  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.
