Greedy seeding procedure for GAs solving a strip packing problem

Autores
Salto, Carolina; Alba Torres, Enrique; Molina, J.M.; Leguizamón, Guillermo
Año de publicación
2007
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this paper, the two-dimensional strip packing problem with 3-stage level patterns is tackled using genetic algorithms (GAs). We evaluate the usefulness of a greedy seeding procedure for creating the initial population, incorporating problem knowledge. This is motivated by the expectation that the seeding will speed up the GA by starting the search in promising regions of the search space. An analysis of the impact of the seeded initial population is offered, together with a complete study of the influence of these modifications on the genetic search. The results show that the use of an appropriate seeding of the initial population outperforms existing GA approaches on all the used problem instances, for all the metrics used, and in fact it represents the new state of the art for this problem.
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Informática
Biology and genetics
Algorithms
genetic algorithms
strip packing
seeding
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:10915/23576

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spelling Greedy seeding procedure for GAs solving a strip packing problemSalto, CarolinaAlba Torres, EnriqueMolina, J.M.Leguizamón, GuillermoCiencias InformáticasInformáticaBiology and geneticsAlgorithmsgenetic algorithmsstrip packingseedingIn this paper, the two-dimensional strip packing problem with 3-stage level patterns is tackled using genetic algorithms (GAs). We evaluate the usefulness of a greedy seeding procedure for creating the initial population, incorporating problem knowledge. This is motivated by the expectation that the seeding will speed up the GA by starting the search in promising regions of the search space. An analysis of the impact of the seeded initial population is offered, together with a complete study of the influence of these modifications on the genetic search. The results show that the use of an appropriate seeding of the initial population outperforms existing GA approaches on all the used problem instances, for all the metrics used, and in fact it represents the new state of the art for this problem.Red de Universidades con Carreras en Informática (RedUNCI)2007-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1512-1524http://sedici.unlp.edu.ar/handle/10915/23576enginfo: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:UNLP2025-10-15T10:48:09Zoai:sedici.unlp.edu.ar:10915/23576Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:48:09.594SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Greedy seeding procedure for GAs solving a strip packing problem
title Greedy seeding procedure for GAs solving a strip packing problem
spellingShingle Greedy seeding procedure for GAs solving a strip packing problem
Salto, Carolina
Ciencias Informáticas
Informática
Biology and genetics
Algorithms
genetic algorithms
strip packing
seeding
title_short Greedy seeding procedure for GAs solving a strip packing problem
title_full Greedy seeding procedure for GAs solving a strip packing problem
title_fullStr Greedy seeding procedure for GAs solving a strip packing problem
title_full_unstemmed Greedy seeding procedure for GAs solving a strip packing problem
title_sort Greedy seeding procedure for GAs solving a strip packing problem
dc.creator.none.fl_str_mv Salto, Carolina
Alba Torres, Enrique
Molina, J.M.
Leguizamón, Guillermo
author Salto, Carolina
author_facet Salto, Carolina
Alba Torres, Enrique
Molina, J.M.
Leguizamón, Guillermo
author_role author
author2 Alba Torres, Enrique
Molina, J.M.
Leguizamón, Guillermo
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Informática
Biology and genetics
Algorithms
genetic algorithms
strip packing
seeding
topic Ciencias Informáticas
Informática
Biology and genetics
Algorithms
genetic algorithms
strip packing
seeding
dc.description.none.fl_txt_mv In this paper, the two-dimensional strip packing problem with 3-stage level patterns is tackled using genetic algorithms (GAs). We evaluate the usefulness of a greedy seeding procedure for creating the initial population, incorporating problem knowledge. This is motivated by the expectation that the seeding will speed up the GA by starting the search in promising regions of the search space. An analysis of the impact of the seeded initial population is offered, together with a complete study of the influence of these modifications on the genetic search. The results show that the use of an appropriate seeding of the initial population outperforms existing GA approaches on all the used problem instances, for all the metrics used, and in fact it represents the new state of the art for this problem.
Red de Universidades con Carreras en Informática (RedUNCI)
description In this paper, the two-dimensional strip packing problem with 3-stage level patterns is tackled using genetic algorithms (GAs). We evaluate the usefulness of a greedy seeding procedure for creating the initial population, incorporating problem knowledge. This is motivated by the expectation that the seeding will speed up the GA by starting the search in promising regions of the search space. An analysis of the impact of the seeded initial population is offered, together with a complete study of the influence of these modifications on the genetic search. The results show that the use of an appropriate seeding of the initial population outperforms existing GA approaches on all the used problem instances, for all the metrics used, and in fact it represents the new state of the art for this problem.
publishDate 2007
dc.date.none.fl_str_mv 2007-10
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dc.language.none.fl_str_mv eng
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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