A study of genotype and phenotype distributions in hybrid evolutionary algorithms to solve the flow shop scheduling problem

Autores
Minetti, Gabriela F.; Alfonso, Hugo; Gallard, Raúl Hector
Año de publicación
2002
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this preliminary study the Flow Shop Scheduling Problem (FSSP) is solved by hybrid Evolutionary Algorithms. The algorithms are obtained as a combination of an evolutionary algorithm, which uses the Multi-Inver-Over operator, and two conventional heuristics (CDS and a modified NEH) which are applied either before the evolution begins or when it ends. Here we analyze the genotype and phenotype distribution over the final population of individuals trying to establish the algorithm behavior. Although the original Evolutionary Algorithm was created to provide solutions to the Traveling Salesman Problems (TSP), it can be used for this particular kind of scheduling problem because they share a common chromosome representation.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Algorithms
Heuristic methods
Scheduling
ARTIFICIAL INTELLIGENCE
flow shop sequencing problem
evolutionary algorithms
heuristics
CDS
NEH
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:10915/23006
Acceder
id SEDICI_d079d7e32f8a7f52eacbc1daa236f3a2
oai_identifier_str oai:sedici.unlp.edu.ar:10915/23006
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A study of genotype and phenotype distributions in hybrid evolutionary algorithms to solve the flow shop scheduling problemMinetti, Gabriela F.Alfonso, HugoGallard, Raúl HectorCiencias InformáticasAlgorithmsHeuristic methodsSchedulingARTIFICIAL INTELLIGENCEflow shop sequencing problemevolutionary algorithmsheuristicsCDSNEHIn this preliminary study the Flow Shop Scheduling Problem (FSSP) is solved by hybrid Evolutionary Algorithms. The algorithms are obtained as a combination of an evolutionary algorithm, which uses the Multi-Inver-Over operator, and two conventional heuristics (CDS and a modified NEH) which are applied either before the evolution begins or when it ends. Here we analyze the genotype and phenotype distribution over the final population of individuals trying to establish the algorithm behavior. Although the original Evolutionary Algorithm was created to provide solutions to the Traveling Salesman Problems (TSP), it can be used for this particular kind of scheduling problem because they share a common chromosome representation.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)2002-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf729-738http://sedici.unlp.edu.ar/handle/10915/23006enginfo: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:UNLP2025-10-15T10:47:52Zoai:sedici.unlp.edu.ar:10915/23006Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:47:52.623SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A study of genotype and phenotype distributions in hybrid evolutionary algorithms to solve the flow shop scheduling problem
title A study of genotype and phenotype distributions in hybrid evolutionary algorithms to solve the flow shop scheduling problem
spellingShingle A study of genotype and phenotype distributions in hybrid evolutionary algorithms to solve the flow shop scheduling problem
Minetti, Gabriela F.
Ciencias Informáticas
Algorithms
Heuristic methods
Scheduling
ARTIFICIAL INTELLIGENCE
flow shop sequencing problem
evolutionary algorithms
heuristics
CDS
NEH
title_short A study of genotype and phenotype distributions in hybrid evolutionary algorithms to solve the flow shop scheduling problem
title_full A study of genotype and phenotype distributions in hybrid evolutionary algorithms to solve the flow shop scheduling problem
title_fullStr A study of genotype and phenotype distributions in hybrid evolutionary algorithms to solve the flow shop scheduling problem
title_full_unstemmed A study of genotype and phenotype distributions in hybrid evolutionary algorithms to solve the flow shop scheduling problem
title_sort A study of genotype and phenotype distributions in hybrid evolutionary algorithms to solve the flow shop scheduling problem
dc.creator.none.fl_str_mv Minetti, Gabriela F.
Alfonso, Hugo
Gallard, Raúl Hector
author Minetti, Gabriela F.
author_facet Minetti, Gabriela F.
Alfonso, Hugo
Gallard, Raúl Hector
author_role author
author2 Alfonso, Hugo
Gallard, Raúl Hector
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Algorithms
Heuristic methods
Scheduling
ARTIFICIAL INTELLIGENCE
flow shop sequencing problem
evolutionary algorithms
heuristics
CDS
NEH
topic Ciencias Informáticas
Algorithms
Heuristic methods
Scheduling
ARTIFICIAL INTELLIGENCE
flow shop sequencing problem
evolutionary algorithms
heuristics
CDS
NEH
dc.description.none.fl_txt_mv In this preliminary study the Flow Shop Scheduling Problem (FSSP) is solved by hybrid Evolutionary Algorithms. The algorithms are obtained as a combination of an evolutionary algorithm, which uses the Multi-Inver-Over operator, and two conventional heuristics (CDS and a modified NEH) which are applied either before the evolution begins or when it ends. Here we analyze the genotype and phenotype distribution over the final population of individuals trying to establish the algorithm behavior. Although the original Evolutionary Algorithm was created to provide solutions to the Traveling Salesman Problems (TSP), it can be used for this particular kind of scheduling problem because they share a common chromosome representation.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description In this preliminary study the Flow Shop Scheduling Problem (FSSP) is solved by hybrid Evolutionary Algorithms. The algorithms are obtained as a combination of an evolutionary algorithm, which uses the Multi-Inver-Over operator, and two conventional heuristics (CDS and a modified NEH) which are applied either before the evolution begins or when it ends. Here we analyze the genotype and phenotype distribution over the final population of individuals trying to establish the algorithm behavior. Although the original Evolutionary Algorithm was created to provide solutions to the Traveling Salesman Problems (TSP), it can be used for this particular kind of scheduling problem because they share a common chromosome representation.
publishDate 2002
dc.date.none.fl_str_mv 2002-10
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
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23006
url http://sedici.unlp.edu.ar/handle/10915/23006
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
729-738
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_ 1846063905880670208
score 13.216834

Publicaciones similares