The Impact of Mutation Probability on Genetic Algorithm Performance in Optimizing Course Scheduling

Computation time plays a crucial role in determining the speed of the genetic algorithm (GA). Critical parameters such as population size, crossover probability (Pc), mutation probability (Pm), and selection significantly influence the time required for the GA to find the optimal solution. Among these parameters, Pm is particularly critical as it directly impacts the mutation process of the parent chromosomes, indicating the importance of the parent chromosomes undergoing mutation. Consequently, selecting the correct Pm value is vital to ensuring the algorithm's efficiency of the mutation process. To examine the effect of Pm on GA performance, a series of simulations were carried out by varying the Pm values from 0.01 to 0.1 while keeping other parameters constant (Pc = 0.85 and population size = 100). The simulations, performed using Matlab R2012b, revealed that a Pm value of 0.06 resulted in the fastest computation time, averaging 0.382 seconds. This suggests that optimizing the scheduling for the electrical engineering program at Universitas Negeri Medan, a Pm value of 0.06, provides the most efficient computational performance.