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The New Era of Hybridisation and Learning in Heuristic Search Design

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The Palgrave Handbook of Operations Research

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.

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Author information

Authors and Affiliations

  1. Kent Business School, University of Kent, Canterbury, UK

    Saïd Salhi

  2. School of Mathematics, Cardiff University, Cardiff, UK

    Jonathan Thompson

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  1. Saïd Salhi
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  2. Jonathan Thompson
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Correspondence to Saïd Salhi .

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Editors and Affiliations

  1. Kent Business School, University of Kent, Canterbury, UK

    Saïd Salhi

  2. Lancaster University, Lancaster, UK

    John Boylan

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Salhi, S., Thompson, J. (2022). The New Era of Hybridisation and Learning in Heuristic Search Design. In: Salhi, S., Boylan, J. (eds) The Palgrave Handbook of Operations Research . Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-96935-6_15

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