Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem
- Autores
- Vilanova, Gabriela; Villagra, Andrea; Pandolfi, Daniel; San Pedro, María Eugenia de; Gallard, Raúl Hector
- Año de publicación
- 2001
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multiple crossovers on multiple parents). Evolutionary algorithms have been successfully applied to solve scheduling problems. MCMP-STUD and MCMP-SRI are novel MCMP variants, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents In this paper the proposal is to generate the stud-breeding individual by means of a robust conventional heuristic, the CDS. In a multirecombined EA, setting of parameters n1 (number of crossovers) and n2 (number of parents) remained as an open question. In previous works; they were empirically determined, or a deterministic rule was applied. In this paper self adaptation of parameters n1 and n2 is implemented, the idea is to code the parameters within the chromosome and undergo genetic operations. Hence it is expected that better parameter values be more intensively propagated. The present paper discusses different multi-recombined methods and contrasts their performance when different parameter control methods are applied, to find the minimum makespan for selected instances of the FSSP.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
Algorithms
Scheduling
ARTIFICIAL INTELLIGENCE
Evolutionary algorithms
Multiple Crossovers
Multiple Parents
Parameter Control. - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/23420
Ver los metadatos del registro completo
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Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problemVilanova, GabrielaVillagra, AndreaPandolfi, DanielSan Pedro, María Eugenia deGallard, Raúl HectorCiencias InformáticasAlgorithmsSchedulingARTIFICIAL INTELLIGENCEEvolutionary algorithmsMultiple CrossoversMultiple ParentsParameter Control.Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multiple crossovers on multiple parents). Evolutionary algorithms have been successfully applied to solve scheduling problems. MCMP-STUD and MCMP-SRI are novel MCMP variants, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents In this paper the proposal is to generate the stud-breeding individual by means of a robust conventional heuristic, the CDS. In a multirecombined EA, setting of parameters n1 (number of crossovers) and n2 (number of parents) remained as an open question. In previous works; they were empirically determined, or a deterministic rule was applied. In this paper self adaptation of parameters n1 and n2 is implemented, the idea is to code the parameters within the chromosome and undergo genetic operations. Hence it is expected that better parameter values be more intensively propagated. The present paper discusses different multi-recombined methods and contrasts their performance when different parameter control methods are applied, to find the minimum makespan for selected instances of the FSSP.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)2001-10info: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/10915/23420enginfo: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:UNLP2025-09-03T10:28:16Zoai:sedici.unlp.edu.ar:10915/23420Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:28:17.271SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
title |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
spellingShingle |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem Vilanova, Gabriela Ciencias Informáticas Algorithms Scheduling ARTIFICIAL INTELLIGENCE Evolutionary algorithms Multiple Crossovers Multiple Parents Parameter Control. |
title_short |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
title_full |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
title_fullStr |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
title_full_unstemmed |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
title_sort |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
dc.creator.none.fl_str_mv |
Vilanova, Gabriela Villagra, Andrea Pandolfi, Daniel San Pedro, María Eugenia de Gallard, Raúl Hector |
author |
Vilanova, Gabriela |
author_facet |
Vilanova, Gabriela Villagra, Andrea Pandolfi, Daniel San Pedro, María Eugenia de Gallard, Raúl Hector |
author_role |
author |
author2 |
Villagra, Andrea Pandolfi, Daniel San Pedro, María Eugenia de Gallard, Raúl Hector |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Algorithms Scheduling ARTIFICIAL INTELLIGENCE Evolutionary algorithms Multiple Crossovers Multiple Parents Parameter Control. |
topic |
Ciencias Informáticas Algorithms Scheduling ARTIFICIAL INTELLIGENCE Evolutionary algorithms Multiple Crossovers Multiple Parents Parameter Control. |
dc.description.none.fl_txt_mv |
Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multiple crossovers on multiple parents). Evolutionary algorithms have been successfully applied to solve scheduling problems. MCMP-STUD and MCMP-SRI are novel MCMP variants, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents In this paper the proposal is to generate the stud-breeding individual by means of a robust conventional heuristic, the CDS. In a multirecombined EA, setting of parameters n1 (number of crossovers) and n2 (number of parents) remained as an open question. In previous works; they were empirically determined, or a deterministic rule was applied. In this paper self adaptation of parameters n1 and n2 is implemented, the idea is to code the parameters within the chromosome and undergo genetic operations. Hence it is expected that better parameter values be more intensively propagated. The present paper discusses different multi-recombined methods and contrasts their performance when different parameter control methods are applied, to find the minimum makespan for selected instances of the FSSP. Eje: Sistemas inteligentes Red de Universidades con Carreras en Informática (RedUNCI) |
description |
Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multiple crossovers on multiple parents). Evolutionary algorithms have been successfully applied to solve scheduling problems. MCMP-STUD and MCMP-SRI are novel MCMP variants, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents In this paper the proposal is to generate the stud-breeding individual by means of a robust conventional heuristic, the CDS. In a multirecombined EA, setting of parameters n1 (number of crossovers) and n2 (number of parents) remained as an open question. In previous works; they were empirically determined, or a deterministic rule was applied. In this paper self adaptation of parameters n1 and n2 is implemented, the idea is to code the parameters within the chromosome and undergo genetic operations. Hence it is expected that better parameter values be more intensively propagated. The present paper discusses different multi-recombined methods and contrasts their performance when different parameter control methods are applied, to find the minimum makespan for selected instances of the FSSP. |
publishDate |
2001 |
dc.date.none.fl_str_mv |
2001-10 |
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 |
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publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/23420 |
url |
http://sedici.unlp.edu.ar/handle/10915/23420 |
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) |
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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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application/pdf |
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