Research And Application Of Intelligent Test Paper Generating Strategy Based On Genetic Algorithm

With rapid development of information technology,online examination system is more and more widely used.As the core of online exams,Intelligent Test Paper Generation(ITPG)demands better performances for efficiency and quality of automatically test paper generating,which however is decided by the design of its algorithm.Genetic algorithms are simple and robust algorithms with global optimization capability,which are not constrained by search space constraints.They are suitable for ITPG.But there are obvious deficiencies of Genetic algorithms,such as immature convergence and short of climbing ability.This thesis researches ITPG methods based on Genetic algorithms and mainly completes the following work.Firstly,the index attributes of questions and papers are analyzed according to the classical test theory,and the relations of certain indexes are briefly introduced,with an assessment criterion.Secondly,the mathematical model of ITPG is proposed.The difference between user expectation and the index of the test paper is calculated according to ITPG's mathematical model,and then the objective function of the test paper is constructed.We present an enhanced method based on BP neural network to optimize the weight coefficient of objective function in ITPG model.Thirdly,a modified genetic algorithm is proposed.We adopt a multi-wheel roulette selection method based on the roulette method as the selection operation to optimize the individual.The optimal individual is reserved to ensure the convergence of the algorithm.Routing selection selects the crossover operation or the mutation operation with different probabilities to speed up the convergence rate of the algorithm.Crossover rate and mutation rate are adjusted adaptively.A reconstruction mutation operator is designed with two parts: crash operator and adaptive operator.Niche technology is imported to maintain population diversity.Experiments demonstrate that the improved genetic algorithm can solve the problem of typical function,especially the processing of multimodal function.Finally,the improved genetic algorithm is applied to ITPG system,and experiments are designed to evaluate the enhanced ITPG system.The experimental results show that the proposed ITPG system is effective and can meet the user's expectations.