Inserting problem-specific knowledge in multirecombined evolutionary algorithms

Autores
Pandolfi, Daniel; San Pedro, María Eugenia de; Villagra, Andrea; Vilanova, Gabriela; Gallard, Raúl Hector
Año de publicación
2002
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In the restricted single-machine common due date problem the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties, while for the weighted tardiness problem the goal is to find a schedule that minimizes the tardiness penalties. Both problems, even in theirs simplest formulations, are an NP-Hard optimization problem. This presentation discusses how problem specific knowledge is inserted into the evolutionary algorithm to enhance its performance.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Inserting problem-specific knowledge
multirecombined evolutionary algorithms
ARTIFICIAL INTELLIGENCE
Knowledge acquisition
Algorithms
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:10915/22065
Acceder
id SEDICI_ffe1c1f6ffd6ede93eef46b29e034c36
oai_identifier_str oai:sedici.unlp.edu.ar:10915/22065
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Inserting problem-specific knowledge in multirecombined evolutionary algorithmsPandolfi, DanielSan Pedro, María Eugenia deVillagra, AndreaVilanova, GabrielaGallard, Raúl HectorCiencias InformáticasInserting problem-specific knowledgemultirecombined evolutionary algorithmsARTIFICIAL INTELLIGENCEKnowledge acquisitionAlgorithmsIn the restricted single-machine common due date problem the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties, while for the weighted tardiness problem the goal is to find a schedule that minimizes the tardiness penalties. Both problems, even in theirs simplest formulations, are an NP-Hard optimization problem. This presentation discusses how problem specific knowledge is inserted into the evolutionary algorithm to enhance its performance.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)2002-05info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf468-472http://sedici.unlp.edu.ar/handle/10915/22065enginfo: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:UNLP2025-09-29T10:54:52Zoai:sedici.unlp.edu.ar:10915/22065Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:54:53.217SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Inserting problem-specific knowledge in multirecombined evolutionary algorithms
title Inserting problem-specific knowledge in multirecombined evolutionary algorithms
spellingShingle Inserting problem-specific knowledge in multirecombined evolutionary algorithms
Pandolfi, Daniel
Ciencias Informáticas
Inserting problem-specific knowledge
multirecombined evolutionary algorithms
ARTIFICIAL INTELLIGENCE
Knowledge acquisition
Algorithms
title_short Inserting problem-specific knowledge in multirecombined evolutionary algorithms
title_full Inserting problem-specific knowledge in multirecombined evolutionary algorithms
title_fullStr Inserting problem-specific knowledge in multirecombined evolutionary algorithms
title_full_unstemmed Inserting problem-specific knowledge in multirecombined evolutionary algorithms
title_sort Inserting problem-specific knowledge in multirecombined evolutionary algorithms
dc.creator.none.fl_str_mv Pandolfi, Daniel
San Pedro, María Eugenia de
Villagra, Andrea
Vilanova, Gabriela
Gallard, Raúl Hector
author Pandolfi, Daniel
author_facet Pandolfi, Daniel
San Pedro, María Eugenia de
Villagra, Andrea
Vilanova, Gabriela
Gallard, Raúl Hector
author_role author
author2 San Pedro, María Eugenia de
Villagra, Andrea
Vilanova, Gabriela
Gallard, Raúl Hector
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Inserting problem-specific knowledge
multirecombined evolutionary algorithms
ARTIFICIAL INTELLIGENCE
Knowledge acquisition
Algorithms
topic Ciencias Informáticas
Inserting problem-specific knowledge
multirecombined evolutionary algorithms
ARTIFICIAL INTELLIGENCE
Knowledge acquisition
Algorithms
dc.description.none.fl_txt_mv In the restricted single-machine common due date problem the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties, while for the weighted tardiness problem the goal is to find a schedule that minimizes the tardiness penalties. Both problems, even in theirs simplest formulations, are an NP-Hard optimization problem. This presentation discusses how problem specific knowledge is inserted into the evolutionary algorithm to enhance its performance.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description In the restricted single-machine common due date problem the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties, while for the weighted tardiness problem the goal is to find a schedule that minimizes the tardiness penalties. Both problems, even in theirs simplest formulations, are an NP-Hard optimization problem. This presentation discusses how problem specific knowledge is inserted into the evolutionary algorithm to enhance its performance.
publishDate 2002
dc.date.none.fl_str_mv 2002-05
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/10915/22065
url http://sedici.unlp.edu.ar/handle/10915/22065
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
468-472
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_ 1844615807018991616
score 13.070432

Publicaciones similares