LAOS: Large Language Model-Driven Adaptive Operator Selection for Evolutionary Algorithms
Yisong Zhang, Guoxing Yi · Proceedings of the Genetic and Evolutionary Computation Conference · 2025
Adaptive Operator Selection (AOS) is a strategy in Evolutionary Algorithms (EAs) that dynamically adjusts the application frequency of operators to enhance search efficiency based on online performance feedback. This paper introduces LAOS, an AOS framework driven by Large Language Models (LLMs). We design a meta-prompt to provide optimization state information (such as optimization progress, best fitness, and population diversity) and operator credit assignment, assisting LLMs in making adaptive decisions. Furthermore, LAOS maintains a dual-layer replay buffer structure: the offline layer records historical experiences under fixed operator strategies, while the online layer accumulates dynamically generated experiences during execution. By employing a similar experience sampling strategy, the framework can provide decision-making support for LLMs, enhancing both the efficiency and accuracy of search strategies. Experimental results on continuous numerical optimization and three categories of combinatorial optimization problems validate the effectiveness and generalization capability of LAOS. This study demonstrates the feasibility of leveraging LLMs for AOS, showcasing their potential in enhancing optimization performance and supporting automated algorithm design.