A genetic approach using direct representation of solution for parallel task scheduling problem

Autores
Esquivel, Susana Cecilia; Gatica, Claudia R.; Gallard, Raúl Hector
Año de publicación
2000
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.
Facultad de Informática
Materia
Ciencias Informáticas
Algoritmos evolutivos
parallel task allocation; genetic algorithm; list scheduling algorithm; schemes of representation; indirect and direct representation; optimisation
Optimización
Procesador paralelo
Programación paralela
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:10915/9397
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/9397
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A genetic approach using direct representation of solution for parallel task scheduling problemEsquivel, Susana CeciliaGatica, Claudia R.Gallard, Raúl HectorCiencias InformáticasAlgoritmos evolutivosparallel task allocation; genetic algorithm; list scheduling algorithm; schemes of representation; indirect and direct representation; optimisationOptimizaciónProcesador paraleloProgramación paralelaEvolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.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/10915/9397enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/pap3.pdfinfo: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:UNLP2025-10-15T10:43:16Zoai:sedici.unlp.edu.ar:10915/9397Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:43:16.888SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A genetic approach using direct representation of solution for parallel task scheduling problem
title A genetic approach using direct representation of solution for parallel task scheduling problem
spellingShingle A genetic approach using direct representation of solution for parallel task scheduling problem
Esquivel, Susana Cecilia
Ciencias Informáticas
Algoritmos evolutivos
parallel task allocation; genetic algorithm; list scheduling algorithm; schemes of representation; indirect and direct representation; optimisation
Optimización
Procesador paralelo
Programación paralela
title_short A genetic approach using direct representation of solution for parallel task scheduling problem
title_full A genetic approach using direct representation of solution for parallel task scheduling problem
title_fullStr A genetic approach using direct representation of solution for parallel task scheduling problem
title_full_unstemmed A genetic approach using direct representation of solution for parallel task scheduling problem
title_sort A genetic approach using direct representation of solution for parallel task scheduling problem
dc.creator.none.fl_str_mv Esquivel, Susana Cecilia
Gatica, Claudia R.
Gallard, Raúl Hector
author Esquivel, Susana Cecilia
author_facet Esquivel, Susana Cecilia
Gatica, Claudia R.
Gallard, Raúl Hector
author_role author
author2 Gatica, Claudia R.
Gallard, Raúl Hector
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Algoritmos evolutivos
parallel task allocation; genetic algorithm; list scheduling algorithm; schemes of representation; indirect and direct representation; optimisation
Optimización
Procesador paralelo
Programación paralela
topic Ciencias Informáticas
Algoritmos evolutivos
parallel task allocation; genetic algorithm; list scheduling algorithm; schemes of representation; indirect and direct representation; optimisation
Optimización
Procesador paralelo
Programación paralela
dc.description.none.fl_txt_mv Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.
Facultad de Informática
description Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.
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/10915/9397
url http://sedici.unlp.edu.ar/handle/10915/9397
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/pap3.pdf
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
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score 13.22299

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