Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods

Autores
Salto, Carolina; Alba, Enrique
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper, we analyze the neighborhood effect in the selection of parents on an evolutionary algorithm. In this line, we compare a cellular genetic algorithm (cGA), which intrinsically uses the neighbor notion in the mating process, with a modified genetic algorithm including the concept of neighborhood in the selection of parents. Additionally, we analyze the neighborhood size considered for the selection of parent, trying to discover if a quasi-optimal size exists. All the analysis is carried out from a traditional analytic sense to a theoretical point of view regarding evolvability measures. The experimental results suggest that the neighbor effect is important in the performance of an evolutionary algorithm and could provide the cGA with higher chances of success in well-known optimization problems. Regarding the neighborhood size, there is an evidence that a range of neighbors of six, plus/minus two, individuals leads to the cGA to perform more efficiently than other considered sizes.
Fil: Salto, Carolina. Universidad Nacional de La Pampa. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina
Fil: Alba, Enrique. Universidad de Málaga; España
Materia
CELLULAR GENETIC ALGORITHMS
NEIGHBORHOOD SIZE
PROBLEM OPTIMIZATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/154756
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Cellular Genetic Algorithms: Understanding the Behavior of Using NeighborhoodsSalto, CarolinaAlba, EnriqueCELLULAR GENETIC ALGORITHMSNEIGHBORHOOD SIZEPROBLEM OPTIMIZATIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In this paper, we analyze the neighborhood effect in the selection of parents on an evolutionary algorithm. In this line, we compare a cellular genetic algorithm (cGA), which intrinsically uses the neighbor notion in the mating process, with a modified genetic algorithm including the concept of neighborhood in the selection of parents. Additionally, we analyze the neighborhood size considered for the selection of parent, trying to discover if a quasi-optimal size exists. All the analysis is carried out from a traditional analytic sense to a theoretical point of view regarding evolvability measures. The experimental results suggest that the neighbor effect is important in the performance of an evolutionary algorithm and could provide the cGA with higher chances of success in well-known optimization problems. Regarding the neighborhood size, there is an evidence that a range of neighbors of six, plus/minus two, individuals leads to the cGA to perform more efficiently than other considered sizes.Fil: Salto, Carolina. Universidad Nacional de La Pampa. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; ArgentinaFil: Alba, Enrique. Universidad de Málaga; EspañaTaylor & Francis2019-07-25info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/154756Salto, Carolina; Alba, Enrique; Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods; Taylor & Francis; Applied Artificial Intelligence; 33; 10; 25-7-2019; 863-8800883-95141087-6545CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/08839514.2019.1646005info:eu-repo/semantics/altIdentifier/doi/10.1080/08839514.2019.1646005info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:59:09Zoai:ri.conicet.gov.ar:11336/154756instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:59:09.457CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods
title Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods
spellingShingle Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods
Salto, Carolina
CELLULAR GENETIC ALGORITHMS
NEIGHBORHOOD SIZE
PROBLEM OPTIMIZATION
title_short Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods
title_full Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods
title_fullStr Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods
title_full_unstemmed Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods
title_sort Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods
dc.creator.none.fl_str_mv Salto, Carolina
Alba, Enrique
author Salto, Carolina
author_facet Salto, Carolina
Alba, Enrique
author_role author
author2 Alba, Enrique
author2_role author
dc.subject.none.fl_str_mv CELLULAR GENETIC ALGORITHMS
NEIGHBORHOOD SIZE
PROBLEM OPTIMIZATION
topic CELLULAR GENETIC ALGORITHMS
NEIGHBORHOOD SIZE
PROBLEM OPTIMIZATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this paper, we analyze the neighborhood effect in the selection of parents on an evolutionary algorithm. In this line, we compare a cellular genetic algorithm (cGA), which intrinsically uses the neighbor notion in the mating process, with a modified genetic algorithm including the concept of neighborhood in the selection of parents. Additionally, we analyze the neighborhood size considered for the selection of parent, trying to discover if a quasi-optimal size exists. All the analysis is carried out from a traditional analytic sense to a theoretical point of view regarding evolvability measures. The experimental results suggest that the neighbor effect is important in the performance of an evolutionary algorithm and could provide the cGA with higher chances of success in well-known optimization problems. Regarding the neighborhood size, there is an evidence that a range of neighbors of six, plus/minus two, individuals leads to the cGA to perform more efficiently than other considered sizes.
Fil: Salto, Carolina. Universidad Nacional de La Pampa. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina
Fil: Alba, Enrique. Universidad de Málaga; España
description In this paper, we analyze the neighborhood effect in the selection of parents on an evolutionary algorithm. In this line, we compare a cellular genetic algorithm (cGA), which intrinsically uses the neighbor notion in the mating process, with a modified genetic algorithm including the concept of neighborhood in the selection of parents. Additionally, we analyze the neighborhood size considered for the selection of parent, trying to discover if a quasi-optimal size exists. All the analysis is carried out from a traditional analytic sense to a theoretical point of view regarding evolvability measures. The experimental results suggest that the neighbor effect is important in the performance of an evolutionary algorithm and could provide the cGA with higher chances of success in well-known optimization problems. Regarding the neighborhood size, there is an evidence that a range of neighbors of six, plus/minus two, individuals leads to the cGA to perform more efficiently than other considered sizes.
publishDate 2019
dc.date.none.fl_str_mv 2019-07-25
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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://hdl.handle.net/11336/154756
Salto, Carolina; Alba, Enrique; Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods; Taylor & Francis; Applied Artificial Intelligence; 33; 10; 25-7-2019; 863-880
0883-9514
1087-6545
CONICET Digital
CONICET
url http://hdl.handle.net/11336/154756
identifier_str_mv Salto, Carolina; Alba, Enrique; Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods; Taylor & Francis; Applied Artificial Intelligence; 33; 10; 25-7-2019; 863-880
0883-9514
1087-6545
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/08839514.2019.1646005
info:eu-repo/semantics/altIdentifier/doi/10.1080/08839514.2019.1646005
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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