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A Classification of Hyper-heuristic Approaches

In: Handbook of Metaheuristics

Author

Listed:
  • Edmund K. Burke

    (University of Nottingham)

  • Matthew Hyde

    (The University of Nottingham)

  • Graham Kendall

    (The University of Nottingham)

  • Gabriela Ochoa

    (The University of Nottingham)

  • Ender Özcan

    (The University of Nottingham)

  • John R. Woodward

    (The University of Nottingham)

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.

Suggested Citation

  • Edmund K. Burke & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & John R. Woodward, 2010. "A Classification of Hyper-heuristic Approaches," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 449-468, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1665-5_15
    DOI: 10.1007/978-1-4419-1665-5_15
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