Crowding under diverse distance criteria for niche formation in multimodal optimization

Autores
Alfonso, Hugo; Gallard, Raúl Hector; Fernandez, Natalia
Año de publicación
2001
Idioma
inglés
Tipo de recurso
documento de conferencia
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.
Eje: Computación evolutiva
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Evolución
Algorithms
Optimization
Genetic algorithms
genetic diversity
genetic drift
niche formation
crowding
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23521
Acceder
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network_name_str SEDICI (UNLP)
spelling Crowding under diverse distance criteria for niche formation in multimodal optimizationAlfonso, HugoGallard, Raúl HectorFernandez, NataliaCiencias InformáticasEvoluciónAlgorithmsOptimizationGenetic algorithmsgenetic diversitygenetic driftniche formationcrowdingNiche 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.Eje: Computación evolutivaRed de Universidades con Carreras en Informática (RedUNCI)2001-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23521enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:37:00Zoai:sedici.unlp.edu.ar:10915/23521Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:37:00.96SEDICI (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
Alfonso, Hugo
Ciencias Informáticas
Evolución
Algorithms
Optimization
Genetic algorithms
genetic diversity
genetic drift
niche formation
crowding
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 Alfonso, Hugo
Gallard, Raúl Hector
Fernandez, Natalia
author Alfonso, Hugo
author_facet Alfonso, Hugo
Gallard, Raúl Hector
Fernandez, Natalia
author_role author
author2 Gallard, Raúl Hector
Fernandez, Natalia
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Evolución
Algorithms
Optimization
Genetic algorithms
genetic diversity
genetic drift
niche formation
crowding
topic Ciencias Informáticas
Evolución
Algorithms
Optimization
Genetic algorithms
genetic diversity
genetic drift
niche formation
crowding
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.
Eje: Computación evolutiva
Red de Universidades con Carreras en Informática (RedUNCI)
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 2001
dc.date.none.fl_str_mv 2001-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23521
url http://sedici.unlp.edu.ar/handle/10915/23521
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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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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