Research And Application Of Key Technology Of Intelligent Course Arrangement Based On Improved Genetic Algorithm

With the continuous development of the Internet and computer technology,intelligent scheduling has become one of the key tasks for colleges and universities in handling teaching management.Faced with a large number of students and teachers,different types of classrooms,limited teaching resources,and restrictive conditions,traditional manual manual arranging courses consume a lot of time and manpower,but it is difficult to solve the above problems of arranging classes.Therefore,it is imminent to implement intelligent scheduling through computers and corresponding algorithms.Scheduling is a typical timetable problem,while the timetable problem is a NPcomplete problem.The focus of solving NP-complete problems depends on the algorit hm chosen.The genetic algorithm can solve the NP complete problem very wel.Genetic algorithms rely on the law of survival of the fittest in Darwinian evolution theory,and rely on natural selection and genetic theory to form a model of simulated biologica l evolution.However,the genetic algorithm will cause premature compression of the solution space due to the existence of supernormal individuals,thus affecting the overall performance of the algorithm.Therefore,this paper first sorts the chromosomes of the curriculum according to the curriculum and teacher's weight in the early stage of the genetic algorithm.According to the constraint conditions,the corresponding fitness function is designed to avoid the occurrence of abnormal individuals,so as to ensure that the solution space will not be compressed prematurely.At the same time,in the later stage of the genetic algorithm,adaptive crossover and mutation operations are used to adaptively adjust the parameters of the genetic algorithm to improve the convergence accuracy of the genetic algorithm and obtain a global optimal solution.In addition,there is a phenomenon of scheduling conflicts in the process of class scheduling.The phenomenon of conflict is caused by the existence of a certain association rule between the various elements of the curriculum,and the genetic algorithm can not find such rules.Apriori algorithm and FP-Growth algorithm are two classical traditio na l association rule algorithms.However,the association rules between the various elements of curriculum scheduling belong to the implicit association rules.The traditio na l association rules algorithm is not enough to solve the implicit association rules.Therefore,this paper first preprocesses the conflicting data set,adopts the weights of the trigonometric membership function in fuzzy mathematics,and then divides the conflicting dataset items into core items and adsorption items,establishes the maximum fuzzy pattern tree,and mines the maximum blurring.Mode items.The maximum fuzzy mode item after mining is the conflict item to be solved.Finally,the problem of class conflict is solved through manual tuition.This article features two innovations.First,the optimal solution of class scheduling is calculated through an improved adaptive genetic algorithm.Second,solve the problem of class conflict through the maximum fuzzy pattern algorithm.Experiments show that the improved genetic algorithm is better than the traditional genetic algorithm in terms of average fitness value and time.At the same time,the use of the maximum fuzzy mode algorithm effectively solves the problem of class scheduling conflicts,thereby improving the solution to the problem of class scheduling.