Research on Intelligent Test Paper Generation Based on Improved Genetic Algorithm
Traditional algorithms of intelligent test paper generation have the disadvantages of slow convergence, low success rate and poor quality. In this article a new method of test paper generation is given which is based on partition binary coding and improved genetic algorithm focused on improving the process of selection. This method uses independent question database. The new method is more efficient and easier to get over premature convergence than the traditional algorithms. It is proved by a number of experiments provided by this article.
improved genetic algorithm intelligent test paper generation mathematical model
Huang Ming Tang Ling Liang Xu
Software Technology Institute, Dalian Jiao Tong University, Dalian 116028
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
2967-2970
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）