Application Research on FSDM-Based GA in Optimizing Curriculum Schedule Model in Universities

In this paper, we first analyze the relationship between curricula, teachers, classes, time slices and classrooms in a graph. Then on the basis of constraint conditions in curriculum schedule practically in universities, we presents its optimization model, in which the fuzzy synthetic decision-making (FSDM) is used to optimize genetic algorithm (GA), and a new GA encoding scheme is employed to design fitness and punishment functions for the curriculum schedule problem. This model effectively improved a running performance, which provides a better implementation approach to improvements of the existing curriculum schedule systems. The experimental results show that fitness values of the FSDM-based GA are of obvious evolutional tendency, the chromosome encoding scheme and the fitness function can meet its requirements preferably, and the more adequate computation resources, the greater possibilities of no restoration for the obtained optimal individual.