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Local search, semantics, and genetic programming

2026-03Artigo científico Acesso aberto
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dc.contributor.authorAnselmi, Fabio
dc.contributor.authorCastelli, Mauro
dc.contributor.authord'Onofrio, Alberto
dc.contributor.authorManzoni, Luca
dc.contributor.authorMariot, Luca
dc.contributor.authorSaletta, Martina
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblSpringer Science Business Media
dc.date.accessioned2026-02-02T15:43:01Z
dc.date.available2026-02-02T15:43:01Z
dc.date.issued2026-03
dc.descriptionAnselmi, F., Castelli, M., d'Onofrio, A., Manzoni, L., Mariot, L., & Saletta, M. (2026). Local search, semantics, and genetic programming: a global analysis. Soft Computing, 30, 1541-1559. https://doi.org/10.1007/s00500-025-11051-7
dc.description.abstractGeometric Semantic Genetic Programming (GSGP) is a powerful variant of Genetic Programming (GP) that defines genetic operators inducing unimodal fitness landscapes. In recent years, a new mutation operator, Geometric Semantic Mutation with Local Search (GSM-LS), has been proposed to include a local search step in the mutation process. The core idea of GSM-LS is to incorporate a linear regression step during mutation, thereby accelerating convergence toward high-quality solutions. While GSM-LS helps the convergence of the evolutionary search, it is prone to overfitting. Thus, it was suggested to apply GSM-LS only for a limited number of generations and then revert to standard geometric semantic mutation. A more recently defined variant of GSGP (called GSGP-reg) also includes a local search step, but shares similar strengths and weaknesses with GSM-LS. Here, we investigate several strategies to mitigate overfitting in GSM-LS and GSGP-reg, ranging from simple regularized regression techniques to adaptive methods that estimate overfitting risk at each mutation. The latter approaches partition the training set into two subsets: one used to perform the mutation, and the other to evaluate the risk of overfitting based on the mutation's impact on held-out data. Experimental evaluations across seven real-world regression benchmarks show that, while plain GSGP underperforms on all datasets, methods incorporating local search often achieve significantly better test performance. For example, on the Airfoil dataset, the GSM-LS variant achieves a median RMSE below 10 compared to 30 with standard GSGP. On the LD50 and Bioavailability datasets, the proposed gen and ridge-regularized variants effectively mitigate overfitting, reducing test RMSE by up to 40% relative to baseline GSGP. We conclude that local search, when used with regularization strategies, enhances GSGP's performance and generalization capability across a diverse range of tasks.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent19
dc.format.extent2961617
dc.identifier.doi10.1007/s00500-025-11051-7
dc.identifier.issn1432-7643
dc.identifier.otherPURE: 147784310
dc.identifier.otherPURE UUID: dd22ef98-8159-42ee-8d6b-a12b9f833f08
dc.identifier.otherScopus: 105028178860
dc.identifier.otherORCID: /0000-0002-8793-1451/work/204497633
dc.identifier.urihttp://hdl.handle.net/10362/199931
dc.identifier.urlhttps://www.scopus.com/pages/publications/105028178860
dc.language.isoeng
dc.peerreviewedyes
dc.relationhttps://doi.org/10.54499/UID/04152/2025
dc.relationhttps://doi.org/10.54499/UID/PRR/04152/2025
dc.subjectGenetic Programming
dc.subjectSemantics
dc.subjectLocal Search
dc.subjectEvolutionary Computation
dc.subjectTheoretical Computer Science
dc.subjectSoftware
dc.subjectGeometry and Topology
dc.titleLocal search, semantics, and genetic programmingen
dc.title.subtitlea global analysisen
dc.typejournal article
degois.publication.firstPage1541
degois.publication.lastPage1559
degois.publication.titleSoft Computing
degois.publication.volume30
dspace.entity.typePublication
rcaap.rightsopenAccess

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