Introducing Crossover in SLIM-GSGP

Pietropolli, Gloria; Farinati, Davide; Manzoni, Luca; Castelli, Mauro; Silva, Sara; Vanneschi, Leonardo

http://hdl.handle.net/10362/182851

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Resumo(s)

The Semantic Learning algorithm based on Inflate and deflate Mutations (SLIM-GSGP, or simply SLIM) is a variant of Geometric Semantic Genetic Programming (GSGP) designed to generate compact and interpretable models while maintaining the beneficial characteristic of GSGP of inducing an error surface without local optima. To date, no crossover operator has been defined for SLIM and the existing SLIM framework relies solely on two mutation operators: inflate and deflate mutation. This paper introduces two novel crossover operators for SLIM: Swap Crossover (XOSw) and Donor Crossover (XODn). These crossovers capitalize on SLIM’s linked-list representation to facilitate genetic exchange while controlling program size. Experimental results on five symbolic regression problems demonstrate that the new crossover operators often improve fitness and reduce model size when compared to standard SLIM and to GSGP. Our findings establish these operators as solid improvements of traditional GSGP crossover.

Descrição

Pietropolli, G., Farinati, D., Manzoni, L., Castelli, M., Silva, S., & Vanneschi, L. (2025). Introducing Crossover in SLIM-GSGP. In B. Xue, L. Manzoni, & I. Bakurov (Eds.), Genetic Programming: 28th European Conference, EuroGP 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23–25, 2025, Proceedings (pp. 103-119). (Lecture Notes in Computer Science; Vol. 15609). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-89991-1_7 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS),(https://doi.org/10.54499/UIDB/04152/2020) the project 2024.07277. IACDC, and the LASIGE Research Unit, ref. UID/000408/2025.

Palavras-chave

SLIM Geometric Semantic Genetic Programming Geometric Semantic Crossover Inflate Mutation Deflate Mutation Theoretical Computer Science General Computer Science