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 in | Evolutionary computation Vol. 31; no. 2; pp. 81 - 122 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
One Rogers Street, Cambridge, MA 02142-1209, USA
MIT Press
01.06.2023
MIT Press Journals, The |
Subjects | |
Online Access | Get full text |
ISSN | 1530-9304 1063-6560 1530-9304 |
DOI | 10.1162/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. |
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Bibliography: | 2023 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ISSN: | 1530-9304 1063-6560 1530-9304 |
DOI: | 10.1162/evco_a_00325 |