Genetic algorithm-based course scheduling system design method

Abstract

The invention discloses a genetic algorithm-based course scheduling system design method, and belongs to the field of algorithm analysis and design in algorithm theories. Aiming at the problems of inaccurate encoding mode, fixed crossover variation rate and unreasonable fitness function of the genetic algorithm in the prior art, the encoding mode of the traditional genetic algorithm is improved, the traditional binary encoding mode is improved into a decimal encoding mode with specific significance, and the encoding mode is more standard and accurate. According to the method, the crossover rate and the mutation rate of the genetic algorithm can automatically change along with the fitness, and the algorithm can be quickly converged to a globally optimal solution by improving the self-adaptive crossover rate and the mutation rate of the genetic algorithm. A fitness function is improved in the aspects of teaching time period distribution uniformity, student curriculum arrangement uniformity, curriculum time period arrangement goodness and the like, experimental data comparison is performed on a traditional genetic algorithm and an improved genetic algorithm, and the performance and efficiency of the improved genetic algorithm in curriculum arrangement are more satisfactory.

Description

technical field [0001] In the present invention, the design is based on the research and design of the genetic algorithm course scheduling system. Through the improvement of the coding method of the traditional genetic algorithm, that is, the traditional binary coding is improved and designed into the decimal coding method, which makes the coding method more standardized and accurate. At the same time, the crossover rate and mutation rate are improved so that they can adapt to changes and quickly converge to the optimal solution. The fitness function is improved in terms of the number of individual conflicts in the population, the evenness of the distribution of teaching time slots, the evenness of class curriculum arrangements, the optimality of course time slot arrangements, and the utilization rate of classroom resources. Through the comparison of the average fitness value of the algorithm and the running time, the improved genetic algorithm is obviously better than the tr...

AI Technical Summary

Problems solved by technology

This resulted in students not being able to take conflicting courses

(2) Classroom arrangement is unreasonable

The classroom arrangement is too small, leading to the behavior of students occupying seats in advance, and many students even stand behind the classroom and the aisle to listen to the class. The crowded classroom space seriously affects the students' concentration in class.

Images