Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range

Online parameter controllers for evolutionary algorithms adjust values of parameters during the run. Recently, a new efficient parameter controller based on reinforcement learning was proposed by Karafotias et al. In this method parameter ranges are discretized into several intervals before the run. However, performing adaptive discretization during the run may increase efficiency of an evolutionary algorithm. Aleti et al. proposed another efficient controller with adaptive discretization. In this paper we propose a parameter controller based on reinforcement learning with adaptive discretization. The proposed controller is compared with the existing parameter adjusting methods on different configurations of an evolutionary algorithm. Results show that the new controller outperforms the other controllers on most of the considered test problems.