Optimizing Course Scheduling Efficiency through Genetic Algorithms

Course scheduling is an important aspect of educational administration in academic institutions. An effective procedure enhances students' educational experience, maximizes resource utilization, and lowers operational expenses. However, course scheduling often faces various constraints and complexities, such as limited space, time and human resources. Therefore, an effective and efficient approach is needed to solve the course scheduling problem. This study implements the genetic Algorithm to solve the problem of optimization course scheduling. This study intends to develop a course scheduling application using genetic algorithm to enhance the effectiveness of course scheduling in educational institutions. There are 8 genetic algorithm procedures for solving problems in this research; Encoding techniques, initial population, fitness function, selection, crossover, mutation, elitism and the condition of iteration is complete when the maximum has been reached, and the fitness value is 1. The best result from 25 iteration and 15 population found at probability of crossover is 0,5 and mutation rate is 10%. The lowest fitness value is 0,09 with the fastest execution time, that is 395 seconds for subjects in odd semester and 563 seconds for subjects in even semester.