- author = "Gloria Pietropolli and Davide Farinati and Luca Manzoni and Mauro Castelli and Leonardo Vanneschi",
- title = "Introducing Crossover in {SLIM-GSGP}",
- booktitle = "European Conference on Genetic Programming, EuroGP 2025",
- year = "2025",
- editor = "Bing Xue and Luca Manzoni and Illya Bakurov",
- volume = "15609",
- series = "LNCS",
- pages = "103--119",
- address = "Trieste",
- month = "23-25 " # apr,
- publisher = "Springer Nature",
- keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, SLIM, Geometric Semantic Crossover, Inflate Mutation, Deflate Mutation",
- isbn13 = "978-3-031-89990-4",
-
URL = "
https://rdcu.be/ejgDO",
-
DOI = "
doi:10.1007/978-3-031-89991-1_7",
- abstract = "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.",
-
notes = "also known as \cite{paper_133_slim_gp_xover}
Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025",
