- author = "Liah Rosenfeld and Davide Farinati and Diogo Rasteiro and Gloria Pietropolli and Karina {Brotto Rebuli} and Sara Silva and Leonardo Vanneschi",
- title = "Slim-gsgp: A Python Library for Non-Bloating {GSGP}",
- booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
- year = "2025",
- editor = "Aniko Ekart and Nelishia Pillay",
- pages = "1026--1034",
- address = "Malaga, Spain",
- series = "GECCO '25",
- month = "14-18 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming",
- isbn13 = "979-8-4007-1465-8",
-
URL = "
https://doi.org/10.1145/3712256.3726398",
-
DOI = "
doi:10.1145/3712256.3726398",
-
code_url = "https://github.com/DALabNOVA/slim",
- size = "9 pages",
- abstract = "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 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.",
-
notes = "GECCO-2025 GP
A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",
