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A Survey on Batch Training in Genetic Programming

2025-06Recensão Acesso aberto
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dc.contributor.authorRosenfeld, Liah
dc.contributor.authorVanneschi, Leonardo
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.date.accessioned2024-12-02T22:58:31Z
dc.date.available2024-12-02T22:58:31Z
dc.date.issued2025-06
dc.descriptionRosenfeld, L., & Vanneschi, L. (2025). A Survey on Batch Training in Genetic Programming. Genetic Programming And Evolvable Machines, 26, 1-28. Article 2. https://doi.org/10.1007/s10710-024-09501-6 --- Open access funding provided by FCT|FCCN (b-on). 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 (https://doi.org/10.54499/UIDB/04152/2020).
dc.description.abstractIn Machine Learning (ML), the use of subsets of training data, referred to as batches, rather than the entire dataset, has been extensively researched to reduce computational costs, improve model efficiency, and enhance algorithm generalization. Despite extensive research, a clear definition and consensus on what constitutes batch training have yet to be reached, leading to a fragmented body of literature that could otherwise be seen as different facets of a unified methodology. To address this gap, we propose a theoretical redefinition of batch training, creating a clearer and broader overview that integrates diverse perspectives. We then apply this refined concepjavascript:void(0);t specifically to Genetic Programming (GP). Although batch training techniques have been explored in GP, the term itself is seldom used, resulting in ambiguity regarding its application in this area. This review seeks to clarify the existing literature on batch training by presenting a new and practical classification system, which we further explore within the specific context of GP. We also investigate the use of dynamic batch sizes in ML, emphasizing the relatively limited research on dynamic or adaptive batch sizes in GP compared to other ML algorithms. By bringing greater coherence to previously disjointed research efforts, we aim to foster further scientific exploration and development. Our work highlights key considerations for researchers designing batch training applications in GP and offers an in-depth discussion of future research directions, challenges, and opportunities for advancement.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent28
dc.identifier.doihttps://doi.org/10.1007/s10710-024-09501-6
dc.identifier.issn1389-2576
dc.identifier.otherPURE: 103016320
dc.identifier.otherPURE UUID: 946d4687-5b1e-45c6-bca8-243a8483cd84
dc.identifier.otherScopus: 85211687955
dc.identifier.otherWOS: 001366871900001
dc.identifier.urihttp://hdl.handle.net/10362/176146
dc.language.isoeng
dc.peerreviewedyes
dc.relationhttps://doi.org/10.54499/UIDB/04152/2020
dc.relationInformation Management Research Center
dc.subjectGenetic programming
dc.subjectBatch training
dc.subjectSampling methods
dc.subjectGeneralization
dc.subjectOverfitting
dc.subjectSoftware
dc.subjectTheoretical Computer Science
dc.subjectHardware and Architecture
dc.subjectComputer Science Applications
dc.titleA Survey on Batch Training in Genetic Programming
dc.typereview
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.citation.endPage28
oaire.citation.startPage1
oaire.citation.titleGenetic Programming And Evolvable Machines
oaire.citation.volume26
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
rcaap.typereview
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)