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Evolving multidimensional transformations for symbolic regression with M3GP

Bibliographic Details
Main Author: Muñoz, Luis
Publication Date: 2019
Other Authors: Trujillo, Leonardo, Silva, Sara, Castelli, Mauro, Vanneschi, Leonardo
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/143625
Summary: 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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spelling 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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:05:00Zoai:run.unl.pt:10362/143625Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:35:39.114345Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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