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A Classification of Hyper-Heuristic Approaches: Revisited

In: Handbook of Metaheuristics

Author

Listed:
  • Edmund K. Burke

    (University of Leicester)

  • Matthew R. Hyde

    (University of Nottingham)

  • Graham Kendall

    (University of Nottingham Malaysia Campus
    University of Nottingham)

  • Gabriela Ochoa

    (University of Stirling)

  • Ender Özcan

    (University of Nottingham)

  • John R. Woodward

    (Queen Mary University of London)

Abstract

Hyper-heuristics comprise a set of approaches that aim to automate the development of computational search methodologies. This chapter overviews previous categorisations of hyper-heuristics and provides a unified classification and definition. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail and recent research trends are highlighted.

Suggested Citation

  • Edmund K. Burke & Matthew R. Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & John R. Woodward, 2019. "A Classification of Hyper-Heuristic Approaches: Revisited," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 453-477, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-91086-4_14
    DOI: 10.1007/978-3-319-91086-4_14
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    Citations

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    Cited by:

    1. Pagnozzi, Federico & Stützle, Thomas, 2021. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints," Operations Research Perspectives, Elsevier, vol. 8(C).
    2. Philipp Heyken Soares & Leena Ahmed & Yong Mao & Christine L Mumford, 2021. "Public transport network optimisation in PTV Visum using selection hyper-heuristics," Public Transport, Springer, vol. 13(1), pages 163-196, March.
    3. Jorge M. Cruz-Duarte & José C. Ortiz-Bayliss & Iván Amaya & Yong Shi & Hugo Terashima-Marín & Nelishia Pillay, 2020. "Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems," Mathematics, MDPI, vol. 8(11), pages 1-23, November.
    4. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Meyer, Patrick & Karimi-Mamaghan, Amir Mohammad & Talbi, El-Ghazali, 2022. "Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art," European Journal of Operational Research, Elsevier, vol. 296(2), pages 393-422.
    5. Vladimir Stanovov & Eugene Semenkin, 2023. "Surrogate-Assisted Automatic Parameter Adaptation Design for Differential Evolution," Mathematics, MDPI, vol. 11(13), pages 1-19, June.
    6. Goerigk, Marc & Hartisch, Michael, 2023. "A framework for inherently interpretable optimization models," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1312-1324.

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