Multiple parents, multiple crossovers and incest prevention in evolutionary computation

Autores
Alfonso, Hugo; Minetti, Gabriela F.; Salto, Carolina; Gallard, Raúl Hector
Año de publicación
1999
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Multimodal optimization is an always present topic in Computer Systems and Networks design and implementation. - Evolutionary Computation is an emergent field which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. The present contribution gives an insight of the current enhancements that can be done in evolutionary techniques, attempting to balance exploitation and explotation to avoid premature convergence during the search process. Multiple parents, multiple crossovers and incest prevention are three different techniques that when combined showed a substantial benefit: The set of suboptimal solutions are concentrated nearby the optimal solution. This paper shows the design, implementation and partial performance results when a combination of multiple crossovers on multiple parents and incest prevention is applied to an evolutionary algorithm optimizing two difficult multimodal functions.
Eje: Redes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
multiple crossovers
Multiple parents
incest prevention
evolutionary computation
ARTIFICIAL INTELLIGENCE
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:10915/22221
Acceder
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network_name_str SEDICI (UNLP)
spelling Multiple parents, multiple crossovers and incest prevention in evolutionary computationAlfonso, HugoMinetti, Gabriela F.Salto, CarolinaGallard, Raúl HectorCiencias Informáticasmultiple crossoversMultiple parentsincest preventionevolutionary computationARTIFICIAL INTELLIGENCEMultimodal optimization is an always present topic in Computer Systems and Networks design and implementation. - Evolutionary Computation is an emergent field which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. The present contribution gives an insight of the current enhancements that can be done in evolutionary techniques, attempting to balance exploitation and explotation to avoid premature convergence during the search process. Multiple parents, multiple crossovers and incest prevention are three different techniques that when combined showed a substantial benefit: The set of suboptimal solutions are concentrated nearby the optimal solution. This paper shows the design, implementation and partial performance results when a combination of multiple crossovers on multiple parents and incest prevention is applied to an evolutionary algorithm optimizing two difficult multimodal functions.Eje: Redes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)1999-05info: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/10915/22221spainfo: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:UNLP2025-09-29T10:54:57Zoai:sedici.unlp.edu.ar:10915/22221Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:54:57.361SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Multiple parents, multiple crossovers and incest prevention in evolutionary computation
title Multiple parents, multiple crossovers and incest prevention in evolutionary computation
spellingShingle Multiple parents, multiple crossovers and incest prevention in evolutionary computation
Alfonso, Hugo
Ciencias Informáticas
multiple crossovers
Multiple parents
incest prevention
evolutionary computation
ARTIFICIAL INTELLIGENCE
title_short Multiple parents, multiple crossovers and incest prevention in evolutionary computation
title_full Multiple parents, multiple crossovers and incest prevention in evolutionary computation
title_fullStr Multiple parents, multiple crossovers and incest prevention in evolutionary computation
title_full_unstemmed Multiple parents, multiple crossovers and incest prevention in evolutionary computation
title_sort Multiple parents, multiple crossovers and incest prevention in evolutionary computation
dc.creator.none.fl_str_mv Alfonso, Hugo
Minetti, Gabriela F.
Salto, Carolina
Gallard, Raúl Hector
author Alfonso, Hugo
author_facet Alfonso, Hugo
Minetti, Gabriela F.
Salto, Carolina
Gallard, Raúl Hector
author_role author
author2 Minetti, Gabriela F.
Salto, Carolina
Gallard, Raúl Hector
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
multiple crossovers
Multiple parents
incest prevention
evolutionary computation
ARTIFICIAL INTELLIGENCE
topic Ciencias Informáticas
multiple crossovers
Multiple parents
incest prevention
evolutionary computation
ARTIFICIAL INTELLIGENCE
dc.description.none.fl_txt_mv Multimodal optimization is an always present topic in Computer Systems and Networks design and implementation. - Evolutionary Computation is an emergent field which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. The present contribution gives an insight of the current enhancements that can be done in evolutionary techniques, attempting to balance exploitation and explotation to avoid premature convergence during the search process. Multiple parents, multiple crossovers and incest prevention are three different techniques that when combined showed a substantial benefit: The set of suboptimal solutions are concentrated nearby the optimal solution. This paper shows the design, implementation and partial performance results when a combination of multiple crossovers on multiple parents and incest prevention is applied to an evolutionary algorithm optimizing two difficult multimodal functions.
Eje: Redes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description Multimodal optimization is an always present topic in Computer Systems and Networks design and implementation. - Evolutionary Computation is an emergent field which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. The present contribution gives an insight of the current enhancements that can be done in evolutionary techniques, attempting to balance exploitation and explotation to avoid premature convergence during the search process. Multiple parents, multiple crossovers and incest prevention are three different techniques that when combined showed a substantial benefit: The set of suboptimal solutions are concentrated nearby the optimal solution. This paper shows the design, implementation and partial performance results when a combination of multiple crossovers on multiple parents and incest prevention is applied to an evolutionary algorithm optimizing two difficult multimodal functions.
publishDate 1999
dc.date.none.fl_str_mv 1999-05
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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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/10915/22221
url http://sedici.unlp.edu.ar/handle/10915/22221
dc.language.none.fl_str_mv spa
language spa
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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