Abstract: The constraints of automatic test paper are optimized, and the algorithm is improved according to the examination hierarchy of knowledge points . The knowledge points weight grading table is used when initializing the population , and the chromosomes in the initial population are selected according to the knowledge weight order so that the population can meet the requirement of the assessment level of the knowledge points from the initial time. In the design of the genetic operators maintains the stability of the population of knowledge point .The results show that the improved algorithm can improve the coverage rate of important knowledge points while guaranteeing the coverage of knowledge points.

Key words: Automatic test paper generation, Genetic algorithms, Knowledge point weight, Assessment level

摘要： 优化了自动组卷的约束条件，并针对知识点的考核层次问题进行了算法改进。在初始化种群时使用知识点权重分级表，按知识点权重顺序来选取试题生成初始种群中的染色体，使种群从最初时起就满足对知识点的考核层次要求，并在遗传算子的设计中保持了种群知识点的稳定性。结果表明，改进算法在保证知识点覆盖率的同时，能提高重要知识点的覆盖率。

关键词: 自动组卷, 遗传算法, 知识点权重, 考核层次