An evolutionary algorithm to track changes of optimum value locations in dynamic environments

Autores
Aragón, Victoria S.; Esquivel, Susana Cecilia
Año de publicación
2004
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Non-stationary, or dynamic, problems change over time. There exist a variety of forms of dynamism. The concept of dynamic environments in the context of this paper means that the fitness landscape changes during the run of an evolutionary algorithm. Genetic diversity is crucial to provide the necessary adaptability of the algorithm to changes. Two mechanism of macromutation are incorporated to the algorithm to maintain genetic diversity in the population. The algorithm was tested on a set of dynamic testing functions provided by a dynamic fitness problem generator. The main goal was to determinate the algorithm´s ability to reacting to changes of optimum values that alter their locations, so that the optimum value can still be tracked when dimensional and multimodal scalability in the functions is adjusted. The effectiveness and limitations of the proposed algorithm is discussed from results empirically obtained.
Facultad de Informática
Materia
Ciencias Informáticas
genetic diversity
macromutation operators
Algorithms
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9493

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network_name_str SEDICI (UNLP)
spelling An evolutionary algorithm to track changes of optimum value locations in dynamic environmentsAragón, Victoria S.Esquivel, Susana CeciliaCiencias Informáticasgenetic diversitymacromutation operatorsAlgorithmsNon-stationary, or dynamic, problems change over time. There exist a variety of forms of dynamism. The concept of dynamic environments in the context of this paper means that the fitness landscape changes during the run of an evolutionary algorithm. Genetic diversity is crucial to provide the necessary adaptability of the algorithm to changes. Two mechanism of macromutation are incorporated to the algorithm to maintain genetic diversity in the population. The algorithm was tested on a set of dynamic testing functions provided by a dynamic fitness problem generator. The main goal was to determinate the algorithm´s ability to reacting to changes of optimum values that alter their locations, so that the optimum value can still be tracked when dimensional and multimodal scalability in the functions is adjusted. The effectiveness and limitations of the proposed algorithm is discussed from results empirically obtained.Facultad de Informática2004-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf127-133http://sedici.unlp.edu.ar/handle/10915/9493enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct04-1.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:50:44Zoai:sedici.unlp.edu.ar:10915/9493Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:44.195SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv An evolutionary algorithm to track changes of optimum value locations in dynamic environments
title An evolutionary algorithm to track changes of optimum value locations in dynamic environments
spellingShingle An evolutionary algorithm to track changes of optimum value locations in dynamic environments
Aragón, Victoria S.
Ciencias Informáticas
genetic diversity
macromutation operators
Algorithms
title_short An evolutionary algorithm to track changes of optimum value locations in dynamic environments
title_full An evolutionary algorithm to track changes of optimum value locations in dynamic environments
title_fullStr An evolutionary algorithm to track changes of optimum value locations in dynamic environments
title_full_unstemmed An evolutionary algorithm to track changes of optimum value locations in dynamic environments
title_sort An evolutionary algorithm to track changes of optimum value locations in dynamic environments
dc.creator.none.fl_str_mv Aragón, Victoria S.
Esquivel, Susana Cecilia
author Aragón, Victoria S.
author_facet Aragón, Victoria S.
Esquivel, Susana Cecilia
author_role author
author2 Esquivel, Susana Cecilia
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
genetic diversity
macromutation operators
Algorithms
topic Ciencias Informáticas
genetic diversity
macromutation operators
Algorithms
dc.description.none.fl_txt_mv Non-stationary, or dynamic, problems change over time. There exist a variety of forms of dynamism. The concept of dynamic environments in the context of this paper means that the fitness landscape changes during the run of an evolutionary algorithm. Genetic diversity is crucial to provide the necessary adaptability of the algorithm to changes. Two mechanism of macromutation are incorporated to the algorithm to maintain genetic diversity in the population. The algorithm was tested on a set of dynamic testing functions provided by a dynamic fitness problem generator. The main goal was to determinate the algorithm´s ability to reacting to changes of optimum values that alter their locations, so that the optimum value can still be tracked when dimensional and multimodal scalability in the functions is adjusted. The effectiveness and limitations of the proposed algorithm is discussed from results empirically obtained.
Facultad de Informática
description Non-stationary, or dynamic, problems change over time. There exist a variety of forms of dynamism. The concept of dynamic environments in the context of this paper means that the fitness landscape changes during the run of an evolutionary algorithm. Genetic diversity is crucial to provide the necessary adaptability of the algorithm to changes. Two mechanism of macromutation are incorporated to the algorithm to maintain genetic diversity in the population. The algorithm was tested on a set of dynamic testing functions provided by a dynamic fitness problem generator. The main goal was to determinate the algorithm´s ability to reacting to changes of optimum values that alter their locations, so that the optimum value can still be tracked when dimensional and multimodal scalability in the functions is adjusted. The effectiveness and limitations of the proposed algorithm is discussed from results empirically obtained.
publishDate 2004
dc.date.none.fl_str_mv 2004-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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info:ar-repo/semantics/articulo
format article
status_str publishedVersion
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dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
127-133
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instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
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instname_str Universidad Nacional de La Plata
instacron_str UNLP
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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