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Modular Differential Evolution

Diederick Vermetten Orcid Logo, Fabio Caraffini Orcid Logo, Anna V. Kononova Orcid Logo, Thomas Bäck Orcid Logo

Proceedings of the Genetic and Evolutionary Computation Conference, Pages: 864 - 872

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.1145/3583131.3590417

Abstract

New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as extensions of a preexisting algorithm. Although these contributions are often compared to the base algorithm, it is challen...

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Published in: Proceedings of the Genetic and Evolutionary Computation Conference
ISBN: 979-8-4007-0119-1
Published: New York, NY, USA ACM 2023
Online Access: http://dx.doi.org/10.1145/3583131.3590417
URI: https://cronfa.swan.ac.uk/Record/cronfa63892
first_indexed 2023-07-16T00:11:34Z
last_indexed 2024-11-25T14:13:03Z
id cronfa63892
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spelling 2023-08-29T11:19:15.3445873 v2 63892 2023-07-16 Modular Differential Evolution d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2023-07-16 MACS New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as extensions of a preexisting algorithm. Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants. This happens because even small changes in the experimental setup, parameter settings, or implementation details can cause results to become incomparable. Modular algorithms offer a way to overcome these challenges. By implementing the algorithmic modifications into a common framework, many algorithm variants can be compared, while ensuring that implementation details match in all versions.In this work, we propose a version of a modular framework for the popular Differential Evolution (DE) algorithm. We show that this modular approach not only aids in comparison but also allows for a much more detailed exploration of the space of possible DE variants. This is illustrated by showing that tuning the settings of modular DE vastly outperforms a set of commonly used DE versions which have been recreated in our framework. We then investigate these tuned algorithms in detail, highlighting the relation between modules and performance on particular problems. Conference Paper/Proceeding/Abstract Proceedings of the Genetic and Evolutionary Computation Conference 864 872 ACM New York, NY, USA 979-8-4007-0119-1 Differential Evolution, Benchmarking, Modular Algorithms, Algorithm Configuration 15 7 2023 2023-07-15 10.1145/3583131.3590417 http://dx.doi.org/10.1145/3583131.3590417 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee 2023-08-29T11:19:15.3445873 2023-07-16T01:08:15.1521698 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Diederick Vermetten 0000-0003-3040-7162 1 Fabio Caraffini 0000-0001-9199-7368 2 Anna V. Kononova 0000-0002-4138-7024 3 Thomas Bäck 0000-0001-6768-1478 4 63892__28175__f22f203a7c8a4a6483c8d2d8718ac6d9.pdf 63892.pdf 2023-07-25T14:56:14.5103638 Output 898684 application/pdf Version of Record true © 2023 Copyright held by the owner/author(s). Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title Modular Differential Evolution
spellingShingle Modular Differential Evolution
Fabio Caraffini
title_short Modular Differential Evolution
title_full Modular Differential Evolution
title_fullStr Modular Differential Evolution
title_full_unstemmed Modular Differential Evolution
title_sort Modular Differential Evolution
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Diederick Vermetten
Fabio Caraffini
Anna V. Kononova
Thomas Bäck
format Conference Paper/Proceeding/Abstract
container_title Proceedings of the Genetic and Evolutionary Computation Conference
container_start_page 864
publishDate 2023
institution Swansea University
isbn 979-8-4007-0119-1
doi_str_mv 10.1145/3583131.3590417
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
url http://dx.doi.org/10.1145/3583131.3590417
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description New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as extensions of a preexisting algorithm. Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants. This happens because even small changes in the experimental setup, parameter settings, or implementation details can cause results to become incomparable. Modular algorithms offer a way to overcome these challenges. By implementing the algorithmic modifications into a common framework, many algorithm variants can be compared, while ensuring that implementation details match in all versions.In this work, we propose a version of a modular framework for the popular Differential Evolution (DE) algorithm. We show that this modular approach not only aids in comparison but also allows for a much more detailed exploration of the space of possible DE variants. This is illustrated by showing that tuning the settings of modular DE vastly outperforms a set of commonly used DE versions which have been recreated in our framework. We then investigate these tuned algorithms in detail, highlighting the relation between modules and performance on particular problems.
published_date 2023-07-15T14:26:11Z
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score 11.047891

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