Optimization of Genetic Algorithm in Courses Scheduling

Abstract

Scheduling courses is an intricate and pivotal part of a university as it impacts the teaching and learning process. The problem frequently occurs is the struggle of placing schedules which is manual, takes a long time, and inaccurate. This paper explores the process and how effective the genetic algorithm method is in solving scheduling problems in lecture environment. The selection of genetic algorithms owes to it produces an optimal scheduling solution. To build a scheduling optimization system, it is essential to collect room data, lecturers, courses, days and hours of teaching. The data collection comes from field studies by observations and interviews. Literature studies are also needed to acquire the basic course scheduling, optimization, genetic algorithms, PHP, MySQL, Bootstrap, and Visual Studio Code. The test outcomes attained the preeminent one with the highest fitness value in the number of generations, populations, the crossover combination and mutation rates. The final result showed that the first chromosome is the finest chromosome produces scheduling with the highest fitness value. The outcomes of the whole algorithm process are consistent with the original predicted data, and the same lecturer is not scheduled to teach more than once at the same time. It is expected that the application of the genetic algorithm method optimizes course scheduling with great outcome.