Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem

Autores
Salto, Carolina; Stark, Natalia; Hugo, Alfonso; Gallard, Raúl Hector
Año de publicación
2001
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Evolutionary algorithms (EAs) have been successfully applied to scheduling problems. Current improvements towards convergence issues in EAs include incest prevention and multiplicity features. A multiplicity feature allows multiple recombination on multiple parents [7, 8, 9, 10]. The method was successfully applied to multimodal optimization problems. As a consequence of this approach it was detected that all individuals of the final population are much more centred on the optimum. This is an important issue when the application requires provision of multiple alternative near-optimal solutions confronting system dynamics as in production planning
Eje: Inteligencia Computacional - Metaheurísticas
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Incest prevention
multicore combinated evolutionary algorithms
job shop scheduling problem
Algorithms
Scheduling
ARTIFICIAL INTELLIGENCE
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:10915/21663
Acceder
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spelling Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problemSalto, CarolinaStark, NataliaHugo, AlfonsoGallard, Raúl HectorCiencias InformáticasIncest preventionmulticore combinated evolutionary algorithmsjob shop scheduling problemAlgorithmsSchedulingARTIFICIAL INTELLIGENCEEvolutionary algorithms (EAs) have been successfully applied to scheduling problems. Current improvements towards convergence issues in EAs include incest prevention and multiplicity features. A multiplicity feature allows multiple recombination on multiple parents [7, 8, 9, 10]. The method was successfully applied to multimodal optimization problems. As a consequence of this approach it was detected that all individuals of the final population are much more centred on the optimum. This is an important issue when the application requires provision of multiple alternative near-optimal solutions confronting system dynamics as in production planningEje: Inteligencia Computacional - MetaheurísticasRed de Universidades con Carreras en Informática (RedUNCI)2001-05info: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/10915/21663enginfo: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:UNLP2025-09-03T10:27:32Zoai:sedici.unlp.edu.ar:10915/21663Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:27:33.203SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem
title Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem
spellingShingle Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem
Salto, Carolina
Ciencias Informáticas
Incest prevention
multicore combinated evolutionary algorithms
job shop scheduling problem
Algorithms
Scheduling
ARTIFICIAL INTELLIGENCE
title_short Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem
title_full Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem
title_fullStr Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem
title_full_unstemmed Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem
title_sort Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem
dc.creator.none.fl_str_mv Salto, Carolina
Stark, Natalia
Hugo, Alfonso
Gallard, Raúl Hector
author Salto, Carolina
author_facet Salto, Carolina
Stark, Natalia
Hugo, Alfonso
Gallard, Raúl Hector
author_role author
author2 Stark, Natalia
Hugo, Alfonso
Gallard, Raúl Hector
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Incest prevention
multicore combinated evolutionary algorithms
job shop scheduling problem
Algorithms
Scheduling
ARTIFICIAL INTELLIGENCE
topic Ciencias Informáticas
Incest prevention
multicore combinated evolutionary algorithms
job shop scheduling problem
Algorithms
Scheduling
ARTIFICIAL INTELLIGENCE
dc.description.none.fl_txt_mv Evolutionary algorithms (EAs) have been successfully applied to scheduling problems. Current improvements towards convergence issues in EAs include incest prevention and multiplicity features. A multiplicity feature allows multiple recombination on multiple parents [7, 8, 9, 10]. The method was successfully applied to multimodal optimization problems. As a consequence of this approach it was detected that all individuals of the final population are much more centred on the optimum. This is an important issue when the application requires provision of multiple alternative near-optimal solutions confronting system dynamics as in production planning
Eje: Inteligencia Computacional - Metaheurísticas
Red de Universidades con Carreras en Informática (RedUNCI)
description Evolutionary algorithms (EAs) have been successfully applied to scheduling problems. Current improvements towards convergence issues in EAs include incest prevention and multiplicity features. A multiplicity feature allows multiple recombination on multiple parents [7, 8, 9, 10]. The method was successfully applied to multimodal optimization problems. As a consequence of this approach it was detected that all individuals of the final population are much more centred on the optimum. This is an important issue when the application requires provision of multiple alternative near-optimal solutions confronting system dynamics as in production planning
publishDate 2001
dc.date.none.fl_str_mv 2001-05
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/10915/21663
url http://sedici.unlp.edu.ar/handle/10915/21663
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)
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
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score 13.13397

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