Evolutionary algorithms with clustering for dynamic fitness landscapes

Autores
Aragón, Victoria S.; 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 maintain 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 anda analyzed.
Facultad de Informática
Materia
Ciencias Informáticas
Algorithms
Information Systems
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:10915/9593

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network_name_str SEDICI (UNLP)
spelling Evolutionary algorithms with clustering for dynamic fitness landscapesAragón, Victoria S.Esquivel, Susana CeciliaCiencias InformáticasAlgorithmsInformation SystemsInterest 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 maintain 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 anda analyzed.Facultad de Informática2005-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf196-203http://sedici.unlp.edu.ar/handle/10915/9593enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Dec05-6.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:10915/9593Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:45.052SEDICI (UNLP) - Universidad Nacional de La Platafalse
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
Aragón, Victoria S.
Ciencias Informáticas
Algorithms
Information Systems
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 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
Algorithms
Information Systems
topic Ciencias Informáticas
Algorithms
Information Systems
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 maintain 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 anda analyzed.
Facultad de Informática
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 maintain 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 anda analyzed.
publishDate 2005
dc.date.none.fl_str_mv 2005-12
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language eng
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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