基于元胞遗传算法的智能组卷的研究

智能组卷是一个包含多重约束条件的目标优化问题，遗传算法的群体搜索策略可以为多目标优化提供较好的解决方案。但传统的遗传算法在组卷过程中存在收敛速度慢、收敛性较差等缺点，组出的试卷质量不高。提出一种新的元胞遗传组卷算法，将群体中的所有元胞按照一定的演化规则演化之后，再进行遗传操作，并把该算法应用到智能组卷中。实验结果表明，新的元胞遗传组卷算法与传统的遗传组卷算法相比，可以有效地提高收敛速度，并能进一步改善收敛性，组出的试卷更加符合人们的要求。

Intelligent test paper construction method based on cellular genetic algorithm

The intelligent test paper construction is a multiple constraints objective optimization problem. The groups search strategy of the genetic algorithm can provide a better solution for multi-objective optimization. Traditional genetic algorithm has shortcomings such as slow convergence and poor convergence in test paper process. The error of the test paper is relatively large. This paper proposes a new intelligent test paper construction method based on cellular genetic algorithm. Compared to the traditional test paper construction method, the novel approach can effectively improve the convergence rate, and further improve the convergence in intelligent test paper. The result is more in line the requirements of users.