research-article

Machine Learning into Metaheuristics: A Survey and Taxonomy

Author:

El-Ghazali TalbiAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 6

Article No.: 129, Pages 1 - 32

https://doi.org/10.1145/3459664

Published: 13 July 2021 Publication History

Abstract

During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this article, we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies that might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem and low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic that need further in-depth investigations.

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Index Terms

Machine Learning into Metaheuristics: A Survey and Taxonomy
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 6

Invited Tutorial

July 2022

799 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3475936

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 13 July 2021

Accepted: 01 March 2021

Revised: 01 February 2021

Received: 01 March 2020

Published in CSUR Volume 54, Issue 6

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https://doi.org/10.1016/j.cor.2025.106989
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