Direct the MOSES Evolutionary Exploration via LLMs

This proposal seeks to promote the application of reinforcement learning principles within the MOSES (Meta-Optimizing Semantic Evolutionary Search) algorithm through the strategic integration of Large Language Models (LLMs). The design is conceptually inspired by Q-learning, and our approach would be to experiment with using LLMs in place of Q-learning functions to estimate the utility of specific evolutionary directions in MOSES. By integrating LLMs into the generational modeling and fitness estimation stages of the evolutionary search, we believe there is potential to guide evolutionary operations to efficiently explore and exploit the search space for optimal solutions.