关键词: 智能组卷, 多约束, 分级寻优, 无回溯算法, 知识点均匀分布

Abstract: Multi-constraint constrains and reduces the test success rate, and it is difficult to make the knowledge points uniformly and automatically distributed in intelligent generating test paper. To solve these above problems, a multi-constraint hierarchical optimization strategy was proposed. It used hierarchical method to reduce the problem size, and used the tree structure to manage knowledge points and realize uniform distribution of knowledge points. With regard to the low success rate and efficiency of small test bank in generating test paper, a forecast calculation algorithm without backtracking was put forward based on hierarchical optimization algorithm to increase the test success rate. The experimental results indicate that the algorithm is suitable for large, medium and small question database, and all of them have good results.

Key words: intelligent test paper generating, multi-constraint, hierarchical optimization, algorithm without backtracking, knowledge uniform distribution