Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP

Bibliographic Details
Main Author: Farinati, Davide
Publication Date: 2025
Other Authors: Pietropolli, Gloria, Vanneschi, Leonardo
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/182852
Summary: Farinati, D., Pietropolli, G., & Vanneschi, L. (2025). Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP. In B. Xue, L. Manzoni, & I. Bakurov (Eds.), Genetic Programming: 28th European Conference, EuroGP 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23–25, 2025, Proceedings (pp. 35-51). (Lecture Notes in Computer Science; Vol. 15609). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-89991-1_3 --- 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).
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spelling Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGPGenetic ProgrammingGeometric Semantic Genetic ProgrammingGeometric MutationMutation StepSymbolic RegressionTheoretical Computer ScienceComputer Science(all)Farinati, D., Pietropolli, G., & Vanneschi, L. (2025). Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP. In B. Xue, L. Manzoni, & I. Bakurov (Eds.), Genetic Programming: 28th European Conference, EuroGP 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23–25, 2025, Proceedings (pp. 35-51). (Lecture Notes in Computer Science; Vol. 15609). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-89991-1_3 --- 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).The Semantic Learning algorithm based on Inflate and deflate Mutation (SLIM) is a promising recent variant of Geometric Semantic Genetic Programming (GSGP) that introduces a new Deflate Geometric Semantic Mutation (DGSM). This operator maintains the key feature of the standard Geometric Semantic Mutation (GSM), inducing a unimodal error surface for any supervised learning problem, while generating smaller offspring than their parents, and thus allowing SLIM to generate compact, and potentially interpretable, final solutions. A key parameter controlling the evolution process in both GSGP and SLIM is the Mutation Step (MS), which regulates the extent of perturbation to the parent semantics. While it is intuitive that the optimal value of MS has a relationship with the scale of the dataset features, to the best of our knowledge no prior research has extensively explored this relationship. In this work, we provide the first comprehensive investigation into this topic. First, we hypothesize a general rule by analyzing results from artificial datasets, and then we confirm these findings with more complex, real-world datasets. This approach offers a solid alternative to the typical hyperparameter tuning approach.Springer Nature Switzerland AGNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNFarinati, DavidePietropolli, GloriaVanneschi, Leonardo2025-04-222026-04-18T00:00:00Z2025-04-22T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion17application/pdfhttp://hdl.handle.net/10362/182852eng978-3-031-89990-40302-9743PURE: 115881088https://doi.org/10.1007/978-3-031-89991-1_3info:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-05-19T01:40:13Zoai:run.unl.pt:10362/182852Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T07:14:12.809319Repositórios 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 Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP
title Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP
spellingShingle Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP
Farinati, Davide
Genetic Programming
Geometric Semantic Genetic Programming
Geometric Mutation
Mutation Step
Symbolic Regression
Theoretical Computer Science
Computer Science(all)
title_short Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP
title_full Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP
title_fullStr Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP
title_full_unstemmed Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP
title_sort Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP
author Farinati, Davide
author_facet Farinati, Davide
Pietropolli, Gloria
Vanneschi, Leonardo
author_role author
author2 Pietropolli, Gloria
Vanneschi, Leonardo
author2_role author
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 Farinati, Davide
Pietropolli, Gloria
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Genetic Programming
Geometric Semantic Genetic Programming
Geometric Mutation
Mutation Step
Symbolic Regression
Theoretical Computer Science
Computer Science(all)
topic Genetic Programming
Geometric Semantic Genetic Programming
Geometric Mutation
Mutation Step
Symbolic Regression
Theoretical Computer Science
Computer Science(all)
description Farinati, D., Pietropolli, G., & Vanneschi, L. (2025). Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP. In B. Xue, L. Manzoni, & I. Bakurov (Eds.), Genetic Programming: 28th European Conference, EuroGP 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23–25, 2025, Proceedings (pp. 35-51). (Lecture Notes in Computer Science; Vol. 15609). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-89991-1_3 --- 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 2025
dc.date.none.fl_str_mv 2025-04-22
2025-04-22T00:00:00Z
2026-04-18T00:00:00Z
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/10362/182852
url http://hdl.handle.net/10362/182852
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-3-031-89990-4
0302-9743
PURE: 115881088
https://doi.org/10.1007/978-3-031-89991-1_3
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
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
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