The Research And Application On Composition Papers System Base On Genetic Algorithm

The problem of composing papers is a multi-objective optimization problem to meet the multi-constrained. Using the traditional mathematical methods to solve the problem of composing papers is very difficult. In the process of auto-generating examination papers, the quality and efficiency of composition papers system are all determined by the design of the composition papers Algorithmic and paper questions-database. Currently, the algorithm to achieve composition papers mainly have random search algorithm, backtracking test algorithm, priority-based algorithm, the error compensation algorithm, genetic algorithm and so on. The paper using composition papers system must be used to the maximum extent possible to meet the needs of users. So, how to design an efficient composition paper algorithm, which quickly generates a set of questions and compose of paper from the questions-database, is the focus of this paper.Genetic Algorithm is global search and optimizing algorithm which simulates the natural selection and genetic mechanism on Darwinian evolution theory. Because of the characteristics that have well global optimization and fast convergence, it is very suitable for solving the problem of composing papers. After analyzing paper properties, this article established the index system, and established a mathematical model of Multi-objective function optimization. Finally, the improved genetic algorithm is applied to composition papers system according to the mathematical model.The system uses Visual Studio 2008 development tools, is based on the technology of ASP.NET 2.0 using C# language, and using the database of SQL Server 2008. In the process of realization system, using the software engineering way, using the RUP development model that is more popular currently, genetic algorithm is applied to the Composition papers system successfully, and in the service outsourcing platform be achieved. The experimental results show that the efficiency of the composition papers and the quality of papers generated has significantly improved than the traditional genetic algorithm. The algorithm is proved to be effective, achievable.