This paper introduces a reinforcement learning (RL) approach to address the challenges associated with configuring and optimizing genetic algorithms (GAs) for solving difficult combinatorial or non-linear problems. The proposed RL+GA method was specifically tested on the flow shop scheduling problem (FSP). The hybrid algorithm incorporates neural networks (NN) and uses the off-policy method Q-learning or the on-policy method Sarsa(0) to control two key genetic algorithm (GA) operators: parent selection mechanism and mutation. At each generation, the RL agent's action is determining the selection method, the probability of the parent selection and the probability of the offspring mutation. This allows the RL agent to dynamically adjust the selection and mutation based on its learned policy. The results of the study highlight the effectiveness of the RL+GA approach in improving the performance of the primitive GA. They also demonstrate its ability to learn and adapt from population diversity and solution improvements over time. This adaptability leads to improved scheduling solutions compared to static parameter configurations while maintaining population diversity throughout the evolutionary process.

该研究论文介绍了一种基于强化学习方法的遗传算法配置与优化的应用，特别针对流水车间调度问题。该混合算法结合了神经网络，并使用离策略方法Q-learning或在策略方法Sarsa(0)控制遗传算法的两个关键操作: 父选择机制和变异。研究结果显示了RL+GA方法在改善原始遗传算法性能方面的有效性，同时表明它可以根据学习策略动态调整选择和变异，以实现人口多样性和解决方案改进的能力。在整个进化过程中，保持人口多样性的同时提供了更好的调度解决方案。

Genetic Algorithm enhanced by Deep Reinforcement Learning in parent
  selection mechanism and mutation : Minimizing makespan in permutation flow
  shop scheduling problems

进化算法在父母选择机制和变异中加强深度强化学习：在排列流水车间调度问题中最小化完成时间