Research On Intelligent Test Paper Generation Based On Improved Genetic Algorithm

With the rapid development of computer technology and artificial intelligence,the research of intelligent test paper generation system has become more and more significant to experts and scholars.The test paper generation is a constrained multi-object optimization problem. Because the generation algorithm is the key factor in test paper generation efficiency and quality, the purpose of this research is to design an effective algorithm in order to make the test paper generated by the system fit the needs furthest.After studying and analyzing many domestic and overseas academic papers,the constraint conditions of the intelligent test paper generation are analyzed according to the education test theory. This paper mainly studies the use of generation algorithm in the test paper generation. After a careful analysis of each binding condition in the test paper, a new mathematical model of intelligence test paper generation system based on the improved genetic algorithm is given. In order to avoid slow convergence and local convergence of simple genetic algorithm for intelligent test paper generation,a kind of improved genetic algorithm has been proposed in this paper. This algorithm can quickly en1arge the search space and to stabilize the individual diversity of the group. And have realized automatic test paper-making with improve genetic algorithm, improved genetic algorithm adopts segment real number code, and then the question number maps gene directly. Real number code avoid decoding procedure, it may enhance operation efficiently. In addition crossover and mutation operation conduct in the interior of each section,it may guarantee the quantity of each type correct matching and different knowledge point of the question of the same type in the process of test paper-making. And the paper analyzed main functions of the intelligent examination system,a intelligent test paper generation system is designed.The results of the experiments show that the new method is more efficient and effective to deal with the problem of intelligent test paper generation.