Controlling individuals growth in semantic genetic programming through elitist replacement

Detalhes bibliográficos
Autor(a) principal: Castelli, Mauro
Data de Publicação: 2016
Outros Autores: Vanneschi, Leonardo, Popovič, Aleš
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
DOI: 10.1155/2016/8326760
Texto Completo: https://doi.org/10.1155/2016/8326760
Resumo: Castelli, M., Vanneschi, L., & Popovič, A. (2016). Controlling individuals growth in semantic genetic programming through elitist replacement. Computational Intelligence And Neuroscience, 2016, [8326760]. https://doi.org/10.1155/2016/8326760
id RCAP_127e57967995f93adb3601c7d0e3b950
oai_identifier_str oai:run.unl.pt:10362/71247
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Controlling individuals growth in semantic genetic programming through elitist replacementNeuroscience(all)Computer Science(all)Mathematics(all)Castelli, M., Vanneschi, L., & Popovič, A. (2016). Controlling individuals growth in semantic genetic programming through elitist replacement. Computational Intelligence And Neuroscience, 2016, [8326760]. https://doi.org/10.1155/2016/8326760In 2012, Moraglio and coauthors introduced new genetic operators for Genetic Programming, called geometric semantic genetic operators. They have the very interesting advantage of inducing a unimodal error surface for any supervised learning problem. At the same time, they have the important drawback of generating very large data models that are usually very hard to understand and interpret. The objective of this work is to alleviate this drawback, still maintaining the advantage. More in particular, we propose an elitist version of geometric semantic operators, in which offspring are accepted in the new population only if they have better fitness than their parents. We present experimental evidence, on five complex real-life test problems, that this simple idea allows us to obtain results of a comparable quality (in terms of fitness), but with much smaller data models, compared to the standard geometric semantic operators. In the final part of the paper, we also explain the reason why we consider this a significant improvement, showing that the proposed elitist operators generate manageable models, while the models generated by the standard operators are so large in size that they can be considered unmanageable.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNCastelli, MauroVanneschi, LeonardoPopovič, Aleš2019-05-30T22:05:35Z20162016-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.1155/2016/8326760eng1687-5265PURE: 2201373http://www.scopus.com/inward/record.url?scp=84956955701&partnerID=8YFLogxKhttps://doi.org/10.1155/2016/8326760info: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:RCAAP2024-05-22T17:39:49Zoai:run.unl.pt:10362/71247Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:39:49Repositório 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 Controlling individuals growth in semantic genetic programming through elitist replacement
title Controlling individuals growth in semantic genetic programming through elitist replacement
spellingShingle Controlling individuals growth in semantic genetic programming through elitist replacement
Controlling individuals growth in semantic genetic programming through elitist replacement
Castelli, Mauro
Neuroscience(all)
Computer Science(all)
Mathematics(all)
Castelli, Mauro
Neuroscience(all)
Computer Science(all)
Mathematics(all)
title_short Controlling individuals growth in semantic genetic programming through elitist replacement
title_full Controlling individuals growth in semantic genetic programming through elitist replacement
title_fullStr Controlling individuals growth in semantic genetic programming through elitist replacement
Controlling individuals growth in semantic genetic programming through elitist replacement
title_full_unstemmed Controlling individuals growth in semantic genetic programming through elitist replacement
Controlling individuals growth in semantic genetic programming through elitist replacement
title_sort Controlling individuals growth in semantic genetic programming through elitist replacement
author Castelli, Mauro
author_facet Castelli, Mauro
Castelli, Mauro
Vanneschi, Leonardo
Popovič, Aleš
Vanneschi, Leonardo
Popovič, Aleš
author_role author
author2 Vanneschi, Leonardo
Popovič, Aleš
author2_role 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 Castelli, Mauro
Vanneschi, Leonardo
Popovič, Aleš
dc.subject.por.fl_str_mv Neuroscience(all)
Computer Science(all)
Mathematics(all)
topic Neuroscience(all)
Computer Science(all)
Mathematics(all)
description Castelli, M., Vanneschi, L., & Popovič, A. (2016). Controlling individuals growth in semantic genetic programming through elitist replacement. Computational Intelligence And Neuroscience, 2016, [8326760]. https://doi.org/10.1155/2016/8326760
publishDate 2016
dc.date.none.fl_str_mv 2016
2016-01-01T00:00:00Z
2019-05-30T22:05:35Z
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 https://doi.org/10.1155/2016/8326760
url https://doi.org/10.1155/2016/8326760
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1687-5265
PURE: 2201373
http://www.scopus.com/inward/record.url?scp=84956955701&partnerID=8YFLogxK
https://doi.org/10.1155/2016/8326760
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 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 mluisa.alvim@gmail.com
_version_ 1822183191566352384
dc.identifier.doi.none.fl_str_mv 10.1155/2016/8326760

Registros relacionados