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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/23521
Ver los metadatos del registro completo
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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:UNLP2025-09-29T10:55:30Zoai: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:13292025-09-29 10:55:30.714SEDICI (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 |
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http://sedici.unlp.edu.ar/handle/10915/23521 |
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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 |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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