Soft target and functional complexity reduction

Detalhes bibliográficos
Autor(a) principal: Vanneschi, Leonardo
Data de Publicação: 2021
Outros Autores: 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/10362/115822
Resumo: Vanneschi, L., & Castelli, M. (2021). Soft target and functional complexity reduction: A hybrid regularization method for genetic programming. Expert Systems with Applications, 177, 1-11. [114929]. https://doi.org/10.1016/j.eswa.2021.114929
id RCAP_ad8bd024eae74e36300f0f6447ea9c6c
oai_identifier_str oai:run.unl.pt:10362/115822
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Soft target and functional complexity reductionA hybrid regularization method for genetic programmingFunctional complexityGenetic programmingHybrid systemRegularizationSoft targetEngineering(all)Computer Science ApplicationsArtificial IntelligenceVanneschi, L., & Castelli, M. (2021). Soft target and functional complexity reduction: A hybrid regularization method for genetic programming. Expert Systems with Applications, 177, 1-11. [114929]. https://doi.org/10.1016/j.eswa.2021.114929Regularization is frequently used in supervised machine learning to prevent models from overfitting. This paper tackles the problem of regularization in genetic programming. We apply, for the first time, soft target regularization, a method recently defined for artificial neural networks, to genetic programming. Also, we introduce a novel measure of functional complexity of the genetic programming individuals, aimed at quantifying their degree of curvature. We experimentally demonstrate that both the use of soft target regularization, and the minimization of the complexity during learning, are often able to reduce overfitting, but they are never able to eliminate it. On the other hand, we demonstrate that the integration of these two strategies into a novel hybrid genetic programming system can completely eliminate overfitting, for all the studied test cases. Last but not least, consistently with what found in the literature, we offer experimental evidence of the fact that the size of the genetic programming models has no correlation with their generalization ability.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNVanneschi, LeonardoCastelli, Mauro2024-01-24T01:31:44Z2021-09-012021-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article11application/pdfhttp://hdl.handle.net/10362/115822eng0957-4174PURE: 29275368https://doi.org/10.1016/j.eswa.2021.114929info: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:RCAAP2024-03-11T04:58:19Zoai:run.unl.pt:10362/115822Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:51.020617Repositório 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 Soft target and functional complexity reduction
A hybrid regularization method for genetic programming
title Soft target and functional complexity reduction
spellingShingle Soft target and functional complexity reduction
Vanneschi, Leonardo
Functional complexity
Genetic programming
Hybrid system
Regularization
Soft target
Engineering(all)
Computer Science Applications
Artificial Intelligence
title_short Soft target and functional complexity reduction
title_full Soft target and functional complexity reduction
title_fullStr Soft target and functional complexity reduction
title_full_unstemmed Soft target and functional complexity reduction
title_sort Soft target and functional complexity reduction
author Vanneschi, Leonardo
author_facet Vanneschi, Leonardo
Castelli, Mauro
author_role author
author2 Castelli, Mauro
author2_role 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 Vanneschi, Leonardo
Castelli, Mauro
dc.subject.por.fl_str_mv Functional complexity
Genetic programming
Hybrid system
Regularization
Soft target
Engineering(all)
Computer Science Applications
Artificial Intelligence
topic Functional complexity
Genetic programming
Hybrid system
Regularization
Soft target
Engineering(all)
Computer Science Applications
Artificial Intelligence
description Vanneschi, L., & Castelli, M. (2021). Soft target and functional complexity reduction: A hybrid regularization method for genetic programming. Expert Systems with Applications, 177, 1-11. [114929]. https://doi.org/10.1016/j.eswa.2021.114929
publishDate 2021
dc.date.none.fl_str_mv 2021-09-01
2021-09-01T00:00:00Z
2024-01-24T01:31:44Z
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/10362/115822
url http://hdl.handle.net/10362/115822
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0957-4174
PURE: 29275368
https://doi.org/10.1016/j.eswa.2021.114929
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 11
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
repository.mail.fl_str_mv
_version_ 1799138039745740800

Registros relacionados