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Emergence of structural bias in differential evolution

Bas van Stein, Fabio Caraffini Orcid Logo, Anna V. Kononova

Proceedings of the Genetic and Evolutionary Computation Conference Companion

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.1145/3449726.3463223

Abstract

Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved. The complexity of these problems is well beyond the boundaries of applicability of exact optimisation algorithms and therefore require modern heuristics to fin...

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Published in: Proceedings of the Genetic and Evolutionary Computation Conference Companion
ISBN: 978-1-4503-8351-6
Published: New York, NY, USA ACM 2021
URI: https://cronfa.swan.ac.uk/Record/cronfa62443
first_indexed 2023-01-25T17:25:23Z
last_indexed 2023-02-21T04:19:04Z
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spelling 2023-02-20T13:37:14.7669522 v2 62443 2023-01-25 Emergence of structural bias in differential evolution d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2023-01-25 MACS Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved. The complexity of these problems is well beyond the boundaries of applicability of exact optimisation algorithms and therefore require modern heuristics to find feasible solutions quickly. These heuristics and their effects are almost always evaluated and explained by particular problem instances. In previous works, it has been shown that many such algorithms show structural bias, by either being attracted to a certain region of the search space or by consistently avoiding regions of the search space, on a special test function designed to ensure uniform 'exploration' of the domain. In this paper, we analyse the emergence of such structural bias for Differential Evolution (DE) configurations and, specifically, the effect of different mutation, crossover and correction strategies. We also analyse the emergence of the structural bias during the run-time of each algorithm. We conclude with recommendations of which configurations should be avoided in order to run DE unbiased. Conference Paper/Proceeding/Abstract Proceedings of the Genetic and Evolutionary Computation Conference Companion ACM New York, NY, USA 978-1-4503-8351-6 8 7 2021 2021-07-08 10.1145/3449726.3463223 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2023-02-20T13:37:14.7669522 2023-01-25T17:22:01.2174959 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Bas van Stein 1 Fabio Caraffini 0000-0001-9199-7368 2 Anna V. Kononova 3 62443__26628__e9a087270e314ea19c922fc5f8ea038a.pdf 62443_VoR.pdf 2023-02-20T13:36:05.6797562 Output 1390388 application/pdf Version of Record true © 2021 Copyright held by the owner/author(s). Released under the terms of a CC-BY license. true eng https://creativecommons.org/licenses/by/4.0/
title Emergence of structural bias in differential evolution
spellingShingle Emergence of structural bias in differential evolution
Fabio Caraffini
title_short Emergence of structural bias in differential evolution
title_full Emergence of structural bias in differential evolution
title_fullStr Emergence of structural bias in differential evolution
title_full_unstemmed Emergence of structural bias in differential evolution
title_sort Emergence of structural bias in differential evolution
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Bas van Stein
Fabio Caraffini
Anna V. Kononova
format Conference Paper/Proceeding/Abstract
container_title Proceedings of the Genetic and Evolutionary Computation Conference Companion
publishDate 2021
institution Swansea University
isbn 978-1-4503-8351-6
doi_str_mv 10.1145/3449726.3463223
publisher ACM
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved. The complexity of these problems is well beyond the boundaries of applicability of exact optimisation algorithms and therefore require modern heuristics to find feasible solutions quickly. These heuristics and their effects are almost always evaluated and explained by particular problem instances. In previous works, it has been shown that many such algorithms show structural bias, by either being attracted to a certain region of the search space or by consistently avoiding regions of the search space, on a special test function designed to ensure uniform 'exploration' of the domain. In this paper, we analyse the emergence of such structural bias for Differential Evolution (DE) configurations and, specifically, the effect of different mutation, crossover and correction strategies. We also analyse the emergence of the structural bias during the run-time of each algorithm. We conclude with recommendations of which configurations should be avoided in order to run DE unbiased.
published_date 2021-07-08T14:22:02Z
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score 11.047523

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