A Novel Genetic Algorithm Based Timetable Generator for Optimized University Timetable Solution

In educational institutes, efficient timetable generation is critical for optimizing resource utilization and fostering a conducive learning environment. Timetable challenges, such as clashes among faculty schedules and lack of real-time updates, underscore the need for innovative solutions in the field. The problem lies in addressing clashes, transparency issues, and inflexibility in manual scheduling systems, leading to disruptions and impacting both students and faculty. We employed a genetic algorithm, combining one-point crossover and mutation operations, to iteratively evolve and adapt timetables to enhances genetic diversity, addresses scheduling conflicts, and promotes the creation of optimal timetables. The results exhibit dynamic and evolving timetables that prioritize efficiency, clash prevention, and overall optimization. The significance lies in fostering an environment conducive to effective teaching and learning, emphasizing fairness, adaptability, and overall well-being. Our work introduces a genetic algorithm-based methodology that not only addresses existing challenges in educational timetable generation but also significantly contributes to fostering a more efficient, transparent, and adaptable scheduling system.