Investigating the parameter space of evolutionary algorithms

Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple ev...

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Detalles Bibliográficos
Autores principales: Sipper, Moshe, Fu, Weixuan, Ahuja, Karuna, Moore, Jason H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5816380/
https://www.ncbi.nlm.nih.gov/pubmed/29467825
http://dx.doi.org/10.1186/s13040-018-0164-x
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author Sipper, Moshe
Fu, Weixuan
Ahuja, Karuna
Moore, Jason H.
author_facet Sipper, Moshe
Fu, Weixuan
Ahuja, Karuna
Moore, Jason H.
author_sort Sipper, Moshe
collection PubMed
description Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple evolutionary algorithm implementations and 25 problems we show that parameter space tends to be rife with viable parameters, at least for the problems studied herein. We discuss the implications of this finding in practice for the researcher employing EC.
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spelling pubmed-58163802018-02-21 Investigating the parameter space of evolutionary algorithms Sipper, Moshe Fu, Weixuan Ahuja, Karuna Moore, Jason H. BioData Min Research Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple evolutionary algorithm implementations and 25 problems we show that parameter space tends to be rife with viable parameters, at least for the problems studied herein. We discuss the implications of this finding in practice for the researcher employing EC. BioMed Central 2018-02-17 /pmc/articles/PMC5816380/ /pubmed/29467825 http://dx.doi.org/10.1186/s13040-018-0164-x Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Sipper, Moshe
Fu, Weixuan
Ahuja, Karuna
Moore, Jason H.
Investigating the parameter space of evolutionary algorithms
title Investigating the parameter space of evolutionary algorithms
title_full Investigating the parameter space of evolutionary algorithms
title_fullStr Investigating the parameter space of evolutionary algorithms
title_full_unstemmed Investigating the parameter space of evolutionary algorithms
title_short Investigating the parameter space of evolutionary algorithms
title_sort investigating the parameter space of evolutionary algorithms
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5816380/
https://www.ncbi.nlm.nih.gov/pubmed/29467825
http://dx.doi.org/10.1186/s13040-018-0164-x
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