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From Six to Two

2026-07-10Documento de conferência Acesso aberto
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dc.contributor.authorVanneschi, Leonardo
dc.contributor.authorRosenfeld, Liah
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.coverage.spatialNew York, United States
dc.date.accessioned2026-07-17T11:29:01Z
dc.date.available2026-07-17T11:29:01Z
dc.date.issued2026-07-10
dc.descriptionVanneschi, L., & Rosenfeld, L. (2026). From Six to Two: SLIM-DUO, a Simplified Extension of SLIM-GSGP. In GECCO'26: Proceedings of the Genetic and Evolutionary Computation Conference (pp. 808-816). ACM - Association for Computing Machinery. https://doi.org/10.1145/3795095.3805151
dc.description.abstractGeometric Semantic Genetic Programming (GSGP) is an extension of Genetic Programming (GP) that captured the interest of researchers because of its ability to induce a unimodal error surface for any supervised learning problem. Although still a recent development, the Semantic Learning with Inflate and Deflate Mutations (SLIM-GSGP) extension of GSGP has already attracted significant attention due to its novel ability to generate offspring that are smaller than their parents, effectively addressing the problem of steady model growth in GSGP. This paper presents SLIM-DUO, an extension of SLIM-GSGP that integrates the six existing SLIMGSGP variants into solely two unified formulations: DUO-MUL and DUO-SUM. This integration streamlines benchmarking and hyperparameter exploration while aiming to retain comparable predictive performance at approximately one third of the computational cost. Across five test problems, SLIM-DUO achieves predictive performance comparable to that of SLIM-GSGP, with model size differences that remain within previously observed dataset-dependent variability among SLIM variants. Overall, SLIM-DUO substantially reduces the computational effort required during both the configuration and benchmarking phases, highlighting a favorable balance between model size and computational complexity and preserving solution quality.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent9
dc.format.extent5177247
dc.identifier.doi10.1145/3795095.3805151
dc.identifier.isbn979-8-4007-2487-9
dc.identifier.otherPURE: 158310909
dc.identifier.otherPURE UUID: e560f1a4-679f-4742-be2a-2ab12e41882d
dc.identifier.otherORCID: /0000-0003-4732-3328/work/221060122
dc.identifier.urihttp://hdl.handle.net/10362/204638
dc.language.isoeng
dc.peerreviewedyes
dc.publisherACM - Association for Computing Machinery
dc.subjectGenetic Programming
dc.subjectGeometric Semantic Genetic Programming
dc.subjectSLIM-GSGP
dc.subjectModel Interpretability
dc.subjectParameter Simplification
dc.titleFrom Six to Twoen
dc.title.subtitleSLIM-DUO, a Simplified Extension of SLIM-GSGPen
dc.typeconference object
degois.publication.firstPage808
degois.publication.lastPage816
degois.publication.titleGECCO'26
degois.publication.titleGenetic and Evolutionary Computation Conference, 2026
dspace.entity.typePublication
rcaap.rightsopenAccess

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