Genetic and Deep Reinforcement Learning-Based Intelligent Course Scheduling for Smart Education

Scheduling courses is a routine yet important task in Smart educational activities. Traditional manual scheduling is time-consuming and labor-intensive, prone to errors, and unable to meet the demands of large-scale scheduling. Classic genetic algorithms for scheduling have issues such as rapid convergence and decreased scheduling efficiency as constraint factors increase. To address these problems in existing scheduling genetic algorithms, we propose a self-learning genetic algorithm for scheduling based on deep reinforcement learning (GAGDRL). The GAGDRL algorithm utilizes the Q-learning algorithm to achieve adaptive adjustment of crossover and mutation parameters, enhancing the search capability of the genetic algorithm. Establishing a parameter dynamic adjustment model for the Markov decision process (MDP) analyzes the state set of the population fitness function to achieve a comprehensive evaluation of the overall performance of the population. Additionally, the deep Q-network (DQN) algorithm is introduced into the scheduling problem to address issues related to the large number of population states and the volume of Q-table data in scheduling. Experimental results show that, compared to classical and improved genetic algorithms for scheduling, the GAGDRL algorithm improves accuracy and optimization capability. The proposed algorithm can also be applied to problems such as exam scheduling, cinema seating arrangements, and airline route planning.