Non-geometric pulse: An adaptive geometricity approach for Genetic Algorithms

Detalhes bibliográficos
Autor(a) principal: Ferreira, José Pedro Mendes Ribeiro do Vale
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/10362/145483
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Non-geometric pulse: An adaptive geometricity approach for Genetic AlgorithmsConvex SearchEvolutionary AlgorithmsGenetic AlgorithmsGeometric semantic operatorsDiversity maintenanceDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceEvolutionary algorithms (EAs) are a family of algorithms inspired by the Darwinian theory of evolution. Mathematics, particularly geometry and topology, allows for the possibility of developing a general geometrical framework and consequently generating deeper insights that may be shared among the different EAs. Genetic Algorithm (GA), inspired by Darwin’s theory of natural selection, is a popular algorithm among EAs. This is a population-based, fitness-oriented algorithm that performs a convex heuristic search to optimize a plethora of problems. Common limitations of GA as well as other EAs have geometrical origins like premature convergence, where the final population’s convex-hull might not include the best solution, called Global Optima. Population diversity maintenance is a key idea that tries to tackle this problem but is often performed through geometrical methods that constantly diminish the search space’s area. In this work, a self-adaptive geometricity approach will be presented. In particular, the non-geometric crossover is strategically employed in a symbiotic relation with geometric crossover, maintaining diversity in a logical way from a geometric/topological grammar standpoint. A comparison with well-known diversity maintenance methods is provided, using common benchmarks that serve as general testing ground for the considered techniques.Castelli, MauroManzoni, LucaRUNFerreira, José Pedro Mendes Ribeiro do Vale2023-10-24T00:31:26Z2022-10-242022-10-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/145483TID:203105702enginfo: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:RCAAP2024-03-11T05:25:54Zoai:run.unl.pt:10362/145483Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:06.206048Repositório 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 Non-geometric pulse: An adaptive geometricity approach for Genetic Algorithms
title Non-geometric pulse: An adaptive geometricity approach for Genetic Algorithms
spellingShingle Non-geometric pulse: An adaptive geometricity approach for Genetic Algorithms
Ferreira, José Pedro Mendes Ribeiro do Vale
Convex Search
Evolutionary Algorithms
Genetic Algorithms
Geometric semantic operators
Diversity maintenance
title_short Non-geometric pulse: An adaptive geometricity approach for Genetic Algorithms
title_full Non-geometric pulse: An adaptive geometricity approach for Genetic Algorithms
title_fullStr Non-geometric pulse: An adaptive geometricity approach for Genetic Algorithms
title_full_unstemmed Non-geometric pulse: An adaptive geometricity approach for Genetic Algorithms
title_sort Non-geometric pulse: An adaptive geometricity approach for Genetic Algorithms
author Ferreira, José Pedro Mendes Ribeiro do Vale
author_facet Ferreira, José Pedro Mendes Ribeiro do Vale
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
Manzoni, Luca
RUN
dc.contributor.author.fl_str_mv Ferreira, José Pedro Mendes Ribeiro do Vale
dc.subject.por.fl_str_mv Convex Search
Evolutionary Algorithms
Genetic Algorithms
Geometric semantic operators
Diversity maintenance
topic Convex Search
Evolutionary Algorithms
Genetic Algorithms
Geometric semantic operators
Diversity maintenance
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2022
dc.date.none.fl_str_mv 2022-10-24
2022-10-24T00:00:00Z
2023-10-24T00:31:26Z
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/10362/145483
TID:203105702
url http://hdl.handle.net/10362/145483
identifier_str_mv TID:203105702
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
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dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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repository.mail.fl_str_mv
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