Slim_gsgp: A Python Library for Non-Bloating GSGP
Liah Rosenfeld, Davide Farinati, Diogo Rasteiro, Gloria Pietropolli, Karina Brotto Rebuli, Sara Silva, Leonardo Vanneschi · Proceedings of the Genetic and Evolutionary Computation Conference · 2025
This paper presents slim_gsgp: an open-source Python library that provides the first ever framework for the Semantic Learning algorithm based on Inflate and deflate Mutation (SLIM-GSGP). Proposed in 2024, SLIM-GSGP is a promising non-bloating variant of Geometric Semantic Genetic Programming (GSGP). slim_gsgp includes all existing SLIM-GSGP variants, as well as traditional GSGP and standard Genetic Programming (GP), facilitating comparative analysis and benchmarking. Additionally, slim_gsgp's parallel computation and semi-modular architecture renders it not only fast but also user-friendly and easily extensible, thereby serving as a valuable resource for researchers aiming to advance this emerging and promising area of research. The source code and documentation can be accessed at https://github.com/DALabNOVA/slim.