Embedding-Based Selection Operators for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8564

@InProceedings{bel-moudden:2025:GECCOcomp,

• author = "Oumaima {Bel Moudden} and Rym Guibadj and Denis Robilliard and Cyril Fonlupt and Abdeslam Kadrani",
• title = "Embedding-Based Selection Operators for Genetic Programming",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
• year = "2025",
• editor = "Aniko Ekart and Nelishia Pillay",
• pages = "591--594",
• address = "Malaga, Spain",
• series = "GECCO '25 Companion",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, code embedding, selection operator, source code representation, convergence optimization: Poster",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3726679",
• DOI = "doi:10.1145/3712255.3726679",
• size = "4 pages",
• abstract = "The field of automatic programming has made significant advances in recent years, particularly through the development of semantic-aware algorithms in Genetic Programming (GP) and the emergence of Large Language Models (LLMs) designed for code. This raises a natural question: can these two approaches be combined to achieve synergistic effects? In this paper, we explore whether the semantic information embedded in vector representations of source code, generated by dedicated LLMs, can enhance GP performance. Specifically, we integrate code representation models expected to capture both syntactic and semantic attributes of GP-generated programs into their embeddings, and propose informed selection operators that leverage these embedded attributes. Experimental results on a diverse set of benchmark problems demonstrate that embedding-based selection methods significantly improve convergence speed in GP. This integration of code embeddings into the selection process offers a promising direction for enhancing the efficiency of GP evolutionary search.",
• notes = "GECCO-2025 GP A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Oumaima Bel Moudden Rym Nesrine Guibadj Denis Robilliard Cyril Fonlupt Abdeslam Kadrani

Citations