Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP

Created by W.Langdon from gp-bibliography.bib Revision:1.8229

@InProceedings{farinati:2025:EuroGP,

• author = "Davide Farinati and Gloria Pietropolli and Leonardo Vanneschi",
• title = "Exploring the Impact of Data Scale on Mutation Step Size in {SLIM-GSGP}",
• booktitle = "European Conference on Genetic Programming, EuroGP 2025",
• year = "2025",
• editor = "Bing Xue and Luca Manzoni and Illya Bakurov",
• series = "LNCS",
• address = "Trieste",
• month = "23-25 " # apr,
• publisher = "Springer Nature",
• note = "Forthcoming",
• keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Geometric Mutation, Mutation Step, Symbolic Regression",
• abstract = "The Semantic Learning algorithm based on Inflate and deflate Mutation (SLIM) is a promising recent variant of Geometric Semantic Genetic Programming (GSGP) that introduces a new Deflate Geometric Semantic Mutation (DGSM). This operator maintains the key feature of the standard Geometric Semantic Mutation (GSM), inducing a unimodal error surface for any supervised learning problem, while generating smaller offspring than their parents, and thus allowing SLIM to generate compact, and potentially interpretable, final solutions. A key parameter controlling the evolution process in both GSGP and SLIM is the Mutation Step (MS), which regulates the extent of perturbation to the parent semantics. While it is intuitive that the optimal value of MS has a relationship with the scale of the dataset features, to the best of our knowledge no prior research has extensively explored this relationship. In this work, we provide the first comprehensive investigation into this topic. First, we hypothesize a general rule by analysing results from artificial datasets, and then we confirm these findings with more complex, real-world datasets. This approach offers a solid alternative to the typical hyperparameter tuning approach.",
• notes = "also known as \cite{paper_117_step_size_in_slim-gsgp}

  Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025",

}

Genetic Programming entries for Davide Farinati Gloria Pietropolli Leonardo Vanneschi

Citations