A criteria to select genetic operators for solving CSP

Autores
Riff Rojas, María Cristina
Año de publicación
2000
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Our interest is to define evolutionary algorithms to solve Constraint Satisfaction Problems (CSP), which indlude benefits of taditional resolution methods of CSPs as well as inherent characteristics of these kind of problems. In this paper we propose a criterion to be able to evaluate the perfomance of genetic operators within evolutionary algorithms that solve CSp.
Facultad de Informática
Materia
Ciencias Informáticas
Algorithms
Optimization
evolutionary algorithms; constraint satisfaction; specialized genetics operators
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9386
Acceder
id SEDICI_4cb6b63b98ad4861132a44413c4ede91
oai_identifier_str oai:sedici.unlp.edu.ar:10915/9386
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A criteria to select genetic operators for solving CSPRiff Rojas, María CristinaCiencias InformáticasAlgorithmsOptimizationevolutionary algorithms; constraint satisfaction; specialized genetics operatorsOur interest is to define evolutionary algorithms to solve Constraint Satisfaction Problems (CSP), which indlude benefits of taditional resolution methods of CSPs as well as inherent characteristics of these kind of problems. In this paper we propose a criterion to be able to evaluate the perfomance of genetic operators within evolutionary algorithms that solve CSp.Facultad de Informática2000info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/9386enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/2015/papers_02/acriteria.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:32:16Zoai:sedici.unlp.edu.ar:10915/9386Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:32:16.892SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A criteria to select genetic operators for solving CSP
title A criteria to select genetic operators for solving CSP
spellingShingle A criteria to select genetic operators for solving CSP
Riff Rojas, María Cristina
Ciencias Informáticas
Algorithms
Optimization
evolutionary algorithms; constraint satisfaction; specialized genetics operators
title_short A criteria to select genetic operators for solving CSP
title_full A criteria to select genetic operators for solving CSP
title_fullStr A criteria to select genetic operators for solving CSP
title_full_unstemmed A criteria to select genetic operators for solving CSP
title_sort A criteria to select genetic operators for solving CSP
dc.creator.none.fl_str_mv Riff Rojas, María Cristina
author Riff Rojas, María Cristina
author_facet Riff Rojas, María Cristina
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Algorithms
Optimization
evolutionary algorithms; constraint satisfaction; specialized genetics operators
topic Ciencias Informáticas
Algorithms
Optimization
evolutionary algorithms; constraint satisfaction; specialized genetics operators
dc.description.none.fl_txt_mv Our interest is to define evolutionary algorithms to solve Constraint Satisfaction Problems (CSP), which indlude benefits of taditional resolution methods of CSPs as well as inherent characteristics of these kind of problems. In this paper we propose a criterion to be able to evaluate the perfomance of genetic operators within evolutionary algorithms that solve CSp.
Facultad de Informática
description Our interest is to define evolutionary algorithms to solve Constraint Satisfaction Problems (CSP), which indlude benefits of taditional resolution methods of CSPs as well as inherent characteristics of these kind of problems. In this paper we propose a criterion to be able to evaluate the perfomance of genetic operators within evolutionary algorithms that solve CSp.
publishDate 2000
dc.date.none.fl_str_mv 2000
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/9386
url http://sedici.unlp.edu.ar/handle/10915/9386
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/2015/papers_02/acriteria.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
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
_version_ 1846782741472870400
score 12.982451

Publicaciones similares