Evolving code with a large language model
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @Article{Hemberg:2024:GPEM,
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author = "Erik Hemberg and Stephen Moskal and Una-May O'Reilly",
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title = "Evolving code with a large language model",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2024",
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volume = "25",
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pages = "Article no: 21",
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note = "Online first",
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keywords = "genetic algorithms, genetic programming, Artificial
Intelligence, AI, LLM, ANN, LLM-GP, Evolutionary
algorithm, Operators",
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ISSN = "1389-2576",
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URL = "https://rdcu.be/dUBQG",
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DOI = "doi:10.1007/s10710-024-09494-2",
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size = "36 pages",
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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 because they enlist an
LLM, using prompting and the LLM 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.",
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notes = "EECS, MIT CSAIL, 32 Vassar St, Cambridge 02139, MA,
USA",
- }
Genetic Programming entries for
Erik Hemberg
Stephen Moskal
Una-May O'Reilly
Citations