Research And Realization Of An System Of Test Paper Composition

The test paper generating is an optimized problem to Muti-objective parameter with certain restriction. The optimization is implemented very difficulty by traditional method. The quality and efficiency of auto-generating test paper is all determined by the test-database designs and getting problems-terms algorithm. Several algorithms have been applied in test paper auto-generation, such as priority strategy, randomization, and trace and trial strategy, genetic algorithm and so on. When encountering with big solution space, multimodal problems, these algorithms are usually inclined to run into local extremum or difficult to solve. Since generated test paper needs to fulfill variable demands with great randomness and rationality, The aim of the research is to design an algorithm that can get a group of test problems terms quickly, which fit the restriction of test requirement. After reading and analyzing a great deal of articles from Domestic and overseas , Genetic Algorithm is selected as the way to produce test paper, and a general-purpose system of auto-generating test paper is designed. Some simulations have been made by MATLAB Genetic Algorithm and Direct Search Toolbox, and got the best result by comparing the different crossover probability and differentiation probability. The graphical user interface is designed by Visual Basic 6.0. it use the Access database as the background information database, input the information of test question, and product test information bank of different subject, at last implement the request of complex test paper generating .The system based on General Genetic Algorithm is designed to search the best answer according to such restriction conditions, like testing question types, terms scalar, teaching time, difficulty degree, distinguish and answer time and so on. Finally, it can find the best answer and get ideal test paper. The main works of the research can be show as follows:1.Establish a model of generating test paper problem and simplify it.2.Improve the traditional genetic algorithm at Chromosome-Code, replacing the Binary-Code with NaturalNum-Code and directly using the questionID as it. So save the time of Coding and Encoding.3.Applying self-adapting theory to improve the Across-probability and Mutation-probability, it making the Genetic Algorithm can finding the optimizing Across-probability and Mutation-probability to suit itself.4.Contrasting the Small-scalar Selecting Algorithm and K-times Optimized Selecting Algorithm through test, we can proved that the former is more advantage than the later by some test-data.