SLIM: the non-bloating genetic programming with geometric semantic mutations and meaningful semantic crossover

Created by W.Langdon from gp-bibliography.bib Revision:1.9194

@Article{Vanneschi:2026:GPEM,

• author = "Leonardo Vanneschi and Sofia Pereira and Davide Farinati",
• title = "{SLIM}: the non-bloating genetic programming with geometric semantic mutations and meaningful semantic crossover",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2026",
• volume = "27",
• pages = "Article no 9",
• note = "Highlights of Genetic Programming 2024 and 2025 Events",
• keywords = "genetic algorithms, genetic programming, Geometric semantic genetic programming, GSGP, Inflate and deflate mutation, Donor crossover, XODn, Symbolic regression, STDGP, SLIM_GSGP, SLIM-R, SLIMMER",
• ISSN = "1389-2576",
• URL = "https://rdcu.be/e8Qcz",
• DOI = "10.1007/s10710-026-09535-y",
• code_url = "https://github.com/DALabNOVA/slim",
• size = "29 pages",
• abstract = "... of creating smaller offspring, offering a novel approach to managing model complexity. Besides deepening and interpreting in greater depth the experimental results, this work further enriches the foundational concepts of SLIM by integrating a new crossover operator, that, contrarily to the traditional geometric semantic crossover, is able to generate individuals of small size. Our comprehensive analysis explores the wider implications of this innovative operator. The novel variant that integrates this crossover is named Semantic Learning algorithm with Inflate/deflate Mutations and MEaningful Recombination (SLIMMER). Experimental results provide strong support for the potential of both SLIM and SLIMMER as effective approaches worthy of further research. As its name suggests, for some test cases SLIMMER demonstrates an enhanced ability to produce even more compact models than SLIM, further reinforcing its promise for applications where interpretability and model simplicity are essential",
• notes = "NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal",

}

Genetic Programming entries for Leonardo Vanneschi Sofia Carreira da Conceicao Alves Pereira Davide Farinati

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