Author(s): Rosenfeld, Liah ; Farinati, Davide ; Rasteiro, Diogo ; Pietropolli, Gloria ; Rebuli, Karina Brotto ; Silva, Sara ; Vanneschi, Leonardo
Date: 2025
Persistent ID: http://hdl.handle.net/10362/185201
Origin: Repositório Institucional da UNL
Subject(s): SLIM-GSGP; Geometric Semantic Genetic Programming; Open Source Library; Extensibility; Python; Computational Theory and Mathematics; Computer Science Applications; Software; Control and Optimization; Logic; Artificial Intelligence
Rosenfeld, L., Farinati, D., Rasteiro, D., Pietropolli, G., Rebuli, K. B., Silva, S., & Vanneschi, L. (2025). Slim_gsgp: A Python Library for Non-Bloating GSGP. In G. Ochoa (Ed.), GECCO '25: Proceedings of the Genetic and Evolutionary Computation Conference (pp. 1026-1034). ACM - Association for Computing Machinery. https://doi.org/10.1145/3712256.3726398 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS (DOI: 10.54499/UIDB/04152/2020) and through the LASIGE R&D Unit (UID/00408/2025).
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.
