Evolving multidimensional transformations for symbolic regression with M3GP
Autor(a) principal: | |
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Data de Publicação: | 2019 |
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/10362/143625 |
Resumo: | Muñoz, L., Trujillo, L., Silva, S., Castelli, M., & Vanneschi, L. (2019). Evolving multidimensional transformations for symbolic regression with M3GP. Memetic computing, 11(2), 111–126. https://doi.org/10.1007/s12293-018-0274-5 |
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Evolving multidimensional transformations for symbolic regression with M3GPData transformationFeature optimizationGenetic programmingSymbolic regressionComputer Science(all)Control and OptimizationMuñoz, L., Trujillo, L., Silva, S., Castelli, M., & Vanneschi, L. (2019). Evolving multidimensional transformations for symbolic regression with M3GP. Memetic computing, 11(2), 111–126. https://doi.org/10.1007/s12293-018-0274-5Multidimensional Multiclass Genetic Programming with Multidimensional Populations (M3GP) was originally proposed as a wrapper approach for supervised classification. M3GP searches for transformations of the form k: Rp→ Rd, where p is the number of dimensions of the problem data, and d is the dimensionality of the transformed data, as determined by the search. This work extends M3GP to symbolic regression, building models that are linear in the parameters using the transformed data. The proposal implements a sequential memetic structure with Lamarckian inheritance, combining two local search methods: a greedy pruning algorithm and least squares parameter estimation. Experimental results show that M3GP outperforms several standard and state-of-the-art regression techniques, as well as other GP approaches. Using several synthetic and real-world problems, M3GP outperforms most methods in terms of RMSE and generates more parsimonious models. The performance of M3GP can be explained by the fact that M3GP increases the maximal mutual information in the new feature space.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNMuñoz, LuisTrujillo, LeonardoSilva, SaraCastelli, MauroVanneschi, Leonardo2022-09-09T22:20:23Z2019-06-012019-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article16application/pdfhttp://hdl.handle.net/10362/143625eng1865-9284PURE: 5917601https://doi.org/10.1007/s12293-018-0274-5info: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:RCAAP2024-03-11T05:22:13Zoai:run.unl.pt:10362/143625Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:51:03.336965Repositório 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 |
Evolving multidimensional transformations for symbolic regression with M3GP |
title |
Evolving multidimensional transformations for symbolic regression with M3GP |
spellingShingle |
Evolving multidimensional transformations for symbolic regression with M3GP Muñoz, Luis Data transformation Feature optimization Genetic programming Symbolic regression Computer Science(all) Control and Optimization |
title_short |
Evolving multidimensional transformations for symbolic regression with M3GP |
title_full |
Evolving multidimensional transformations for symbolic regression with M3GP |
title_fullStr |
Evolving multidimensional transformations for symbolic regression with M3GP |
title_full_unstemmed |
Evolving multidimensional transformations for symbolic regression with M3GP |
title_sort |
Evolving multidimensional transformations for symbolic regression with M3GP |
author |
Muñoz, Luis |
author_facet |
Muñoz, Luis Trujillo, Leonardo Silva, Sara Castelli, Mauro Vanneschi, Leonardo |
author_role |
author |
author2 |
Trujillo, Leonardo Silva, Sara Castelli, Mauro Vanneschi, Leonardo |
author2_role |
author author 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 |
Muñoz, Luis Trujillo, Leonardo Silva, Sara Castelli, Mauro Vanneschi, Leonardo |
dc.subject.por.fl_str_mv |
Data transformation Feature optimization Genetic programming Symbolic regression Computer Science(all) Control and Optimization |
topic |
Data transformation Feature optimization Genetic programming Symbolic regression Computer Science(all) Control and Optimization |
description |
Muñoz, L., Trujillo, L., Silva, S., Castelli, M., & Vanneschi, L. (2019). Evolving multidimensional transformations for symbolic regression with M3GP. Memetic computing, 11(2), 111–126. https://doi.org/10.1007/s12293-018-0274-5 |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-06-01 2019-06-01T00:00:00Z 2022-09-09T22:20:23Z |
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/10362/143625 |
url |
http://hdl.handle.net/10362/143625 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1865-9284 PURE: 5917601 https://doi.org/10.1007/s12293-018-0274-5 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
16 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 |
reponame_str |
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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