On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
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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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 |
dc.format.none.fl_str_mv |
20 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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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