An Adaptive Evolutionary Programming Algorithm Based on Q Learning
基于Q学习的适应性进化规划算法

Selection of mutation strategies plays an important role in evolutionary programming, and adaptively selecting a mutation strategy in each evolutionary step can achieve good performance. A mutation strategy is evaluated and selected only based on the one-step performance of mutation operators in classical adaptive evolutionary programming, and the performance of mutation operators in the delayed mutation steps is ignored. This paper proposes a novel adaptive mutation strategy based on Q learning --- QEP (Q learning based evolutionary programming). In this algorithm, several candidate mutation operators are used and each is considered as an action. The evolutionary performance of delayed mutation steps is considered in calculating the Q values for each mutation operator and the mutation operator that maximizes the learned Q values is the optimal one. Experimental results show that the proposed mutation strategy achieves better performance than the existing algorithms.