An Investigation of Geometric Semantic GP with Linear Scaling

Bibliographic Details
Main Author: Nadizar, Giorgia
Publication Date: 2023
Other Authors: Garrow, Fraser, Sakallioglu, Berfin, Canonne, Lorenzo, Silva, Sara, Vanneschi, Leonardo
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/158170
Summary: Nadizar, G., Garrow, F., Sakallioglu, B., Canonne, L., Silva, S., & Vanneschi, L. (2023). An Investigation of Geometric Semantic GP with Linear Scaling. In GECCO’23: Proceedings of the 2023 Genetic and Evolutionary Computation Conference (pp. 1165-1174). Association for Computing Machinery (ACM). https://doi.org/10.1145/3583131.3590418 --- Funding: This work was partially supported by FCT, Portugal, through funding of research units MagIC/NOVA IMS (UIDB/04152/2020) and LASIGE (UIDB/00408/2020 and UIDP/00408/2020). We also wish to thank the SPECIES Society and Anna Esparcia-Alcázar for organizing the SPECIES Summer School 2022, which brought us together and gave us the chance to start this collaboration
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spelling An Investigation of Geometric Semantic GP with Linear ScalingSymbolic RegressionGeometric Semantic Genetic ProgrammingLinear ScalingGenetic ProgrammingArtificial IntelligenceSoftwareTheoretical Computer ScienceNadizar, G., Garrow, F., Sakallioglu, B., Canonne, L., Silva, S., & Vanneschi, L. (2023). An Investigation of Geometric Semantic GP with Linear Scaling. In GECCO’23: Proceedings of the 2023 Genetic and Evolutionary Computation Conference (pp. 1165-1174). Association for Computing Machinery (ACM). https://doi.org/10.1145/3583131.3590418 --- Funding: This work was partially supported by FCT, Portugal, through funding of research units MagIC/NOVA IMS (UIDB/04152/2020) and LASIGE (UIDB/00408/2020 and UIDP/00408/2020). We also wish to thank the SPECIES Society and Anna Esparcia-Alcázar for organizing the SPECIES Summer School 2022, which brought us together and gave us the chance to start this collaborationGeometric semantic genetic programming (GSGP) and linear scaling (LS) have both, independently, shown the ability to outperform standard genetic programming (GP) for symbolic regression. GSGP uses geometric semantic genetic operators, different from the standard ones, without altering the fitness, while LS modifies the fitness without altering the genetic operators. So far, these two methods have already been joined together in only one practical application. However, to the best of our knowledge, a methodological study on the pros and cons of integrating these two methods has never been performed. In this paper, we present a study of GSGP-LS, a system that integrates GSGP and LS. The results, obtained on five hand-tailored benchmarks and six real-life problems, indicate that GSGP-LS outperforms GSGP in the majority of the cases, confirming the expected benefit of this integration. However, for some particularly hard datasets, GSGP-LS overfits training data, being outperformed by GSGP on unseen data. Additional experiments using standard GP, with and without LS, confirm this trend also when standard crossover and mutation are employed. This contradicts the idea that LS is always beneficial for GP, warning the practitioners about its risk of overfitting in some specific cases.ACM - Association for Computing MachineryNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNNadizar, GiorgiaGarrow, FraserSakallioglu, BerfinCanonne, LorenzoSilva, SaraVanneschi, Leonardo2023-09-22T22:20:43Z2023-07-152023-07-15T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion10application/pdfhttp://hdl.handle.net/10362/158170eng979-8-4007-0119-1PURE: 66711554https://doi.org/10.1145/3583131.3590418info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:14:36Zoai:run.unl.pt:10362/158170Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:45:09.082488Repositórios 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 An Investigation of Geometric Semantic GP with Linear Scaling
title An Investigation of Geometric Semantic GP with Linear Scaling
spellingShingle An Investigation of Geometric Semantic GP with Linear Scaling
Nadizar, Giorgia
Symbolic Regression
Geometric Semantic Genetic Programming
Linear Scaling
Genetic Programming
Artificial Intelligence
Software
Theoretical Computer Science
title_short An Investigation of Geometric Semantic GP with Linear Scaling
title_full An Investigation of Geometric Semantic GP with Linear Scaling
title_fullStr An Investigation of Geometric Semantic GP with Linear Scaling
title_full_unstemmed An Investigation of Geometric Semantic GP with Linear Scaling
title_sort An Investigation of Geometric Semantic GP with Linear Scaling
author Nadizar, Giorgia
author_facet Nadizar, Giorgia
Garrow, Fraser
Sakallioglu, Berfin
Canonne, Lorenzo
Silva, Sara
Vanneschi, Leonardo
author_role author
author2 Garrow, Fraser
Sakallioglu, Berfin
Canonne, Lorenzo
Silva, Sara
Vanneschi, Leonardo
author2_role author
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 Nadizar, Giorgia
Garrow, Fraser
Sakallioglu, Berfin
Canonne, Lorenzo
Silva, Sara
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Symbolic Regression
Geometric Semantic Genetic Programming
Linear Scaling
Genetic Programming
Artificial Intelligence
Software
Theoretical Computer Science
topic Symbolic Regression
Geometric Semantic Genetic Programming
Linear Scaling
Genetic Programming
Artificial Intelligence
Software
Theoretical Computer Science
description Nadizar, G., Garrow, F., Sakallioglu, B., Canonne, L., Silva, S., & Vanneschi, L. (2023). An Investigation of Geometric Semantic GP with Linear Scaling. In GECCO’23: Proceedings of the 2023 Genetic and Evolutionary Computation Conference (pp. 1165-1174). Association for Computing Machinery (ACM). https://doi.org/10.1145/3583131.3590418 --- Funding: This work was partially supported by FCT, Portugal, through funding of research units MagIC/NOVA IMS (UIDB/04152/2020) and LASIGE (UIDB/00408/2020 and UIDP/00408/2020). We also wish to thank the SPECIES Society and Anna Esparcia-Alcázar for organizing the SPECIES Summer School 2022, which brought us together and gave us the chance to start this collaboration
publishDate 2023
dc.date.none.fl_str_mv 2023-09-22T22:20:43Z
2023-07-15
2023-07-15T00:00:00Z
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/10362/158170
url http://hdl.handle.net/10362/158170
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 979-8-4007-0119-1
PURE: 66711554
https://doi.org/10.1145/3583131.3590418
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 10
application/pdf
dc.publisher.none.fl_str_mv ACM - Association for Computing Machinery
publisher.none.fl_str_mv ACM - Association for Computing Machinery
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
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
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
repository.mail.fl_str_mv info@rcaap.pt
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