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This paper introduces a reinforcement learning (RL) approach to address thechallenges associated with configuring and optimizing genetic algorithms (GAs)for solving difficult combinatorial or non-linear problems. The proposed RL+GAmethod was specifically tested on the flow shop scheduling problem (FSP). Thehybrid algorithm incorporates neural networks (NN) and uses the off-policymethod Q-learning or the on-policy method Sarsa(0) to control two key geneticalgorithm (GA) operators: parent selection mechanism and mutation. At eachgeneration, the RL agent's action is determining the selection method, theprobability of the parent selection and the probability of the offspringmutation. This allows the RL agent to dynamically adjust the selection andmutation based on its learned policy. The results of the study highlight theeffectiveness of the RL+GA approach in improving the performance of theprimitive GA. They also demonstrate its ability to learn and adapt frompopulation diversity and solution improvements over time. This adaptabilityleads to improved scheduling solutions compared to static parameterconfigurations while maintaining population diversity throughout theevolutionary process.

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