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Handbook of Heuristics pp 489–545Cite as

Hyper-heuristics

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

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

Michael G. Epitropakis is supported by a grant from the Engineering and Physical Sciences Research Council (EPSRC Grant No. EP/J017515/1), by a Microsoft Azure Grant 2014, and by a Lancaster University Early Career Internal Grant (A100699). This support is gratefully acknowledged.

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Authors and Affiliations

  1. Data Science Institute, Department of Management Science, Lancaster University Management School, Lancaster University, Lancaster, UK

    Michael G. Epitropakis

  2. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK

    Edmund K. Burke

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Correspondence to Michael G. Epitropakis .

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Editors and Affiliations

  1. Statistics and Operations Research Department, University of Valencia, Valencia, Spain

    Rafael Martí

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Amazon.com, Inc. and University of Washington, Seattle, Washington, USA

    Dr. Mauricio G. C. Resende

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Epitropakis, M.G., Burke, E.K. (2018). Hyper-heuristics. In: Martí, R., Pardalos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_32

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