Evolving code with a large language model
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10710_2024_Article_9494.pdf
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2.89 MB
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Checksum (MD5)
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Author(s)
Hemberg, Erik
Moskal, Stephen
O’Reilly, Una-May
Date Issued
September 12, 2024
Journal
Genetic Programming and Evolvable Machines
Publisher
Springer US
Citation
Hemberg, E., Moskal, S. & O’Reilly, UM. Evolving code with a large language model. Genet Program Evolvable Mach 25, 21 (2024).
Version
Final published version
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 general LLM-based evolutionary algorithm designed to evolve code. Like GP, it uses evolutionary operators, but its designs and implementations of those operators significantly 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 presentations that range from 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10710-024-09494-2
