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Geometric semantic GP with linear scaling

Bibliographic Details
Main Author: Nadizar, Giorgia
Publication Date: 2024
Other Authors: Sakallioglu, Berfin, Garrow, Fraser, Silva, Sara, Vanneschi, Leonardo
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/168307
Summary: Nadizar, G., Sakallioglu, B., Garrow, F., Silva, S., & Vanneschi, L. (2024). Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolution. Genetic Programming And Evolvable Machines, 25(2), 1-24. Article 17. https://doi.org/10.1007/s10710-024-09488-0 --- Open access funding provided by Università degli Studi di Trieste within the CRUI-CARE Agreement. This work was partially supported by FCT, Portugal, through funding of research units MagIC/NOVA IMS (UIDB/04152/2020) and LASIGE (UIDB/00408/2020 and UIDP/00408/2020).
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spelling Geometric semantic GP with linear scalingDarwinian versus Lamarckian evolutionSymbolic regressionGeometric semantic genetic programmingLinear scalingLamarckian evolutionGenetic programmingSoftwareTheoretical Computer ScienceHardware and ArchitectureComputer Science ApplicationsNadizar, G., Sakallioglu, B., Garrow, F., Silva, S., & Vanneschi, L. (2024). Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolution. Genetic Programming And Evolvable Machines, 25(2), 1-24. Article 17. https://doi.org/10.1007/s10710-024-09488-0 --- Open access funding provided by Università degli Studi di Trieste within the CRUI-CARE Agreement. This work was partially supported by FCT, Portugal, through funding of research units MagIC/NOVA IMS (UIDB/04152/2020) and LASIGE (UIDB/00408/2020 and UIDP/00408/2020).Geometric Semantic Genetic Programming (GSGP) has shown notable success in symbolic regression with the introduction of Linear Scaling (LS). This achievement stems from the synergy of the geometric semantic genetic operators of GSGP with the scaling of the individuals for computing their fitness, which favours programs with a promising behaviour. However, the initial combination of GSGP and LS (GSGP-LS) underutilised the potential of LS, scaling individuals only for fitness evaluation, neglecting to incorporate improvements into their genetic material. In this paper we propose an advancement, GSGP with Lamarckian LS (GSGP-LLS), wherein we update the individuals in the population with their scaling coefficients in a Lamarckian fashion, i.e., by inheritance of acquired traits. We assess GSGP-LS and GSGP-LLS against standard GSGP for the task of symbolic regression on five hand-tailored benchmarks and six real-life problems. On the former ones, GSGP-LS and GSGP-LLS both consistently improve GSGP, though with no clear global superiority between them. On the real-world problems, instead, GSGP-LLS steadily outperforms GSGP-LS, achieving faster convergence and superior final performance. Notably, even in cases where LS induces overfitting on challenging problems, GSGP-LLS surpasses GSGP-LS, due to its slower and more localised optimisation steps.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNNadizar, GiorgiaSakallioglu, BerfinGarrow, FraserSilva, SaraVanneschi, Leonardo2024-06-06T00:57:37Z2024-122024-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article24application/pdfhttp://hdl.handle.net/10362/168307eng1389-2576PURE: 92769244https://doi.org/10.1007/s10710-024-09488-0info: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:RCAAP2024-12-02T01:35:19Zoai:run.unl.pt:10362/168307Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:55:10.505143Repositórios 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 Geometric semantic GP with linear scaling
Darwinian versus Lamarckian evolution
title Geometric semantic GP with linear scaling
spellingShingle Geometric semantic GP with linear scaling
Nadizar, Giorgia
Symbolic regression
Geometric semantic genetic programming
Linear scaling
Lamarckian evolution
Genetic programming
Software
Theoretical Computer Science
Hardware and Architecture
Computer Science Applications
title_short Geometric semantic GP with linear scaling
title_full Geometric semantic GP with linear scaling
title_fullStr Geometric semantic GP with linear scaling
title_full_unstemmed Geometric semantic GP with linear scaling
title_sort Geometric semantic GP with linear scaling
author Nadizar, Giorgia
author_facet Nadizar, Giorgia
Sakallioglu, Berfin
Garrow, Fraser
Silva, Sara
Vanneschi, Leonardo
author_role author
author2 Sakallioglu, Berfin
Garrow, Fraser
Silva, Sara
Vanneschi, Leonardo
author2_role author
author
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 Nadizar, Giorgia
Sakallioglu, Berfin
Garrow, Fraser
Silva, Sara
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Symbolic regression
Geometric semantic genetic programming
Linear scaling
Lamarckian evolution
Genetic programming
Software
Theoretical Computer Science
Hardware and Architecture
Computer Science Applications
topic Symbolic regression
Geometric semantic genetic programming
Linear scaling
Lamarckian evolution
Genetic programming
Software
Theoretical Computer Science
Hardware and Architecture
Computer Science Applications
description Nadizar, G., Sakallioglu, B., Garrow, F., Silva, S., & Vanneschi, L. (2024). Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolution. Genetic Programming And Evolvable Machines, 25(2), 1-24. Article 17. https://doi.org/10.1007/s10710-024-09488-0 --- Open access funding provided by Università degli Studi di Trieste within the CRUI-CARE Agreement. This work was partially supported by FCT, Portugal, through funding of research units MagIC/NOVA IMS (UIDB/04152/2020) and LASIGE (UIDB/00408/2020 and UIDP/00408/2020).
publishDate 2024
dc.date.none.fl_str_mv 2024-06-06T00:57:37Z
2024-12
2024-12-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/168307
url http://hdl.handle.net/10362/168307
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1389-2576
PURE: 92769244
https://doi.org/10.1007/s10710-024-09488-0
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 24
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
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