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dc.contributor.author
Salto, Carolina
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dc.contributor.author
Alba, Enrique
dc.date.available
2022-04-08T15:22:14Z
dc.date.issued
2019-07-25
dc.identifier.citation
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
dc.identifier.issn
0883-9514
dc.identifier.uri
http://hdl.handle.net/11336/154756
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Taylor & Francis
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
CELLULAR GENETIC ALGORITHMS
dc.subject
NEIGHBORHOOD SIZE
dc.subject
PROBLEM OPTIMIZATION
dc.subject.classification
Ciencias de la Computación
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dc.subject.classification
Ciencias de la Computación e Información
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-04-06T16:03:29Z
dc.identifier.eissn
1087-6545
dc.journal.volume
33
dc.journal.number
10
dc.journal.pagination
863-880
dc.journal.pais
Estados Unidos
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dc.journal.ciudad
Filadelfia
dc.description.fil
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
dc.description.fil
Fil: Alba, Enrique. Universidad de Málaga; España
dc.journal.title
Applied Artificial Intelligence
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/08839514.2019.1646005
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1080/08839514.2019.1646005
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