Evolutionary algorithms with clustering for dynamic fitness landscapes

Autores
Aragon, Victoria Soledad; Esquivel, Susana Cecilia
Año de publicación
2005
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Interest on dynamic multimodal functions risen over the last years since many real problems have this feature. On these problems, the goal is no longer to find the global optimal, but to track their progression through the space as closely as possible. This paper presents three evolutionary algorithms for dynamic fitness landscapes. In order to mantain diversity in the population they use two clustering techniques and a macromutation operator. Besides, this paper compares two crossover operators: arithmetic and multiparents two points, respectively. Effectiveness and limitations of each algorithm are discuss and analyzed.
Fil: Aragon, Victoria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Esquivel, Susana Cecilia. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Materia
DYNAMIC MULTIMODAL FUNCTIONS
EVOLUTIONARY ALGORITHMS
CLUSTERING ALGORITHMS
MACROMUTATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/158250
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/158250
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Evolutionary algorithms with clustering for dynamic fitness landscapesAragon, Victoria SoledadEsquivel, Susana CeciliaDYNAMIC MULTIMODAL FUNCTIONSEVOLUTIONARY ALGORITHMSCLUSTERING ALGORITHMSMACROMUTATIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Interest on dynamic multimodal functions risen over the last years since many real problems have this feature. On these problems, the goal is no longer to find the global optimal, but to track their progression through the space as closely as possible. This paper presents three evolutionary algorithms for dynamic fitness landscapes. In order to mantain diversity in the population they use two clustering techniques and a macromutation operator. Besides, this paper compares two crossover operators: arithmetic and multiparents two points, respectively. Effectiveness and limitations of each algorithm are discuss and analyzed.Fil: Aragon, Victoria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaFil: Esquivel, Susana Cecilia. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaUniversidad Nacional de La Plata. Facultad de Informática2005-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/158250Aragon, Victoria Soledad; Esquivel, Susana Cecilia; Evolutionary algorithms with clustering for dynamic fitness landscapes; Universidad Nacional de La Plata. Facultad de Informática; Journal of Computer Science & Technology; 5; 4; 12-2005; 196-2031666-60461666-6038CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://journal.info.unlp.edu.ar/JCST/article/view/836info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:21:57Zoai:ri.conicet.gov.ar:11336/158250instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:21:57.963CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Evolutionary algorithms with clustering for dynamic fitness landscapes
title Evolutionary algorithms with clustering for dynamic fitness landscapes
spellingShingle Evolutionary algorithms with clustering for dynamic fitness landscapes
Aragon, Victoria Soledad
DYNAMIC MULTIMODAL FUNCTIONS
EVOLUTIONARY ALGORITHMS
CLUSTERING ALGORITHMS
MACROMUTATION
title_short Evolutionary algorithms with clustering for dynamic fitness landscapes
title_full Evolutionary algorithms with clustering for dynamic fitness landscapes
title_fullStr Evolutionary algorithms with clustering for dynamic fitness landscapes
title_full_unstemmed Evolutionary algorithms with clustering for dynamic fitness landscapes
title_sort Evolutionary algorithms with clustering for dynamic fitness landscapes
dc.creator.none.fl_str_mv Aragon, Victoria Soledad
Esquivel, Susana Cecilia
author Aragon, Victoria Soledad
author_facet Aragon, Victoria Soledad
Esquivel, Susana Cecilia
author_role author
author2 Esquivel, Susana Cecilia
author2_role author
dc.subject.none.fl_str_mv DYNAMIC MULTIMODAL FUNCTIONS
EVOLUTIONARY ALGORITHMS
CLUSTERING ALGORITHMS
MACROMUTATION
topic DYNAMIC MULTIMODAL FUNCTIONS
EVOLUTIONARY ALGORITHMS
CLUSTERING ALGORITHMS
MACROMUTATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Interest on dynamic multimodal functions risen over the last years since many real problems have this feature. On these problems, the goal is no longer to find the global optimal, but to track their progression through the space as closely as possible. This paper presents three evolutionary algorithms for dynamic fitness landscapes. In order to mantain diversity in the population they use two clustering techniques and a macromutation operator. Besides, this paper compares two crossover operators: arithmetic and multiparents two points, respectively. Effectiveness and limitations of each algorithm are discuss and analyzed.
Fil: Aragon, Victoria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Fil: Esquivel, Susana Cecilia. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
description Interest on dynamic multimodal functions risen over the last years since many real problems have this feature. On these problems, the goal is no longer to find the global optimal, but to track their progression through the space as closely as possible. This paper presents three evolutionary algorithms for dynamic fitness landscapes. In order to mantain diversity in the population they use two clustering techniques and a macromutation operator. Besides, this paper compares two crossover operators: arithmetic and multiparents two points, respectively. Effectiveness and limitations of each algorithm are discuss and analyzed.
publishDate 2005
dc.date.none.fl_str_mv 2005-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/158250
Aragon, Victoria Soledad; Esquivel, Susana Cecilia; Evolutionary algorithms with clustering for dynamic fitness landscapes; Universidad Nacional de La Plata. Facultad de Informática; Journal of Computer Science & Technology; 5; 4; 12-2005; 196-203
1666-6046
1666-6038
CONICET Digital
CONICET
url http://hdl.handle.net/11336/158250
identifier_str_mv Aragon, Victoria Soledad; Esquivel, Susana Cecilia; Evolutionary algorithms with clustering for dynamic fitness landscapes; Universidad Nacional de La Plata. Facultad de Informática; Journal of Computer Science & Technology; 5; 4; 12-2005; 196-203
1666-6046
1666-6038
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://journal.info.unlp.edu.ar/JCST/article/view/836
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universidad Nacional de La Plata. Facultad de Informática
publisher.none.fl_str_mv Universidad Nacional de La Plata. Facultad de Informática
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846082612670496768
score 13.22299

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