基于遗传算法的自动组卷系统的设计与实现

组卷问题是一个在一定约束条件下的多目标参数优化问题,采用传统的数学方法求解十分困难,自动组卷的效率和质量完全取决于试题库设计以及抽题算法的设计。本文以省级《数据结构》精品课程建设为背景,在分析传统组卷算法的优缺点和组卷策略参数的基础上,选用遗传算法,设计并实现了一个自动组卷系统。该算法按照试题类型、数量、难度、区分度、分值和时间等约束条件进行快速搜索并寻找最优解,其中采用分组自然数编码,减少了染色体长度空间;运用自适应理论改进交叉概率及变异概率,使得算法总能找到合适的交叉和变异概率。系统采用C#.NET编程实现,目前已应用于实际教学,取得了良好的教学效果。

Design and Implementation of an Automatic Test Paper Generation System Based on Genetic Algorithms

The generation of test papers is an optimized problem with multi-objective parameters under a certain restrictive condition.The optimization is implemented very difficultly by traditional methods.The quality and efficiency of auto-generation is determined by the design of test question databases and algorithms to extract questions.Based on the construction of the provincial excellent course Data Structure,an analysis of the features of traditional test paper algorithms and the parameters of strategy,this paper presents the design and realization of an automatic test paper generation system based on genetic algorithms.This algorithm searches for the best answer according to such restrictive conditions as test question types,quantity,difficulty level,difference level,score and answering time.In addition,the natural code is used in this algorithm in order to decrease the space of chromosomes.The crossover probability and the mutation probability are improved with the self-adaptation theory,so that the proper numbers of crossover probability and mutation probability can be found.After the accomplishment based on C#.NET,the system has been applied in practice and achieved good effect.