LLM-Driven Evolution of Metaheuristic Components for GNBG Benchmark

This paper is devoted to the implementation of the LLM Designed Evolutionary Algorithms, using a GNBG-generated Test Suite. Traditional metaheuristics face challenges in maintaining efficiency and effectiveness as complexity increases. Our research addresses these issues by focusing on enhancing individual methods within a class rather than reorganizing entire classes - a relatively unexplored strategy in black-box continuous optimization. By integrating Large-Language Models (LLMs), we evolve specific functions or components, offering novel insights into improving metaheuristic performance.