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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/23576
Ver los metadatos del registro completo
id |
SEDICI_73d1aa049b92b4833582e311b9987cb3 |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/23576 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
network_name_str |
SEDICI (UNLP) |
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 |
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/10915/23576 |
url |
http://sedici.unlp.edu.ar/handle/10915/23576 |
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 1512-1524 |
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_ |
1846063908432904192 |
score |
13.22299 |