一种改进的遗传算法及其在组卷系统中的应用  

An Improved Genetic Algorithm and Application to Test Paper Auto-Generation

作　　者：尹红卫[1] 刘云如[1] 易叶青[1] 

机构地区：[1]湖南人文科技学院计算机系,娄底417000

出　　处：《现代计算机》2006年第5期66-70,共5页Modern Computer

摘　　要：针对遗传算法容易出现早熟和收敛速度慢的问题,根据群体适应值分布的变化特点,提出一种新启发性的基于小生境技术的自适应遗传算法(ANGA)。其基本思想是:根据群体中各个个体的适应值分布情况加以启发,引入一个自适应的常数Cmin,通过自适应调整Cmin以适时改变群体适应值的分布,优化了各个个体被选择的概率,并以目前的计算机等级考试三级信息管理技术的组卷为例,采用ANGA算法进行了仿真计算。仿真结果表明,该算法能够在较短的时间内完成组卷,组卷效率、成功率高,对初值不敏感。To deal with the prematurity and the low convergence speed of genetic algorithm, a new adaptive genetic algorithm based on niches （ANGA） was developed according to the variety of population fitness distribution. The basic idea is as follows： inspired by the variety of population fitness distribution, a self-adaptive constant Cmin. was introduced. By adjusting Cmin according to the population fitness distribution, the selection probability of each population was optimized. The test paper auto-generation of the present ＂National computer rank examination（3）： information management technical＂ was taken as an example. The tests results indicate that： ANGA can be successfully applied in test paper auto-generation system; The simulation results show that the algorithm can finish a calculation within a short time, and the speed is quite fast, furthermore, the success rate is high, and the sensitivity to initial value is dull.

关 键 词：一遗传算法 组卷 小生境 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]