A self-adaptive recombination method in evolutionary algorithms for solving epistatic problems

Autores
Stark, Natalia; Salto, Carolina
Año de publicación
2011
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
There are many different forms of recombination operators available in literature. However, it is difficult to determine a priori which one is the best suited for a given problem. This issue encourages us to propose an adaptive evolutionary algorithm to solve the NK landscape problem, which dynamically selects the recombination operator from an operator pool during the evolution; this removes the need of specifying a single recombinator operator ad-hoc. We compare the performance of our adaptive proposal against traditional evolutionary algorithms in a numerical way. Our experiments show that the simple adaptive mechanism has a good performance among all the evaluated ones on high dimensional landscapes with an additional reduction in pretuning time.
Presentado en XII Workshop Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Algorithms
recombination operator; evolutionary algorithm; epistatic problems
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:10915/18571
Acceder
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network_name_str SEDICI (UNLP)
spelling A self-adaptive recombination method in evolutionary algorithms for solving epistatic problemsStark, NataliaSalto, CarolinaCiencias InformáticasAlgorithmsrecombination operator; evolutionary algorithm; epistatic problemsThere are many different forms of recombination operators available in literature. However, it is difficult to determine a priori which one is the best suited for a given problem. This issue encourages us to propose an adaptive evolutionary algorithm to solve the NK landscape problem, which dynamically selects the recombination operator from an operator pool during the evolution; this removes the need of specifying a single recombinator operator ad-hoc. We compare the performance of our adaptive proposal against traditional evolutionary algorithms in a numerical way. Our experiments show that the simple adaptive mechanism has a good performance among all the evaluated ones on high dimensional landscapes with an additional reduction in pretuning time.Presentado en XII Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2011-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1-10http://sedici.unlp.edu.ar/handle/10915/18571enginfo: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:UNLP2025-10-15T10:46:09Zoai:sedici.unlp.edu.ar:10915/18571Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:46:10.12SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A self-adaptive recombination method in evolutionary algorithms for solving epistatic problems
title A self-adaptive recombination method in evolutionary algorithms for solving epistatic problems
spellingShingle A self-adaptive recombination method in evolutionary algorithms for solving epistatic problems
Stark, Natalia
Ciencias Informáticas
Algorithms
recombination operator; evolutionary algorithm; epistatic problems
title_short A self-adaptive recombination method in evolutionary algorithms for solving epistatic problems
title_full A self-adaptive recombination method in evolutionary algorithms for solving epistatic problems
title_fullStr A self-adaptive recombination method in evolutionary algorithms for solving epistatic problems
title_full_unstemmed A self-adaptive recombination method in evolutionary algorithms for solving epistatic problems
title_sort A self-adaptive recombination method in evolutionary algorithms for solving epistatic problems
dc.creator.none.fl_str_mv Stark, Natalia
Salto, Carolina
author Stark, Natalia
author_facet Stark, Natalia
Salto, Carolina
author_role author
author2 Salto, Carolina
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Algorithms
recombination operator; evolutionary algorithm; epistatic problems
topic Ciencias Informáticas
Algorithms
recombination operator; evolutionary algorithm; epistatic problems
dc.description.none.fl_txt_mv There are many different forms of recombination operators available in literature. However, it is difficult to determine a priori which one is the best suited for a given problem. This issue encourages us to propose an adaptive evolutionary algorithm to solve the NK landscape problem, which dynamically selects the recombination operator from an operator pool during the evolution; this removes the need of specifying a single recombinator operator ad-hoc. We compare the performance of our adaptive proposal against traditional evolutionary algorithms in a numerical way. Our experiments show that the simple adaptive mechanism has a good performance among all the evaluated ones on high dimensional landscapes with an additional reduction in pretuning time.
Presentado en XII Workshop Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description There are many different forms of recombination operators available in literature. However, it is difficult to determine a priori which one is the best suited for a given problem. This issue encourages us to propose an adaptive evolutionary algorithm to solve the NK landscape problem, which dynamically selects the recombination operator from an operator pool during the evolution; this removes the need of specifying a single recombinator operator ad-hoc. We compare the performance of our adaptive proposal against traditional evolutionary algorithms in a numerical way. Our experiments show that the simple adaptive mechanism has a good performance among all the evaluated ones on high dimensional landscapes with an additional reduction in pretuning time.
publishDate 2011
dc.date.none.fl_str_mv 2011-10
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/10915/18571
url http://sedici.unlp.edu.ar/handle/10915/18571
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
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dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
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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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