Genetic Algorithm With Three-Dimensional Population Dominance Strategy for University Course Timetabling Problem

Abstract

In recent years, with the growing expansion of the recruitment scale and the further reform in teaching, how to use the limited teacher resources and the limited classroom resources to schedule a reasonable university course timetable has gotten great interest. In this paper, the authors firstly hashed over the university course timetabling problem, and then they presented the related mathematical model and constructed the relevant solution framework. Subsequently, in view of characteristics of the university course timetabling problem, they introduced genetic algorithm to solve the university course timetabling problem and proposed many improvement strategies which include the three-dimensional coding strategy, the fitness function design strategy, the initial population generation strategy, the population dominance strategy, the adaptive crossover probability strategy, and the adaptive mutation probability strategy to optimize genetic algorithm. Simulation results show that the proposed genetic algorithm can solve the university course timetabling problem effectively.