Research on intelligent test paper auto-generating algorithm based on improved GA

A high quality test paper auto-generating is generally at the cost of considerable time. In order to handle the contradiction between quality and speed of combination, an improved subsection-single-point crossover and mutation GA based on real segment coding and condition matrix is proposed in the thesis. In the design of genetic operators roulette wheel selection, subsection-single-point crossover and mutation policy are synthetically used. And the population can be evolved continuously by the optimal-store policy. The algorithm has been proved feasible and effective through test and comparison between other algorithms. At last the validating system enhances the users' condition constraints for test paper through manual inching, thus the system is more simple and practical.