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On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers

2023-12Artigo científico Acesso aberto
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dc.contributor.authorPietropolli, Gloria
dc.contributor.authorManzoni, Luca
dc.contributor.authorPaoletti, Alessia
dc.contributor.authorCastelli, Mauro
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblSpringer Science Business Media
dc.date.accessioned2023-11-06T22:09:37Z
dc.date.available2023-11-06T22:09:37Z
dc.date.issued2023-12
dc.descriptionPietropolli, G., Manzoni, L., Paoletti, A., & Castelli, M. (2023). On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers. Genetic Programming And Evolvable Machines, 24(2 Special Issue on Highlights of Genetic Programming 2022 Events), 1-20. [16]. https://doi.org/10.21203/rs.3.rs-2229748/v1, https://doi.org/10.1007/s10710-023-09463-1---Open access funding provided by Università degli Studi di Trieste within the CRUI-CARE Agreement. This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the Project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS
dc.description.abstractGeometric semantic genetic programming (GSGP) is a popular form of GP where the effect of crossover and mutation can be expressed as geometric operations on a semantic space. A recent study showed that GSGP can be hybridized with a standard gradient-based optimized, Adam, commonly used in training artificial neural networks.We expand upon that work by considering more gradient-based optimizers, a deeper investigation of their parameters, how the hybridization is performed, and a more comprehensive set of benchmark problems. With the correct choice of hyperparameters, this hybridization improves the performances of GSGP and allows it to reach the same fitness values with fewer fitness evaluations.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent20
dc.format.extent2472160
dc.identifier.doi10.21203/rs.3.rs-2229748/v1
dc.identifier.issn1389-2576
dc.identifier.otherPURE: 72363135
dc.identifier.otherPURE UUID: 6383b255-30cf-4597-b621-c19471123afe
dc.identifier.otherScopus: 85175168117
dc.identifier.otherWOS: 001088866300001
dc.identifier.otherORCID: /0000-0002-8793-1451/work/151444375
dc.identifier.urihttp://hdl.handle.net/10362/159617
dc.identifier.urlhttps://www.scopus.com/pages/publications/85175168117
dc.identifier.urlhttps://github.com/gpietrop/GradientBasedGSGP
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001088866300001
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectAdam
dc.subjectEvolutionary algorithm
dc.subjectGeometric semantic genetic programming
dc.subjectStochastic gradient descent
dc.subjectSoftware
dc.subjectTheoretical Computer Science
dc.subjectHardware and Architecture
dc.subjectComputer Science Applications
dc.titleOn the Hybridization of Geometric Semantic GP with Gradient-based Optimizersen
dc.typejournal article
degois.publication.firstPage1
degois.publication.issue2 Special Issue on Highlights of Genetic Programming 2022 Events
degois.publication.lastPage20
degois.publication.titleGenetic Programming And Evolvable Machines
degois.publication.volume24
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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