Multiple crossover per couple and fitness proportional couple selection in genetic algorithms

Autores
Esquivel, Susana Cecilia; Leiva, Héctor Ariel; Gallard, Raúl Hector
Año de publicación
1997
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Contrasting with conventional approaches to crossover, Multiple Crossover Per Couple (MCPC) is an alternative, recently proposed [1], approach under which more than one crossover operation for each mating pair is allowed. In genetic algorithms, Proportional Selection (PS) is a popular method to select individuals for mating based on their fitness values. The Fitness Proportional Couple Selection (FPCS) approach, is a new selection method which creates an intermediate population of couples from where, subsequently, couples are selected for crossing-over based on couple fitness. This paper proposes the combined use of MCPC and FPCS. Outstanding performance was achieved by joining both methods when optimising hard testing multimodal and unimodal functions. Some of these results and their comparison against results from conventional approaches are shown.
Eje: Procesamiento distribuido y paralelo. Tratamiento de señales
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
genetic algorithms
selection
crossover
function optimisation
Algorithms
Parallel processing
Distributed
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:10915/23895

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network_name_str SEDICI (UNLP)
spelling Multiple crossover per couple and fitness proportional couple selection in genetic algorithmsEsquivel, Susana CeciliaLeiva, Héctor ArielGallard, Raúl HectorCiencias Informáticasgenetic algorithmsselectioncrossoverfunction optimisationAlgorithmsParallel processingDistributedContrasting with conventional approaches to crossover, Multiple Crossover Per Couple (MCPC) is an alternative, recently proposed [1], approach under which more than one crossover operation for each mating pair is allowed. In genetic algorithms, Proportional Selection (PS) is a popular method to select individuals for mating based on their fitness values. The Fitness Proportional Couple Selection (FPCS) approach, is a new selection method which creates an intermediate population of couples from where, subsequently, couples are selected for crossing-over based on couple fitness. This paper proposes the combined use of MCPC and FPCS. Outstanding performance was achieved by joining both methods when optimising hard testing multimodal and unimodal functions. Some of these results and their comparison against results from conventional approaches are shown.Eje: Procesamiento distribuido y paralelo. Tratamiento de señalesRed de Universidades con Carreras en Informática (RedUNCI)1997info: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/10915/23895enginfo: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:UNLP2025-10-15T10:48:14Zoai:sedici.unlp.edu.ar:10915/23895Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:48:15.272SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Multiple crossover per couple and fitness proportional couple selection in genetic algorithms
title Multiple crossover per couple and fitness proportional couple selection in genetic algorithms
spellingShingle Multiple crossover per couple and fitness proportional couple selection in genetic algorithms
Esquivel, Susana Cecilia
Ciencias Informáticas
genetic algorithms
selection
crossover
function optimisation
Algorithms
Parallel processing
Distributed
title_short Multiple crossover per couple and fitness proportional couple selection in genetic algorithms
title_full Multiple crossover per couple and fitness proportional couple selection in genetic algorithms
title_fullStr Multiple crossover per couple and fitness proportional couple selection in genetic algorithms
title_full_unstemmed Multiple crossover per couple and fitness proportional couple selection in genetic algorithms
title_sort Multiple crossover per couple and fitness proportional couple selection in genetic algorithms
dc.creator.none.fl_str_mv Esquivel, Susana Cecilia
Leiva, Héctor Ariel
Gallard, Raúl Hector
author Esquivel, Susana Cecilia
author_facet Esquivel, Susana Cecilia
Leiva, Héctor Ariel
Gallard, Raúl Hector
author_role author
author2 Leiva, Héctor Ariel
Gallard, Raúl Hector
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
genetic algorithms
selection
crossover
function optimisation
Algorithms
Parallel processing
Distributed
topic Ciencias Informáticas
genetic algorithms
selection
crossover
function optimisation
Algorithms
Parallel processing
Distributed
dc.description.none.fl_txt_mv Contrasting with conventional approaches to crossover, Multiple Crossover Per Couple (MCPC) is an alternative, recently proposed [1], approach under which more than one crossover operation for each mating pair is allowed. In genetic algorithms, Proportional Selection (PS) is a popular method to select individuals for mating based on their fitness values. The Fitness Proportional Couple Selection (FPCS) approach, is a new selection method which creates an intermediate population of couples from where, subsequently, couples are selected for crossing-over based on couple fitness. This paper proposes the combined use of MCPC and FPCS. Outstanding performance was achieved by joining both methods when optimising hard testing multimodal and unimodal functions. Some of these results and their comparison against results from conventional approaches are shown.
Eje: Procesamiento distribuido y paralelo. Tratamiento de señales
Red de Universidades con Carreras en Informática (RedUNCI)
description Contrasting with conventional approaches to crossover, Multiple Crossover Per Couple (MCPC) is an alternative, recently proposed [1], approach under which more than one crossover operation for each mating pair is allowed. In genetic algorithms, Proportional Selection (PS) is a popular method to select individuals for mating based on their fitness values. The Fitness Proportional Couple Selection (FPCS) approach, is a new selection method which creates an intermediate population of couples from where, subsequently, couples are selected for crossing-over based on couple fitness. This paper proposes the combined use of MCPC and FPCS. Outstanding performance was achieved by joining both methods when optimising hard testing multimodal and unimodal functions. Some of these results and their comparison against results from conventional approaches are shown.
publishDate 1997
dc.date.none.fl_str_mv 1997
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
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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
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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