Full Inclusive Genetic Programming

Detalhes bibliográficos
Autor(a) principal: Marchetti, Francesco
Data de Publicação: 2024
Outros Autores: Castelli, Mauro, Bakurov, Illya, Vanneschi, Leonardo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/172917
Resumo: Marchetti, F., Castelli, M., Bakurov, I., & Vanneschi, L. (2024). Full Inclusive Genetic Programming. In 2024 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC60901.2024.10611808 --- This work was partially supported by FCT, Portugal, through funding of research unit MagIC/NOVA IMS (UIDB/04152/2020); and by the SPECIES Society through the SPECIES Scholarship 2022.
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spelling Full Inclusive Genetic ProgrammingGenetic ProgrammingPopulation’s DiversitySymbolic RegressionPMLB BenchmarksPopulation InitializationArtificial IntelligenceComputer Science ApplicationsComputer Vision and Pattern RecognitionComputational MathematicsControl and OptimizationMarchetti, F., Castelli, M., Bakurov, I., & Vanneschi, L. (2024). Full Inclusive Genetic Programming. In 2024 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC60901.2024.10611808 --- This work was partially supported by FCT, Portugal, through funding of research unit MagIC/NOVA IMS (UIDB/04152/2020); and by the SPECIES Society through the SPECIES Scholarship 2022.This manuscript presents an improved version of the Inclusive Genetic Programming (IGP) algorithm. The IGP was developed to promote and maintain the population's genotypic diversity and showed superior performance compared to standard Genetic Programming (GP). In this work, two modifications to the IGP are proposed: first, the diversity promotion and maintenance mechanism is enhanced with information from the phenotype of the individuals rather than only the genotype; second, the Evolutionary Demes Despeciation Algorithm - V2 (EDDA-V2) is used to initialize the population. The phenotype is considered to differentiate the individuals also according to their behaviour rather than only their structure, while EDDA-V2 is employed to start the evolution with a simultaneously diverse and fit population, contrary to traditional initialization techniques. The algorithms incorporating these improvements are called Full Inclusive Genetic Programming (FIGP) and FIGP _E, respectively with and without the EDDA-V2 initialization. The experimental results, performed over eight benchmarks and considering six algorithms, demonstrate the superior performance of FIGP and FIGP _E in comparison to other GP formulations. Moreover, the EDDA-V2 initialization allows for a significant reduction of the computational time.Institute of Electrical and Electronics Engineers (IEEE)Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNMarchetti, FrancescoCastelli, MauroBakurov, IllyaVanneschi, Leonardo20242026-08-08T00:00:00Z2024-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion8application/pdfhttp://hdl.handle.net/10362/172917eng979-8-3503-0837-2PURE: 85913472https://doi.org/10.1109/CEC60901.2024.10611808info:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-10-07T01:41:13Zoai:run.unl.pt:10362/172917Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T18:55:27.841067Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Full Inclusive Genetic Programming
title Full Inclusive Genetic Programming
spellingShingle Full Inclusive Genetic Programming
Marchetti, Francesco
Genetic Programming
Population’s Diversity
Symbolic Regression
PMLB Benchmarks
Population Initialization
Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Computational Mathematics
Control and Optimization
title_short Full Inclusive Genetic Programming
title_full Full Inclusive Genetic Programming
title_fullStr Full Inclusive Genetic Programming
title_full_unstemmed Full Inclusive Genetic Programming
title_sort Full Inclusive Genetic Programming
author Marchetti, Francesco
author_facet Marchetti, Francesco
Castelli, Mauro
Bakurov, Illya
Vanneschi, Leonardo
author_role author
author2 Castelli, Mauro
Bakurov, Illya
Vanneschi, Leonardo
author2_role author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Marchetti, Francesco
Castelli, Mauro
Bakurov, Illya
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Genetic Programming
Population’s Diversity
Symbolic Regression
PMLB Benchmarks
Population Initialization
Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Computational Mathematics
Control and Optimization
topic Genetic Programming
Population’s Diversity
Symbolic Regression
PMLB Benchmarks
Population Initialization
Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Computational Mathematics
Control and Optimization
description Marchetti, F., Castelli, M., Bakurov, I., & Vanneschi, L. (2024). Full Inclusive Genetic Programming. In 2024 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC60901.2024.10611808 --- This work was partially supported by FCT, Portugal, through funding of research unit MagIC/NOVA IMS (UIDB/04152/2020); and by the SPECIES Society through the SPECIES Scholarship 2022.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-01T00:00:00Z
2026-08-08T00:00:00Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/172917
url http://hdl.handle.net/10362/172917
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 979-8-3503-0837-2
PURE: 85913472
https://doi.org/10.1109/CEC60901.2024.10611808
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv 8
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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