Evolving code with a large language model

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.