MCPC: another approach to crossover in genetic algorithms

Autores
Esquivel, Susana Cecilia; Gallard, Raúl Hector; Michalewicz, Zbigniew
Año de publicación
1995
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian strive for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where . no algorithm is known. The main operator, which is the driving force of genetic algorithms, IS crossover. It combines the features of two parents and produces two offspring. This paper propases a Multiple Crossover Per Couple (MCPC) approach as an altemate method for crossover operators.
Eje: Diseño de algoritmos
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Algorithms
genetic algorithms
genetic operators
crossover
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:10915/24282

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spelling MCPC: another approach to crossover in genetic algorithmsEsquivel, Susana CeciliaGallard, Raúl HectorMichalewicz, ZbigniewCiencias InformáticasAlgorithmsgenetic algorithmsgenetic operatorscrossoverGenetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian strive for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where . no algorithm is known. The main operator, which is the driving force of genetic algorithms, IS crossover. It combines the features of two parents and produces two offspring. This paper propases a Multiple Crossover Per Couple (MCPC) approach as an altemate method for crossover operators.Eje: Diseño de algoritmosRed de Universidades con Carreras en Informática (RedUNCI)1995-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf141-150http://sedici.unlp.edu.ar/handle/10915/24282enginfo: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:UNLP2025-09-03T10:28:34Zoai:sedici.unlp.edu.ar:10915/24282Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:28:34.909SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv MCPC: another approach to crossover in genetic algorithms
title MCPC: another approach to crossover in genetic algorithms
spellingShingle MCPC: another approach to crossover in genetic algorithms
Esquivel, Susana Cecilia
Ciencias Informáticas
Algorithms
genetic algorithms
genetic operators
crossover
title_short MCPC: another approach to crossover in genetic algorithms
title_full MCPC: another approach to crossover in genetic algorithms
title_fullStr MCPC: another approach to crossover in genetic algorithms
title_full_unstemmed MCPC: another approach to crossover in genetic algorithms
title_sort MCPC: another approach to crossover in genetic algorithms
dc.creator.none.fl_str_mv Esquivel, Susana Cecilia
Gallard, Raúl Hector
Michalewicz, Zbigniew
author Esquivel, Susana Cecilia
author_facet Esquivel, Susana Cecilia
Gallard, Raúl Hector
Michalewicz, Zbigniew
author_role author
author2 Gallard, Raúl Hector
Michalewicz, Zbigniew
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Algorithms
genetic algorithms
genetic operators
crossover
topic Ciencias Informáticas
Algorithms
genetic algorithms
genetic operators
crossover
dc.description.none.fl_txt_mv Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian strive for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where . no algorithm is known. The main operator, which is the driving force of genetic algorithms, IS crossover. It combines the features of two parents and produces two offspring. This paper propases a Multiple Crossover Per Couple (MCPC) approach as an altemate method for crossover operators.
Eje: Diseño de algoritmos
Red de Universidades con Carreras en Informática (RedUNCI)
description Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian strive for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where . no algorithm is known. The main operator, which is the driving force of genetic algorithms, IS crossover. It combines the features of two parents and produces two offspring. This paper propases a Multiple Crossover Per Couple (MCPC) approach as an altemate method for crossover operators.
publishDate 1995
dc.date.none.fl_str_mv 1995-10
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
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dc.language.none.fl_str_mv eng
language eng
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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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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
141-150
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