Universal learning machine with genetic programming

Detalhes bibliográficos
Autor(a) principal: Re, Alessandro
Data de Publicação: 2019
Outros Autores: Vanneschi, Leonardo, Castelli, Mauro
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: https://doi.org/10.5220/0007808101150122
Resumo: Re, A., Vanneschi, L., & Castelli, M. (2019). Universal learning machine with genetic programming. In J. J. Merelo, J. Garibaldi, A. Linares-Barranco, K. Madani, K. Warwick, & K. Warwick (Eds.), Proceedings of the 11th International Joint Conference on Computational Intelligence (Vol. 1, pp. 115-122). (IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence). Viena: SciTePress.
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spelling Universal learning machine with genetic programmingEnsemblesGenetic programmingGeometric semantic genetic programmingMachine learningMaster algorithmArtificial IntelligenceComputational Theory and MathematicsRe, A., Vanneschi, L., & Castelli, M. (2019). Universal learning machine with genetic programming. In J. J. Merelo, J. Garibaldi, A. Linares-Barranco, K. Madani, K. Warwick, & K. Warwick (Eds.), Proceedings of the 11th International Joint Conference on Computational Intelligence (Vol. 1, pp. 115-122). (IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence). Viena: SciTePress.This paper presents a proof of concept. It shows that Genetic Programming (GP) can be used as a "universal" machine learning method, that integrates several different algorithms, improving their accuracy. The system we propose, called Universal Genetic Programming (UGP) works by defining an initial population of programs, that contains the models produced by several different machine learning algorithms. The use of elitism allows UGP to return as a final solution the best initial model, in case it is not able to evolve a better one. The use of genetic operators driven by semantic awareness is likely to improve the initial models, by combining and mutating them. On three complex real-life problems, we present experimental evidence that UGP is actually able to improve the models produced by all the studied machine learning algorithms in isolation.SciTePress - Science and Technology PublicationsInformation Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNRe, AlessandroVanneschi, LeonardoCastelli, Mauro2019-11-12T05:04:12Z2019-01-012019-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion8application/pdfhttps://doi.org/10.5220/0007808101150122eng9789897583841PURE: 15379539http://www.scopus.com/inward/record.url?scp=85074267111&partnerID=8YFLogxKhttps://doi.org/10.5220/0007808101150122info: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:RCAAP2024-11-18T01:40:17Zoai:run.unl.pt:10362/87065Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:13:27.812882Repositórios 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 Universal learning machine with genetic programming
title Universal learning machine with genetic programming
spellingShingle Universal learning machine with genetic programming
Re, Alessandro
Ensembles
Genetic programming
Geometric semantic genetic programming
Machine learning
Master algorithm
Artificial Intelligence
Computational Theory and Mathematics
title_short Universal learning machine with genetic programming
title_full Universal learning machine with genetic programming
title_fullStr Universal learning machine with genetic programming
title_full_unstemmed Universal learning machine with genetic programming
title_sort Universal learning machine with genetic programming
author Re, Alessandro
author_facet Re, Alessandro
Vanneschi, Leonardo
Castelli, Mauro
author_role author
author2 Vanneschi, Leonardo
Castelli, Mauro
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 Re, Alessandro
Vanneschi, Leonardo
Castelli, Mauro
dc.subject.por.fl_str_mv Ensembles
Genetic programming
Geometric semantic genetic programming
Machine learning
Master algorithm
Artificial Intelligence
Computational Theory and Mathematics
topic Ensembles
Genetic programming
Geometric semantic genetic programming
Machine learning
Master algorithm
Artificial Intelligence
Computational Theory and Mathematics
description Re, A., Vanneschi, L., & Castelli, M. (2019). Universal learning machine with genetic programming. In J. J. Merelo, J. Garibaldi, A. Linares-Barranco, K. Madani, K. Warwick, & K. Warwick (Eds.), Proceedings of the 11th International Joint Conference on Computational Intelligence (Vol. 1, pp. 115-122). (IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence). Viena: SciTePress.
publishDate 2019
dc.date.none.fl_str_mv 2019-11-12T05:04:12Z
2019-01-01
2019-01-01T00:00:00Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.5220/0007808101150122
url https://doi.org/10.5220/0007808101150122
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 9789897583841
PURE: 15379539
http://www.scopus.com/inward/record.url?scp=85074267111&partnerID=8YFLogxK
https://doi.org/10.5220/0007808101150122
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 8
application/pdf
dc.publisher.none.fl_str_mv SciTePress - Science and Technology Publications
publisher.none.fl_str_mv SciTePress - Science and Technology Publications
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
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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
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