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
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/24282
Ver los metadatos del registro completo
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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:UNLP2025-09-29T10:55:49Zoai: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:13292025-09-29 10:55:50.089SEDICI (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 |
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1995-10 |
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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 |
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http://sedici.unlp.edu.ar/handle/10915/24282 |
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| dc.language.none.fl_str_mv |
eng |
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eng |
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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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