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A Study on the Dynamics and Effectiveness of the Deflate Geometric Semantic Mutation

2026-06Artigo científico Acesso embargado
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dc.contributor.authorFarinati, Davide
dc.contributor.authorPietropolli, Gloria
dc.contributor.authorVanneschi, Leonardo
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblInstitute of Electrical and Electronics Engineers (IEEE)
dc.date.accessioned2025-10-07T21:53:20Z
dc.date.embargoedUntil2027-09-17
dc.date.issued2026-06
dc.descriptionFarinati, D., Pietropolli, G., & Vanneschi, L. (2025). A Study on the Dynamics and Effectiveness of the Deflate Geometric Semantic Mutation. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2025.3611226 --- %ABS4%
dc.description.abstractGeometric Semantic Genetic Programming (GSGP) is a variant of Genetic Programming (GP) that induces an error surface without local minima for supervised learning tasks. However, GSGP is limited by the fact that its operators produce increasingly large individuals, leading to overly complex models. The slim addresses this issue by introducing a deflate geometric semantic mutation capable of producing offspring smaller than their parents. Preliminary studies show that slim can maintain accuracy comparable to traditional GSGP while generating much smaller models. However, a thorough analysis of this mutation remains lacking. This work fills that gap by conducting a detailed study of the deflate mutation, focusing on its behavior and practical value. Our results show that, when applied at the right stage of evolution, deflate mutation mitigates overfitting and yields compact, accurate models. This is also the first study to explore the timing and interaction of inflate and deflate mutations in slim, demonstrating how deflation enhances generalization and reduces overfitting. We support our conclusions with a comprehensive experimental approach, including comparisons between exclusive use of inflate mutation and alternating it with deflation. We also evaluate numerical indicators such as improvement rate and training effectiveness. The consistency across these methods reinforces our findings and highlights the deflate mutation as a robust regularization strategy. Finally, when compared to established non-evolutionary machine learning methods, SLIM shows competitive performance. Overall, this study confirms SLIM as a promising direction for GP and lays the foundation for future research.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent10
dc.format.extent1394465
dc.identifier.doi10.1109/TEVC.2025.3611226
dc.identifier.issn1089-778X
dc.identifier.otherPURE: 128984092
dc.identifier.otherPURE UUID: 9bced4aa-9912-48eb-8004-9996fb7ae4c8
dc.identifier.otherScopus: 105016741642
dc.identifier.otherWOS: 001784195800029
dc.identifier.otherORCID: /0000-0003-4732-3328/work/192593830
dc.identifier.urihttp://hdl.handle.net/10362/189109
dc.identifier.urlhttps://www.scopus.com/pages/publications/105016741642
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001784195800029
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.subjectGeometric Semantic Genetic Programming
dc.subjectMutation
dc.subjectDeflate Mutation
dc.subjectSoftware
dc.subjectTheoretical Computer Science
dc.subjectComputational Theory and Mathematics
dc.titleA Study on the Dynamics and Effectiveness of the Deflate Geometric Semantic Mutationen
dc.typejournal article
degois.publication.firstPage1098
degois.publication.issue3
degois.publication.lastPage1107
degois.publication.titleIEEE Transactions on Evolutionary Computation
degois.publication.volume30
dspace.entity.typePublication
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