GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming

Detalhes bibliográficos
Autor(a) principal: Trujillo, Leonardo
Data de Publicação: 2022
Outros Autores: Muñoz Contreras, Jose Manuel, Hernandez, Daniel E., Castelli, Mauro, Tapia, Juan J.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/138207
Resumo: Trujillo, L., Muñoz Contreras, J. M., Hernandez, D. E., Castelli, M., & Tapia, J. J. (2022). GSGP-CUDA — A CUDA framework for Geometric Semantic Genetic Programming. SoftwareX, 18, 1-7. [101085]. https://doi.org/10.1016/j.softx.2022.101085 -------------------------------Funding Information: We thank Perla Juárez-Smith for her help implementing some of the source code used in this software. We also thank the Tecnológico Nacional de México/IT de Tijuana and CITEDI-IPN for their administrative and technical assistance in the development of this work. This research was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), Portugal by the projects GADgET ( DSAIPA/DS/0022/2018 ) and AICE ( DSAIPA/DS/0113/2019 ). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding No. P5-0410 ). Funding for this work was also provided by TecNM (Mexico) 2020 through the research project “Resolución de múltiples problemas de aprendizaje supervisado de manera simultánea con programación genética”.
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spelling GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic ProgrammingGenetic ProgrammingGeometric Semantic Genetic ProgrammingCUDAGPUSoftwareComputer Science ApplicationsTrujillo, L., Muñoz Contreras, J. M., Hernandez, D. E., Castelli, M., & Tapia, J. J. (2022). GSGP-CUDA — A CUDA framework for Geometric Semantic Genetic Programming. SoftwareX, 18, 1-7. [101085]. https://doi.org/10.1016/j.softx.2022.101085 -------------------------------Funding Information: We thank Perla Juárez-Smith for her help implementing some of the source code used in this software. We also thank the Tecnológico Nacional de México/IT de Tijuana and CITEDI-IPN for their administrative and technical assistance in the development of this work. This research was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), Portugal by the projects GADgET ( DSAIPA/DS/0022/2018 ) and AICE ( DSAIPA/DS/0113/2019 ). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding No. P5-0410 ). Funding for this work was also provided by TecNM (Mexico) 2020 through the research project “Resolución de múltiples problemas de aprendizaje supervisado de manera simultánea con programación genética”.Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently than operating at the syntax level like most GP systems. Efficient implementations of GSGP in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP and the most efficient, which exploits the intrinsic parallelism of GSGP using GPUs. Results show speedups greater than 1, 000× relative to the state-of-the-art sequential implementation, during the model training process. Additionally, our implementation allows the user to seamlessly make inferences over new data through the best evolved model, opening the possibility of using GSGP on Big Data problems.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNTrujillo, LeonardoMuñoz Contreras, Jose ManuelHernandez, Daniel E.Castelli, MauroTapia, Juan J.2022-05-18T22:47:45Z2022-06-012022-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article7application/pdfhttp://hdl.handle.net/10362/138207engPURE: 43986256https://doi.org/10.1016/j.softx.2022.101085info: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:RCAAP2024-03-11T05:15:32Zoai:run.unl.pt:10362/138207Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:49:01.855948Repositório 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-CUDA: A CUDA framework for Geometric Semantic Genetic Programming
title GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming
spellingShingle GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming
Trujillo, Leonardo
Genetic Programming
Geometric Semantic Genetic Programming
CUDA
GPU
Software
Computer Science Applications
title_short GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming
title_full GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming
title_fullStr GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming
title_full_unstemmed GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming
title_sort GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming
author Trujillo, Leonardo
author_facet Trujillo, Leonardo
Muñoz Contreras, Jose Manuel
Hernandez, Daniel E.
Castelli, Mauro
Tapia, Juan J.
author_role author
author2 Muñoz Contreras, Jose Manuel
Hernandez, Daniel E.
Castelli, Mauro
Tapia, Juan J.
author2_role author
author
author
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 Trujillo, Leonardo
Muñoz Contreras, Jose Manuel
Hernandez, Daniel E.
Castelli, Mauro
Tapia, Juan J.
dc.subject.por.fl_str_mv Genetic Programming
Geometric Semantic Genetic Programming
CUDA
GPU
Software
Computer Science Applications
topic Genetic Programming
Geometric Semantic Genetic Programming
CUDA
GPU
Software
Computer Science Applications
description Trujillo, L., Muñoz Contreras, J. M., Hernandez, D. E., Castelli, M., & Tapia, J. J. (2022). GSGP-CUDA — A CUDA framework for Geometric Semantic Genetic Programming. SoftwareX, 18, 1-7. [101085]. https://doi.org/10.1016/j.softx.2022.101085 -------------------------------Funding Information: We thank Perla Juárez-Smith for her help implementing some of the source code used in this software. We also thank the Tecnológico Nacional de México/IT de Tijuana and CITEDI-IPN for their administrative and technical assistance in the development of this work. This research was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), Portugal by the projects GADgET ( DSAIPA/DS/0022/2018 ) and AICE ( DSAIPA/DS/0113/2019 ). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding No. P5-0410 ). Funding for this work was also provided by TecNM (Mexico) 2020 through the research project “Resolución de múltiples problemas de aprendizaje supervisado de manera simultánea con programación genética”.
publishDate 2022
dc.date.none.fl_str_mv 2022-05-18T22:47:45Z
2022-06-01
2022-06-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/138207
url http://hdl.handle.net/10362/138207
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 43986256
https://doi.org/10.1016/j.softx.2022.101085
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 7
application/pdf
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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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