Ensembled Geometric Semantic Genetic Programming: An ensemble-based initialization technique for Geometric Semantic Genetic Programming

Detalhes bibliográficos
Autor(a) principal: Rosenfeld, Liah
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/140850
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Ensembled Geometric Semantic Genetic Programming: An ensemble-based initialization technique for Geometric Semantic Genetic ProgrammingGenetic ProgrammingGeometric Semantic Genetic ProgrammingInitialization methodsEnsemble methodsMeta-learningMachine LearningEvolutionary AlgorithmsDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsMachine learning is a scientific field that seeks to empower computers with the ability to learn without having to be explicitly programmed. Given the ever growing number of sophisticated intelligent machine learning algorithms, investigators can find themselves “stuck” in a time and resources consuming process of model searching, tuning and testing. Bearing this in mind, Ensemble Methods and meta-learning algorithms have emerged as an attempt to automate the process of combining several different models in an intelligent, adaptive way. Within the field of machine learning, a specific set of algorithms, called Evolutionary Algorithms, mimic Darwin’s Theory of Evolution. As their name indicates, these algorithms incorporate concepts of evolution to the task learning process. They do so by evolving a set of individuals (i.e., possible solutions to a given problem) under pressures of natural selection and "survival of the fittest" mechanisms. Genetic Programming (GP) is an Evolutionary Algorithm that evolves computer programs (i.e., individuals) in order to perform a mapping between input and output features. Its extension, Geometric Semantic Genetic Programming (GSGP), allows us to perform the evolution and variation of the individuals on the semantic space (i.e., the space in which the output vectors lay) rather than on their syntax based structure. The generation of the initial population, commonly known as the initialization, has been proven to be a key factor for both GP and GSGP’s performance. This work proposes an initialization technique for GSGP that utilizes the combinatory power of Ensemble Methods in order to generate a “fit” initial population based on the predictions of a wide variety of machine learning algorithms, called Base Learners, making use of GSGP’s ability to evolve individuals solely based on their semantics (i.e., predictions). This initialization technique is called Ensembled Geometric Semantic Genetic Programming as it utilizes GSGP as a learning combiner that merges the knowledge obtained from different machine learning techniques by evolving their semantics via crossing between and mutating them over the course of several generations. The performance of the proposed initialization method (EGSGP) was tested on three different case studies. Results show that EGSGP significantly outperforms traditional GSGP (with the exception of one case study where EGSGP’s improved performance in comparison to GSGP did not translate into statistically significant results). Additionally, results show that EGSGP produces better results than the best base learner from the initial population being, thus, able to improve it. This improvement was present in all case studies only yielding, however, statistically significant results in one of them.Vanneschi, LeonardoRUNRosenfeld, Liah2022-06-27T13:50:46Z2022-05-042022-05-04T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/140850TID:203028422enginfo: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:RCAAP2024-03-11T05:18:02Zoai:run.unl.pt:10362/140850Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:49:48.360843Repositório 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 Ensembled Geometric Semantic Genetic Programming: An ensemble-based initialization technique for Geometric Semantic Genetic Programming
title Ensembled Geometric Semantic Genetic Programming: An ensemble-based initialization technique for Geometric Semantic Genetic Programming
spellingShingle Ensembled Geometric Semantic Genetic Programming: An ensemble-based initialization technique for Geometric Semantic Genetic Programming
Rosenfeld, Liah
Genetic Programming
Geometric Semantic Genetic Programming
Initialization methods
Ensemble methods
Meta-learning
Machine Learning
Evolutionary Algorithms
title_short Ensembled Geometric Semantic Genetic Programming: An ensemble-based initialization technique for Geometric Semantic Genetic Programming
title_full Ensembled Geometric Semantic Genetic Programming: An ensemble-based initialization technique for Geometric Semantic Genetic Programming
title_fullStr Ensembled Geometric Semantic Genetic Programming: An ensemble-based initialization technique for Geometric Semantic Genetic Programming
title_full_unstemmed Ensembled Geometric Semantic Genetic Programming: An ensemble-based initialization technique for Geometric Semantic Genetic Programming
title_sort Ensembled Geometric Semantic Genetic Programming: An ensemble-based initialization technique for Geometric Semantic Genetic Programming
author Rosenfeld, Liah
author_facet Rosenfeld, Liah
author_role author
dc.contributor.none.fl_str_mv Vanneschi, Leonardo
RUN
dc.contributor.author.fl_str_mv Rosenfeld, Liah
dc.subject.por.fl_str_mv Genetic Programming
Geometric Semantic Genetic Programming
Initialization methods
Ensemble methods
Meta-learning
Machine Learning
Evolutionary Algorithms
topic Genetic Programming
Geometric Semantic Genetic Programming
Initialization methods
Ensemble methods
Meta-learning
Machine Learning
Evolutionary Algorithms
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2022
dc.date.none.fl_str_mv 2022-06-27T13:50:46Z
2022-05-04
2022-05-04T00:00:00Z
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/10362/140850
TID:203028422
url http://hdl.handle.net/10362/140850
identifier_str_mv TID:203028422
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
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