Genetic Algorithm with Reinforcement Learning based Parameter Optimisation

Optimisation of parameters in Genetic algorithms (GA) can improve the speed and accuracy of the solution produced, but well optimised parameters are dependant on the problem being solved, and the substantial additional cost of spending time pre-computing good parameters can offset the benefit. This research investigates the use of reinforcement learning algorithms to optimise the parameters of the GA during its runtime. Specifically, we propose a variant of the GA method which embeds the Q-learning algorithm to select an optimal mutation rate at each iteration. Evaluating with a set of benchmark functions, the proposed GA model with Q-learning shows promising performance with lower mean scores than those of the original GA for most test functions. In particular, the Q-learning algorithm shows a promising emergent behaviour, i.e. selecting a high mutation rate when the population variance is low to increase swarm and search diversity. Evaluated using diverse unimodal and multimodal numerical optimisation problems, the proposed model outperforms several baseline GAs with a statistical significance.