Crowding under diverse distance criteria for niche formation in multimodal optimization

Autores
Fernandez, Natalia; Alfonso, Hugo; Gallard, Raúl Hector
Año de publicación
2000
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Niche formation allows evolutionary algorithms to be used when the location and maintenance of multiple solutions appertaining to diverse areas of the phenotypic space is required. Consequently the application field can be extended to multiobjective optimization, simulation of complex systems and multimodal function optimization. In this later case a conventional evolutionary algorithm tends to group the final population around the fittest individual. Thus, other areas of interest in the search process are lost. Niching methods permits the maintenance of solutions located around these areas of interest. This contribution briefly describe problems preventing niche formation in conventional genetic algorithms, a crowding method for niche formation and analysis of results when optimizing two multimodal functions.
Facultad de Informática
Materia
Ciencias Informáticas
genetic algorithms; genetic diversity; genetic drift; niche formation; crowding
Informática
Algoritmos evolutivos
Optimización
Métodos heurísticos
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:10915/9399
Acceder
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network_name_str SEDICI (UNLP)
spelling Crowding under diverse distance criteria for niche formation in multimodal optimizationFernandez, NataliaAlfonso, HugoGallard, Raúl HectorCiencias Informáticasgenetic algorithms; genetic diversity; genetic drift; niche formation; crowdingInformáticaAlgoritmos evolutivosOptimizaciónMétodos heurísticosNiche formation allows evolutionary algorithms to be used when the location and maintenance of multiple solutions appertaining to diverse areas of the phenotypic space is required. Consequently the application field can be extended to multiobjective optimization, simulation of complex systems and multimodal function optimization. In this later case a conventional evolutionary algorithm tends to group the final population around the fittest individual. Thus, other areas of interest in the search process are lost. Niching methods permits the maintenance of solutions located around these areas of interest. This contribution briefly describe problems preventing niche formation in conventional genetic algorithms, a crowding method for niche formation and analysis of results when optimizing two multimodal functions.Facultad de Informática2000info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/9399enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/pap5.pdfinfo: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:UNLP2025-09-29T10:50:40Zoai:sedici.unlp.edu.ar:10915/9399Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:40.261SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Crowding under diverse distance criteria for niche formation in multimodal optimization
title Crowding under diverse distance criteria for niche formation in multimodal optimization
spellingShingle Crowding under diverse distance criteria for niche formation in multimodal optimization
Fernandez, Natalia
Ciencias Informáticas
genetic algorithms; genetic diversity; genetic drift; niche formation; crowding
Informática
Algoritmos evolutivos
Optimización
Métodos heurísticos
title_short Crowding under diverse distance criteria for niche formation in multimodal optimization
title_full Crowding under diverse distance criteria for niche formation in multimodal optimization
title_fullStr Crowding under diverse distance criteria for niche formation in multimodal optimization
title_full_unstemmed Crowding under diverse distance criteria for niche formation in multimodal optimization
title_sort Crowding under diverse distance criteria for niche formation in multimodal optimization
dc.creator.none.fl_str_mv Fernandez, Natalia
Alfonso, Hugo
Gallard, Raúl Hector
author Fernandez, Natalia
author_facet Fernandez, Natalia
Alfonso, Hugo
Gallard, Raúl Hector
author_role author
author2 Alfonso, Hugo
Gallard, Raúl Hector
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
genetic algorithms; genetic diversity; genetic drift; niche formation; crowding
Informática
Algoritmos evolutivos
Optimización
Métodos heurísticos
topic Ciencias Informáticas
genetic algorithms; genetic diversity; genetic drift; niche formation; crowding
Informática
Algoritmos evolutivos
Optimización
Métodos heurísticos
dc.description.none.fl_txt_mv Niche formation allows evolutionary algorithms to be used when the location and maintenance of multiple solutions appertaining to diverse areas of the phenotypic space is required. Consequently the application field can be extended to multiobjective optimization, simulation of complex systems and multimodal function optimization. In this later case a conventional evolutionary algorithm tends to group the final population around the fittest individual. Thus, other areas of interest in the search process are lost. Niching methods permits the maintenance of solutions located around these areas of interest. This contribution briefly describe problems preventing niche formation in conventional genetic algorithms, a crowding method for niche formation and analysis of results when optimizing two multimodal functions.
Facultad de Informática
description Niche formation allows evolutionary algorithms to be used when the location and maintenance of multiple solutions appertaining to diverse areas of the phenotypic space is required. Consequently the application field can be extended to multiobjective optimization, simulation of complex systems and multimodal function optimization. In this later case a conventional evolutionary algorithm tends to group the final population around the fittest individual. Thus, other areas of interest in the search process are lost. Niching methods permits the maintenance of solutions located around these areas of interest. This contribution briefly describe problems preventing niche formation in conventional genetic algorithms, a crowding method for niche formation and analysis of results when optimizing two multimodal functions.
publishDate 2000
dc.date.none.fl_str_mv 2000
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/9399
url http://sedici.unlp.edu.ar/handle/10915/9399
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/pap5.pdf
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
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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