  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.
