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Handbook of Metaheuristics pp 449–468Cite as

A Classification of Hyper-heuristic Approaches

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 146))

Abstract

The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present an overview of previous categorisations of hyper-heuristics and provide a unified classification and definition, which capture the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goals are to clarify the mainfeatures of existing techniques and to suggest new directions for hyper-heuristic research.

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Notes

  1. 1.

    According to the genetic programming literature, programs can be represented in ways other than trees. Research has already established the efficacy of both linear and graph-based genetic programming systems.

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Authors and Affiliations

  1. Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer Science, University of Nottingham, Nottingham, UK

    Edmund K. Burke

  2. The University of Nottingham, Nottingham, UK

    Matthew Hyde, Graham Kendall, Gabriela Ochoa, Ender Özcan & John R. Woodward

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  1. Edmund K. Burke
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  2. Matthew Hyde
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  3. Graham Kendall
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  4. Gabriela Ochoa
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  5. Ender Özcan
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  6. John R. Woodward
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Correspondence to Edmund K. Burke .

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  1. , Département de mathématiques et génie in, École Polytechnique Montréal, Montreal, H3C 3A7, Québec, Canada

    Michel Gendreau

  2. Dépt. Informatique et Recherche, Opérationnelle, Université de Montreal, Pavillon André-Aisenstadt, Montreal, H3C 3J7, Québec, Canada

    Jean-Yves Potvin

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Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Woodward, J.R. (2010). A Classification of Hyper-heuristic Approaches. In: Gendreau, M., Potvin, JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 146. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1665-5_15

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