Adaptive operator selection in heuristic optimization utilizing generalized experience with reinforcement learning

Combinatorial optimization problems are inherently complex and difficult to solve. Although evolutionary and swarm intelligence algorithms have proven effectiveness, achieving an optimal balance between exploration and exploitation (EvE) remains a significant challenge. The growing trend of employing multiple operators with adaptive operator selection schemes aims to address EvE issues; however, there remains a significant demand for utilizing tailored adaptive selection schemes in search. Reinforcement Learning (RL) has recently been proposed as an efficient technique to customize and develop highly effective and adaptive selection mechanisms. However, scalability continuous to pose a significant challenge. This paper presents a novel RL-based approach that establishes a generalized and scalable framework for acquiring, processing, and leveraging experiential knowledge for both immediate and long-term use. The experimental results provide evidence of a certain level of success in validating the proposed framework.