Nurse Rostering Problem, by Khairul Anwar and Mohammed A. Awadallah and Ahamad Tajudin Khader and Mohammed Azmi Al-Betar, the IEEE Symposium Series on Computational Intelligence (SSCI14), Orlando/Florida, USA, 2014
  • HyperILS: An Effective Iterated Local Search Hyperheuristic for Combinatorial Optimisation, by Gabriela Ochoa and Edmund Burke, the 10th International Conference on the Practice and Theory of Automated Timetabling (PATAT14), York, UK, 2014
  • Hyperheuristics based on Parameterized Metaheuristic Schemes, by Jose-Matias Cutillas-Lozano and Francisco Almeida and Domingo Gimenez, the International Conference on Metaheuristics and Nature Inspired Computing (META14), Marrakech, Morocco, 2014 [PDF]
  • Hyperion2: A Toolkit for Meta-Hyper Heuristic Research, by Alexander E. I. Brownlee and Jerry Swan and Ender Ozcan and Andrew J. Parkes, the 16th Annual Conference on Genetic and Evolutionary Computation (GECCO) -- the Workshop on Evolutionary Computation Software Systems (EvoSoft) address = Voncouver/BC, Canada, 2014
  • Investigation into an Evolutionary Algorithm Hyperheuristic for the Nurse Rostering Problem, by Christopher Rae and Nelishia Pillay, the 10th International Conference on the Practice and Theory of Automated Timetabling (PATAT14), York, UK, 2014
  • Modified Choice Function Heuristic Selection for the Multidimensional Knapsack Problem, by John H. Drake and Ender Ozcan and Edmund K. Burke, the 8th International Conference on Genetic and Evolutionary Computing (ICGEC14), Nanchang, China, 2014 [PDF]
  • On the Life-long Learning Capabilities of a NELLI*: a Hyper-heuristic Optimisation System, by Emma Hart and Kevin Sim, the 13th International Conference on Parallel Problem Solving From Nature (PPSN14), Ljubljana, Slovenia, 2014 [PDF]
  • Plug-and-Play hyper-heuristics: an extended formulation, by Ryser-Welch, Patricia and Miller, Julian F, 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems, IEEE, 2014 [PDF] [ABSTRACT]

    Hyper-heuristics is a very active field that is developing all the time. This area of bio-inspired intelligent systems covers a wide range of algorithms selection techniques. This type of self-organising mechanism uses heuristics to optimise heuristics. Many discussions focus on the quality of solutions of the problems obtained from the hyper-heuristics, very little discussion concentrates on the generated algorithms themselves. Some hyper-heuristic frameworks tend to be highly constrained, their limited instruction sets prevent the state-of-the-art algorithms from being expressed. In addition, often the generated algorithms are not human-readable. In this paper, we propose a possible extension of some existing hyper-heuristic formulations, so that some of the current open issues can be addressed and it becomes possible to produce self-organizing heuristics that adapt themselves automatically to the environment when the class of problems changes. This together with the analysis of the evolved algorithms, may lead to human-competitive results.

  • Stochastic Hyper-Heuristic for the Winner Determination Problem in Combinatorial Auctions, by Dalila Boughaci and Mourad Lassouaoui, the 6th International Conference on Management of computational and collective IntElligence in Digital EcoSystems (MEDES14), Buraidah Al Qassim, Saudi Arabia, 2014
  • Template Method Hyper-Heuristics, by John R. Woodward and Jerry Swan, the Workshop on Metaheuristic Design Patterns (MetaDeeP), the 16th Annual Conference on Genetic and Evolutionary Computation (GECCO14), Voncouver/BC, Canada, 2014
  • The Entity-to-Algorithm Allocation Problem: Extending the Analysis, by Jacomine Grobler and Andries P. Engelbrecht and Graham Kendall and V.S.S. Yadavalli, the IEEE Symposium Series on Computational Intelligence (SSCI14), Orlando/Florida, USA, 2014
  • Towards a Distributed Hyperheuristic Deploy Architecture, by Enrique Urra and Daniel Cabrera-Paniagua and Claudio Cubillos, the 7th Euro American Conference on Telematics and Information Systems (EATIS14), Valparaiso, Chile, 2014 [PDF]
  • Hyper-heuristics, by Peter Ross, Search Methodologies, 2014 [PDF]
  • An empirical study of meta-and hyper-heuristic search for multi-objective release planning, by Zhang, Yuanyuan and Harman, Mark and Ochoa, Gabriela and Ruhe, Guenther and Brinkkemper, Sjaak, UCL Research Note, RN/14/07, 2014 [PDF] [ABSTRACT]

    A variety of meta-heuristic search algorithms have been introduced for optimising software release planning. However, there has been no comprehensive empirical study of different search algorithms across multiple different real world datasets. In this paper we present an empirical study of global, local and hybrid meta- and hyper-heuristic search based algorithms on 10 real world datasets. We find that the hyper-heuristics are particularly effective. For example, the hyper-heuristic genetic algorithm significantly outperformed the other six approaches (and with high effect size) for solution quality 85% of the time, and was also faster than all others 70% of the time. Furthermore, correlation analysis reveals that it scales well as the number of requirements increases.

  • A Framework for Hyper-Heuristic Optimisation of Conceptual Aircraft Structural Designs, by Jonathan George Allen, PhD Thesis, School of Engineering and Computing Sciences, Durham University, 2014 [PDF]
  • An Investigation of Multi-objective Hyper-heuristics for Multi-objective Optimisation, by Mashael Maashi, PhD Thesis, School of Computer Science, University of Nottingham, 2014
  • Crossover Control in Selection Hyper-Heuristics: Case Studies Using MKP and HyFlex, by John Drake, PhD Thesis, School of Computer Science, University of Nottingham, 2014
  • Hyper-heuristics in Dynamic Environments, by Berna Kiraz, PhD Thesis, Department of Computer Engineering, Istanbul Technical University, 2014
  • Multi-stage Hyper-heuristics for Optimisation Problems, by Ahmed Kheiri, PhD Thesis, School of Computer Science, University of Nottingham, 2014
  • Scheduling an Automotive Manufacturing Facility Using Hyper-Heuristics, by Sarah Brown, MSc Thesis, Department of Mathematics, University of Guelph, 2014 [PDF]

2013 (60 publications)

  • A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem, by Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan, IEEE Transactions on Evolutionary Computation, 17(5), IEEE, 2013 [PDF]
  • A General Multi-objective Hyper-heuristic for Water Distribution Network Design with Discolouration Risk, by Kent McClymont and Ed Keedwell and Dragan Savic and Mark Randall-Smith, Journal of Hydroinformatics, 15(3), IWA Publishing, 2013 [PDF]
  • A Greedy Gradient-Simulated Annealing Hyper-heuristic, by Murat Kalender and Ahmed Kheiri and Ender Ozcan and Edmund K. Burke, Soft Computing, 17(12), Springer, 2013 [PDF]
  • A Hybrid Multi-population Framework for Dynamic Environments Combining Online and Offline Learning, by Gonul Uludag and Berna Kiraz and A. Sima Etaner-Uyar and Ender Ozcan, Soft Computing, 17(12), Springer, 2013 [PDF]
  • A Hyper-heuristic Approach to Aircraft Structural Design Optimization, by Jonathan Allen and Graham Coates and Jon Trevelyan, Structural and Multidisciplinary Optimization, 48(4), Springer, 2013 [PDF]
  • A Hyper-heuristic Approach to Sequencing by Hybridization of DNA Sequences, by Jacek Blazewicz and Edmund Burke and Graham Kendall and Wojciech Mruczkiewicz and Ceyda Oguz and Aleksandra Swiercz, Annals of Operations Research, 207(1), Springer, 2013 [PDF]
  • A Mixture Experiments Multi-objective Hyper-heuristic, by Jose A. Vazquez-Rodriguez and Sanja Petrovic, Journal of the Operational Research Society, 64, Palgrave Macmillan, 2013 [PDF]
  • A New Hyper-heuristic as a General Problem Solver: an Implementation in HyFlex, by Mustafa Misir and Katja Verbeeck and Patrick De Causmaecker and Greet Vanden Berghe, Journal of Scheduling, 16(3), Springer, 2013 [PDF]
  • A New Model and a Hyper-heuristic Approach for Two-dimensional Shelf Space Allocation, by Ruibin Bai and Tom Van Woensel and Graham Kendall and Edmund Burke, 4OR: A Quarterly Journal of Operations Research, 11(1), Springer, 2013 [PDF]
  • A New Tabu Search-based Hyper-heuristic Algorithm for Solving Construction Leveling Problems with Limited Resource Availabilities, by Georgios Koulinas and Konstantinos Anagnostopoulos, Automation in Construction, 31, Elsevier, 2013 [PDF]
  • A Parametric Hybrid Method for the Traveling Salesman Problem, by Gozde Kizilates and Fidan Nuriyeva, Mathematical and Computational Apllications, 18(3), ASR, 2013 [PDF]
  • Adaptive Selection of Heuristics for Assigning Time Slots and Rooms in Exam Timetables, by Amr Soghier and Rong Qu, Applied Intelligence, 39(2), Springer, 2013 [PDF]
  • An Evolutionary-based Hyper-Heuristic Approach for Optimal Construction of Group Method of Data Handling Networks, by J. Gascon-Moreno and S. Salcedo-Sanz and B. Saavedra-Moreno and L. Carro-Calvo and A. Portilla-Figueras, Information Sciences, 247, Elsevier, 2013 [PDF]
  • An Investigation on the Generality Level of Selection Hyper-heuristics under Different Empirical Conditions, by Mustafa Misir and Katja Verbeeck and Patrick De Causmaecker and Greet Vanden Berghe, Applied Soft Computing, Elsevier, Elsevier, 2013 [PDF]
  • Bacterial Foraging based Hyper-heuristic for Resource Scheduling in Grid Computing, by Rajni Aron and Inderveer Chana, Future Generation Computer Systems, 29(3), Elsevier, 2013 [PDF]
  • Competitive Travelling Salesmen Problem: A Hyper-heuristic Approach, by Graham Kendall and Jiawei Li, Journal of the Operational Research Society, 64, Palgrave Macmillan, 2013 [PDF]
  • Evolutionary Generation of Dispatching Rule Sets for Complex Dynamic Scheduling Problems, by Christoph W. Pickardt and Torsten Hildebrandt and Jurgen Branke and Jens Heger and Bernd Scholz-Reiter, International Journal of Production Economics, 145(1), Elsevier, 2013 [PDF]
  • Grammatical Evolution Hyper-heuristic for Combinatorial Optimization Problems, by Nasser R. Sabar and Masri Ayob and Graham Kendall and Rong Qu, IEEE Transactions on Evolutionary Computation, 17(6), IEEE, 2013 [PDF]
  • Hybridizing Genetic Algorithms and Particle Swarm Optimization Transplanted into a Hyper-Heuristic System for Solving University Course Timetabling Problem, by Morteza Alinia Ahandani and Mohammad Taghi Vakil Baghmisheh, WSEAS Transactions on Computers, 12(3), WSEAS, 2013 [PDF]
  • Hyper-heuristic Applied to Nuclear Reactor Core Design, by Roberto Pinheiro Domingos and G.M.Platt, Journal of Physics, 410(1), IOP, 2013 [PDF]
  • Hyper-heuristics: A Survey of the State of the Art, by Edmund Burke and Michel Gendreau and Matthew Hyde and Graham Kendall and Gabriela Ochoa and Ender Ozcan and Rong Qu, Journal of the Operational Research Society, 64, Palgrave Macmillan, 2013 [PDF]
  • Learning Vector Quantization for Variable Ordering in Constraint Satisfaction Problems, by Jose Carlos Ortiz-Bayliss and Hugo Terashima-Marin and Santiago Enrique Conant-Pablos, Pattern Recognition Letters, 34(4), Elsevier, 2013 [PDF]
  • On the Investigation of Hyper-heuristics on a Financial Forecasting Problem, by Michael Kampouridis and Abdullah Alsheddy and Edward Tsang, Annals of Mathematics and Artificial Intelligence, 68(4), Springer, 2013 [PDF]
  • Parameter Tuning of a Choice-function based Hyperheuristic using Particle Swarm Optimization, by Broderick Crawford and Ricardo Soto and Eric Monfroy and Wenceslao Palma and Fernando Paredes, Expert Systems with Applications, 40(5), Elsevier, 2013 [PDF]
  • Scalability and Robustness of Parallel Hyperheuristics Applied to a Multiobjectivised Frequency Assignment Problem, by Carlos Segura and Eduardo Segredo and Coromoto Leon, Soft Computing, 17(6), Springer, 2013 [PDF]
  • Scheduling and Inspection Planning in Software Development Projects using Multi-objective Hyper-heuristic Evolutionary Algorithm, by A. Charan Kumari and K. Srinivas, International Journal of Software Engineering & Applications, 4(3), AIRCC, 2013 [PDF]
  • Selection Hyper-heuristics in Dynamic Environments, by Berna Kiraz and A. Sima Uyar and Ender Ozcan, Journal of the Operational Research Society, 64, Palgrave Macmillan, 2013 [PDF]
  • Towards an object-oriented pattern proposal for heuristic structures of diverse abstraction levels, by Urra, Enrique and Cabrera-Paniagua, Daniel and Cubillos, Claudio, Jornadas Chilenas de Computacin, 2013 [PDF] [ABSTRACT]

    In the optimisation field, the term heuristic is associated with mechanisms for problem solving, ranging from simple algorithms to complex learning techniques. Recent research has focused on developing more appropriate environments for the design and implementation of heuristics. Particularly, the software design consideration is restricted only to the application of design patterns; a detailed discussion regarding methodological background and architectural design approaches is not adequately considered. In this work, we want to discuss software design issues, from an object-oriented perspective, which can be useful to develop heuristics methods considering different abstraction levels, ranging from specialized components to more general-purpose architectures. A theoretical algorithmic model is presented, which forms the basis for a design pattern proposal named Flowchart pattern. We provide a case study of a new heuristic construction framework that uses the pattern at its core, and we discuss how such tool has been used in the implementation of a comprehensive hyperheuristic architecture. The framework usage and the modular structure provided by the hyperheuristic architecture demonstrates how the pattern allows to construct objectual representations of algorithms, and the main consequence is the direct decoupling of an algorithm's structure, its logic behaviour and the data that it treats, which allows for the development of highly dynamic structures that can be modified even at runtime. This approach may open new alternatives in which applied optimisation and software design meet.

  • A Choice Function Hyper-Heuristic for the Winner Determination Problem, by Mourad Lassouaoui and Dalila Boughaci, the 6th International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO13), Canterbury, UK, 2013
  • A Genetic Programming Hyper-heuristic: Turning Features into Heuristics for Constraint Satisfaction, by Jose Carlos Ortiz-Bayliss and Ender Ozcan and Andrew J. Parkes and Hugo Terashima-Marin, the 13th Annual Workshop on Computational Intelligence (UKCI13), Surrey, UK, 2013 [PDF]
  • A Grouping Hyper-heuristic Framework based on Linear Linkage Encoding For Graph Coloring, by Anas Elhag and Ender Ozcan, the 13th Annual Workshop on Computational Intelligence (UKCI13), Surrey, UK, 2013 [PDF]
  • A Hyper-heuristic with a Round Robin Neighbourhood Selection, by Ahmed Kheiri and Ender Ozcan, the 13th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP13), Vienna, Austria, 2013 [PDF]
  • A Runtime Analysis of Simple Hyper-heuristics: To Mix or Not to Mix Operators, by Per Kristian Lehre and Ender Ozcan, the 12th International Workshop on Foundations of Genetic Algorithms (FOGA13), Adelaide, Australia, 2013 [PDF] [ABSTRACT]

    There is a growing body of work in the field of hyper-heuristics. Hyper-heuristics are high level search methodologies that operate on the space of heuristics to solve hard computational problems. A frequently used hyper-heuristic framework mixes a predefined set of low level heuristics during the search process. While most of the work on such selection hyper-heuristics in the literature are empirical, we analyse the runtime of the hyper-heuristics rigorously. Our initial analysis shows that mixing heuristics could lead to exponentially faster search than individual (deterministically chosen) heuristics on chosen problems. Both mixing of variation operators and mixing of acceptance criteria are investigated on some selected problems. It is shown that mixing operators is only efficient with the right mixing distribution (parameter setting). Additionally, some of the existing adaptation mechanisms for mixing operators are also evaluated.

  • An Ant-based Selection Hyper-heuristic for Dynamic Environments, by Berna Kiraz and Sima Etaner Uyar and Ender Ozcan, the 13th European Conference on the Applications of Evolutionary Computation (EvoApplications13), Vienna, Austria, 2013 [PDF]
  • An Asynchronous Reinforcement Learning Hyper-Heuristic Algorithm for Flow Shop Problem, by Wen Shi and Xueyan Song and Cuiling Yu and Jizhou Sun, the 12th IASTED International Conference on Artificial Intelligence and Applications (AIA13), Innsbruck, Austria, 2013 [PDF]
  • Batched Mode Hyper-heuristics, by Shahriar Asta and Ender Ozcan and Andrew J. Parkes, the 7th Learning and Intelligent OptimizatioN Conference (LION13), Catania, Italy, 2013 [PDF]
  • Dimension Reduction in the Search for Online Bin Packing Policies, by Shahriar Asta and Ender Ozcan and Andrew J. Parkes, the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO13), Amsterdam, Netherlands, 2013 [PDF]
  • Evolutionary Hyperheuristic for Capacitated Vehicle Routing Problem, by Jaromir Mlejnek and Jiri Kubalik, the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO13), Amsterdam, Netherlands, 2013 [PDF]
  • Exploring Heuristic Interactions in Constraint Satisfaction Problems: A Closer Look at the Hyper-Heuristic Space, by J.C. Ortiz-Bayliss and H. Terashima-Marin and E. Ozcan and A.J. Parkes and S.E. Conant-Pablos, the 2013 IEEE Congress on Evolutionary Computation (CEC13), Cancun, Mexico, 2013 [PDF]
  • Fuzzy Hyperheuristic Framework for GA Parameters Tuning, by Gudino-Penaloza, Fernando and Gonzalez-Mendoza, Miguel and Mora-Vargas, Jaime and Hernandez-Gress, Neil, Proceedings of the 12th Mexican International Conference on Artificial Intelligence (MICAI), IEEE, 2013 [PDF] [ABSTRACT]

    A fuzzy based hyperheuristic system is used for Genetic Algorithm self adaption. A fuzzy Takagi-Sugeno Inference System is used as High level Heuristic and the GA is used as Low-level heuristic. The framework allows to the system to automatically adjust their own parameters without the need for manual adjustment. The fuzzy system to handle uncertainty about which or in what proportion should adjust the parameters.

  • Generalizing Hyper-heuristics via Apprenticeship Learning, by Shahriar Asta and Ender Ozcan and Andrew J. Parkes and Sima Etaner Uyar, the 13th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP13), Vienna, Austria, 2013 [PDF]
  • Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model, by Kevin Sim and Emma Hart, the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO13), Amsterdam, Netherlands, 2013 [PDF]
  • Generation of VNS Components with Grammatical Evolution for Vehicle Routing, by John Drake and Nikolaos Kililis and Ender Ozcan, the 16th European Conference on Genetic Programming (EuroGP13), Vienna, Austria, 2013 [PDF] [ABSTRACT]

    The vehicle routing problem (VRP) is a family of problems whereby a fleet of vehicles must service the commodity demands of a set of geographically scattered customers from one or more depots, subject to a number of constraints. Early hyper-heuristic research focussed on selecting and applying a low-level heuristic at a given stage of an optimisation process. Recent trends have led to a number of approaches being developed to automatically generate heuristics for a number of combinatorial optimisation problems. Previous work on the VRP has shown that the application of hyper-heuristic approaches can yield successful results. In this paper we investigate the potential of grammatical evolution as a method to evolve the components of a variable neighbourhood search (VNS) framework. In particular, two components are generated; constructive heuristics to create initial solutions and neighbourhood move operators to change the state of a given solution. The proposed method is tested on standard benchmark instances of two common VRP variants.

  • Group Decision Making in Selection Hyper-heuristics, by Ender Ozcan and Mustafa Misir and Ahmed Kheiri, the 13th Annual Workshop on Computational Intelligence (UKCI13), Surrey, UK, 2013 [PDF]
  • HH-DSL: A Domain Specific Language for Selection Hyper-heuristics, by Hilal Kevser Cora and H. Turgut Uyar and A. Sima Etanar-Uyar, the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO13), Amsterdam, Netherlands, 2013 [PDF] [ABSTRACT]

    A domain specific language (DSL) is a programming language which provides a natural notation and suitable data structures to express solutions to problems of a targeted domain. Although using a general purpose programming language together with a special library for the domain is common practice, it still requires a considerable amount of programming knowledge, making it hard for domain experts who might have limited or no programming skills. In the CHeSC (Cross-domain Heuristic Search Challenge) competition, researchers and practitioners from different research fields use the HyFlex platform to develop hyper-heuristics. The domain specific language proposed in this study aims to help these researchers to focus on hyper-heuristic development rather than the details of Java programming.

  • HH-evolver: A System for Domain-specific, Hyper-heuristic Evolution, by Achiya Elyasaf and Moshe Sipper, the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO), Amsterdam, Netherlands, 2013 [PDF] [ABSTRACT]

    We present HH-Evolver, a tool for domain-specific, hyper-heuristic evolution. HH-Evolver automates the design of domain-specific heuristics for planning domains. Hyper-heuristics generated by our tool can be used with combinatorial search algorithms such as A* and IDA* for solving problems of a given domain. HH-Evolver has a rich GUI that enables easy operation, including: running experiments in parallel, pausing and resuming experiments, and saving them and analyzing the results. Implementing new domains and heuristics with HH-Evolver is easily accomplished.

  • Harmony Search-Based Hyper-Heuristic for Examination Timetabling, by Khairul Anwar and Ahamad Tajudin Khader and Mohammed Azmi Al-Betar and Mohammed A. Awadallah, the 9th IEEE Colloquium on Signal Processing and its Applications (CSPA13), Kuala Lumpur, Malaysia, 2013
  • Hyper heuristic based Production Process Scheduling to Improve Productivity in Sustainable Manufacturing, by Hendro Wicaksono E.V. Prohl J. Ovtcharova, the 22nd International Conference on Production Research (ICPR13), Iguassu Falls, Brazil, 2013
  • Late Acceptance-based Selection Hyper-heuristics for Cross-domain Heuristic Search, by Warren Jackson and Ender Ozcan and John H. Drake, the 13th Annual Workshop on Computational Intelligence (UKCI13), Surrey, UK, 2013 [PDF]
  • Learning Selection Strategies for Evolutionary Algorithms, by Nuno Lourenco and Francisco Baptista Pereira and Ernesto Costa, the 11th Biennial International Conference on Artificial Evolution (EA), Bordeaux, France, 2013 [PDF] [ABSTRACT]

    Hyper-Heuristics is a recent area of research concerned with the automatic design of algorithms. In this paper we propose a grammar-based hyper-heuristic to automate the design of an Evolutionary Algorithm component, namely the parent selection mechanism. More precisely, we present a grammar that defines the number of individuals that should be selected, and how they should be chosen in order to adjust the selective pressure. Knapsack Problems are used to assess the capacity to evolve selection strategies. The results obtained show that the proposed approach is able to evolve general selection methods that are competitive with the ones usually described in the literature.

  • Learning to Solve Bin Packing Problems with an Immune Inspired Hyper-heuristic, by Kevin Sim and Emma Hart and Ben Paechter, the 12th European Conference on Artificial Life (ECAL13), Taormina, Italy, 2013 [PDF]
  • Memetic Algorithms for Cross-domain Heuristic Search, by Ender Ozcan and Shahriar Asta and Cevriye Altintas, the 13th Annual Workshop on Computational Intelligence (UKCI13), Surrey, UK, 2013 [PDF]
  • Parhyflex: A framework for parallel hyper-heuristics, by Van Onsem, Willem and Demoen, Bart, Proceedings of the 25th Benelux Conference on Artificial Intelligence (BNAIC), Delft, Netherlands, 2013 [PDF] [ABSTRACT]

    A framework called ParHyFlex and its underlying principle are presented. ParHyFlex is based on the sequential HyFlex framework and also supports the implementation of different hyper-heuristics in a parallel setting which the programmer does not need to be aware of. its most novel feature is the way the search space of a process is influenced by experience learned by other processes. ParHyFlex was tested on the Maximum Satisfiability Problem where it gives good speed-ups. While ParHyFlex cannot compete with tailor-made solvers for most problems, it offers a framework for specifying new hyper-heuristics as well as a parallel environment for solving new problems.

  • Software Effort Prediction: A Hyper-Heuristic Decision-Tree based Approach, by Marcio P. Basgalupp and Rodrigo C. Barros and Tiago S. Da and Andre C. P. L. F. De Carvalho, the 28th ACM Symposium on Applied Computing (SAC13), Coimbra, Portugal, 2013 [PDF]
  • The Importance of the Learning Conditions in Hyper-heuristics, by Nuno Lourenco and Francisco Baptista Pereira and Ernesto Costa, the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO13), Amsterdam, Netherlands, 2013 [PDF]
  • Towards a method for automatically evolving bayesian network classifiers, by de Sa, Alex Guimar~aes Cardoso and Pappa, Gisele Lobo, Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO), ACM, 2013 [PDF] [ABSTRACT]

    When faced with a new machine learning problem, selecting which classifier is the best to perform the task at hand is a very hard problem. Most solutions proposed in the literature are based on meta-learning, and use meta-data about the problem to recommend an effective algorithm to solve the task. This paper proposes a new approach to this problem: to build an algorithm tailored to the application problem at hand. More specifically, we propose an evolutionary algorithm (EA) to automatically evolve Bayesian Network Classifiers (BNCs). The method receives as input a list of the main components of BNC algorithms, and uses an EA to encode these components. Given an input dataset, the method tests different combinations of components to that specific application domain. The method was tested in 10 UCI datasets, and compared to three classical BNCs and a greedy search algorithm. Results show that the current algorithms can indeed be improved, but that the EA is currently outperformed by the greedy search.

  • Boosting Metaheuristic Search Using Reinforcement Learning, by Tony Wauters and Katja Verbeeck and Patrick De Causmaecker and Greet Vanden Berghe, Hybrid Metaheuristics, 2013 [PDF]
  • Evolving Bin Packing Heuristic Using Micro-Differential Evolution with Indirect Representation, by Marco Aurelio Sotelo-Figueroa and Hector Jose Puga Soberanes and Juan Martin Carpio and Hector J. Fraire Huacuja and Laura Cruz Reyes and Jorge Alberto Soria Alcaraz, Recent Advances on Hybrid Intelligent Systems, 2013 [PDF]
  • Learning combinatorial interaction testing strategies using hyperheuristic search, by Jia, Yue and Cohen, Myra B and Harman, Mark and Petke, Justyna, UCL Research Note, RN/13/07, 2013 [PDF] [ABSTRACT]

    Two decades of bespoke Combinatorial Interaction Testing (CIT) algorithm development have left software engineers with a bewildering choice of configurable system testing techniques. This paper introduces a single hyperheuristic algorithm that earns CIT strategies, providing a single generalist approach. We report experiments that show that our algorithm competes with known best solutions across constrained and unconstrained problems. For all 26 real world subjects and 29 of the 30 constrained benchmark problems studied, it equals or improves upon the best known result. We also present evidence that our algorithm's strong generic performance is caused by its effective unsupervised learning. Hyperheuristic search is thus a promising way to relocate CIT design intelligence from human to machine.

  • Selection Hyper-heuristics for Healthcare Scheduling, by Monica Banerjea-Brodeur, PhD Thesis, School of Computer Science, University of Nottingham, 2013 [PDF]

2012 (88 publications)

  • A Flexible and Adaptive Hyper-heuristic Approach for (Dynamic) Capacitated Vehicle Routing Problems, by Pablo Garrido and Carlos Castro, Fundamenta Informaticae, 119(1), IOS Press, 2012 [PDF]
  • A Graph Coloring Constructive Hyper-heuristic for Examination Timetabling Problems, by Nasser R. Sabar and Masri Ayob and Rong Qu and Graham Kendall, Applied Intelligence, 37(1), Springer, 2012 [PDF]
  • A Guide-and-Observe Hyper-heuristic Approach to the Eternity II Puzzle, by Tony Wauters and Wim Vancroonenburg and Greet Vanden Berghe, Journal of Mathematical Modelling and Algorithms, 11(3), Springer, 2012 [PDF]
  • A Hyper-heuristic for the Longest Common Subsequence Problem, by Farzaneh Sadat Tabataba and Sayyed Rasoul Mousavi, Computational Biology and Chemistry, 36, Elsevier, 2012 [PDF]
  • A Hyperheuristic Approach to Examination Timetabling Problems: Benchmarks and a New Problem from Practice, by Peter Demeester and Burak Bilgin and Patrick De Causmaecker and Greet Vanden Berghe, Journal of Scheduling, 15(1), Springer, 2012 [PDF]
  • A Simulated Annealing Hyper-heuristic Methodology for Flexible Decision Support, by Ruibin Bai and Jacek Blazewicz and Edmund Burke and Graham Kendall and Barry McCollum, 4OR: A Quarterly Journal of Operations Research, 10(1), Springer, 2012 [PDF]
  • A Study of Evolutionary Algorithm Selection Hyper-Heuristics for the One-Dimensional Bin-Packing Problem-Solving, by Nelishia Pillay, South African Computer Journal, 48, SACJ, 2012 [PDF]
  • A stochastic hyperheuristic for unsupervised matching of partial information, by Kieran Greer, Advances in Artificial Intelligence, 2012, Hindawi, 2012 [PDF]
  • An Empirical Study of Hyperheuristics for Managing Very Large Sets of Low-level Heuristics, by Stephen Remde and Peter Cowling and Keshav Dahal and Nic Colledge and Evgeny Selensky, Journal of the Operational Research Society, 63(3), Palgrave Macmillan, 2012 [PDF]
  • An Evolutionary Non-Linear Great Deluge Approach for Solving Course Timetabling Problems, by Joe Henry Obit and Djamila Ouelhadj and Dario Landa-Silva and Rayner Alfred, International Journal of Computer Networks and Wireless Communications, 9(4-2), IJCSI, 2012 [PDF]
  • An Integrated Approach for Optimization of Solid Rocket Motor, by Ali Kamran and Liang Guozhu, Aerospace Science and Technology, 17(1), Elsevier, 2012 [PDF]
  • Analysis and Optimization of Channel Allocation Strategies in Cellular Network, by K.V. Narayanaswamy, International Journal of Computer Science Issues, 2(6), IRACST, 2012 [PDF]
  • Automatic Design of Decision-tree Induction Algorithms Tailored to Flexible-Receptor Docking Data, by Rodrigo C Barros and Ana T Winck and Karina S Machado and Marcio P Basgalupp and Andre CPLF de Carvalho and Duncan D Ruiz and Osmar Norberto de Souza, BMC Bioinformatics, 13(1), Biomed, 2012 [PDF]
  • Automating the Packing Heuristic Design Process with Genetic Programming, by Edmund Burke and Matthew Hyde and Graham Kendall and John Woodward, Evolutionary Computation, 20(1), MIT, 2012 [PDF]
  • Bespoke Set of Heuristics for Solving Curriculum Scheduling Problems, by Aftab Ahmed and M. Ali and W. Hussain and Abdul Hussain Shah Bukhari, Sindh University Research Journal, 44(2), Sindh University, 2012 [PDF]
  • Building General Hyper-Heuristics for Multi-Objective Cutting Stock Problems, by Juan Carlos Gomez and Hugo Terashima-Marin, Computacion y Sistemas, 16(3), Instituto Politecnico Nacional, 2012 [PDF]
  • Calibrating Continuous Multi-objective Heuristics using Mixture Experiments, by Jose Antonio Vazquez-Rodriguez and Sanja Petrovic, Journal of Heuristics, 18(5), Springer, 2012 [PDF]
  • Evolving Hyper-Heuristics for the Uncapacitated Examination Timetabling Problem, by Nelishia Pillay, Journal of the Operational Research Society, 63(1), Palgrave Macmillan, 2012
  • Grammatical Evolution of Local Search Heuristics, by Edmund Burke and Matthew Hyde and Graham Kendall, IEEE Transactions on Evolutionary Computation, 16(3), IEEE, 2012 [PDF]
  • Hybrid Particle Swarm Optimization Transplanted into a Hyper-Heuristic Structure for Solving Examination Timetabling Problem, by Morteza Alinia Ahandani and Mohammad Taghi Vakil Baghmisheh and Mohammad Ali Badamchi Zadeh and Sehraneh Ghaemi, Swarm and Evolutionary Computation, 7, Elsevier, 2012 [PDF]
  • Hyper heuristic based on Great Deluge and its Variants for Exam Timetabling Problem, by Ei Shwe Sin and Nang Saing Moon Kham, International Journal of Artificial Intelligence & Applications, 3(1), AIRCC, 2012 [PDF]
  • Hyper-Heuristics with Low Level Parameter Adaptation, by Zhilei Ren and He Jiang and Jifeng Xuan and Zhongxuan Luo, Evolutionary Computation, 20(2), MIT, 2012 [PDF]
  • Hyper-heuristics for Cross-Domain Search, by T. Cichowicz and M. Drozdowski and M. Frankiewicz and G. Pawlak and F. Rytwinski and J. Wasilewski, Bulletin of the Polish Academy of Sciences Technical Sciences, 60(4), JPAC, 2012 [PDF]
  • Monte Carlo Hyper-heuristics for Examination Timetabling, by Edmund Burke and Graham Kendall and Mustafa Misir and Ender Ozcan, Annals of Operations Research, 196(1), Springer, 2012 [PDF]
  • Multi-Objective Optimization of a Stacked Neural Network Using an Evolutionary Hyper-Heuristic, by Renata Furtuna and Silvia Curteanu and Florin Leon, Applied Soft Computing, 12(1), Elsevier, 2012 [PDF]
  • One Hyperheuristic Approach to Two Timetabling Problems in Health Care, by Burak Bilgin and Peter Demeester and Mustafa Misir and Wim Vancroonenburg and Greet Vanden Berghe, Journal of Heuristics, 18(3), Springer, 2012 [PDF]
  • Software Module Clustering using a Fast Multi-objective Hyper-heuristic Evolutionary Algorithm, by Charan A. Kumari and K. Srinivas, International Journal of Applied Information Systems, 5(6), FCS, 2012 [PDF]
  • A Co-evolutionary Hyper-heuristic for ROADEF/EURO Challenge 2012 Machine Reassignment Problem, by Wojciech Jaskowski and Piotr Gawron and Marcin Szubert and Bartosz Wieloch, the 25th EURO Conference on Operational Research (EURO12), Vilnius, Lithuania, 2012
  • A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem, by Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan, the IEEE Congress on Evolutionary Computation (IEEE CEC12), Brisbane, Australia, 2012 [PDF]
  • A Framework to Hybridise PBIL and a Hyper-heuristic for Dynamic Environments, by Gonul Uludag and Berna Kiraz and Sima Uyar and Ender Ozcan, the 12th International Conference on Parallel Problem Solving From Nature (PPSN12), Taormina, Italy, 2012 [PDF] [ABSTRACT]

    Selection hyper-heuristic methodologies explore the space of heuristics which in turn explore the space of candidate solutions for solving hard computational problems. This study investigates the performance of approaches based on a framework that hybridizes selection hyper-heuristics and population based incremental learning (PBIL), mixing offline and online learning mechanisms for solving dynamic environment problems. The experimental results over well known benchmark instances show that the approach is generalized enough to provide a good average performance over different types of dynamic environments.

  • A Genetic Programming Approach to Hyper-Heuristic Feature Selection, by Rachel Hunt and Kourosh Neshatian and Mengjie Zhang, the 9th International Conference on Simulated Evolution And Learning (SEAL12), LNCS vol.7673/2012, Hanoi, Vietnam, 2012 [PDF]
  • A Genetic Programming Hyper-Heuristic for the Multidimensional Knapsack Problem, by John Drake and Matthew Hyde and Khaled Ibrahim and Ender Ozcan, the 11th IEEE International Conference on Cybernetic Intelligent Systems (IEEE CIS12), Limerick, Ireland, 2012 [PDF] [ABSTRACT]

    Purpose: Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem. Design/methodology/approach: Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. Findings: The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results. Originality/value: In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort.

  • A Greedy Gradient-Simulated Annealing Hyper-heuristic for a Curriculum-based Course Timetabling Problem, by Murat Kalender and Ahmed Kheiri and Ender Ozcan and Edmund Burke, the 12th UK Workshop on Computational Intelligence (UKCI12), Edinburgh, Scotland, 2012 [PDF] [ABSTRACT]

    The course timetabling problem is a well known constraint optimization problem which has been of interest to researchers as well as practitioners. Due to the NP-hard nature of the problem, the traditional exact approaches might fail to find a solution even for a given instance. Hyper-heuristics which search the space of heuristics for high quality solutions are alternative methods that have been increasingly used in solving such problems. In this study, a curriculum based course timetabling problem at Yeditepe University is described. An improvement oriented heuristic selection strategy combined with a simulated annealing move acceptance as a hyper-heuristic utilizing a set of low level constraint oriented neighbourhood heuristics is investigated for solving this problem. The proposed hyper-heuristic was initially developed to handle a variety of problems in a particular domain with different properties considering the nature of the low level heuristics. On the other hand, a goal of hyper-heuristic development is to build methods which are general. Hence, the proposed hyper-heuristic is applied to six other problem domains and its performance is compared to different state-of-the-art hyper-heuristics to test its level of generality. The empirical results show that the proposed method is sufficiently general and powerful.

  • A Hyper-Heuristic Classifier for One Dimensional Bin Packing Problems: Improving Classification Accuracy by Attribute Evolution, by Kevin Sim and Emma Hart and Ben Paechter, the 12th International Conference on Parallel Problem Solving From Nature (PPSN12), LNCS, 7492, Taormina, Italy, 2012 [PDF] [ABSTRACT]

    A hyper-heuristic for the one dimensional bin packing problem is presented that uses an Evolutionary Algorithm (EA) to evolve a set of attributes that characterise a problem instance. The EA evolves divisions of variable quantity and dimension that represent ranges of a bin's capacity and are used to train a k-nearest neighbour algorithm. Once trained the classifier selects a single deterministic heuristic to solve each one of a large set of unseen problem instances. The evolved classifier is shown to achieve results significantly better than are obtained by any of the constituent heuristics when used in isolation.

  • A Hyper-Heuristic Evolutionary Algorithm for Automatically Designing Decision-Tree Algorithms, by Rodrigo C. Barros and Marcio P. Basgalupp and Andre Carlos Ponce Leon Ferreira de Carvalho and Alex Alves Freitas, the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO12), Philadelphia/Pennsylvania, USA, 2012 [PDF] [ABSTRACT]

    Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes a hyper-heuristic evolutionary algorithm for automatically generating decision-tree induction algorithms, named HEAD-DT. We perform extensive experiments in 20 public data sets to assess the performance of HEAD-DT, and we compare it to traditional decision-tree algorithms such as C4.5 and CART. Results show that HEAD-DT can generate algorithms that significantly outperform C4.5 and CART regarding predictive accuracy and F-Measure.

  • A Hyper-heuristic Approach to Optimizing Emergency Response, by Duncan T. Wilson and Glenn I. Hawe and Graham Coates and Roger S. Crouch, the 4th International Conference on Metaheuristics and Nature Inspired Computing (META), Port El-Kantaoui, Tunusia, 2012
  • A Hyper-heuristic Approach to Parallel Assembly Line Balancing Problems, by Gokhan Secme and Lale Ozbakir and Ender Ozcan, the 25th Conference of European Chapter on Combinatorial Optimization (ECCO12), Antalya, Turkey, 2012
  • A Hyper-heuristic Clustering Algorithm, by Chun-Wei Tsai and Huei-Jyun Song and Ming-Chao Chiang, the IEEE International Conference on Systems, Man, and Cybernetics (SMC12), Seoul, Korea, 2012 [PDF] [ABSTRACT]

    The so-called heuristics have been widely used in solving combinatorial optimization problems because they provide a simple but effective way to find an approximate solution. These technologies are very useful for users who do not need the exact solution but who care very much about the response time. For every existing heuristic algorithm has its pros and cons, a hyper-heuristic clustering algorithm based on the diversity detection and improvement detection operators to determine when to switch from one heuristic algorithm to another is presented to improve the clustering result in this paper. Several well-known datasets are employed to evaluate the performance of the proposed algorithm. Simulation results show that the proposed algorithm can provide a better clustering result than the state-of-the-art heuristic algorithms compared in this paper, namely, k-means, simulated annealing, tabu search, and genetic k-means algorithm.

  • A Hyper-heuristic Inspired by Pearl Hunting, by C.Y. Chan and Fan Xue and W.H. Ip and C.F. Cheung, the 6th Learning and Intelligent OptimizatioN Conference (LION12), LNCS vol.7219, Paris, France, 2012
  • A Hyperheuristic Approach for Guiding Enumeration in Constraint Solving, by Broderick Crawford and Carlos Castro and Eric Monfroy and Ricardo Soto and Wenceslao Palma and Fernando Paredes, the 2nd EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation, Mexico City, Mexico, 2012 [PDF]
  • A New Hyperheuristic Algorithm for Cross Domain Search Problems, by Andreas Lehrbaum and Nysret Musliu, the 6th Learning and Intelligent OptimizatioN Conference (LION12), LNCS vol.7219, Paris, France, 2012
  • A Non-Adaptive Stochastic Local Search Algorithm for the CHeSC 2011 Competition, by Franco Mascia and Thomas Stutzle, the 6th Learning and Intelligent OptimizatioN Conference (LION12), LNCS vol.7219, Paris, France, 2012
  • A Preliminary Study into the Use of an Evolutionary Algorithm Hyper-heuristic to Solve the Nurse Rostering Problem, by Christopher Rae and Nelishia Pillay, the 4th World Congress on Nature and Biologically Inspired Computing (NaBIC12), Mexico City, Mexico, 2012 [PDF] [ABSTRACT]

    This paper reports on an initial attempt to solve the nurse rostering problem using an evolutionary algorithm selection perturbative hyper-heuristic. The main aim of this study is to get a feel for the potential of such a hyper-heuristic in solving the nurse rostering problem. This will be used to direct future extensions of this work. This study identifies low-level perturbative heuristics for this domain as well as a representation, initial population generation method, evaluation and selection methods, and genetic operator for the evolutionary algorithm hyper-heuristic. The approach was tested on six problems from the first international nurse rostering competition. The performance of the hyper-heuristic was found to be comparable to that of other methods applied to the same problems. The study has shown the potential of this approach and also identified future extensions of this work.

  • A Study of Hyper-heuristics for Examination Timetabling, by Ender Ozcan and Anas Elhag and Viral Shah, the 9th International Conference on the Practice and Theory of Automated Timetabling (PATAT12), Son, Norway, 2012
  • A Time-Complexity Analysis of Hyper-Heuristics, by Per Kristian Lehre and Ender Ozcan, the 25th Conference of European Chapter on Combinatorial Optimization (ECCO12), Antalya, Turkey, 2012
  • A VNS-based Hyper-heuristic with Adaptive Computational Budget of Local Search, by Ping-Che Hsiao and Tsung-Che Chiang and Li-Chen Fu, the IEEE Congress on Evolutionary Computation (IEEE CEC12), Brisbane, Australia, 2012 [PDF]
  • A Vehicle Routing Domain for the HyFlex Hyper-heuristics Framework, by James Walker and Gabriela Ochoa and Michel Gendreau and Edmund Burke, the 6th Learning and Intelligent OptimizatioN Conference (LION12), LNCS vol.7219, Paris, France, 2012
  • Adaptive Evolutionary Algorithms and Extensions to the HyFlex Hyper-heuristic Framework, by Gabriela Ochoa and James Walker and Matthew Hyde and Tim Curtois, the 12th International Conference on Parallel Problem Solving From Nature (PPSN12), LNCS vol.7492, Taormina, Italy, 2012 [PDF]
  • An Improved Choice Function Heuristic Selection for Cross Domain Heuristic Search, by John Drake and Ender Ozcan and Edmund Burke, the 12th International Conference on Parallel Problem Solving From Nature (PPSN12), LNCS vol.7492, Taormina, Italy, 2012 [PDF] [ABSTRACT]

    Hyper-heuristics are a class of high-level search technologies to solve computationally difficult problems which operate on a search space of low-level heuristics rather than solutions directly. A iterative selection hyper-heuristic framework based on single-point search relies on two key components, a heuristic selection method and a move acceptance criteria. The Choice Function is an elegant heuristic selection method which scores heuristics based on a combination of three different measures and applies the heuristic with the highest rank at each given step. Each measure is weighted appropriately to provide balance between intensification and diversification during the heuristic search process. Choosing the right parameter values to weight these measures is not a trivial process and a small number of methods have been proposed in the literature. In this study we describe a new method, inspired by reinforcement learning, which controls these parameters automatically. The proposed method is tested and compared to previous approaches over a standard benchmark across six problem domains.

  • An Integrated Approach to Optimising Container Processes at Multimodal Seaport Terminals, by Erhan Kozan and Brad Casey, the 25th EURO Conference on Operational Research (EURO12), Vilnius, Lithuania, 2012
  • An Intelligent Hyper-heuristic Framework for CHeSC 2011, by Mustafa Misir and Katja Verbeeck and Patrick De Causmaecker and Greet Vanden Berghe, the 6th Learning and Intelligent OptimizatioN Conference (LION12), LNCS vol.7219, Paris, France, 2012
  • Analysing the Adaptation Level of Parallel Hyperheuristics Applied to Multiobjectivised Benchmark Problems, by Carlos Segura and Eduardo Segredo and Coromoto Leon, the 20th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP12), Munich, Germany, 2012 [PDF]
  • Autoconstructive evolution for structural problems, by Harrington, Kyle I and Spector, Lee and Pollack, Jordan B and O'Reilly, Una-May, Proceedings of the 14th annual conference companion on Genetic and evolutionary computation, ACM, 2012 [PDF] [ABSTRACT]

    While most hyper-heuristics search for a heuristic that is later used to solve classes of problems, autoconstructive evolution represents an alternative which simultaneously searches both heuristic and solution space. In this study we contrast autoconstructive evolution, in which intergenerational variation is accomplished by the evolving programs themselves, with a genetic programming system, PushGP, to understand the dynamics of this hybrid approach. A problem size scaling analysis of these genetic programming techniques is performed on structural problems. These problems involve fewer domain-specific features than most model problems while maintaining core features representative of program search. We use two such problems, Order and Majority, to study autoconstructive evolution in the Push programming language.

  • Automatic Discovery of Optimisation Search Heuristics for Two Dimensional Strip Packing Using Genetic Programming, by Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan, the 9th International Conference on Simulated Evolution And Learning (SEAL12), LNCS vol.7673/2012, Hanoi, Vietnam, 2012 [PDF]
  • Evaluation of a Family of Reinforcement Learning Cross-domain Optimization Heuristics, by Luca Di Gaspero and Tommaso Urli, the 6th Learning and Intelligent OptimizatioN Conference (LION12), LNCS vol.7219, Paris, France, 2012 [PDF]
  • Evolving evolutionary algorithms, by Lourencco, Nuno and Pereira, Francisco and Costa, Ernesto, Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO), ACM, 2012 [PDF] [ABSTRACT]

    This paper proposes a Grammatical Evolution framework to the automatic design of Evolutionary Algorithms. We define a grammar that has the ability to combine components regularly appearing in existing evolutionary algorithms, aiming to achieve novel and fully functional optimization methods. The problem of the Royal Road Functions is used to assess the capacity of the framework to evolve algorithms. Results show that the computational system is able to evolve simple evolutionary algorithms that can effectively solve Royal Road instances. Moreover, some unusual design solutions, competitive with standard approaches, are also proposed by the grammatical evolution framework.

  • Five Phase and Genetic Hive Hyper-heuristics for the Cross-Domain Search, by Tomasz Cichowicz and Maciej Drozdowski and Michal Frankiewicz and Grzegorz Pawlak and Filip Rytwinski and Jacek Wasilewski, the 6th Learning and Intelligent OptimizatioN Conference (LION12), LNCS vol.7219, Paris, France, 2012
  • Heuristic Selection in a Multi-phase Hybrid Approach for Dynamic Environments, by Gonul Uludag and Berna Kiraz and Sima Uyar and Ender Ozcan, the 12th UK Workshop on Computational Intelligence (UKCI12), Edinburgh, Scotland, 2012 [PDF] [ABSTRACT]

    An iterative selection hyper-heuristic method controls and mixes a set of low-level heuristics while solving a given problem. A low-level heuristic is selected and employed for improving a (set of) solution(s) at each step. This study investigates the influence of different heuristic selection methods within a population based incremental learning algorithm and hyper-heuristic based hybrid multiphase framework for solving dynamic environment problems. Even though the hybrid method delivers a good overall performance, it is superior in cyclic environments. The empirical results show that a heuristic selection method that relies on a fixed permutation of the underlying low-level heuristics, combined with a strategy that guarantees diversity when the environment changes is more successful than the learning approaches across different dynamic environments produced using a well known benchmark generator.

  • Heuristics for Car Setup Optimisation in TORCS, by Muhammet Kole and Sima Etaner-Uyar and Berna Kiraz, the 12th UK Workshop on Computational Intelligence (UKCI12), Edinburgh, Scotland, 2012 [PDF] [ABSTRACT]

    A TORCS-based (The Open Racing Car Simulator) car setup optimisation problem requires a search for the best parameter settings of a race car that improves its performance across different types of race tracks. This problem often exhibits a noisy environment due to the properties of the race track as well as the components of the car. Selection hyper-heuristics are methodologies that control and mix different predefined set of heuristics during the search process for solving computationally hard problems. In this study, we represent the car setup problem as a real valued optimisation problem and investigate the performance of different approaches including a set of heuristics and their combination controlled by a selection hyper-heuristic framework. The results show that selection hyper-heuristics and a tuned heuristic perform well and are promising approaches even in a dynamically changing, noisy environment.

  • HyFlex: A Benchmark Framework for Cross-domain Heuristic Search, by Gabriela Ochoa and Matthew Hyde and Tim Curtois and Jose A. Vazquez-Rodriguez and James Walker and Michel Gendreau and Graham Kendall and Barry McCollum and Andrew J. Parkes and Sanja Petrovic and Edmund Burke, the 12th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP12), Malaga, Spain, 2012 [PDF]
  • HySST: Hyper-heuristic Search Strategies and Timetabling, by Ahmed Kheiri and Ender Ozcan and Andrew J. Parkes, the 9th International Conference on the Practice and Theory of Automated Timetabling (PATAT12), Son, Norway, 2012 [PDF]
  • Hyper-Heuristic Based on Iterated Local Search Driven by Evolutionary Algorithm, by Jiri Kubalik, the 12th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP12), Malaga, Spain, 2012 [PDF]
  • Hyper-Heuristics for Educational Timetabling, by Nelishia Pillay, the 9th International Conference on the Practice and Theory of Automated Timetabling (PATAT12), Son, Norway, 2012 [PDF] [ABSTRACT]

    Hyper-heuristics aim at providing generalized solutions to combinatorial optimization problems. Educational timetabling encompasses university examination timetabling, university course timetabling and school timetabling. This paper provides an overview of the use of hyper- heuristics to solve educational timetabling problems. The paper then proposes future research directions focusing on using hyper-heuristics to provide a generalized solution over the domain of educational timetabling instead of for a specific timetabling problem.

  • Hyper-heuristic Applied to Nuclear Reactor Core Design, by Roberto P. Domingos and Gustavo M. Platt, the 1st International Conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE12), Budapest, Hungary, 2012 [PDF] [ABSTRACT]

    The design of nuclear reactors gives rises to a series of optimization problems because of the need for high efficiency, availability and maintenance of security levels. Gradient-based techniques and linear programming have been applied, as well as genetic algorithms and particle swarm optimization. The nonlinearity, multimodality and lack of knowledge about the problem domain makes de choice of suitable meta-heuristic models particularly challenging. In this work we solve the optimization problem of a nuclear reactor core design through the application of an optimal sequence of meta-heuritics created automatically. This combinatorial optimization model is known as hyper-heuristic.

  • Hyper-heuristic to Construct Magic Squares, by Ahmed Kheiri and Ender Ozcan, the 3rd Student Conference on Operational Research (SCOR12), Nottingham, UK, 2012 [PDF]
  • Improving the Performance of Vector Hyper-heuristics through Local Search, by Jose Carlos Ortiz-Bayliss and Hugo Terashima-Marin and Ender Ozcan and Andrew Parkes, the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO12), Philadelphia/Pennsylvania, USA, 2012 [PDF] [ABSTRACT]

    Hyper-heuristics enable us to selectively apply the most suitable low-level heuristic depending on the properties of the problem at hand. They can be used for solving Constraint Satisfaction Problems (CSP) in different ways considering the variety of hyper-heuristics and low-level heuristics. A particular approach which has been receiving attention in the recent years is based on variable ordering using hyper-heuristics. A hyper-heuristic decides the next variable to process using a set of predefined heuristics considering the features that describe the instance at a given point during the search in this framework. This study explores an approach in which each hyper-heuristic is represented as a set of vectors mapping instance features to heuristics for variable ordering. The results suggest that the proposed approach is able to combine the strengths of different heuristics and compensate for their weaknesses performing better than each heuristic in isolation across a range of instances.

  • Investigating the Use of Local Search for Improving Meta-Hyper-Heuristic Performance, by Jacomine Grobler and Andries P. Engelbrecht and Graham Kendall and Sarma Yadavalli, the IEEE Congress on Evolutionary Computation (IEEE CEC12), Brisbane, Australia, 2012 [PDF]
  • Landscape Analysis for Hyperheuristic Bayesian Network Structure Learning on Unseen Problems, by Yanghui Wu and John McCall and David Corne and Olivier Regnier-Coudert, the IEEE Congress on Evolutionary Computation (IEEE CEC12), Brisbane, Australia, 2012 [PDF]
  • Modelling Parameterized Shared-Memory Hyperheuristics for Auto-tuning, Presentation, by Jose-Matias Cutillas-Lozano and Domingo Gimenez and Luis-Gabino Cutillas-Lozano, the 12th International Conference Computational and Mathematical Methods in Science and Engineering (CMMSE12), La Manga-Murcia, Spain, 2012 [PDF]
  • Multi Objective Learning Classifier Systems Based Hyperheuristics for Modularised Fleet Mix Problem, by Kamran Shafi and Axel Bender and Hussein A. Abbass, the 9th International Conference on Simulated Evolution And Learning (SEAL12), LNCS, 7673, Hanoi, Vietnam, 2012 [PDF] [ABSTRACT]

    This paper presents an offline multi-objective hyperheuristic for the Modularised Fleet Mix Problem (MFMP) using Learning Classifier Systems (LCS). The LCS based hyperheuristic is built from multi-objective low-level heuristics that are derived from an existing MFMP solver. While the low-level heuristics use multi-objective evolutionary algorithms to search non-dominated solutions, the LCS based hyperheuristic applies the non-dominance concept at the primitive heuristic level. Two LCS, namely the eXtended Classifier System (XCS) and the sUpervised Classifier System (UCS) are augmented by multi-objective reward and accuracy functions, respectively. The results show that UCS performs better than XCS: the hyperheuristic learned by the UCS is able to select low-level heuristics which create MFMP solutions that, in terms of a distance-based convergence metric, are closer to the derived global Pareto curves on a large set of MFMP test scenarios than the solutions created by heuristics that are selected by the XCS hyperheuristic.

  • Multiobjective Hyper heuristic Scheme for System Design and Optimization, by Amer Farhan Rafique, the 9th International Conference on Mathematical Problems in Engineering, Aerospace and Sciences (ICNPAA12), Vienna, Austria, 2012 [PDF]
  • Round-robin Strategy-based Selection Hyper-heuristic, by Ender Ozcan and Ahmed Kheiri, the 25th Conference of European Chapter on Combinatorial Optimization (ECCO12), Antalya, Turkey, 2012 [PDF]
  • Simulation-based Optimization for Semiconductor Manufacturing using Hyper-heuristics, by Tobias Uhlig and Falk Stefan Pappert and Oliver Rose, the 2012 Winter Simulation Conference (WSC12), Berlin, Germany, 2012 [PDF]
  • Suggestive Therapeutic Pathways Using Hyper-Heuristics, by Prapa Rattadilok and Mahdi Mahfouf and Jonathan Ross and Gary Mills and George Panoutsos and Abdelhafid Zeghbib and Mouloud Denai, the 8th IFAC Symposium on Biological and Medical Systems, Budapest, Hungary, 2012 [PDF] [ABSTRACT]

    Therapeutic decision support can be used to promptly assist clinical decision making process. This paper presents a new approach to interpreting multiple data streams in intensive care environments, the resulting model can be used to correct and maintain patients' health whilst treating underlying illnesses. Rather than simply directing which treatments to be applied, multiple suggestive treatment pathways can be provided allowing several "what-if" scenarios to choose from. Hyper-heuristics are used to guide the treatments and therapeutic pathways selection. Algorithmic validation is made using a human cardiovascular system model parameterised with various post surgery conditions.

  • The Automatic Generation of Mutation Operators for Genetic Algorithms, by John Woodward and Jerry Swan, the Workshop on Evolutionary Computation for the Automated Design of Algorithms - the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO12), Philadelphia/Pennsylvania, USA, 2012 [PDF] [ABSTRACT]

    We automatically generate mutation operators for Genetic Algorithms (GA) and tune them to problem instances drawn from a given problem class. By so doing, we perform metalearning in which the base-level contains GAs (which learn about problem instances), and the meta-level contains GAmutation operators (which learn about problem classes). We use Register Machines to explore a constrained design space for mutation operators. We show how two commonly used mutation operators (viz. one-point and uniform mutation) can be expressed in this framework. Iterated local search is used to search the space of mutation operators, and on a test-bed of 7 problem classes we identify machine-designed mutation operators which outperform their human counterparts.

  • The Effect of the Set of Low-level Heuristics on the Performance of Selection Hyper-heuristics, by Mustafa Misir and Katja Verbeeck and Patrick De Causmaecker and Greet Vanden Berghe, the 12th International Conference on Parallel Problem Solving From Nature (PPSN12), LNCS vol.7492, Taormina, Italy, 2012 [PDF] [ABSTRACT]

    The present study investigates the effect of heuristic sets on the performance of several selection hyper-heuristics. The performance of selection hyper-heuristics is strongly dependant on low-level heuristic sets employed for solving target problems. Therefore, the generality of hyper-heuristics should be examined across various heuristic sets. Unlike the majority of hyper-heuristics research, where the low-level heuristic set is considered given, the present study investigates the influence of the low-level heuristics on the hyper-heuristic's performance. To achieve this, a number of heuristic sets was generated for the patient admission scheduling problem by setting the parameters of a set of parametric heuristics with specific values. These values were set such that nine heuristic sets with different improvement capabilities, speed characteristics and size were generated. A group of hyper-heuristics with certain selection mechanisms and acceptance criteria having dissimilar intensification/diversification abilities were taken from the literature enabling a comprehensive analysis. The experimental results indicated that different hyper-heuristics perform superiorly on distinct heuristic sets. The results can be explained and hence result in hyper-heuristic design recommendations.

  • The Impact of the Bin Packing Problem Structure in Hyper-heuristic Performance, by Eunice Lopez-Camacho and Hugo Terashima-Marin and Santiago Enrique Conant-Pablos, the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO12), Philadelphia/Pennsylvania, USA, 2012 [PDF]
  • hypDE: A Hyper-Heuristic Based on Differential Evolution for Solving Constrained Optimization Problems, by Jose Carlos Villela Tinoco and Carlos A. Coello Coello, the 2nd EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation, Mexico City, Mexico, 2012 [PDF]
  • Hyper heuristic based on Great Deluge and its Variants for Exam Timetabling Problem, by Ei Shwe Sin and Nang Saing Moon Kham, Arxiv preprint arXiv:1202.1891, 2012 [PDF]
  • A Hyperheuristic for Generating Timetables in the XHSTT Format, by Mathijs ter Braak, MSc Thesis, Faculty of Science and Technology, University of Twente, 2012 [PDF]
  • Advanced Models and Solution Methods for Automation of Personnel Rostering Optimisation, by Burak Bilgin, PhD Thesis, Department of Computer Science, KU Leuven, 2012 [PDF]
  • Evolutionary Hyper-Heuristics for Heuristic Selection, by Jakub Weberschinke, MSc Thesis, Department of Cybernetics, Czech Technical University in Prague, 2012 [PDF]
  • Intelligent Hyper-heuristics: A Tool for Solving Generic Optimisation Problems, by Mustafa Misir, PhD Thesis, Department of Computer Science, KU Leuven, 2012 [PDF] [ABSTRACT]

    Designing a dedicated search and optimisation algorithm is a time-consuming process requiring an in-depth analysis of the problem. The resulting algorithm is expected to be effective for solving a given set of target problem instances. However, since the algorithm is dedicated, it is hard to adapt and to apply to other problems. Meta-heuristics were brought in to cope with this drawback. Nevertheless, in most of the meta-heuristic studies, the employed meta-heuristics have been implemented as rather problem-dependent methodologies. Hyper-heuristics furnish problem-independent management opportunities differently from such search and optimisation algorithms. The present dissertation focuses on the generality of hyper-heuristics. It thereby aims at designing intelligent hyper-heuristics so that generality is facilitated. While most works on hyper-heuristics make use of the term generality in describing the potential for solving various problems, the performance changes across different domains have only rarely been reported. Additionally, there are other generality related elements such as the performance variations over distinct heuristic sets, that are usually ignored. This means that there is no study fully discussing generality questions while providing a hyper-heuristic design capable of addressing them.To this end, the factors affecting the hyper-heuristics' generality are determined and several novel hyper-heuristic components are developed based on these factors. Then, the hyper-heuristics using the new components are tested across various problem domains on different heuristic sets, while also varying the experimental limits. First, each developed hyper-heuristic is applied to only one problem domain. The performance of these hyper-heuristics is compared with other algorithms encountered in the literature. The information gathered during these experiments is used later on to design a highly adaptive, intelligent selection hyper-heuristic.The ultimate result of the present PhD research is called the Generic Intelligent Hyper-heuristic (GIHH). It is equipped with multiple online adaptive hyper-heuristic procedures and decision mechanisms for simultaneously coordinating them. GIHH is expected to evolve for different search environments without human intervention. A simplified version of GIHH is tested via a series of experiments on three problems from practice to measure its generality level. A comprehensive performance analysis is conducted using a group of selection hyper-heuristics only involving heuristic selection and move acceptance mechanisms from the literature. The analysis provides strong conclusions about when a hyper-heuristic with certain characteristics has advantages or disadvantages.Finally, GIHH is tested on other challenging combinatorial optimisation problems under different empirical conditions. The computational results indicate that GIHH is effective in solving the target instances from distinct problem domains. Additionally, GIHH won the first international cross domain heuristic search challenge 2011 against 19 high-level algorithms developed by the other academic competitors. The winning hyper-heuristic was then used to investigate the performance and contribution of low-level heuristics while simultaneously solving three problems with routing and rostering characteristics. This completely new application of a hyper-heuristic offers promising perspectives for supporting dedicated heuristic development.

  • Metaheuristics for a Multimodal Home-health Care Scheduling Problem, by Gerhard Hiermann, MSc Thesis, Faculty of Informatics, Vienna University of Technology, 2012 [PDF]
  • Novel Hyper-Heuristic Approaches in Exam Timetabling, by Amr Soghier, PhD Thesis, School of Computer Science, University of Nottingham, 2012
  • Problem Dependent Metaheuristic Performance in Bayesian Network Structure Learning, by Yanghui Wu, PhD Thesis, Robert Gordon University, 2012 [PDF]
  • Reinforcement Learning Enhanced Heuristic Search for Combinatorial Optimization, by Tony Wauters, PhD Thesis, Department of Computer Science, KU Leuven, 2012 [PDF]
  • Survivable Virtual Topology Design in Optical WDM Networks Using Nature-Inspired Algorithms, by Fatma Corut Ergin, PhD Thesis, Informatics Institute, Istanbul Technical University, 2012 [PDF]

2011 (57 publications)

  • A Comparative Study On Three Hyper-Heuristic Approaches For Solving Benchmark Scheduling Problems, by Aftab Ahmed and Abdul Hussain Shah Bukhari and Imdad Ali Ismaili, Sindh University Research Journal, 43(2), Sindh University, 2011 [PDF] [ABSTRACT]

    he research work compares the outcome and solving capabilities of three prominent algorithms. Each algorithm are separately implemented as higher level heuristic to manage the group of low level heuristics (LLHs) in order to solve the benchmark university scheduling instances. The study comprises over Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Evolutionary Algorithm (EA). All these optimization techniques are highly appraised for their skills to handle the complex problems. A number of classical operators and parameters have been examined with each hyper-heuristics due to high diversity in datasets. Secondly, Domain specified Low Level Heuristics have been designed under several operational classifications. In addition, obtaining effective deployment and utilization of the academic resources to the greatest extent are counted as supplementary but essential advantages of the research work.

  • A Hyper-Heuristic Using GRASP with Path-Relinking: A Case Study of the Nurse Rostering Problem, by He Jiang and Junying Qiu and Jifeng Xuan, European Journal of Information Technology Research, 4(2), IGI Global, 2011 [PDF]
  • Hyper Heuristic Approach for Design and Optimization of Satellite Launch Vehicle, by Amer Farhan Rafique and Linshu He and Ali Kamran and Qasim Zeeshan, Chinese Journal of Aeronautics, 24(2), Elsevier, 2011 [PDF]
  • Hyper-Heuristic Algorithm: A Cross-Domain Problem Solving Model, by He Jiang, Communications of the Chinese Computer Federation (Chinese edition), 7(3), CCF, 2011 [PDF]
  • Hyper-Heuristic Approach For Solving Scheduling Problem: A Case Study, by Aftab Ahmed and Abdul Wahid Shaikh and Mazhar Ali and Abdul Hussain Shah Bukhari, Australian Journal of Basic and Applied Sciences, 5(9), INSI, 2011 [PDF]
  • Hyper-heuristic Approaches for the Response Time Variability Problem, by Alberto Garcia-Villoria and Said Salhi and Albert Corominas and Rafael Pastor, European Journal of Operational Research, 211(1), Elsevier, 2011 [PDF]
  • Integrating Neural Networks and Logistic Regression to Underpin Hyper-heuristic Search, by Jingpeng Li and Edmund Burke and Rong Qu, Knowledge-Based Systems, 24(2), Elsevier, 2011 [PDF]
  • Parallel Hyperheuristics for the Frequency Assignment Problem, by Carlos Segura and Gara Miranda and Coromoto Leon, Memetic Computing, 3(1), Springer, 2011 [PDF]
  • Particle Swarm Based Hyper-Heuristic For Tackling Real World Examinations Scheduling Problem, by Aftab Ahmed and Mazhar Ali and Ahthasham Sajid and Abdul Hussain Shah Bukhari, Australian Journal of Basic and Applied Sciences, 5(10), INSI, 2011 [PDF]
  • Resource-Constrained Critical Path Scheduling by a GRASP based Hyperheuristic, by Konstantinos Anagnostopoulos and Georgios Koulinas, Journal of Computing in Civil Engineering, 1(1), ASCE, 2011 [PDF]
  • A Genetic Programming based Hyper-heuristic Approach for Combinatorial Optimisation, by Su Nguyen and Mengjie Zhang, the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO11), Dublin, Ireland, 2011 [PDF]
  • A Hyper-Heuristic Approach to Evolving Algorithms for Bandwidth Reduction Based on Genetic Programming, by Behrooz Koohestani and Riccardo Poli, the 31st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (SGAI11), Cambridge, UK, 2011 [PDF]
  • A Hyper-heuristic Approach for the Ready-Mixed Concrete Delivery Problem, by Wim Vancroonenburg and Mustafa Misir and Greet Vanden Berghe, the 25th Belgian Conference on Operations Research (ORBEL11), Gent, Belgium, 2011
  • A Hyper-heuristic Approach to Design and Tuning Heuristic Methods for Web Document Clustering, by Carlos Cobos and Martha Mendoza and Elizabeth Leon, the IEEE Congress on Evolutionary Computation (IEEE CEC11), New Orleans, USA, 2011 [PDF]
  • A Hyper-heuristic based on Random Gradient, Greedy and Dominance, by Ender Ozcan and Ahmed Kheiri, the 26th International Symposium on Computer and Information Sciences (IEEE ISCIS11), London, UK, 2011 [PDF]
  • A Hyper-heuristic for Solving One and Two-dimensional Bin Packing Problems, by Eunice Lopez-Camacho and Hugo Terashima-Marin and Peter Ross, the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO11), Dublin, Ireland, 2011 [PDF]
  • A Hyperheuristic Approach for Dynamic Enumeration Strategy Selection in Constraint Satisfaction, by Broderick Crawford and Ricardo Soto and Carlos Castro and Eric Monfroy, the 4th International Work-Conference on Interplay between Natural and Artificial Computation: New Challenges on Bioinspired Applications (IWINAC11), LNCS vol.6687/2011, Canary Islands, Spain, 2011 [PDF]
  • A New Hyper-heuristic Implementation in HyFlex: a Study on Generality, by Mustafa Misir and Katja Verbeeck and Patrick De Causmaecker and Greet Vanden Berghe, the 5th Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA11), Phoenix/Arizona, USA, 2011 [PDF]
  • A New Hyper-heuristic Implementation in HyFlex: a Study on Generality, by Mustafa Misir and Patrick De Causmaecker and Greet Vanden Berghe and Katja Verbeeck, the 23rd Benelux Conference on Artificial Intelligence (BNAIC11), Gent, Belgium, 2011 [PDF]
  • A Reinforcement Learning approach for the Cross-Domain Heuristic Search Challenge, by Tommaso Urli and Luca Di Gaspero, the 9th Metaheuristics International Conference (MIC11), Udine, Italy, 2011 [PDF]
  • A Selection Hyper-heuristic for Scheduling Deliveries of Ready-Mixed Concrete, by Mustafa Misir and Wim Vancroonenburg and Katja Verbeeck and Greet Vanden Berghe, the 9th Metaheuristics International Conference (MIC11), Udine, Italy, 2011 [PDF]
  • Adaptive Iterated Local Search for Cross-domain Optimisation, by Edmund Burke and Michel Gendreau and Gabriela Ochoa and James Walker, the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO11), Dublin, Ireland, 2011 [PDF]
  • An Adaptive Selection Hyper-heuristic for CHeSC 2011, by Mustafa Misir and Patrick De Causmaecker and Greet Vanden Berghe and Katja Verbeeck, OR53 Annual Conference, Nottingham, UK, 2011 [PDF]
  • An Adaptive Tie Breaking and Hybridisation Hyper-Heuristic for Exam Timetabling Problems, by Edmund Burke and Rong Qu and Amr Soghier, the 5th International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO11), Cluj Napoca, Romania, 2011 [PDF]
  • An Introduction to New Application Domains for the Home Care Scheduling Problem, by Pieter Smet and Mustafa Misir and Greet Vanden Berghe, the 25th Belgian Conference on Operations Research (ORBEL11), Gent, Belgium, 2011
  • An Investigation of Selection Hyper-heuristics in Dynamic Environments, by Berna Kiraz and Sima Etaner Uyar and Ender Ozcan, the 11th European Conference on the Applications of Evolutionary Computation (EvoApplications11), LNCS vol.6624/2011, Torino, Italy, 2011 [PDF]
  • Analysing the Adaptation Level of Parallel Hyperheuristics Applied to Mono-objective Optimisation Problems, by Eduardo Segredo and Carlos Segura and Coromoto Leon, the 5th International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO11), Cluj Napoca, Romania, 2011 [PDF]
  • Ant-Q Hyper-heuristic Approach for Solving 2-dimensional Cutting Stock Problem, by Khamassi Imen and Hammami Moez and Ghedira Khaled, the IEEE Symposium on Swarm Intelligence (IEEE SIS11), Paris, France, 2011 [PDF]
  • Automated Heuristic Design, by Gabriela Ochoa and Matthew Hyde and Edmund Burke, the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO11), Dublin, Ireland, 2011 [PDF]
  • Automatically Designing Selection Heuristics, by John Woodward and Jerry Swan, the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO11), Dublin, Ireland, 2011 [PDF]
  • Controlling Crossover in a Selection Hyper-heuristic Framework, by John Drake and Ender Ozcan and Edmund Burke, CS Technical Report NOTTCS-TR-SUB-1104181638-4244, OR53 Annual Conference, Nottingham, UK, 2011 [PDF]
  • Cooperating of Local Searches based Hyperheuristic Approach for Solving Traveling Salesman Problem, by Montazeri Mitra and Abbas Bahrololoum and Hossein Nezamabadi-pour and Mahdieh Soleymani Baghshah and Mahdieh Montazeri, the 3rd International Conference on Evolutionary Computation Theory and Applications (ECTA11), Paris, France, 2011
  • Design of a Generic Selection Hyper-heuristic, by Mustafa Misir and Katja Verbeeck and Greet Vanden Berghe and Patrick De Causmaecker, the 25th Belgian Conference on Operations Research (ORBEL11), Gent, Belgium, 2011
  • Evolution of Neural Networks Topologies and Learning Parameters to Produce Hyper-heuristics for Constraint Satisfaction Problems, by Jose Carlos Ortiz-Bayliss and Hugo Terashima-Marin and Peter Ross and Santiago Enrique Conant-Pablos, the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO11), Dublin, Ireland, 2011 [PDF]
  • Experimental Comparison of Selection Hyper-heuristics for the Short-Term Electrical Power Generation Scheduling Problem, by Argun Berberoglu and Sima Etaner Uyar, the 11th European Conference on the Applications of Evolutionary Computation (EvoApplications11), LNCS vol.6625/2011, Torino, Italy, 2011 [PDF]
  • Frequency Distribution Based Hyper-Heuristic for the Bin-Packing Problem, by He Jiang and Shuyan Zhang and Jifeng Xuan and Youxi Wu, the 11th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP11), LNCS vol.6622/2011, Torino, Italy, 2011 [PDF]
  • Genetic Programming Hyper-heuristic for Solving Dynamic Production Scheduling Problem, by Luciana Abednego and Dwi Hendratmo, the 3rd International Conference on Electrical Engineering and Informatics (ICEEI11), Bandung, Indonesia, 2011 [PDF]
  • HYPERION - A Recursive Hyper-heuristic Framework, by Jerry Swan and Ender Ozcan and Graham Kendall, the 5th Learning and Intelligent OptimizatioN Conference (LION11), LNCS vol.6683/2011, Rome, Italy, 2011 [PDF]
  • Hyperheuristic Encoding Scheme for Multi-objective Guillotine Cutting Problems, by Jesica de Armas and Gara Miranda and Coromoto Leon, the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO11), Dublin, Ireland, 2011 [PDF]
  • Hyperheuristic for the Parameter Tuning of a Bio-Inspired Algorithm of Query Routing in P2P Networks, by Paula Hernandez and Claudia Gomez and Laura Cruz and Alberto Ochoa and Norberto Castillo and Gilberto Rivera, the 10th Mexican International Conference on Artificial Intelligence (MICAI11), LNAI vol.7095/2011, Puebla, Mexico, 2011 [PDF]
  • Investigation of Hyper-Heuristics for Designing Survivable Virtual Topologies in Optical WDM Networks, by Fatma Corut Ergin and Sima Etaner Uyar and Aysegul Gencata Yayimli, the 11th European Conference on the Applications of Evolutionary Computation (EvoApplications11), LNCS vol.6625/2011, Torino, Italy, 2011 [PDF]
  • MYNDA: An IDSS Generator with Hyperheuristic Attribute Reduction, by Abdul Razak Hamdan, the IEEE International Conference on Electrical Engineering and Informatics (IEEE ICEEI11), Bandung, Indonesia, 2011 [PDF]
  • Markov Chain Hyper-heuristic (MCHH): an Online Selective Hyper-heuristic for Multi-objective Continuous Problems, by Kent McClymont and Edward C. Keedwell, the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO11), Dublin, Ireland, 2011 [PDF]
  • New Bounds for the Relaxed Traveling Tournament Problems using an Artificial Immune Algorithm, by Leslie Perez and Maria-Cristina Riff, the IEEE Congress on Evolutionary Computation (IEEE CEC11), New Orleans, USA, 2011 [PDF]
  • On the Idea of Evolving Decision Matrix Hyper-heuristics for Solving Constraint Satisfaction Problems, by Jose Carlos Ortiz-Bayliss and Hugo Terashima-Marin and Ender Ozcan and Andrew Parkes, the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO11), Dublin, Ireland, 2011 [PDF]
  • Policy Matrix Evolution for Generation of Heuristics, by Ender Ozcan and Andrew Parkes, the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO11), Dublin, Ireland, 2011 [PDF]
  • Reinforcement Learning with EGD based Hyper heuristic System for Exam Timetabling Problem, by Ei Shwe Sin, the IEEE International Conference on Cloud Computing and Intelligent Systems (IEEE CCIS11), Beijing, China, 2011 [PDF]
  • Security Personnel Routing and Rostering: a Hyper-heuristic Approach, by Mustafa Misir and Pieter Smet and Katja Verbeeck and Greet Vanden Berghe, the 3rd International Conference on Applied Operational Research (ICAOR11), LNMS vol.3, Istanbul, Turkey, 2011 [PDF]
  • Survivable Cross-layer Virtual Topology Design using a Hyper-heuristic Approach, by Fatma Corut Ergin and Aysegul Yayimli and Sima Uyar, the 13th International Conference on Transparent Optical Networks (IEEE ICTON11), Stockholm, Sweden, 2011 [PDF]
  • Using Hyperheuristics under a GP Framework for Financial Forecasting, by Michael Kampouridis and Edward Tsang, the 5th Learning and Intelligent OptimizatioN Conference (LION11), LNCS vol.6683/2011, Rome, Italy, 2011 [PDF]
  • A Hyper-heuristic with Learning Automata for the Traveling Tournament Problem, by Mustafa Misir and Tony Wauters and Katja Verbeeck and Greet Vanden Berghe, Metaheuristics: Intelligent Decision Making, 2011 [PDF]
  • Academic Timetabling Design Using Hyper-Heuristics, by Jorge Soria-Alcaraz and Martin Carpio-Valadez and Hugo Tereshima-Marin, Soft Computing for Intelligent Control and Mobile Robotics, 2011 [PDF]
  • Non-Linear Great Deluge with Reinforcement Learning for University Course Timetabling, by Joe Henry Obit and Dario Landa-Silva and Marc Sevaux and Djamila Ouelhadj, Metaheuristics: Intelligent Decision Making, 2011 [PDF]
  • HyFlex: A Benchmark Framework for Cross-domain Heuristic Search, by Edmund Burke and Tim Curtois and Matthew Hyde and Gabriela Ochoa and Jose A. Vazquez-Rodriguez, Arxiv preprint arXiv:1107.5462, 2011 [PDF]
  • A New Hyperheuristic Algorithm for Cross-Domain Search Problems, by Andreas Lehrbaum, MSc Thesis, Faculty of Informatics, Vienna University of Technology, 2011 [PDF]
  • Hyper-heuristics for Grouping Problems, by Murat Birben, MSc Thesis, Department of Computer Engineering, Yeditepe University, 2011
  • Optimising Container Processes at Multimodal Seaport Terminals : an Integrated Approach and Application, by Bradley Casey, PhD Thesis, Mathematical Sciences School, Queensland University of Technology, 2011 [PDF]

2010 (43 publications)

  • A Cooperative Hyper-heuristic Search Framework, by Djamila Ouelhadj and Sanja Petrovic, Journal of Heuristics, 16(6), Springer, 2010 [PDF]
  • A Genetic Programming Hyper-Heuristic Approach for Evolving 2-Dimensional Strip Packing Heuristics, by Edmund Burke and Matthew Hyde and Graham Kendall and John Woodward, IEEE Transactions on Evolutionary Computation, 14(6), IEEE, 2010 [PDF]
  • A New Dispatching Rule based Genetic Algorithm for the Multi-objective Job Shop Problem, by Jose Antonio Vazquez Rodriguez and Sanja Petrovic, Journal of Heuristics, 16(6), Springer, 2010 [PDF]
  • A Reinforcement Learning - Great Deluge Hyperheuristic for Examination Timetabling, by Ender Ozcan and Mustafa Misir and Gabriela Ochoa and Edmund Burke, International Journal of Applied Metaheuristic Computing, 1(1), IGI Global, 2010 [PDF]
  • A Scatter Search based Hyper-heuristic for Sequencing a Mixed-model Assembly Line, by Jaime Cano-Belman and Roger Z. Rios-Mercado and Joaquin Bautista, Journal of Heuristics, 16(6), Springer, 2010 [PDF]
  • An Investigation and Extension of a Hyper-heuristic Framework, by Prapa Rattadilok, Informatica, 34(4), Slovenian Society Informatika, 2010 [PDF]
  • Coalition-based Metaheuristic: a Self-Adaptive Metaheuristic using Reinforcement Learning and Mimetism, by David Meignan and Abderrafiaa Koukam and Jean-Charles Creput, Journal of Heuristics, 16(6), Springer, 2010 [PDF]
  • DVRP: a Hard Dynamic Combinatorial Optimisation Problem Tackled by an Evolutionary Hyper-heuristic, by Pablo Garrido and Maria Cristina Riff, Journal of Heuristics, 16(6), Springer, 2010 [PDF]
  • Design of a Hyperheuristic for Production Scheduling in Job Shop Environments, by Omar Danilo Castrillon and William Ariel Sarache and Jaime Alberto Giraldo, Ingeniare. Revista Chilena de Ingenieria, 18(2), Universidad de Tarapaca, 2010 [PDF]
  • Generalized Hyper-heuristics for Solving 2D Regular and Irregular Packing Problems, by Hugo Terashima-Marin and Peter Ross and Claudia J. Farias Zarate and Eunice Lopez-Camacho and Manuel Valenzuela-Rendon, Annals of Operations Research, 179(1), Springer, 2010 [PDF]
  • Multiobjective Optimization for Water Distribution System Design Using a Hyperheuristic, by Darian Raad and Alexander Sinske and Jan van Vuuren, Journal of Water Resources Planning and Management, 136(5), ASCE, 2010 [PDF]
  • A Coevolutionary, Hyper Heuristic approach to the Optimization of Three-dimensional Process Plant Layouts - A Comparative Study, by Marcus Furuholmen and Kyrre Glette and Mats Hovin and Jim Torresen, the IEEE Congress on Evolutionary Computation (IEEE CEC10), Barcelona, Spain, 2010 [PDF]
  • A Constructive Hyper-heuristics for Rough Set Attribute Reduction, by Salwani Abdullah and Nasser Sabar and Mohd Zakree Ahmad Nazri and Hamza Turabieh and Barry McCollum, the 10th International Conference on Intelligent Systems Design and Applications (ISDA10), Cairo, Egypt, 2010 [PDF]
  • A Hyper-Heuristic Approach for the Unit Commitment Problem, by Argun Berberoglu and Sima Uyar, the 10th European Conference on the Applications of Evolutionary Computation (EvoApplications10), LNCS vol.6025/2010, Istanbul, Turkey, 2010 [PDF]
  • A Hyper-heuristic Approach to Strip Packing Problems, by Edmund Burke and Qiang Guo and Graham Kendall, the 11th Conference on Parallel Problem Solving from Nature (PPSN10), LNCS vol.6238/2011, Krakow, Poland, 2010 [PDF]
  • A Hyper-heuristic Combined with a Greedy Shuffle Approach to the Nurse Rostering Competition, by Burak Bilgin and Peter Demeester and Mustafa Misir and Wim Vancroonenburg and Greet Vanden Berghe and Tony Wauters, the 8th International Conference on the Practice and Theory of Automated Timetabling (PATAT10), Belfast, Northern Ireland, 2010 [PDF]
  • A Hyperheuristic Approach for Constraint Solving, by Broderick Crawford and Carlos Castro and Eric Monfroy, the IEEE Electronics, Robotics and Automotive Mechanics Conference (IEEE CERMA10), Cuernavaca, Mexico, 2010 [PDF]
  • A Study into the Use of Hyper-heuristics to Solve the School Timetabling Problem, by Nelishia Pillay, the Annual Research Conference of the South African Institute for Computer Scientists and Information Technologists (SAICSIT10), Bela Bela, South Africa, 2010 [PDF]
  • A Study of Different Hyper-heuristics for Sequencing by Hybridization Problem, by Aleksandra Swiercz and Wojciech Mruczkiewicz and Jacek Blazewicz and Graham Kendall and Edmund Burke, the 24th EURO Conference on Operational Research (EURO10), Lisbon, Portugal, 2010
  • A Two Phase Hyper-heuristic Approach for Solving the Eternity II Puzzle, by Tony Wauters and Wim Vancrooenburg and Greet Vanden Berghe, the 2nd International Conference on Metaheuristics and Nature Inspired Computing (META10), Djerba Island, Tunisia, 2010 [PDF]
  • Adaptive Selection of Heuristics for Improving Constructed Exam Timetables, by Edmund Burke and Rong Qu and Amr Soghier, the 8th International Conference on the Practice and Theory of Automated Timetabling (PATAT10), Belfast, Northern Ireland, 2010 [PDF]
  • Alternative Hyper-heuristic Strategies for Multi-method Global Optimization, by Jacomine Grobler and Andries Petrus Engelbrecht and Graham Kendall and Sarma Yadavalli, the IEEE Congress on Evolutionary Computation (IEEE CEC10), Barcelona, Spain, 2010 [PDF]
  • An Agent-Based Hyper-Heuristic Approach to Combinatorial Optimization Problems, by Richard Malek, the 2nd IEEE International Conference on Intelligent Computing and Intelligent Systems (IEEE ICIS10), Xiamen, China, 2010 [PDF]
  • An Empirical Study into the Structure of Heuristic Combinations in an Evolutionary Algorithm Hyper-heuristic for the Examination Timetabling Problem, by Nelishia Pillay, the Annual Research Conference of the South African Institute for Computer Scientists and Information Technologists (SAICSIT10), Bela Bela, South Africa, 2010 [PDF]
  • An Evolutionary Algorithm Hyper-heuristic for Producing Feasible Timetables for the Curriculum based University Course Timetabling Problem, by Rosanne Els and Nelishia Pillay, the 2nd World Congress on Nature and Biologically Inspired Computing (NaBIC10), Kitakyushu, Japan, 2010 [PDF]
  • Ant based Hyper Heuristics with Space Reduction: a Case Study of the p-Median Problem, by Zhilei Ren and He Jiang and Jifeng Xuan and Zhongxuan Luo, the 11th Conference on Parallel Problem Solving from Nature (PPSN10), LNCS vol.6238/2011, Krakow, Poland, 2010 [PDF]
  • Approximating Multi-Objective Hyper-Heuristics for Solving 2D Irregular Cutting Stock Problems, by Juan Carlos Gomez and Hugo Terashima-Marin, the 9th Mexican International Conference on Artificial Intelligence (MICAI10), LNCS vol.6438/2010, Pachuca, Mexico, 2010 [PDF]
  • Co-evolutionary Hyper-heuristic Method for Auction based Scheduling, by Shaheen Fatima and Mohamed Bader-El-Den, the IEEE Congress on Evolutionary Computation (IEEE CEC10), Barcelona, Spain, 2010 [PDF]
  • Codifications in Evolutionary Algorithms for the Multi-Objective 2D Guillotine Strip Packing Problem, by Jesica de Armas and Gara Miranda and Coromoto Leon, the 24th EURO Conference on Operational Research (EURO10), Lisbon, Portugal, 2010
  • Combined Blackbox and AlgebRaic Architecture (CBRA), by Andrew J. Parkes, the 8th International Conference on the Practice and Theory of Automated Timetabling (PATAT10), Belfast, Northern Ireland, 2010 [PDF]
  • Evolving Hyper Heuristic-Based Solvers for Rush Hour and FreeCell, by Ami Hauptman and Achiya Elyasaf and Moshe Sipper, the 3rd Annual Symposium on Combinatorial Search (SOCS10), Atlanta/Georgia, USA, 2010 [PDF]
  • Generating Dispatching Rules for Semiconductor Manufacturing to Minimize Weighted Tardiness, by Christoph W. Pickardt and Jurgen Branke and Torsten Hildebrandt and Jens Heger and Bernd Scholz-Reiter, the 42nd Winter Simulation Conference (WSC10), Baltimore/Maryland, USA, 2010 [PDF]
  • Hyper-heuristic Approach for Assigning Patients to Hospital Rooms, by Wim Vancroonenburg and Mustafa Misir and Burak Bilgin and Peter Demeester and Greet Vanden Berghe, the 8th International Conference on the Practice and Theory of Automated Timetabling (PATAT10), Belfast, Northern Ireland, 2010 [PDF]
  • Hyper-heuristic Approaches for the Dynamic Generalized Assignment Problem, by Berna Kiraz and Haluk Topcuoglu, the 10th International Conference on Intelligent Systems Design and Applications (ISDA10), Cairo, Egypt, 2010 [PDF]
  • Hyper-heuristics Learning a Varying Set of Low-level Heuristics, by Mustafa Misir and Katja Verbeeck and Greet Vanden Berghe and Patrick De Causmaecker, the 24th Belgian Conference on Operations Research (ORBEL10), Liege, Belgium, 2010
  • Hyper-heuristics with a Dynamic Heuristic Set for the Home Care Scheduling Problem, by Mustafa Misir and Katja Verbeeck and Patrick De Causmaecker and Greet Vanden Berghe, the IEEE Congress on Evolutionary Computation (IEEE CEC10), Barcelona, Spain, 2010 [PDF]
  • Iterated Local Search vs. Hyper-heuristics: Towards General-Purpose Search Algorithms, by Edmund Burke and Tim Curtois and Matthew Hyde and Graham Kendall and Gabriela Ochoa and Sanja Petrovic and Jose Antonio Vazquez Rodriguez and Michel Gendreau, the IEEE Congress on Evolutionary Computation (IEEE CEC10), Barcelona, Spain, 2010 [PDF]
  • Problem-state Representations in a Hyper-heuristic Approach for the 2D Irregular BPP, by Eunice Lopez-Camacho and Hugo Terashima-Marin and Peter Ross and Manuel Valenzuela-Rendon, the 12th Annual Conference on Genetic and Evolutionary Computation (GECCO10), Portland/Oregon, USA, 2010 [PDF]
  • Scheduling English Football Fixtures over the Holiday Period using Hyper-heuristics, by Jonathon Gibbs and Graham Kendall and Ender Ozcan, the 11th Conference on Parallel Problem Solving from Nature (PPSN10), LNCS vol.6238/2011, Krakow, Poland, 2010 [PDF]
  • A Classification of Hyper-heuristic Approaches, by Edmund Burke and Matthew Hyde and Graham Kendall and Gabriela Ochoa and Ender Ozcan and John Woodward, Handbook of Metaheuristics, 2010 [PDF]
  • A Genetic Programming Hyper-Heuristic Approach to Automated Packing, by Matthew Hyde, PhD Thesis, School of Computer Science, University of Nottingham, 2010 [PDF]
  • Developing Novel Meta-heuristic, Hyper-heuristic and Cooperative Search for Course Timetabling Problems, by Joe Henry Obit, PhD Thesis, School of Computer Science, University of Nottingham, 2010 [PDF]
  • Heuristic Approaches for Real World Timetabling Problems in Education and Health Care, by Peter Demeester, PhD Thesis, Department of Computer Science, KU Leuven, 2010 [PDF]

2009 (38 publications)

  • A Study of Heuristic Combinations for Hyper-heuristic Systems for the Uncapacitated Examination Timetabling Problem, by Nelishia Pillay and Wolfgang Banzhaf, European Journal of Operational Research, 197(2), Elsevier, 2009 [PDF]
  • Adaptive Automated Construction of Hybrid Heuristics for Exam Timetabling and Graph Colouring Problems, by Rong Qu and Edmund Burke and Barry McCollum, European Journal of Operational Research, 198(2), Elsevier, 2009 [PDF]
  • An Investigation of Fuzzy Multiple Heuristic Orderings in the Construction of University Examination Timetables, by Hishammuddin Asmuni and Edmund Burke and Jonathan Garibaldi and Barry McCollum and Andrew Parkes, Computers and Operations Research, 36(4), Elsevier, 2009 [PDF]
  • Evolving Timetabling Heuristics using a Grammar-based Genetic Programming Hyper-heuristic Framework, by Mohamed Bader-El-Den and Riccardo Poli and Shaheen Fatima, Memetic Computing, 1(3), Springer, 2009 [PDF]
  • Hybridizations within a Graph-based Hyper-heuristic Framework for University Timetabling Problems, by Rong Qu and Edmund Burke, International Journal of the Operational Research Society, 60(9), Palgrave Macmillan, 2009 [PDF]
  • A Choice Function to Dynamic Selection of Enumeration Strategies Solving Constraint Satisfaction Problems, by Broderick Crawford and Mauricio Montecinos and Carlos Castroy and Eric Monfroy, the 1st International Conference of Soft Computing and Pattern Recognition (SOCPAR09), Malacca, Malaysia, 2009 [PDF]
  • A Greedy Hyper-heuristic in Dynamic Environments, by Ender Ozcan and Sima Etaner Uyar and Edmund Burke, the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO09), Montreal, Canada, 2009 [PDF]
  • A Hyper-heuristic Approach to the Home Care Scheduling Problem, by Mustafa Misir and Katja Verbeeck and Greet Vanden Berghe and Patrick De Causmaecker, the 14th Belgian-French-German Conference on Optimization (BFG09), Leuven, Belgium, 2009 [PDF]
  • A Hyper-heuristic Approach to the Patient Admission Scheduling Problem, by Mustafa Misir and Burak Bilgin and Peter Demeester and Katja Verbeeck and Patrick De Causmaecker and Greet Vanden Berghe, the 35th International Conference of Operational Research Applied to Health Services (ORAHS09), Leuven, Belgium, 2009 [PDF]
  • A Hyperheuristic Approach to Belgian Nurse Rostering Problems, by Burak Bilgin and Patrick De Causmaecker and Greet Vanden Berghe, the 4th Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA09), Dublin, Ireland, 2009 [PDF]
  • A Hyperheuristic Approach to Select Enumeration Strategies in Constraint Programming, by Broderick Crawford and Mauricio Montecinos and Carlos Castro and Eric Monfroy, the 9th WSEAS International Conference on Applied Informatics and Communications (AIC09), Moscow, Russia, 2009 [PDF]
  • A Memetic Algorithm and a Parallel Hyperheuristic Island-based Model for a 2D Packing Problem, by Coromoto Leon and Gara Miranda and Carlos Segura, the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO09), Montreal, Canada, 2009 [PDF]
  • A Multi-level Search Framework for Asynchronous Cooperation of Multiple Hyper-heuristics, by Djamila Ouelhadj and Sanja Petrovic and Ender Ozcan, the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO09), Montreal, Canada, 2009 [PDF]
  • A Neuro-evolutionary Approach to Produce General Hyper-heuristics for the Dynamic Variable Ordering in Hard Binary Constraint Satisfaction Problems, by Jose Carlos Ortiz-Bayliss and Hugo Terashima-Marin and Peter Ross and Jorge Ivan Fuentes-Rosado and Manuel Valenzuela-Rendon, the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO09), Montreal, Canada, 2009 [PDF]
  • A New Learning Hyper-heuristic for the Traveling Tournament Problem, by Mustafa Misir and Tony Wauters and Katja Verbeeck and Greet Vanden Berghe, the 8th Metaheuristics International Conference (MIC09), Hamburg, Germany, 2009 [PDF]
  • A Self-organising Hyper-heuristic Framework, by Ender Ozcan and Mustafa Misir and Edmund Burke, the 4th Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA09), Dublin, Ireland, 2009 [PDF]
  • Adaptive Selection of Heuristics within a GRASP for Exam Timetabling Problems, by Edmund Burke and Rong Qu, the 4th Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA09), Dublin, Ireland, 2009 [PDF]
  • Analyzing the Landscape of a Graph based Hyper-heuristic for Timetabling Problems, by Gabriela Ochoa and Rong Qu and Edmund Burke, the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO09), Montreal, Canada, 2009 [PDF]
  • Dispatching Rules for Production Scheduling: a Hyper-heuristic Landscape Analysis, by Gabriela Ochoa and Jose Antonio Vazquez-Rodriguez and Sanja Petrovic and Edmund Burke, the IEEE Congress on Evolutionary Computation (IEEE CEC09), Trondheim, Norway, 2009 [PDF]
  • Distributed Hyper-heuristics for Real Parameter Optimization, by Marco Biazzini and Balazs Banhelyi and Alberto Montresor and Mark Jelasity, the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO09), Montreal, Canada, 2009 [PDF]
  • Evolution of Hyperheuristics for the Biobjective Graph Coloring Problem using Multiobjective Genetic Programming, by Paresh Tolay and Rajeev Kumar, the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO09), Montreal, Canada, 2009 [PDF]
  • Evolving Hyper-Heuristics for the Uncapacitated Examination Timetabling Problem, by Nelishia Pillay, the 4th Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA09), Dublin, Ireland, 2009 [PDF]
  • Evolving Reusable 3D Packing Heuristics with Genetic Programming, by Sam Allen and Edmund Burke and Matthew Hyde and Graham Kendall, the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO09), Montreal, Canada, 2009 [PDF]
  • Examination Timetabling Using Late Acceptance Hyper-heuristics, by Ender Ozcan and Yuri Bykov and Murat Birben and Edmund Burke, the IEEE Congress on Evolutionary Computation (IEEE CEC09), Trondheim, Norway, 2009 [PDF]
  • Grammar-based Genetic Programming for Timetabling, by Mohamed Bader-El-Den and Riccardo Poli, the IEEE Congress on Evolutionary Computation (IEEE CEC09), Trondheim, Norway, 2009 [PDF]
  • Hyper-heuristics: Raising the Level of Generality, by Mustafa Misir and Patrick De Causmaecker and Katja Verbeeck and Greet Vanden Berghe, the 23rd Belgian Conference on Operations Research (ORBEL09), Leuven, Belgium, 2009 [PDF]
  • Landscape Analysis of Simple Perturbative Hyper-heuristics, by Ibrahim Maden and Sima Uyar and Ender Ozcan, the 15th International Conference on Soft Computing (MENDEL09), Brno, Czech Republic, 2009 [PDF]
  • Learning and Using Hyper-heuristics for Variable and Value Ordering in Constraint Satisfaction Problems, by Sean A. Bittle and Mark S. Fox, the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO09), Montreal, Canada, 2009 [PDF]
  • Multilevel Search for Choosing Hyper-heuristics, by Ender Ozcan and Edmund Burke, the 4th Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA09), Dublin, Ireland, 2009 [PDF]
  • Multilevel Search for Evolving the Acceptance Criteria of a Hyper-Heuristic, by Matthew Hyde and Ender Ozcan and Edmund Burke, the 4th Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA09), Dublin, Ireland, 2009 [PDF]
  • Non-Linear Great Deluge with Learning Mechanism for Solving the Course Timetabling Problem, by Joe Henry Obit and Dario Landa Silva and Djamila Ouelhadj and Marc Sevaux, the 8th Metaheuristics International Conference (MIC09), Hamburg, Germany, 2009 [PDF]
  • Stable solving of CVRPs using hyperheuristics, by Pablo Garrido and Carlos Castro, the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO09), Montreal, Canada, 2009 [PDF]
  • There is a Free Lunch for Hyper-Heuristics, Genetic Programming and Computer Scientists, by Riccardo Poli and Mario Graff, the 12th European Conference on Genetic Programming (EuroGP09), Tubingen, Germany, 2009 [PDF] [ABSTRACT]

    In this paper we prove that in some practical situations, there is a free lunch for hyper-heuristics, i.e., for search algorithms that search the space of solvers, searchers, meta-heuristics and heuristics for problems. This has consequences for the use of genetic programming as a method to discover new search algorithms and, more generally, problem solvers. Furthermore, it has also rather important philosophical consequences in relation to the efforts of computer scientists to discover useful novel search algorithms.

  • Evolving Effective Incremental Solvers for SAT with a Hyper-heuristic Framework based on Genetic Programming, by Mohamed Bader-El-Den and Riccardo Poli, Genetic Programming Theory and Practice VI, 2009 [PDF]
  • Exploring Hyper-heuristic Methodologies with Genetic Programming, by Edmund Burke and Matthew Hyde and Graham Kendall and Gabriela Ochoa and John Woodward, Collaborative Computational Intelligence, Springer, 2009 [PDF]
  • Hyper-heuristics for Sequencing by Hybridisation Problem, by Wojciech Mruczkiewicz, Master Thesis, Institute of Computing Science, Poznan University of Technology, 2009 [PDF] [ABSTRACT]

    The objective of this work was twofold. First of all a hyper-heuristic approach to the SBH problem was to be applied. In order to solve the SBH problem with the use of a general hyper-heuristic framework a number of software tools have been created. Design and development of the framework is based on the current research in the field of optimisation techniques. The second goal of this thesis was to check the behaviour and performance of the hyper-heuristic methods. Hyper-heuristics allow to change the set of moves that solver is capable of performing quickly and with a great flexibility. Sensitivity of modifications of this kind is examined in this work. In an ideal case hyper-heuristic method should work almost equally good with every reasonable set of allowed moves. The goal of hyper-heuristic algorithms is to learn and predict which moves are going to improve the solution and which are not. Thus, the aim of this work is to establish a verifiable use case for hyper-heuristic algorithms based on the solution to the SBH problem.

  • Investigation of the Role of Genetic Programming in a Hyper-Heuristic Framework for Combinatorial Optimization Problems, by Mohamed Bader-El-Den, PhD Thesis, School of Computer Science and Electronic Engineering, University of Essex, 2009
  • Tolerable Constructive Graph-Based Hyper-Heuristic Algorithm For Examination Timetabling, by Shahrzad Mohammad Pour, MSc Thesis, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 2009 [PDF]

2008 (15 publications)

  • A Comprehensive Analysis of Hyper-heuristics, by Ender Ozcan and Burak Bilgin and Emin Erkan Korkmaz, Intelligent Data Analysis, 12(1), IOS, 2008 [PDF]
  • A Hyper-Heuristic Approach for Efficient Resource Scheduling in Grid, by Mary Saira Bhanu and Nagamaputhur Gopalan, International Journal of Computers, Communications & Control, Universitatea Agora, 2008 [PDF]
  • Automated Discovery of Local Search Heuristics for Satisfiability Testing, by Alex Fukunaga, Evolutionary Computation, 16(1), MIT, 2008 [PDF]
  • Heuristic, Meta-heuristic and Hyper-heuristic Approaches for Fresh Produce Inventory Control and Shelf Space Allocation, by Ruibin Bai and Edmund Burke and Graham Kendall, Journal of the Operational Research Society, 59(10), Palgrave Macmillan, 2008 [PDF]
  • Mining the Data from a Hyperheuristic Approach using Associative Classification, by Fadi Thabtah and Peter Cowling, Expert Systems with Applications, 34(2), Elsevier, 2008 [PDF]
  • A Cooperative Distributed Hyper-heuristic Framework for Scheduling, by Djamila Ouelhadj and Sanja Petrovic, the IEEE International Conference on Systems, Man, and Cybernetics (SMC08), Singapore, 2008 [PDF]
  • A Model and a Hyperheuristic Approach for Automated Assignment of Patients to Beds in a Hospital, by Burak Bilgin and Peter Demeester and Greet Vanden Berghe and Tony Wauters, the 2nd International Conference on Metaheuristics and Nature Inspired Computing (META08), Hammamet, Tunusia, 2008
  • A Study of Simulated Annealing Hyperheuristics, by Edmund Burke and Graham Kendall and Mustafa Misir and Ender Ozcan, the 7th International Conference of Practice and Theory of Automated Timetabling (PATAT08), Montreal, Canada, 2008 [PDF]
  • Cost-Benefit Investigation of a Genetic-Programming Hyperheuristic, by Robert Keller and Riccardo Poli, the 8th International Conference on Evolution Artificielle (EA08), LNCS vol.4926, Tours, France, 2008 [PDF]
  • Learning Heuristic Selection in Hyperheuristics for Examination Timetabling, by Edmund Burke and Mustafa Misir and Gabriela Ochoa and Ender Ozcan, the 7th International Conference of Practice and Theory of Automated Timetabling (PATAT08), Montreal, Canada, 2008 [PDF]
  • Parallel hyperheuristic: a self-adaptive island-based model for multi-objective optimization, by Leon, Coromoto and Miranda, Gara and Segura, Carlos, Proceedings of the 10th annual conference on Genetic and evolutionary computation, ACM, 2008 [PDF] [ABSTRACT]

    This work presents a new parallel model for the solution of multi-objective optimization problems. The model combines a parallel island-based scheme with a hyperheuristic approach in order to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths of one algorithm can compensate for the weaknesses of another. Computational results demonstrate that the model grants more computational resources to those algorithms that show a more promising behaviour.

  • Self-adaptive Hyperheuristic and Greedy Search, by Robert Keller and Riccardo Poli, the IEEE Congress on Evolutionary Computation (IEEE CEC08), Hong Kong, 2008 [PDF]
  • Subheuristic Search and Scalability in a Hyperheuristic, by Robert Keller and Riccardo Poli, the 10th Annual Conference on Genetic and Evolutionary Computation (GECCO08), Atlanta/Georgia, USA, 2008 [PDF]
  • Hyperheuristics: Recent Developments, by Konstantin Chakhlevitch and Peter Cowling, Adaptive and Multilevel Metaheuristics, Springer, 2008 [PDF]
  • Group Decision Making for Move Acceptance in Hyperheuristics, by Mustafa Misir, MSc Thesis, Department of Computer Engineering, Yeditepe University, 2008

2007 (11 publications)

  • A Graph-Based Hyper Heuristic for Educational Timetabling Problems, by Edmund Burke and Barry McCollum and Ammon Meisels and Sanja Petrovic and Rong Qu, European Journal of Operational Research, 176(1), Elsevier, 2007 [PDF]
  • A Hyper-Heuristic for Descriptive Rule Induction, by Tho Hoan Pham, International Journal of Data Warehousing and Mining, 3(1), IGI Global, 2007 [PDF]
  • A Simulated Annealing Hyper-heuristic for Determining Shipper Sizes for Storage and Transportation, by Kathryn Dowsland and Eric Soubeiga and Edmund Burke, European Journal of Operational Research, 179(3), Elsevier, 2007 [PDF]
  • An Evolutionary Hyperheuristic to Solve Strip-Packing Problems, by Pablo Garrido and Maria-Cristina Riff, the 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL07), LNCS vol.4881/2007, Birmingham, UK, 2007 [PDF]
  • An Investigation of Population-based Hyper-heuristics for Graph Colouring, by Edmund Burke and Nam Pham and Rong Qu, the 7th Metaheuristics International Conference (MIC07), Montreal, Canada, 2007 [PDF]
  • Collaboration Between Hyperheuristics to Solve Strip-Packing Problems, by Pablo Garrido and Maria-Cristina Riff, the 12th International Fuzzy Systems Association World Congress (IFSA07), LNCS vol.4529/2007, Cancun, Mexico, 2007 [PDF]
  • Comparing Two Models to Generate Hyper-heuristics for the 2D-Regular Bin-Packing Problem, by Hugo Terashima-Marin and Claudia J. Farias Zarate and Peter Ross and Manuel Valenzuela-Rendon, the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO07), New York, USA, 2007 [PDF]
  • Generating SAT Local-Search Heuristics Using a GP Hyper-Heuristic Framework, by Mohamed Bader-El-Den and Riccardo Poli, the 8th International Conference on Evolution Artificielle (EA07), LNCS vol.4926/2008, Tours, France, 2007 [PDF]
  • A New Hyperheuristic: IDWalk based Hyper-heuristic Strategy, by Mustafa Misir, B.Sc. Graduation Project Report, 2007
  • An Investigation of a Hyper-heuristic Ant Algorithm for the Travelling Tournament Problem, by Pai-Chun Chen, MSc Thesis, School of Computer Science, University of Nottingham, 2007
  • Ant Algorithm Hyperheuristic Approaches for Scheduling Problems, by Ross O'Brien, MSc Thesis, School of Computer Science, University of Nottingham, 2007 [PDF]

2006 (7 publications)

  • A Simulated Annealing Approach to the Traveling Tournament Problem, by Aris Anagnostopoulos and Laurent Michel and Pascal Van Hentenryck and Yannis Vergados, Journal of Scheduling, 9(2), Springer, 2006 [PDF]
  • Case Based Heuristic Selection for Timetabling Problems, by Edmund Burke and Sanja Petrovic and Rong Qu, Journal of Scheduling, 9(2), Springer, 2006 [PDF]
  • A GA-based Method to Produce Generalized Hyper-heuristics for the 2D-regular Cutting Stock Problem, by Hugo Terashima-Marin and Claudia J. Farias Zarate and Peter Ross and Manuel Valenzuela-Rendon, the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO06), Seattle/Washington, USA, 2006 [PDF]
  • An Experimental Study on Hyper-Heuristics and Exam Scheduling, by Burak Bilgin and Ender Ozcan and Emin Erkan Korkmaz, the 6th International Conference on the Practice and Theory of Automated Timetabling (PATAT06), LNCS vol.3867/2007, Brono, Czech Republic, 2006 [PDF]
  • Evolving Bin Packing Heuristics with Genetic Programming, by Edmund Burke and Matthew Hyde and Graham Kendall, the 9th Parallel Problem Solving from Nature (PPSN06), LNCS vol.4193/2006, Reykjavik, Iceland, 2006 [PDF]
  • Hill Climbers and Mutational Heuristics in Hyperheuristics, by Ender Ozcan and Burak Bilgin and Emin Erkan Korkmaz, the 9th Parallel Problem Solving from Nature (PPSN06), LNCS vol.4193/2006, Reykjavik, Iceland, 2006 [PDF]
  • by Burak Bilgin, MSc Thesis, Department of Computer Engineering, Yeditepe University, 2006

2005 (13 publications)

  • An Ant Algorithm Hyperheuristic for the Project Presentation Scheduling Problem, by Edmund Burke and Graham Kendall and Dario Landa Solva and Ross O'Brien and Eric Soubeiga, the IEEE Congress on Evolutionary Computation (IEEE CEC05), Edinburgh, Scotland, 2005 [PDF]
  • Choosing the Fittest Subset of Low Level Heuristics in a Hyperheuristic Framework, by Konstantin Chakhlevitch and Peter Cowling, the 5th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP05), LNCS vol.3448/2005, Lausanne, Switzerland, 2005 [PDF]
  • Forming Hyper-heuristics with GAs when Solving 2D-regular Cutting Stock Problems, by Hugo Tereshima-Marin and Armando Moran-Saavedra and Peter Ross, the IEEE Congress on Evolutionary Computation (IEEE CEC05), Edinburgh, Scotland, 2005 [PDF]
  • Fuzzy Multiple Heuristic Ordering for Course Timetabling, by Hishammuddin Asmuni and Edmund Burke and Jonathan Garibaldi, the 5th United Kingdom Workshop on Computational Intelligence (UKCI05), London, UK, 2005 [PDF]
  • Hybrid Variable Neighbourhood Hyperheuristics for Exam Timetabling Problems, by Rong Qu and Edmund Burke, the 6th Metaheuristics International Conference (MIC05), Vienna, Austria, 2005
  • An Investigation of Automated Planograms Using a Simulated Annealing based Hyper-heuristics, by Ruibin Bai and Graham Kendall, Meta-heuristics: Progress as Real Problem Solvers, 2005 [PDF]
  • Hyper-heuristics, by Peter Ross, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Technique, 2005 [PDF]
  • Multi-objective Hyper-heuristic Approaches for Space Allocation and Timetabling, by Edmund Burke and Dario Landa Silva and Eric Soubeiga, Meta-heuristics: Progress as Real Problem Solvers, 2005 [PDF]
  • Analysing the High Level Heuristics within a Graph Based Hyper-heuristic, by Rong Qu and Edmund Burke, CS Technical Report No: NOTTCS-TR-2005-3, 2005 [PDF]
  • An Investigation of Novel Approaches for Optimising Retail Shelf Space Allocation, by Ruibin Bai, PhD Thesis, School of Computer Science, University of Nottingham, 2005 [PDF]
  • An Investigation of a Genetic Algorithm Based Hyper-Heuristic Applied to Scheduling Problems, by Limin Han, PhD Thesis, School of Computer Science, University of Nottingham, 2005
  • Optimisation of Surface Mount Device Placement Machine in Printed Circuit Board Assembly, by Masri Ayob, PhD Thesis, School of Computer Science, University of Nottingham, 2005 [PDF]
  • Tabu Search Based Hyper-Heuristic Approaches for Examination Timetabling, by Naimah Mohd Hussin, PhD Thesis, School of Computer Science, University of Nottingham, 2005 [PDF]

2004 (6 publications)

  • Channel Assignment Optimisation Using a Hyper-heuristic, by Graham Kendall and Mazlan Mohamad, the IEEE Conference on Cybernetics and Intelligent Systems (IEEE CIS04), Singapore, 2004 [PDF]
  • Channel Assignment in Cellular Communication Using a Great Deluge Hyper-heuristic, by Graham Kendall and Mazlan Mohamad, the 12th IEEE International Conference on Network (IEEE ICON04), Singapore, 2004 [PDF]
  • Distributed Choice Function Hyper-heuristics for Timetabling and Scheduling, by Prapa Rattadilok and Andy Gaw and Raymond Kwan, the 5th International Conference on the Practice and Theory of Automated Timetabling (PATAT04), LNCS vol.3616/2005, Pittsburg/Pennsylvania, USA, 2004 [PDF]
  • Fuzzy Multiple Heuristic Orderings for Examination Timetabling, by Hishammuddin Asmuni and Edmund Burke and Jonathan Garibaldi and Barry McCollum, the 5th International Conference on the Practice and Theory of Automated Timetabling (PATAT04), LNCS vol.3616/2005, Pittsburg/Pennsylvania, USA, 2004 [PDF]
  • Hyper-heuristics applied to Class and Exam Timetabling Problems, by Peter Ross and Javier G. Marin-Blazquez and Emma Hart, the IEEE Congress on Evolutionary Computation (IEEE CEC04), Portland/Oregon , USA, 2004 [PDF]
  • A Distributed Hyper-heuristic for Scheduling, by Prapa Rattadilok and Andy Gaw and Raymond Kwan, CS Technical Report No: 2004.01, 2004 [PDF]

2003 (8 publications)

  • A Tabu-Search Hyper-Heuristic for Timetabling and Rostering, by Edmund Burke and Graham Kendall and Eric Soubeiga, Journal of Heuristics, 9(3), Springer, 2003 [PDF]
  • A Monte Carlo Hyper-Heuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine, by Masri Ayob and Graham Kendall, the 4th International Conference on Intelligent Technologies (InTech03), Chiang Mai, Thailand, 2003 [PDF]
  • A Pheromone-based Look-Ahead Hyper-heuristic for Time-tabling Problems, by Edmund Burke and Moshe Dror and Graham Kendall and Ross O'Brien and David Redrup and Eric Soubeiga, the 1st Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA03), Nottingham, UK, 2003 [PDF]
  • An Ant Algorithm Hyper-heuristic, by Edmund Burke and Graham Kendall and Ross O'Brien and David Redrup and Eric Soubeiga, the 5th Metaheuristics International Conference (MIC03), Kyoto, Japan, 2003
  • Hyperheuristic Approaches for Multiobjective Optimisation, by Edmund Burke and Dario Landa Silva and Eric Soubeiga, the 5th Metaheuristics International Conference (MIC03), Kyoto, Japan, 2003 [PDF]
  • Learning a Procedure that can Solve Hard Bin-packing Problems: a New GA-based Approach to Hyperheuristics, by Peter Ross and Emma Hart and Javier G. Marin-Blazquez and Sonia Schulenburg, the 5th Annual Conference on Genetic and Evolutionary Computation (GECCO03), LNCS vol.2724/2003, Chicago/Illinois, USA, 2003 [PDF]
  • Hyper-Heuristics: An Emerging Direction in Modern Search Technology, by Edmund Burke and Emma Hart and Graham Kendall and Jim Newall and Peter Ross and Sonia Schulenburg, Handbook of Meta-Heuristics, 2003 [PDF]
  • Development and Application of Hyperheuristics to Personnel Scheduling, by Eric Soubeiga, PhD Thesis, School of Computer Science, University of Nottingham, 2003 [PDF]

2002 (8 publications)

  • An Adaptive Length Chromosome Hyperheuristic Genetic Algorithm for a Trainer Scheduling Problem, by Peter Cowling and Graham Kendall and Limin Han, the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL02), Orchid Country Club, Singapore, 2002 [PDF]
  • An Investigation of a Hyperheuristic Genetic Algorithm Applied to a Trainer Scheduling Problem, by Peter Cowling and Graham Kendall and Limin Han, the IEEE Congress on Evolutionary Computation (IEEE CEC02), Honolulu/Hawaii, USA, 2002 [PDF]
  • Case-Based Reasoning as a Heuristic Selector in a Hyper-Heuristic for Course Timetabling Problems, by Sanja Petrovic and Rong Qu, the 6th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES02), Crema, Italy, 2002 [PDF]
  • Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems, by Peter Ross and Sonia Schulenburg and Javier G. Marin-Blazquez and Emma Hart, the 4th Annual Conference on Genetic and Evolutionary Computation (GECCO02), New York, USA, 2002 [PDF]
  • Hyperheuristics: A Robust Optimisation Method Applied to Nurse Scheduling, by Peter Cowling and Graham Kendall and Eric Soubeiga, the 7th Conference on Parallel Problem Solving from Nature (PPSN02), Granada, Spain, 2002 [PDF]
  • Hyperheuristics: A Tool for Rapid Prototyping in Scheduling and Optimisation, by Peter Cowling and Graham Kendall and Eric Soubeiga, the 7th European Workshops on the Applications of Evolutionary Computation (EvoWorkshops02), LNCS vol.2279/2002, Kinsale, Ireland, 2002 [PDF]
  • Knowledge Discovery in a Hyper-heuristic for Course Timetabling Using Case-Based Reasoning, by Edmund Burke and Bart L. MacCarthy and Sanja Petrovic and Rong Qu, the 4th International Conference on the Practice and Theory of Automated Timetabling (PATAT02), Gent, Belgium, 2002 [PDF]
  • Case-Based Reasoning for Course Timetabling Problems, by Rong Qu, PhD Thesis, School of Computer Science, University of Nottingham, 2002 [PDF]

2001 (1 publication)

  • A Parameter-Free Hyperheuristic for Scheduling a Sales Summit, by Peter Cowling and Graham Kendall and Eric Soubeiga, the 4th Metaheuristics International Conference (MIC01), Porto, Portugal, 2001 [PDF]

2000 (2 publications)

  • A Hyperheuristic Approach to Scheduling a Sales Summit, by Peter Cowling and Graham Kendall and Eric Soubeiga, the 3rd International Conference on the Practice and Theory of Automated Timetabling (PATAT00), LNCS vol.2079/2001, Constance, Germany, 2000 [PDF] [ABSTRACT]

    The concept of a hyperheuristic is introduced as an approach that operates at a higher lever of abstraction than current metaheuristic approaches. The hyperheuristic manages the choice of which lowerlevel heuristic method should be applied at any given time, depending upon the characteristics of the region of the solution space currently under exploration. We analyse the behaviour of several different hyperheuristic approaches for a real-world personnel scheduling problem. Results obtained show the effectiveness of our approach for this problem and suggest wider applicability of hyperheuristic approaches to other problems of scheduling and combinatorial optimisation.

  • Neighborhood Structures for Personnel Scheduling: A Summit Meeting Scheduling Problem, by Peter Cowling and Eric Soubeiga, the 3rd International Conference on the Practice and Theory of Automated Timetabling (PATAT00), Constance, Germany, 2000

1999 (1 publication)

  • Evolution of Constraint Satisfaction Strategies in Examination Timetabling, by Hugo Terashima-Marin and Peter Ross and Manuel Valenzuela-Rendon, the 1st Annual Conference on Genetic and Evolutionary Computation (GECCO99), Orlando/Florida, USA, 1999 [PDF] [ABSTRACT]

    This paper describes an investigation of solving Examination Timetabling Problems (ETTPs) with Genetic Algorithms (GAs) using a non-direct chromosome representation based on evolving the configuration of Constraint Satisfaction methods. There are two aims. The first is to circumvent the problems posed by a direct chromosome representation for the ETTP that consists of an array of events in which each value represents the timeslot which the corresponding event is assigned to. The second is to show that the adaptation of particular features in both the instance of the problem to be solved and the strategies used to solve it provides encouraging results for real ETTPs. There is much scope for investigating such approaches further, not only for the ETTP, but also for other related scheduling problems.