基于改进遗传算法智能组卷的研究

组卷问题是解决在一个多约束条件下的多目标参数优化的问题，在研究组卷数学模型的基础上，对遗传算法进行了改进，通过对染色体分段编码、确定适应度函数、分段初始化种群、交叉概率和变异概率的自适应调整及最优个体保存策略措施，实现了智能组卷。通过实验数据测试结果表明，改进遗传算法比简单遗传算法能更好地完成组卷，有更高地组卷效率，有较好地实用性。

Research on Iintelligent Auto Generating Test Paper Based on Improved Genetic Algorithm

Test paper and its essence is to solve the multi-objective optimization problem of a multi constraint conditions, based on the research on the mathematical model of test paper, genetic algorithm is improved, the chromosome segment coding, fitness function, to determine the population initialization, adaptive crossover probability and mutation probability adjustment and the best individual preservation strategy such measures, the intelligent test paper method. The results show that, the improved genetic algorithm can better complete the test paper than the traditional genetic algorithm, has higher working efficiency.