一种改进的遗传算法及其在组卷系统中的应用

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.

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

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.