A Survey on Batch Training in Genetic Programming

Detalhes bibliográficos
Autor(a) principal: Rosenfeld, Liah
Data de Publicação: 2024
Outros Autores: Vanneschi, Leonardo
Tipo de documento: Outros
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/176146
Resumo: Rosenfeld, 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).
id RCAP_5c87103f94d9ae3e7492814829a10001
oai_identifier_str oai:run.unl.pt:10362/176146
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling A Survey on Batch Training in Genetic ProgrammingGenetic programmingBatch trainingSampling methodsGeneralizationOverfittingSoftwareTheoretical Computer ScienceHardware and ArchitectureComputer Science ApplicationsRosenfeld, 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).In 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.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNRosenfeld, LiahVanneschi, Leonardo2024-12-02T22:58:31Z2025-062025-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/other28application/pdfhttp://hdl.handle.net/10362/176146eng1389-2576PURE: 103016320https://doi.org/10.1007/s10710-024-09501-6info: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:RCAAP2024-12-23T01:37:22Zoai:run.unl.pt:10362/176146Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:17:47.952159Repositórios 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 A Survey on Batch Training in Genetic Programming
title A Survey on Batch Training in Genetic Programming
spellingShingle A Survey on Batch Training in Genetic Programming
Rosenfeld, Liah
Genetic programming
Batch training
Sampling methods
Generalization
Overfitting
Software
Theoretical Computer Science
Hardware and Architecture
Computer Science Applications
title_short A Survey on Batch Training in Genetic Programming
title_full A Survey on Batch Training in Genetic Programming
title_fullStr A Survey on Batch Training in Genetic Programming
title_full_unstemmed A Survey on Batch Training in Genetic Programming
title_sort A Survey on Batch Training in Genetic Programming
author Rosenfeld, Liah
author_facet Rosenfeld, Liah
Vanneschi, Leonardo
author_role author
author2 Vanneschi, Leonardo
author2_role 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 Rosenfeld, Liah
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Genetic programming
Batch training
Sampling methods
Generalization
Overfitting
Software
Theoretical Computer Science
Hardware and Architecture
Computer Science Applications
topic Genetic programming
Batch training
Sampling methods
Generalization
Overfitting
Software
Theoretical Computer Science
Hardware and Architecture
Computer Science Applications
description Rosenfeld, 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).
publishDate 2024
dc.date.none.fl_str_mv 2024-12-02T22:58:31Z
2025-06
2025-06-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/other
format other
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/176146
url http://hdl.handle.net/10362/176146
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1389-2576
PURE: 103016320
https://doi.org/10.1007/s10710-024-09501-6
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 28
application/pdf
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
_version_ 1833597998327660544

Registros relacionados