On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers

Detalhes bibliográficos
Autor(a) principal: Pietropolli, Gloria
Data de Publicação: 2023
Outros Autores: Manzoni, Luca, Paoletti, Alessia, Castelli, Mauro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/159617
Resumo: Pietropolli, G., Manzoni, L., Paoletti, A., & Castelli, M. (2023). On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers. Genetic Programming And Evolvable Machines, 24(2 Special Issue on Highlights of Genetic Programming 2022 Events), 1-20. [16]. https://doi.org/10.21203/rs.3.rs-2229748/v1, https://doi.org/10.1007/s10710-023-09463-1---Open access funding provided by Università degli Studi di Trieste within the CRUI-CARE Agreement. 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 On the Hybridization of Geometric Semantic GP with Gradient-based OptimizersAdamEvolutionary algorithmGeometric semantic genetic programmingStochastic gradient descentSoftwareTheoretical Computer ScienceHardware and ArchitectureComputer Science ApplicationsPietropolli, G., Manzoni, L., Paoletti, A., & Castelli, M. (2023). On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers. Genetic Programming And Evolvable Machines, 24(2 Special Issue on Highlights of Genetic Programming 2022 Events), 1-20. [16]. https://doi.org/10.21203/rs.3.rs-2229748/v1, https://doi.org/10.1007/s10710-023-09463-1---Open access funding provided by Università degli Studi di Trieste within the CRUI-CARE Agreement. 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 IMSGeometric semantic genetic programming (GSGP) is a popular form of GP where the effect of crossover and mutation can be expressed as geometric operations on a semantic space. A recent study showed that GSGP can be hybridized with a standard gradient-based optimized, Adam, commonly used in training artificial neural networks.We expand upon that work by considering more gradient-based optimizers, a deeper investigation of their parameters, how the hybridization is performed, and a more comprehensive set of benchmark problems. With the correct choice of hyperparameters, this hybridization improves the performances of GSGP and allows it to reach the same fitness values with fewer fitness evaluations.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNPietropolli, GloriaManzoni, LucaPaoletti, AlessiaCastelli, Mauro2023-11-06T22:09:37Z2023-122023-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article20application/pdfhttp://hdl.handle.net/10362/159617eng1389-2576PURE: 72363135https://doi.org/10.21203/rs.3.rs-2229748/v1info: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:RCAAP2024-03-11T05:42:00Zoai:run.unl.pt:10362/159617Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:57:37.076490Repositório 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 On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers
title On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers
spellingShingle On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers
Pietropolli, Gloria
Adam
Evolutionary algorithm
Geometric semantic genetic programming
Stochastic gradient descent
Software
Theoretical Computer Science
Hardware and Architecture
Computer Science Applications
title_short On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers
title_full On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers
title_fullStr On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers
title_full_unstemmed On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers
title_sort On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers
author Pietropolli, Gloria
author_facet Pietropolli, Gloria
Manzoni, Luca
Paoletti, Alessia
Castelli, Mauro
author_role author
author2 Manzoni, Luca
Paoletti, Alessia
Castelli, Mauro
author2_role author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Pietropolli, Gloria
Manzoni, Luca
Paoletti, Alessia
Castelli, Mauro
dc.subject.por.fl_str_mv Adam
Evolutionary algorithm
Geometric semantic genetic programming
Stochastic gradient descent
Software
Theoretical Computer Science
Hardware and Architecture
Computer Science Applications
topic Adam
Evolutionary algorithm
Geometric semantic genetic programming
Stochastic gradient descent
Software
Theoretical Computer Science
Hardware and Architecture
Computer Science Applications
description Pietropolli, G., Manzoni, L., Paoletti, A., & Castelli, M. (2023). On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers. Genetic Programming And Evolvable Machines, 24(2 Special Issue on Highlights of Genetic Programming 2022 Events), 1-20. [16]. https://doi.org/10.21203/rs.3.rs-2229748/v1, https://doi.org/10.1007/s10710-023-09463-1---Open access funding provided by Università degli Studi di Trieste within the CRUI-CARE Agreement. 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 2023-11-06T22:09:37Z
2023-12
2023-12-01T00:00:00Z
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/10362/159617
url http://hdl.handle.net/10362/159617
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1389-2576
PURE: 72363135
https://doi.org/10.21203/rs.3.rs-2229748/v1
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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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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