EASE-ing into Global Optimization with LLMs

This paper presents an extended abstract describing an entry into the LLM for evolutionary algorithms tailored benchmarking competition using a GNBG-generated test suite. This study explores the generative design of optimization algorithms using large language models (LLMs) within the EASE modular framework, which supports iterative prompting and feedback-driven refinement. Across five successive generations, we observed a progressive transformation in algorithmic structure. The findings suggest opportunities for further research into the role of prompt design, feedback phrasing, and framework architecture in guiding the emergence of more task-adaptive, domain-specialized algorithmic behavior.