Research On Course Scheduling Problems Based On Self-Adaptive Immune Genetic Algorithm

Course Scheduling problem is a multi-objective optimization problem, which is one of Timetable Problems (Timetable Problems, abbreviated TTP), and which is also the NP complete problem. Genetic algorithm is a mechanism, which refers to the natural selection and natural inheritance in biosphere, with the using of group's searching technique algorithms. Genetic algorithm is especially appropriate for dealing with traditional searching methods, which can hardly solve the complex and nonlinear problems. In recent years, using the genetic algorithm to solve the Course Scheduling problem is a hot point.This thesis carries out research in the following areas:using the adaptive immune genetic algorithm for solving the Course Scheduling problem, the design of extracting vaccines and immunization algorithm, effectively improving the degradation of traditional genetic algorithm. This thesis analyses the soft and hard constraints conditions of the course scheduling problems, and, quantifying the soft constraints, as well as establishing a corresponding mathematical model. According to the actual needs of the course scheduling, the thesis analyses the impact of combined classes on the course scheduling, and establishes a fitness function in accordance with the distributed uniformity of the combined classes and classes'time. Besides, the thesis also designs the corresponding selection algorithm, crossover operator, mutation operator, and the immune operator for the improvement of the above problems.