Research On Course Scheduling Problem Based On The Improved Hybrid Genetic Algorithm

In order to ensure the teaching quality, the college needs to make a set of standardized teaching plans, while arranging curriculum schedule is to be an important part of the teaching plans. With the increasing quantity of college students, the size of data and the constraints grow lager. Under the limited teaching resources, curriculum scheduling problem has become more and more complex. Artificial course scheduling has been difficult to complete the schedule arrangement work requirements. Therefore, using the computer to solve curriculum scheduling problem has become a pressing matter.Curriculum Scheduling Problem is a typical multi-objective combination optimization problem with multiple constraints. In the early70’s, curriculum scheduling problem has been proved to be a NP complete problem. Because of its complex, it has not been solved well for decades. The purpose of this study is trying to find a better way to solve the complex problem.Genetic Algorithm (GA) is a kind of adaptive random search algorithm based on natural selection and evolution. GA is parallel, common, and stable, which is an effective method to solve the NP complete problem. At present, using GA to solve curriculum scheduling problem has become a hot research for many scholars and universities. Greedy algorithm is a kind of simpler, more rapid design technology. The work of this paper is conducting a deep research on the university curriculum scheduling problem by using the improved hybrid genetic algorithm based on insufficiency of a single intelligent algorithm.The paper starts by providing an overview about curriculum scheduling problem. Then, it introduces the research status and development trend of this issue at home and abroad. Thirdly, the problem model is described with a set of courses, rooms, instructors and student groups Then summarize the structure, function, characteristics of genetic algorithm and the greedy algorithm, and analyze the main characteristics and the status quo of combination optimization theory. Aiming at the shortage of the genetic algorithm, the greedy strategy and optimal preservation strategy is introduced into the genetic algorithm, which effectively guides the crossover and mutation operations. Using greedy strategy enlarges the searching space and avoids the weakness of standard genetic algorithm. The realization of computer program proves that the improved hybrid genetic algorithm is fully applicable to curriculum scheduling problem, and it has higher efficiency. The obtained results also show that the proposed solutions can provide a new idea for the development of curriculum scheduling problem.