Conference Paper/Proceeding/Abstract 431 views 58 downloads
Using structural bias to analyse the behaviour of modular CMA-ES
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Swansea University Author:
Fabio Caraffini
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DOI (Published version): 10.1145/3520304.3534035
Abstract
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a commonly used iterative optimisation heuristic for optimising black-box functions. CMA-ES comes in many flavours with different configuration settings. In this work, we investigate whether CMA-ES suffers from structural bias and which...
Published in: | Proceedings of the Genetic and Evolutionary Computation Conference Companion |
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ISBN: | 978-1-4503-9268-6 |
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New York, NY, USA
ACM
2022
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URI: | https://cronfa.swan.ac.uk/Record/cronfa62444 |
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2023-02-20T13:33:44.8131335 v2 62444 2023-01-25 Using structural bias to analyse the behaviour of modular CMA-ES d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2023-01-25 SCS The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a commonly used iterative optimisation heuristic for optimising black-box functions. CMA-ES comes in many flavours with different configuration settings. In this work, we investigate whether CMA-ES suffers from structural bias and which modules and parameters affect the strength and type of structural bias. Structural bias occurs when an algorithm or a component of the algorithm biases the search towards a specific direction in the search space irrespective of the objective function. In addition to this investigation, we propose a method to assess the relationship between structural bias and the performance of configurations with different types of bias on the BBOB suite of benchmark functions. Surprisingly for such a popular algorithm, 90.3% of the 1 620 CMA-ES configurations were found to have Structural Bias. Some interesting patterns between module settings and bias types are presented and further insights are discussed. Conference Paper/Proceeding/Abstract Proceedings of the Genetic and Evolutionary Computation Conference Companion ACM New York, NY, USA 978-1-4503-9268-6 19 7 2022 2022-07-19 10.1145/3520304.3534035 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Another institution paid the OA fee 2023-02-20T13:33:44.8131335 2023-01-25T17:27:17.2232013 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Diederick Vermetten 1 Fabio Caraffini 0000-0001-9199-7368 2 Bas van Stein 3 Anna V. Kononova 4 62444__26627__cb843fa9e6824082ac549f3f1353772a.pdf 62444_VoR.pdf 2023-02-20T13:24:46.0473030 Output 2472372 application/pdf Version of Record true © 2022 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 |
Using structural bias to analyse the behaviour of modular CMA-ES |
spellingShingle |
Using structural bias to analyse the behaviour of modular CMA-ES Fabio Caraffini |
title_short |
Using structural bias to analyse the behaviour of modular CMA-ES |
title_full |
Using structural bias to analyse the behaviour of modular CMA-ES |
title_fullStr |
Using structural bias to analyse the behaviour of modular CMA-ES |
title_full_unstemmed |
Using structural bias to analyse the behaviour of modular CMA-ES |
title_sort |
Using structural bias to analyse the behaviour of modular CMA-ES |
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d0b8d4e63d512d4d67a02a23dd20dfdb |
author_id_fullname_str_mv |
d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini |
author |
Fabio Caraffini |
author2 |
Diederick Vermetten Fabio Caraffini Bas van Stein Anna V. Kononova |
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Conference Paper/Proceeding/Abstract |
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Proceedings of the Genetic and Evolutionary Computation Conference Companion |
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2022 |
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Swansea University |
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978-1-4503-9268-6 |
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10.1145/3520304.3534035 |
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ACM |
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Faculty of Science and Engineering |
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Faculty of Science and Engineering |
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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 |
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a commonly used iterative optimisation heuristic for optimising black-box functions. CMA-ES comes in many flavours with different configuration settings. In this work, we investigate whether CMA-ES suffers from structural bias and which modules and parameters affect the strength and type of structural bias. Structural bias occurs when an algorithm or a component of the algorithm biases the search towards a specific direction in the search space irrespective of the objective function. In addition to this investigation, we propose a method to assess the relationship between structural bias and the performance of configurations with different types of bias on the BBOB suite of benchmark functions. Surprisingly for such a popular algorithm, 90.3% of the 1 620 CMA-ES configurations were found to have Structural Bias. Some interesting patterns between module settings and bias types are presented and further insights are discussed. |
published_date |
2022-07-19T04:22:04Z |
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1763754455117856768 |
score |
11.021648 |