Augmented Genetic Algorithm v2 with Reinforcement Learning for PDN Decap Optimization

Genetic algorithms (GAs) use many hyperparameters, and tuning these parameters can determine the optimization performance. A GA with an augmented initial population was proposed for decap optimization but it had convergence issues by getting stuck in the local minimum. This work uses a reinforcement learning (RL) approach to adaptively tune the hyperparameters of GA during its operation. With this approach, the agent tries to change the parameters so that the GA does not get stuck in the local minimum. The proposed method combining the RL agent and Augmented GA showed better performance in terms of solution quality and time cost. Overall, in all the cases tested, the proposed method showed better performance than the Augmented GA without RL.