GSGP-C++ 2.0

Detalhes bibliográficos
Autor(a) principal: Castelli, Mauro
Data de Publicação: 2019
Outros Autores: Manzoni, Luca
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.1016/j.softx.2019.100313
Resumo: Castelli, M., & Manzoni, L. (2019). GSGP-C++ 2.0: A geometric semantic genetic programming framework. SoftwareX, 10, [100313]. https://doi.org/10.1016/j.softx.2019.100313
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spelling GSGP-C++ 2.0A geometric semantic genetic programming frameworkGenetic programmingMachine learningSemanticsSoftwareComputer Science ApplicationsCastelli, M., & Manzoni, L. (2019). GSGP-C++ 2.0: A geometric semantic genetic programming framework. SoftwareX, 10, [100313]. https://doi.org/10.1016/j.softx.2019.100313Geometric semantic operators (GSOs) for Genetic Programming have been widely investigated in recent years, producing competitive results with respect to standard syntax based operator as well as other well-known machine learning techniques. The usage of GSOs has been facilitated by a C++ framework that implements these operators in a very efficient manner. This work presents a description of the system and focuses on a recently implemented feature that allows the user to store the information related to the best individual and to evaluate new data in a time that is linear with respect to the number of generations used to find the optimal individual. The paper presents the main features of the system and provides a step by step guide for interested users or developers.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNCastelli, MauroManzoni, Luca2019-09-17T22:48:16Z2019-07-012019-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article4application/pdfhttps://doi.org/10.1016/j.softx.2019.100313engPURE: 14716677http://www.scopus.com/inward/record.url?scp=85071718915&partnerID=8YFLogxKhttps://doi.org/10.1016/j.softx.2019.100313info: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:RCAAP2024-03-11T04:36:22Zoai:run.unl.pt:10362/81595Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:36:07.041866Repositório 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 GSGP-C++ 2.0
A geometric semantic genetic programming framework
title GSGP-C++ 2.0
spellingShingle GSGP-C++ 2.0
Castelli, Mauro
Genetic programming
Machine learning
Semantics
Software
Computer Science Applications
title_short GSGP-C++ 2.0
title_full GSGP-C++ 2.0
title_fullStr GSGP-C++ 2.0
title_full_unstemmed GSGP-C++ 2.0
title_sort GSGP-C++ 2.0
author Castelli, Mauro
author_facet Castelli, Mauro
Manzoni, Luca
author_role author
author2 Manzoni, Luca
author2_role author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Castelli, Mauro
Manzoni, Luca
dc.subject.por.fl_str_mv Genetic programming
Machine learning
Semantics
Software
Computer Science Applications
topic Genetic programming
Machine learning
Semantics
Software
Computer Science Applications
description Castelli, M., & Manzoni, L. (2019). GSGP-C++ 2.0: A geometric semantic genetic programming framework. SoftwareX, 10, [100313]. https://doi.org/10.1016/j.softx.2019.100313
publishDate 2019
dc.date.none.fl_str_mv 2019-09-17T22:48:16Z
2019-07-01
2019-07-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.1016/j.softx.2019.100313
url https://doi.org/10.1016/j.softx.2019.100313
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 14716677
http://www.scopus.com/inward/record.url?scp=85071718915&partnerID=8YFLogxK
https://doi.org/10.1016/j.softx.2019.100313
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