Evolutionary reinforcement learning based search optimization: student research abstract

Nature has always inspired researchers to find the best solutions to the toughest of problems. In this article, we proposed a search optimization algorithm based on a refined Plant Growth Simulation Algorithm (PGSA) that uses reinforcement learning. The method combines the heuristic based PGSA with reinforcement learning techniques where natural selection is used as a feedback, thus combining evolutionary algorithms with learning. This enables us to achieve a highly optimized algorithm for growth point search that simulates the evolutionary techniques seen in nature. The proposed method reduces the feasible sets of growth points in each iteration, thereby reducing the required run times of load flow, objective function evaluation and morphactin concentration calculation.