Analysis of the use of repulsors to improve generalization ability in genetic programming : an application to symbolic regression problems

Detalhes bibliográficos
Autor(a) principal: Canelhas, Jorge Miguel Silvestre
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/28931
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
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spelling Analysis of the use of repulsors to improve generalization ability in genetic programming : an application to symbolic regression problemsGenetic algorithmGenetic programmingMachine learningRepulsorOverfittingSymbolic regressionDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementGenetic Algorithms are bio-inspired metaheuristics that solve optimization problems; they are evolutionary algorithms that mimic the biological processes of evolution and natural selection to evolve solutions to a given problem. Genetic programming consists of the creation of programs employing GAs to evolve them. In both GA and GP, the algorithm starts with a random solution to a problem that is improved generation after generation building it on the positive traits of the previous generation and discarding traits that do not improve the solution. Repulsors consist of giving the learning algorithm some prior knowledge on the outcome of previous generations on a test set, to try to replace solutions that performed poorly on the data set with better ones. This thesis aims to test and document if the use of repulsors can change the behavior of GP, improve its learning rate and reduce overfitting thus also improving the generalization abilities? Overfitting is a problem in many machine learning algorithms, genetic programming (GP) is also affected by it, one of the objectives of this dissertation is to assess if overfitting can be reduced by using knowledge on the prior behavior of programs generated by GP on a validation data set, and, applying this knowledge to change the selection phase penalizing solutions similar to those that generalized poorly before. These poorly performing solutions will be called repulsors and are the main topic of this dissertation. We developed a program that implemented standard and repulsor based genetic programming. The program was then executed several times over some datasets and collect the results. Finally, the results were compared, and conclusions were taken. The results indicate that the use of repulsors produces better results on the training set and in the test set, this leads us to conclude that the use of repulsors has a positive effect on the performance of GP. The results indicate that the use of repulsors does indeed produce better results. On the training phase, seven out of the nine datasets showed improved algorithm performance when learning. In the test sets, the algorithm presented better generalization ability on five out of nine datasets. Studies could be extended to the use of multi-objective optimization when selecting individuals, and the extension of the repulsor list to other (independent) runs with the same parameters and dataset.Vanneschi, LeonardoRUNCanelhas, Jorge Miguel Silvestre2018-01-24T15:25:37Z2018-01-092018-01-09T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/28931TID:201826801enginfo: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:RCAAP2024-03-11T04:15:41Zoai:run.unl.pt:10362/28931Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:29:06.840964Repositório 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 Analysis of the use of repulsors to improve generalization ability in genetic programming : an application to symbolic regression problems
title Analysis of the use of repulsors to improve generalization ability in genetic programming : an application to symbolic regression problems
spellingShingle Analysis of the use of repulsors to improve generalization ability in genetic programming : an application to symbolic regression problems
Canelhas, Jorge Miguel Silvestre
Genetic algorithm
Genetic programming
Machine learning
Repulsor
Overfitting
Symbolic regression
title_short Analysis of the use of repulsors to improve generalization ability in genetic programming : an application to symbolic regression problems
title_full Analysis of the use of repulsors to improve generalization ability in genetic programming : an application to symbolic regression problems
title_fullStr Analysis of the use of repulsors to improve generalization ability in genetic programming : an application to symbolic regression problems
title_full_unstemmed Analysis of the use of repulsors to improve generalization ability in genetic programming : an application to symbolic regression problems
title_sort Analysis of the use of repulsors to improve generalization ability in genetic programming : an application to symbolic regression problems
author Canelhas, Jorge Miguel Silvestre
author_facet Canelhas, Jorge Miguel Silvestre
author_role author
dc.contributor.none.fl_str_mv Vanneschi, Leonardo
RUN
dc.contributor.author.fl_str_mv Canelhas, Jorge Miguel Silvestre
dc.subject.por.fl_str_mv Genetic algorithm
Genetic programming
Machine learning
Repulsor
Overfitting
Symbolic regression
topic Genetic algorithm
Genetic programming
Machine learning
Repulsor
Overfitting
Symbolic regression
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
publishDate 2018
dc.date.none.fl_str_mv 2018-01-24T15:25:37Z
2018-01-09
2018-01-09T00:00:00Z
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/10362/28931
TID:201826801
url http://hdl.handle.net/10362/28931
identifier_str_mv TID:201826801
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
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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
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