LAPC: Large Language Model-Powered Adaptive Parameter Control for Evolutionary Algorithms

Parameter setting is a critical challenge in Evolutionary Algorithms (EAs) as it directly impacts optimization performance. However, fixed parameter configurations often fail to guarantee optimal algorithm behavior throughout the optimization process. Despite numerous dynamic parameter control methods, no universal solution has emerged. In this work, we introduce Large Language Model-Powered Adaptive Parameter Control (LAPC), a novel framework for adaptive parameter control in EAs. Our framework leverages an innovative prompt design that incorporates key optimization metrics, including progress, mean fitness, and mean pairwise distance. Additionally, a replay buffer is employed to sample similar optimization experiences, helping guide large language models (LLMs) in selecting appropriate parameter control strategies. To reduce search space and inference complexity, we also design a discrete parameter output strategy. We apply LAPC to control parameters in (1 + 1)-ES and Genetic Algorithms (GAs), targeting numerical optimization problems and the Traveling Salesman Problem (TSP), respectively. Experimental results demonstrate that LAPC consistently outperforms existing methods, such as the one-fifth rule and Deep Q-Networks (DQN). Moreover, we present comparative studies on the effectiveness of the LAPC module, the impact of parameter update intervals, and the effect of the dimensionality of the discrete parameter output. Our results highlight the potential of LLMs as general-purpose tools for complex decision-making tasks.