Hyper-heuristics

Michael G. Epitropakis () and Edmund K. Burke ()
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Michael G. Epitropakis: Lancaster University Management School, Lancaster University, Data Science Institute, Department of Management Science
Edmund K. Burke: Queen Mary University of London, School of Electronic Engineering and Computer Science

Chapter 17 in Handbook of Heuristics, 2018, pp 489-545 from Springer

Abstract: Abstract This chapter presents a literature review of the main advances in the field of hyper-heuristics, since the publication of a survey paper in 2013. The chapter demonstrates the most recent advances in hyper-heuristic foundations, methodologies, theory, and application areas. In addition, a simple illustrative selection hyper-heuristic framework is developed as a case study. This is based on the well-known Iterated Local Search algorithm and is presented to provide a tutorial style introduction to some of the key basic issues. A brief discussion about the implementation process in addition to the decisions that had to be made during the implementation is presented. The framework implements an action selection model that operates on the perturbation stage of the Iterated Local Search algorithm to adaptively select among various low-level perturbation heuristics. The performance and efficiency of the developed framework is evaluated across six well-known real-world problem domains.

Keywords: Hyper-heuristics; Heuristics; Meta-heuristics; Evolutionary computation; Optimization; Search; Machine learning; Multi-objective optimization; Combinatorial optimization; Black box optimization; Dynamic optimization; Scheduling; Timetabling; Packing; Iterated local search (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-07124-4_32

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DOI: 10.1007/978-3-319-07124-4_32

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