Computer Science > Neural and Evolutionary Computing
arXiv:2401.07102 (cs)
[Submitted on 13 Jan 2024]
Title:Evolving Code with A Large Language Model
View a PDF of the paper titled Evolving Code with A Large Language Model, by Erik Hemberg and 2 other authors
View PDFHTML (experimental)Abstract:Algorithms that use Large Language Models (LLMs) to evolve code arrived on the Genetic Programming (GP) scene very recently. We present LLM GP, a formalized LLM-based evolutionary algorithm designed to evolve code. Like GP, it uses evolutionary operators, but its designs and implementations of those operators radically differ from GP's because they enlist an LLM, using prompting and the LLM's pre-trained pattern matching and sequence completion capability. We also present a demonstration-level variant of LLM GP and share its code. By addressing algorithms that range from the formal to hands-on, we cover design and LLM-usage considerations as well as the scientific challenges that arise when using an LLM for genetic programming.
| Comments: | 34 pages, 9 figures, 6 Tables |
| Subjects: | Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.8 |
| Cite as: | arXiv:2401.07102 [cs.NE] |
| (orarXiv:2401.07102v1 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2401.07102 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Evolving Code with A Large Language Model, by Erik Hemberg and 2 other authors
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