<![CDATA[Scheduling university courses is a complex challenge involving multiple variables, such as time allocation, room assignment, lecturer availability, and student requirements. This study explores the implementation of a genetic algorithm as a solution for generating optimal and efficient schedules. The genetic algorithm operates through the principles of selection, crossover, and mutation to progressively explore the solution space. Experiments were conducted using parameters of 50 individuals and 40 chromosomes, yielding an optimal schedule at the 124th iteration with a maximum fitness value (fitness = 1). The results indicate that the fitness value of individuals increases as generations progress, affirming the genetic algorithm's capability to achieve optimization iteratively. However, the stochastic nature of the algorithm leads to variations in the number of generations required to reach optimal results, influenced by the problem's complexity and the number of chromosomes. This study demonstrates that genetic algorithms are highly effective in solving complex scheduling problems with significant efficiency, producing solutions that meet constraints and support more structured operations. The algorithm contributes substantially to the development of automated scheduling systems in educational institutions and other sectors.]]>
Copyrights © 2025
