Optimizing Course Scheduling with Genetic Algorithms: A Dynamic Approach

This study explores the application of Genetic Algorithms (GAs) for optimizing course scheduling in educational institutions. Traditional manual scheduling methods are often time-consuming and result in suboptimal solutions due to the complexity and scale of the task. GAs, inspired by natural selection, offer a robust solution by iteratively applying selection, crossover, and mutation to evolve optimal schedules. Using Python and libraries such as NumPy, Matplotlib, and DEAP, the GA was tested through various simulations. The results indicated that GAs significantly improve scheduling efficiency, minimizing conflicts and optimizing resource utilization. Larger populations yielded better fitness values but required more computation time. The hybrid GA approach outperformed manual methods, producing higher quality timetables that adhered better to constraints. A user-friendly interface was developed to facilitate efficient data management and schedule generation. This study confirms that GAs, especially when combined with hybrid techniques, offer a robust solution for the complex problem of course scheduling. Future research should refine these algorithms, explore new hybrid approaches, and address practical implementation challenges to fully harness the potential of GAs in academic scheduling.