基于自适应遗传优化k-means算法的高校学情分析

Analysis of College Student Situation based on Adaptive Genetic Optimization K-Means Algorithm

为对高校学生学习过程与学习行为进行深度分析,帮助教师实现精准化教学,本文基于某高校计算机及相关专业学生数字逻辑课程学习过程相关数据,探索一种自适应策略的遗传优化k-means算法来进行高校学情分析。首先针对k-means算法存在的不足,提出通过遗传算法的交叉操作和变异操作获取最优解,同时通过自适应策略动态地调整交叉概率和变异概率,避免过早产生次优解;其次对学生数字逻辑学习过程相关数据执行自适应策略的遗传优化k-means算法;最后对算法执行结果进行分析。结果表明,本文研究的基于自适应策略的遗传优化k-means算法能够获得更加有效的分析结果。

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.