A Classification of Hyper-heuristic Approaches

  • Edmund K. Burke
  • Matthew Hyde
  • Graham Kendall
  • Gabriela Ochoa
  • Ender Özcan
  • John R. Woodward
Part of the International Series in Operations Research & Management Science book series (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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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Edmund K. Burke
    • 1
  • Matthew Hyde
    • 2
  • Graham Kendall
    • 2
  • Gabriela Ochoa
    • 2
  • Ender Özcan
    • 2
  • John R. Woodward
    • 2
  1. 1.Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.The University of NottinghamNottinghamUK

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