Author(s): Rosenfeld, Liah ; Farinati, Davide ; Rasteiro, Diogo ; Pietropolli, Gloria ; Rebuli, Karina Brotto ; Silva, Sara ; Vanneschi, Leonardo
Date: 2024
Persistent ID: http://hdl.handle.net/10362/175912
Origin: Repositório Institucional da UNL
Project/scholarship: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT; info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00408%2F2020/PT; info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00408%2F2020/PT;
Rosenfeld, L., Farinati, D., Rasteiro, D., Pietropolli, G., Rebuli, K. B., Silva, S., & Vanneschi, L. (2024). Slim: a Python Library for the non-bloating SLIM-GSGP algorithm [poster]. 1. Poster session presented at Data Research Meetup by MagIC, Lisbon, Portugal. --- 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~(UIDB/00408/2020 and UIDP/00408/2020).
This poster presents Slim: 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 by Vanneschi in 2024, SLIM-GSGP is a promising non-bloating variant of Geometric Semantic Genetic Programming (GSGP). The Slim library includes all existing SLIM-GSGP variants, as well as traditional GSGP and standard Genetic Programming (GP), facilitating comparative analysis and benchmarking. Additionally, Slim’s semi-modular architecture and parallel computation renders it not only fast but also user-friendly and easily extensible, thereby fostering progress in this emerging area of research.
