Analysis of College Student Situation based on Adaptive Genetic Optimization K-Means Algorithm
In order to conduct in-depth analysis of the learning process and behavior of college students and help teachers achieve precise teaching,this article explores an adaptive strategy of genetic optimization k-means algorithm based on data related to the learning process of the digital logic course for computer and related majors in a certain university for college student situation analysis.Firstly,according to the shortcomings of the k-means algorithm,it is proposed to obtain the optimal solution through genetic algorithm's crossover and mutation operations.At the same time,an adaptive strategy is used to dynamically adjust the crossover and mutation probabilities to avoid premature generation of suboptimal solutions.Then,execute an adaptive genetic optimization k-means algorithm on the relevant data of the students'learning process of digital logic.Finally,analyze the execution results of the algorithm.The analysis results indicate that the genetic optimization k-means algorithm based on adaptive strategy studied in this article can obtain more effective analysis results.
analysis of college student situationk-means algorithmgenetic optimizationadaptive
NETL
NSTL
万方数据
