A study of performance of stochastic universal sampling versus proportional selection on genetic algorithms

Autores
Minetti, Gabriela F.; Salto, Carolina; Alfonso, Hugo; Gallard, Raúl Hector
Año de publicación
1999
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Selection mechanisms favour reproduction of better individuals imposing a direction on the search process. According to this it is expected that the effective number of offspring of an individual in the next generation would always agree with the algorithmic sampling frequencies. This does not happens due to sampling errors. Stochastic universal sampling is a method that tries to remedy this problem. This presentation discusses performance results on evolutionary algorithms optimizing a set of highly multimodal functions and a hard unimodal function, under Proportional selection and stochastic universal sampling. Contrasting results are shown.
Eje: Redes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
stochastic universal sampling
proportional selection
genetic algorithms
ARTIFICIAL INTELLIGENCE
Algorithms
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:10915/22220
Acceder
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network_name_str SEDICI (UNLP)
spelling A study of performance of stochastic universal sampling versus proportional selection on genetic algorithmsMinetti, Gabriela F.Salto, CarolinaAlfonso, HugoGallard, Raúl HectorCiencias Informáticasstochastic universal samplingproportional selectiongenetic algorithmsARTIFICIAL INTELLIGENCEAlgorithmsSelection mechanisms favour reproduction of better individuals imposing a direction on the search process. According to this it is expected that the effective number of offspring of an individual in the next generation would always agree with the algorithmic sampling frequencies. This does not happens due to sampling errors. Stochastic universal sampling is a method that tries to remedy this problem. This presentation discusses performance results on evolutionary algorithms optimizing a set of highly multimodal functions and a hard unimodal function, under Proportional selection and stochastic universal sampling. Contrasting results are shown.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/10915/22220enginfo: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:UNLP2025-10-15T10:47:37Zoai:sedici.unlp.edu.ar:10915/22220Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:47:37.398SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A study of performance of stochastic universal sampling versus proportional selection on genetic algorithms
title A study of performance of stochastic universal sampling versus proportional selection on genetic algorithms
spellingShingle A study of performance of stochastic universal sampling versus proportional selection on genetic algorithms
Minetti, Gabriela F.
Ciencias Informáticas
stochastic universal sampling
proportional selection
genetic algorithms
ARTIFICIAL INTELLIGENCE
Algorithms
title_short A study of performance of stochastic universal sampling versus proportional selection on genetic algorithms
title_full A study of performance of stochastic universal sampling versus proportional selection on genetic algorithms
title_fullStr A study of performance of stochastic universal sampling versus proportional selection on genetic algorithms
title_full_unstemmed A study of performance of stochastic universal sampling versus proportional selection on genetic algorithms
title_sort A study of performance of stochastic universal sampling versus proportional selection on genetic algorithms
dc.creator.none.fl_str_mv Minetti, Gabriela F.
Salto, Carolina
Alfonso, Hugo
Gallard, Raúl Hector
author Minetti, Gabriela F.
author_facet Minetti, Gabriela F.
Salto, Carolina
Alfonso, Hugo
Gallard, Raúl Hector
author_role author
author2 Salto, Carolina
Alfonso, Hugo
Gallard, Raúl Hector
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
stochastic universal sampling
proportional selection
genetic algorithms
ARTIFICIAL INTELLIGENCE
Algorithms
topic Ciencias Informáticas
stochastic universal sampling
proportional selection
genetic algorithms
ARTIFICIAL INTELLIGENCE
Algorithms
dc.description.none.fl_txt_mv Selection mechanisms favour reproduction of better individuals imposing a direction on the search process. According to this it is expected that the effective number of offspring of an individual in the next generation would always agree with the algorithmic sampling frequencies. This does not happens due to sampling errors. Stochastic universal sampling is a method that tries to remedy this problem. This presentation discusses performance results on evolutionary algorithms optimizing a set of highly multimodal functions and a hard unimodal function, under Proportional selection and stochastic universal sampling. Contrasting results are shown.
Eje: Redes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description Selection mechanisms favour reproduction of better individuals imposing a direction on the search process. According to this it is expected that the effective number of offspring of an individual in the next generation would always agree with the algorithmic sampling frequencies. This does not happens due to sampling errors. Stochastic universal sampling is a method that tries to remedy this problem. This presentation discusses performance results on evolutionary algorithms optimizing a set of highly multimodal functions and a hard unimodal function, under Proportional selection and stochastic universal sampling. Contrasting results are shown.
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
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/10915/22220
url http://sedici.unlp.edu.ar/handle/10915/22220
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)
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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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