Application of genetic algorithm concept on course scheduling

Abstract

Genetic Algorithms have parameters such as crossover probabilities and mutation probabilities used based on entering population numbers and number of generations. Of the two entries, nine rules were obtained which would produce crossover probabilities and mutation probabilities. One problem that can be solved using a genetic algorithm is the scheduling of courses. In the preparation of course scheduling, it takes quite a long time and needs a very high accuracy. Therefore, the purpose of this study is to implement genetic algorithms on lecture scheduling problems. So that the accuracy and speed in determining the class schedule can be fulfilled. The test results show that applying a genetic algorithm can obtain the course schedule without any collision in one iteration process.