Stream on Hyper-heuristics

the 30th European Conference on Operational Research (EURO 2019), June 23-26, 2019

Call for Abstracts (Max. 1500 characters)

  • Session 1: Selection Hyper-heuristics (Submission Code: 39acc144)

  • Session 2: Generation Hyper-heuristics (Submission Code: ffd15a62)

Hyper-heuristics are problem-independent generic solvers which have been successfully applied to a wide range of combinatorial search problems both from academia and real-world, such as timetabling, scheduling, routing, rostering, cutting and packing. The studies on this field is mainly considered under two categories (Figure 1), namely Selection and Generation Hyper-heuristics. Selection Hyper-heuristics operate by automatically choosing (low-level) heuristics from an existing heuristic set while the latter type focuses on generating heuristics from scratch based on predefined components. These hyper-heuristics can have certain learning capabilities by incorporating Offline and Online learning. Offline refers to learning before a hyper-heuristic run, mostly in the form of un-/supervised learning. Online is about learning while a problem (~instance) is being solved, likely to be based on reinforcement learning. It is also possible to design hyper-heuristics without learning. Besides the learning aspect, the type of heuristics may differ as constructive and perturbative heuristics.

Hyper-heuristic Classification

Figure 1 - Hyper-heuristic Classification (Burke et. al (2010))

The aim of this stream is to gather researchers studying hyper-heuristics to share their research on all the aforementioned hyper-heuristic variations as well as the strategies developed to support hyper-heuristics.

This stream will be organized in connection with the Task Force on Hyper-heuristics within the Technical Committee of Intelligent Systems and Applications at the IEEE Computational Intelligence Society.

Important Dates
Venue:  -  University College Dublin (UCD), Dublin/Ireland