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An Investigation of Geometric Semantic GP with Linear Scaling

2023-07-15Documento de conferência Acesso aberto
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dc.contributor.authorNadizar, Giorgia
dc.contributor.authorGarrow, Fraser
dc.contributor.authorSakallioglu, Berfin
dc.contributor.authorCanonne, Lorenzo
dc.contributor.authorSilva, Sara
dc.contributor.authorVanneschi, Leonardo
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.date.accessioned2023-09-22T22:20:43Z
dc.date.available2023-09-22T22:20:43Z
dc.date.issued2023-07-15
dc.descriptionNadizar, 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
dc.description.abstractGeometric 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.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent10
dc.format.extent1077496
dc.identifier.doi10.1145/3583131.3590418
dc.identifier.isbn979-8-4007-0119-1
dc.identifier.otherPURE: 66711554
dc.identifier.otherPURE UUID: 3740b278-576f-42f8-bf26-e998e9f63637
dc.identifier.othercrossref: 10.1145/3583131.3590418
dc.identifier.otherScopus: 85167697497
dc.identifier.otherWOS: 001031455100130
dc.identifier.otherORCID: /0000-0003-4732-3328/work/151426827
dc.identifier.urihttp://hdl.handle.net/10362/158170
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001031455100130
dc.identifier.urlhttps://www.scopus.com/pages/publications/85167697497
dc.identifier.urlhttps://dl.acm.org/doi/10.1145/3583131.3590418
dc.language.isoeng
dc.peerreviewedyes
dc.publisherACM - Association for Computing Machinery
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectSymbolic Regression
dc.subjectGeometric Semantic Genetic Programming
dc.subjectLinear Scaling
dc.subjectGenetic Programming
dc.subjectArtificial Intelligence
dc.subjectSoftware
dc.subjectTheoretical Computer Science
dc.titleAn Investigation of Geometric Semantic GP with Linear Scalingen
dc.typeconference object
degois.publication.firstPage1165
degois.publication.lastPage1174
degois.publication.titleGECCO’23
degois.publication.titleThe Genetic and Evolutionary Computation Conference (GECCO 2023)
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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