SLIM-GSGP: The Non-bloating Geometric Semantic Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Vanneschi:2024:EuroGP,

• author = "Leonardo Vanneschi",
• editor = "Mario Giacobini and Bing Xue and Luca Manzoni",
• title = "{SLIM-GSGP}: The Non-bloating Geometric Semantic Genetic Programming",
• booktitle = "EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming",
• year = "2024",
• volume = "14631",
• series = "LNCS",
• pages = "125--141",
• publisher = "Springer",
• address = "Aberystwyth",
• month = "3-5 " # apr,
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Inflate and Deflate Mutations, Model Interpretability",
• isbn13 = "978-3-031-56957-9",
• URL = "https://rdcu.be/dOkvz",
• DOI = "doi:10.1007/978-3-031-56957-9_8",
• size = "17 pages",
• abstract = "Geometric semantic genetic programming (GSGP) is a successful variant of genetic programming (GP), able to induce a unimodal error surface for all supervised learning problems. However, a limitation of GSGP is its tendency to generate offspring larger than their parents, resulting in continually growing program sizes. This leads to the creation of models that are often too complex for human comprehension. This paper presents a novel GSGP variant, the Semantic Learning algorithm with Inflate and deflate Mutations (SLIM-GSGP). SLIM_GSGP retains the essential theoretical characteristics of traditional GSGP, including the induction of a unimodal error surface and introduces a novel geometric semantic mutation, the deflate mutation, which generates smaller offspring than its parents. The study introduces four SLIMGSGP variants and presents experimental results demonstrating that, across six symbolic regression test problems, SLIM GSGP consistently evolves models with equal or superior performance on unseen data compared to traditional GSGP and standard GP. These SLIM-GSGP models are significantly smaller than those produced by traditional GSGP and are either smaller or of comparable size to standard GP models. Notably, the compactness of SLIM_GSGP models allows for human interpretation.",
• notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in conjunction with EvoCOP2024, EvoMusArt2024 and EvoApplications2024",

}

Genetic Programming entries for Leonardo Vanneschi

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