A Study of Dynamic Populations in Geometric Semantic Genetic Programming

Detalhes bibliográficos
Autor(a) principal: Farinati, Davide
Data de Publicação: 2023
Outros Autores: Bakurov, Illya, Vanneschi, Leonardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/156900
Resumo: Farinati, D., Bakurov, I., & Vanneschi, L. (2023). A Study of Dynamic Populations in Geometric Semantic Genetic Programming. Information Sciences, 648(November), 1-21. [119513]. https://doi.org/10.1016/j.ins.2023.119513 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
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spelling A Study of Dynamic Populations in Geometric Semantic Genetic ProgrammingDynamic PopulationsGenetic ProgrammingGeometric semantic genetic programmingSemantic neighbourhoodSoftwareControl and Systems EngineeringTheoretical Computer ScienceComputer Science ApplicationsInformation Systems and ManagementArtificial IntelligenceFarinati, D., Bakurov, I., & Vanneschi, L. (2023). A Study of Dynamic Populations in Geometric Semantic Genetic Programming. Information Sciences, 648(November), 1-21. [119513]. https://doi.org/10.1016/j.ins.2023.119513 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.Allowing the population size to variate during the evolution can bring advantages to evolutionary algorithms (EAs), retaining computational effort during the evolution process. Dynamic populations use computational resources wisely in several types of EAs, including genetic programming. However, so far, a thorough study on the use of dynamic populations in Geometric Semantic Genetic Programming (GSGP) is missing. Still, GSGP is a resource-greedy algorithm, and the use of dynamic populations seems appropriate. This paper adapts algorithms to GSGP to manage dynamic populations that were successful for other types of EAs and introduces two novel algorithms. The novel algorithms exploit the concept of semantic neighbourhood. These methods are assessed and compared through a set of eight regression problems. The results indicate that the algorithms outperform standard GSGP, confirming the suitability of dynamic populations for GSGP. Interestingly, the novel algorithms that use semantic neighbourhood to manage variation in population size are particularly effective in generating robust models even for the most difficult of the studied test problems.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNFarinati, DavideBakurov, IllyaVanneschi, Leonardo2023-08-25T22:19:48Z2023-11-012023-11-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article21application/pdfhttp://hdl.handle.net/10362/156900eng0020-0255PURE: 68879326https://doi.org/10.1016/j.ins.2023.119513info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:39:12Zoai:run.unl.pt:10362/156900Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:56:29.809339Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A Study of Dynamic Populations in Geometric Semantic Genetic Programming
title A Study of Dynamic Populations in Geometric Semantic Genetic Programming
spellingShingle A Study of Dynamic Populations in Geometric Semantic Genetic Programming
Farinati, Davide
Dynamic Populations
Genetic Programming
Geometric semantic genetic programming
Semantic neighbourhood
Software
Control and Systems Engineering
Theoretical Computer Science
Computer Science Applications
Information Systems and Management
Artificial Intelligence
title_short A Study of Dynamic Populations in Geometric Semantic Genetic Programming
title_full A Study of Dynamic Populations in Geometric Semantic Genetic Programming
title_fullStr A Study of Dynamic Populations in Geometric Semantic Genetic Programming
title_full_unstemmed A Study of Dynamic Populations in Geometric Semantic Genetic Programming
title_sort A Study of Dynamic Populations in Geometric Semantic Genetic Programming
author Farinati, Davide
author_facet Farinati, Davide
Bakurov, Illya
Vanneschi, Leonardo
author_role author
author2 Bakurov, Illya
Vanneschi, Leonardo
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Farinati, Davide
Bakurov, Illya
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Dynamic Populations
Genetic Programming
Geometric semantic genetic programming
Semantic neighbourhood
Software
Control and Systems Engineering
Theoretical Computer Science
Computer Science Applications
Information Systems and Management
Artificial Intelligence
topic Dynamic Populations
Genetic Programming
Geometric semantic genetic programming
Semantic neighbourhood
Software
Control and Systems Engineering
Theoretical Computer Science
Computer Science Applications
Information Systems and Management
Artificial Intelligence
description Farinati, D., Bakurov, I., & Vanneschi, L. (2023). A Study of Dynamic Populations in Geometric Semantic Genetic Programming. Information Sciences, 648(November), 1-21. [119513]. https://doi.org/10.1016/j.ins.2023.119513 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
publishDate 2023
dc.date.none.fl_str_mv 2023-08-25T22:19:48Z
2023-11-01
2023-11-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/156900
url http://hdl.handle.net/10362/156900
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0020-0255
PURE: 68879326
https://doi.org/10.1016/j.ins.2023.119513
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 21
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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