A critical examination of large language model capabilities in iteratively refining differential evolution algorithm

Abstract

In this study, we investigate the applicability, challenges, and effectiveness of the advanced large language model GPT 4 Turbo in enhancing the selected metaheuristic algorithm, which is Differential Evolution. Our research primarily examines whether iterative, repetitive prompting could lead to progressive improvements in algorithm performance. We also explore the potential of developing enhanced algorithms through this process that markedly surpass the established baseline in terms of performance. In addition, the impact of the model's temperature parameter on these improvements is evaluated. As part of our diverse testing approach, we conduct an experiment where the best-performing algorithm from the initial phase is used as a new baseline. This step is to determine if further refinement via GPT 4 Turbo can achieve even better algorithmic efficiency. Finally, we have performed the benchmarking comparison of selected enhanced variants against the top three algorithms from the CEC 2022 competition.