An Adaptive Evolutionary Programming Algorithm Based on Q Learning

Abstract

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 clas- sical 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 program- ming).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 calculat- ing 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.