A multi-population hybrid Genetic Programming System

Detalhes bibliográficos
Autor(a) principal: Galvão, Bernardo Gil Câmara
Data de Publicação: 2017
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/25160
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling A multi-population hybrid Genetic Programming SystemMachine LearningStatisticsComputational IntelligenceGenetic ProgrammingGenetic AlgorithmEvolutionary AlgorithmOptimization AlgorithmOptimization ProblemOverfittingSemantic AwarenessDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn the last few years, geometric semantic genetic programming has incremented its popularity, obtaining interesting results on several real life applications. Nevertheless, the large size of the solutions generated by geometric semantic genetic programming is still an issue, in particular for those applications in which reading and interpreting the final solution is desirable. In this thesis, a new parallel and distributed genetic programming system is introduced with the objective of mitigating this drawback. The proposed system (called MPHGP, which stands for Multi-Population Hybrid Genetic Programming) is composed by two types of subpopulations, one of which runs geometric semantic genetic programming, while the other runs a standard multi-objective genetic programming algorithm that optimizes, at the same time, fitness and size of solutions. The two subpopulations evolve independently and in parallel, exchanging individuals at prefixed synchronization instants. The presented experimental results, obtained on five real-life symbolic regression applications, suggest that MPHGP is able to find solutions that are comparable, or even better, than the ones found by geometric semantic genetic programming, both on training and on unseen testing data. At the same time, MPHGP is also able to find solutions that are significantly smaller than the ones found by geometric semantic genetic programming.Vanneschi, LeonardoRUNGalvão, Bernardo Gil Câmara2017-11-09T16:51:14Z2017-11-022017-11-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/25160TID:201748630enginfo: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:RCAAP2024-03-11T04:13:11Zoai:run.unl.pt:10362/25160Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:28:13.414128Repositório 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 A multi-population hybrid Genetic Programming System
title A multi-population hybrid Genetic Programming System
spellingShingle A multi-population hybrid Genetic Programming System
Galvão, Bernardo Gil Câmara
Machine Learning
Statistics
Computational Intelligence
Genetic Programming
Genetic Algorithm
Evolutionary Algorithm
Optimization Algorithm
Optimization Problem
Overfitting
Semantic Awareness
title_short A multi-population hybrid Genetic Programming System
title_full A multi-population hybrid Genetic Programming System
title_fullStr A multi-population hybrid Genetic Programming System
title_full_unstemmed A multi-population hybrid Genetic Programming System
title_sort A multi-population hybrid Genetic Programming System
author Galvão, Bernardo Gil Câmara
author_facet Galvão, Bernardo Gil Câmara
author_role author
dc.contributor.none.fl_str_mv Vanneschi, Leonardo
RUN
dc.contributor.author.fl_str_mv Galvão, Bernardo Gil Câmara
dc.subject.por.fl_str_mv Machine Learning
Statistics
Computational Intelligence
Genetic Programming
Genetic Algorithm
Evolutionary Algorithm
Optimization Algorithm
Optimization Problem
Overfitting
Semantic Awareness
topic Machine Learning
Statistics
Computational Intelligence
Genetic Programming
Genetic Algorithm
Evolutionary Algorithm
Optimization Algorithm
Optimization Problem
Overfitting
Semantic Awareness
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2017
dc.date.none.fl_str_mv 2017-11-09T16:51:14Z
2017-11-02
2017-11-02T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/25160
TID:201748630
url http://hdl.handle.net/10362/25160
identifier_str_mv TID:201748630
dc.language.iso.fl_str_mv eng
language eng
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
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