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.

翻译：本文提出一种强化学习方法以解决遗传算法在配置与优化过程中应对复杂组合或非线性问题时所面临的挑战。所提出的RL+GA方法专门针对流水车间调度问题进行了测试。该混合算法融合了神经网络,并采用离策略方法Q-learning或在线策略方法Sarsa(0)来控制遗传算法的两个关键算子：父代选择机制与变异操作。在每一代进化中,强化学习智能体的行为决定了选择方法、父代选择概率以及子代变异概率。这使得强化学习智能体能够根据其学习到的策略动态调整选择与变异操作。研究结果凸显了RL+GA方法在提升原始遗传算法性能方面的有效性,同时证明了其通过种群多样性与解质量的持续改进进行学习与自适应调整的能力。与静态参数配置相比,这种自适应性在保持整个进化过程中种群多样性的同时,能够获得更优的调度解方案。