Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range
Arkady Rost, Irina Petrova, Буздалова Арина Сергеевна · 2016
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.