Automated Timetable Generation Using Genetic Algorithms: A Heuristic Optimization Approach

Abstract - Timetable scheduling is a critical and complex optimization problem frequently encountered in educational institutions and other domains requiring resource allocation. Traditional heuristic methods often fall short in handling the diverse and dynamic constraints involved. This paper investigates the application of Genetic Algorithms (GAs) to solve the timetable scheduling problem effectively. GAs, inspired by natural selection, offer a robust framework for exploring a vast solution space and evolving optimal or near-optimal timetables. Through a combination of rigorous constraint modeling, algorithmic design, and empirical validation, this study demonstrates that GA-based systems significantly enhance scheduling efficiency, adaptability, and quality. Key Words: Time Table Scheduling, Genetic Algorithms, Optimization, Evolutionary Computation.