A deep-belief network approach for course scheduling

The scheduling of academic courses is a problem in which a weekly schedule is produced for educational purposes. Many different types of scheduling problems exist at various universities in accordance with their laws, needs, and constraints. These problems fall into the category of NP-hard problems and are incredibly complex. In this paper, an intelligent system for scheduling courses using the deep-belief network is proposed. The reason why the proposed system is intelligent is that it can learn the constraints, inputs, and other necessary parameters in one step by receiving the inputs as well as the training needed by the deep-belief network. The deep-belief network used has one output layer, four hidden layers, and four input layers. The experimental results of this research show that the deep-belief network proposed for the scheduling of academic courses provides a better score, less error, and execution time compared with Sequence-Based Selection Hyper-Heuristic (SSHH) approach.