面向自动组卷问题的改进类电磁算法

Improved electromagnetism-like mechanism algorithm for automatic test paper problem

类电磁算法(EM)中局部搜索是按一定步长进行线性搜索,在这个范围内寻找个体在某一维上的最优值。由于步长的限定,求得的该维上最优值可能远离实际的最优值。采用遗传算法(GA)中选择因子和交叉因子可以很好地解决这一问题。在组卷系统中,通过基于遗传算法改进的类电磁算法(Based Genetic Electromagnetism-like Mechanism Algorithm,GEM)与GA算法以及采用线性局部搜索的EM算法实验的比较,证明该算法有更高的组卷效率。

Local search of Electromagnetism-like Mechanism algorithm（EM） is linear searched by a certain step length,which finds the optimal value in a particular dimension in this individual.As the limit of step length,the optimal value may be far from the actual value of a particular dimension.To solve this problem, the select and crossover factor of Genetic Algorithm （GA） is the good way.In the automatic test paper system, compared with the Based Genetic Electromagnetism-like Mechanism Algorithm（GEM）,GA and the EM of linear search in experiments show that the GEM has higher efficiency test paper.