基于优化初始种群的自动组卷算法  

Automatic Generation Algorithms Based on Optimization Initial Population

作　　者：唐永红[1] 龚安[1] 王超[1] 

机构地区：[1]中国石油大学(华东),山东青岛266580

出　　处：《计算机与现代化》2012年第7期38-40,43,共4页Computer and Modernization

摘　　要：针对遗传算法和改进遗传算法初始种群的不确定性,提出一种优化初始种群的遗传算法。该算法首先进行局部寻优,将局部较优解按题型的不同随机组合,组成遗传算法的初始种群,然后通过遗传交叉和单点变异得到一个较优解,最后将算法应用到《数据库系统》课程的自动组卷。结果表明,该方法有很好的组卷效率,能较好满足组卷的要求。Aiming at the uncertainty of initial population of the genetic algorithm and the improved genetic algorithm, this paper proposes an optimized initial generation genetic algorithm. Firstly the algorithm finds the local optimized solution, then different types of solutions random combinations, gets the formation of initial generation of genetic algorithm. The next, an optimal solution by genetic crossover and single-point mutation is got. Finally, the algorithm is applied to the automatic generation of database sys- tems course. The results show that the method has good efficiency to the examination paper, the requirements of the examination paper can be well positioned to meet.

关 键 词：遗传算法 优化初始种群 单点变异 自动组卷 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]