@inbook{3510318f11e246c882b39f2dc743e0e6,
title = "Exploring Non-bloating Geometric Semantic Genetic Programming",
abstract = "Recently, a new variant of Geometric Semantic Genetic Programming(GSGP) was introduced that, while maintaining the property of inducing a unimodal error surface for all supervised learning problems, is able to generate models that are compact enough to be interpretable by humans. This variant is called the Semantic Learning algorithm based on Inflate and deflate Mutation (SLIM\_GSGP) and, as the name suggests, it is based on two types of mutation: one (inflate) that generates offspring larger than their parents, similar to traditional geometric semantic mutation, and the other (deflate) that generates offspring smaller than their parents. This chapter reviews and extends the initial work on SLIM\_GSGP by introducing two novel variants, thoroughly studying the geometric characteristics of the SLIM\_GSGP operators and discussing the experimental results and their interpretation in greater depth. The main conclusion is that SLIM\_GSGP is a very promising method, warranting significant investment in future research.",
author = "Leonardo Vanneschi and Davide Farinati and Diogo Rasteiro and Liah Rosenfeld and Gloria Pietropolli and Sara Silva",
note = "https://doi.org/10.54499/UID/04152/2025\# https://doi.org/10.54499/UID/PRR/04152/2025\# https://doi.org/10.54499/UIDB/00408/2020\# https://doi.org/10.54499/UIDP/00408/2020\# Vanneschi, L., Farinati, D., Rasteiro, D., Rosenfeld, L., Pietropolli, G., \& Silva, S. (2025). Exploring Non-bloating Geometric Semantic Genetic Programming. In S. M. Winkler, W. Banzhaf, T. Hu, \& A. Lalejini (Eds.), Genetic Programming Theory and Practice XXI (pp. 237-258). (Genetic and Evolutionary Computation). Springer Singapore. https://doi.org/10.1007/978-981-96-0077-9\_12 --- This work was supported by national funds through FCT (Funda{\c c}{\~a}o para a Ci{\~a}ncia e a Tecnologia), under the project UIDB/04152/2020 (https://doi.org/10.54499/UIDB/04152/2020)—Centro de Investiga{\c c}{\~a}o em Gest{\~a}o de Informa{\c c}{\~a}o (MagIC)/NOVA IMS and through the LASIGE R\&D Unit (UIDB/00408/2020 (https://doi.org/10.54499/UIDB/00408/2020) and UIDP/00408/2020 (https://doi.org/10.54499/UIDP/00408/2020)).",
year = "2025",
month = feb,
day = "28",
doi = "10.1007/978-981-96-0077-9\_12",
language = "English",
isbn = "978-981-96-0076-2",
series = "Genetic and Evolutionary Computation",
publisher = "Springer Singapore",
pages = "237--258",
editor = "Winkler, \{Stephan M.\} and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini",
booktitle = "Genetic Programming Theory and Practice XXI",
address = "Singapore",
}