Implementing Genetic Algorithms for Curriculum Scheduling and Optimization in Higher Education

This research explores the application of Genetic Algorithms (GA) as a novel approach to address the complex task of curriculum scheduling and optimization in higher education institutions. The conventional methods for creating and optimizing academic schedules often prove inefficient in handling the intricate constraints and preferences inherent in educational environments. The study begins by outlining the challenges associated with curriculum scheduling, including factors such as room availability, faculty constraints, student preferences, and course prerequisites. It then delves into the theoretical foundations of GA, illustrating how these algorithms can be adapted to model the optimization problem inherent in curriculum scheduling. The implementation phase involves the development of a customized GA tailored to the specific requirements of higher education scheduling. The algorithm is designed to evolve and improve schedules over successive generations, a variety of constraints and objectives. The research also explores the integration of user-defined parameters to accommodate institutional policies and stakeholder preferences. Results show that GAs greatly improve schedule adaptation and optimization, adding valuable insights to the growing research on evolutionary algorithms in education. The accuracy of the proposed model is 98. 52% which is 9.44% higher than existing methods.