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.