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The New Era of Hybridisation and Learning in Heuristic Search Design

In: The Palgrave Handbook of Operations Research

Author

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  • Saïd Salhi

    (University of Kent)

  • Jonathan Thompson

    (Cardiff University)

Abstract

This chapter aims to extend on the overview of heuristic and metaheuristics described in chapter [51] by focussing on the new developments related to hybridisation and learning when designing an effective heuristic, metaheuristic, or machine learning technique. This will include a wider discussion on hybridisation, deep learning, and a brief overview of machine learning and big data. Some of the mechanisms that enhance their implementation by turning these heuristic-based techniques into powerful, efficient, and practical optimisation/statistical tools are discussed. This is attributed to the incorporation of speed-up mechanisms that can be inspired from data structure, neighbourhood reduction, cost function approximation, and parallelisation among others. The chapter also provides a highlight of potential research avenues that can be worth exploring.

Suggested Citation

  • Saïd Salhi & Jonathan Thompson, 2022. "The New Era of Hybridisation and Learning in Heuristic Search Design," Springer Books, in: Saïd Salhi & John Boylan (ed.), The Palgrave Handbook of Operations Research, chapter 0, pages 501-538, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-96935-6_15
    DOI: 10.1007/978-3-030-96935-6_15
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