SLIM_GSGP

Bibliographic Details
Main Author: Vanneschi, Leonardo
Publication Date: 2024
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/172918
Summary: Vanneschi, L. (2024). SLIM_GSGP: The Non-bloating Geometric Semantic Genetic Programming. In M. Giacobini, B. Xue, & L. Manzoni (Eds.), Genetic Programming: 27th European Conference, EuroGP 2024, Held as Part of EvoStar 2024 Aberystwyth, UK, April 3–5, 2024 Proceedings (pp. 125-141). (Lecture Notes in Computer Science; Vol. 14631). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-56957-9_8 --- 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
id RCAP_532d3837c29df1c75434c7192b954b0e
oai_identifier_str oai:run.unl.pt:10362/172918
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 SLIM_GSGPThe Non-bloating Geometric Semantic Genetic ProgrammingGenetic ProgrammingGeometric Semantic Genetic ProgrammingInflate and Deflate MutationsModel InterpretabilityTheoretical Computer ScienceComputer Science(all)Vanneschi, L. (2024). SLIM_GSGP: The Non-bloating Geometric Semantic Genetic Programming. In M. Giacobini, B. Xue, & L. Manzoni (Eds.), Genetic Programming: 27th European Conference, EuroGP 2024, Held as Part of EvoStar 2024 Aberystwyth, UK, April 3–5, 2024 Proceedings (pp. 125-141). (Lecture Notes in Computer Science; Vol. 14631). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-56957-9_8 --- 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 IMSGeometric semantic genetic programming (GSGP) is a successful variant of genetic programming (GP), able to induce a unimodal error surface for all supervised learning problems. However, a limitation of GSGP is its tendency to generate offspring larger than their parents, resulting in continually growing program sizes. This leads to the creation of models that are often too complex for human comprehension. This paper presents a novel GSGP variant, the Semantic Learning algorithm with Inflate and deflate Mutations (SLIM_GSGP). SLIM_GSGP retains the essential theoretical characteristics of traditional GSGP, including the induction of a unimodal error surface and introduces a novel geometric semantic mutation, the deflate mutation, which generates smaller offspring than its parents. The study introduces four SLIM_GSGP variants and presents experimental results demonstrating that, across six symbolic regression test problems, SLIM_GSGP consistently evolves models with equal or superior performance on unseen data compared to traditional GSGP and standard GP. These SLIM_GSGP models are significantly smaller than those produced by traditional GSGP and are either smaller or of comparable size to standard GP models. Notably, the compactness of SLIM_GSGP models allows for human interpretation.Springer Nature Switzerland AGNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNVanneschi, Leonardo2025-03-29T01:32:49Z2024-03-282024-03-28T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion17application/pdfhttp://hdl.handle.net/10362/172918eng978-3-031-56956-20302-9743PURE: 87159507https://doi.org/10.1007/978-3-031-56957-9_8info: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:RCAAP2025-03-31T01:53:45Zoai:run.unl.pt:10362/172918Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T18:55:27.888682Repositórios 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 SLIM_GSGP
The Non-bloating Geometric Semantic Genetic Programming
title SLIM_GSGP
spellingShingle SLIM_GSGP
Vanneschi, Leonardo
Genetic Programming
Geometric Semantic Genetic Programming
Inflate and Deflate Mutations
Model Interpretability
Theoretical Computer Science
Computer Science(all)
title_short SLIM_GSGP
title_full SLIM_GSGP
title_fullStr SLIM_GSGP
title_full_unstemmed SLIM_GSGP
title_sort SLIM_GSGP
author Vanneschi, Leonardo
author_facet Vanneschi, Leonardo
author_role 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 Vanneschi, Leonardo
dc.subject.por.fl_str_mv Genetic Programming
Geometric Semantic Genetic Programming
Inflate and Deflate Mutations
Model Interpretability
Theoretical Computer Science
Computer Science(all)
topic Genetic Programming
Geometric Semantic Genetic Programming
Inflate and Deflate Mutations
Model Interpretability
Theoretical Computer Science
Computer Science(all)
description Vanneschi, L. (2024). SLIM_GSGP: The Non-bloating Geometric Semantic Genetic Programming. In M. Giacobini, B. Xue, & L. Manzoni (Eds.), Genetic Programming: 27th European Conference, EuroGP 2024, Held as Part of EvoStar 2024 Aberystwyth, UK, April 3–5, 2024 Proceedings (pp. 125-141). (Lecture Notes in Computer Science; Vol. 14631). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-56957-9_8 --- 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
publishDate 2024
dc.date.none.fl_str_mv 2024-03-28
2024-03-28T00:00:00Z
2025-03-29T01:32:49Z
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/10362/172918
url http://hdl.handle.net/10362/172918
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-3-031-56956-2
0302-9743
PURE: 87159507
https://doi.org/10.1007/978-3-031-56957-9_8
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 17
application/pdf
dc.publisher.none.fl_str_mv Springer Nature Switzerland AG
publisher.none.fl_str_mv Springer Nature Switzerland AG
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_ 1833597757309321216

Similar Items