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针对传统遗传算法收敛速度慢、稳定性能差的缺陷,根据云计算思想提出一种遗传算法的考试系统组卷算法。该算法利用正态云模型的随机性和倾向性,动态调整遗传算法的个体选择适应度值和交叉概率和变异概率,以加快算法向最优解的逼近速度,可以在试题库中按照试题类型、试题数量、曝光度等约束条件进行快速搜索,系统通过选择、交叉和变异等操作,从试题库中自动地查找和组织出一些不同类型、不同难度、不同章节范围的试题来组成一套最佳的试卷,实现了快速自动组卷功能。
Aiming at the shortcomings of traditional GA, such as slow convergence rate and poor stability, a genetic algorithm test paper assembly algorithm based on cloud computing is proposed. The algorithm uses the stochastic and propensity of the normal cloud model to dynamically adjust individual selection fitness and crossover probability and mutation probability of genetic algorithm to speed up the approximation of the algorithm to the optimal solution. , The number of questions, exposure and other constraints for rapid search, the system through the selection, cross and mutation and other operations, from the test database automatically find and organize a number of different types, different levels of difficulty, different chapters of questions to form a set of the most Good papers, to achieve a fast automatic paper volume function.