An empirical study on the feature selection ability of SLIM-GSGP

Created by W.Langdon from gp-bibliography.bib Revision:1.8576

@InProceedings{farinati:2025:GECCOcomp,

• author = "Davide Farinati and Leonardo Vanneschi",
• title = "An empirical study on the feature selection ability of {SLIM-GSGP}",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
• year = "2025",
• editor = "Aniko Ekart and Nelishia Pillay",
• pages = "607--610",
• address = "Malaga, Spain",
• series = "GECCO '25 Companion",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, geometric semantic genetic programming, feature selection, symbolic regression: Poster",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3726642",
• DOI = "doi:10.1145/3712255.3726642",
• size = "4 pages",
• abstract = "Feature Selection (FS) is a key characteristic of any Machine Learning method. Genetic Programming (GP) performs it inherently, using evolution pressure to exclude redundant or irrelevant features. However, this ability is lost in Geometric Semantic Genetic Programming (GSGP), where Geometric Semantic Operator (GSO) keep adding genetic material to the individuals, inevitably adding noisy features. This work focuses on comparing the FS abilities of GSGP and Semantic Learning algorithm based on Inflate and deflate Mutations (SLIM), a promising new variant that employs Deflate Geometric Semantic Mutation (DGSM), a genetic operator that is able to remove genetic material while still inducing an unimodal fitness landscape. The experimental results show how SLIM has superior FS abilities compared to GSGP.",
• notes = "GECCO-2025 GP A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Davide Farinati Leonardo Vanneschi

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