Evolving code with a large language model

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 op...

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Bibliographic Details
Published inGenetic programming and evolvable machines Vol. 25; no. 2
Main Authors Hemberg, Erik, Moskal, Stephen, O’Reilly, Una-May
Format Journal Article
LanguageEnglish
Published New York Springer US 01.12.2024
Springer Nature B.V
Subjects
Artificial Intelligence
Biomedical Engineering and Bioengineering
Compilers
Computer Science
Electrical Engineering
Evolutionary algorithms
Genetic algorithms
Interpreters
Large language models
Operators
Pattern matching
Programming Languages
Programming Techniques
Software Engineering/Programming and Operating Systems
Operators
Large language models
Genetic programming
Evolutionary algorithm
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Summary: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.
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ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-024-09494-2