Evolutionary Algorithms for Parameter Optimization—Thirty Years Later

Thirty years, 1993–2023, is a huge time frame in science. We address some major developments in the field of evolutionary algorithms, with applications in parameter optimization, over these 30 years. These include the covariance matrix adaptation evolution strategy and some fast-growing fields such...

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Published inEvolutionary computation Vol. 31; no. 2; pp. 81 - 122
Main Authors Bäck, Thomas H. W., Kononova, Anna V., van Stein, Bas, Wang, Hao, Antonov, Kirill A., Kalkreuth, Roman T., de Nobel, Jacob, Vermetten, Diederick, de Winter, Roy, Ye, Furong
Format Journal Article
LanguageEnglish
Published One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.06.2023
MIT Press Journals, The
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ISSN1530-9304
1063-6560
1530-9304
DOI10.1162/evco_a_00325

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Summary:Thirty years, 1993–2023, is a huge time frame in science. We address some major developments in the field of evolutionary algorithms, with applications in parameter optimization, over these 30 years. These include the covariance matrix adaptation evolution strategy and some fast-growing fields such as multimodal optimization, surrogate-assisted optimization, multiobjective optimization, and automated algorithm design. Moreover, we also discuss particle swarm optimization and differential evolution, which did not exist 30 years ago, either. One of the key arguments made in the paper is that we need fewer algorithms, not more, which, however, is the current trend through continuously claiming paradigms from nature that are suggested to be useful as new optimization algorithms. Moreover, we argue that we need proper benchmarking procedures to sort out whether a newly proposed algorithm is useful or not. We also briefly discuss automated algorithm design approaches, including configurable algorithm design frameworks, as the proposed next step toward designing optimization algorithms automatically, rather than by hand.
Bibliography:2023
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ISSN:1530-9304
1063-6560
1530-9304
DOI:10.1162/evco_a_00325