Embedding-Based Selection Operators for Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8519
- @InProceedings{bel-moudden:2025:GECCOcomp,
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author = "Oumaima {Bel Moudden} and Rym Guibadj and
Denis Robilliard and Cyril Fonlupt and Abdeslam Kadrani",
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title = "Embedding-Based Selection Operators for Genetic
Programming",
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booktitle = "Proceedings of the 2025 Genetic and Evolutionary
Computation Conference Companion",
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year = "2025",
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editor = "Aniko Ekart and Nelishia Pillay",
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pages = "591--594",
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address = "Malaga, Spain",
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series = "GECCO '25 Companion",
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month = "14-18 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, code
embedding, selection operator, source code
representation, convergence optimization: Poster",
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isbn13 = "979-8-4007-1464-1",
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URL = "
https://doi.org/10.1145/3712255.3726679",
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DOI = "
doi:10.1145/3712255.3726679",
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size = "4 pages",
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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.",
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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