Reinforcement Learning in Adaptive Control of Genetic Algorithm Parameters
Abstract
Reinforcement Learning in Adaptive Control of Genetic Algorithm Parameters
Incoming article date: 13.06.2025The article presents a novel approach for adaptive control of genetic algorithm parameters using reinforcement learning methods. The use of the Q-learning algorithm enables dynamic adjustment of mutation and crossover probabilities based on the current population state and the evolutionary process progress. Experimental results demonstrate that this method offers a more efficient solution for optimization problems compared to the classical genetic algorithm and previously developed approaches employing artificial neural networks. Tests conducted on the Rastrigin and Shaffer functions confirm the advantages of the new method in problems characterized by a large number of local extrema and high dimensionality. The article details the theoretical foundations, describes the implementation of the proposed hybrid model, and thoroughly analyzes experimental results. Conclusions highlight the method's adaptability, efficiency, and potential for application in complex optimization scenarios.
Keywords: genetic algorithm, reinforcement learning, adaptive control, Q-learning, global optimization, Rastrigin function, Shaffer function