Research on Intelligent Generating Test Paper Based on Parallel Genetic Algorithm

  • Jianjun Li
  • Meng Wang
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 115)


The aim of test paper composing is to compose an optimization test paper that satisfies the parameters which the user inputs, so the test paper composing problem is a classical multi-objective linear programming problem. This paper proposes an intelligent algorithm to generating test paper based on Parallel genetic algorithm, and provides a set of schemes of making papers of different degree of difficulties display in normal distribution. The algorithm adopts a new decimal system of subsection code, improves the traditional method of initializing the population and optimizes course of search. The experiment proves that this algorithm has better performance thus is more practical.


Parallel genetic algorithm artificial intelligence Intelligent Generating Paper 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, M., Jin, H., Wang, X.: Research on set at random algorithm in intelligent generating test paper. Computer Engineering and Design 27(19), 3583–3585 (2006)Google Scholar
  2. 2.
    Wang, Y., Zhang, Z., Cui, J.: Mathematical Model and Algorithm of Intelligent Test Paper Auto-generation System from Item Pool. Systems Engineering —Theory & Practice (9), 85–97 (2004)Google Scholar
  3. 3.
    Dong, M., Huo, J., Wang, X.: Model Management System for IRT-based Test Construction. Journal of University of Science and Technology of China 34(5), 612–617 (2004)Google Scholar
  4. 4.
    DeJong, K.A., Spears, W.M.: Using Genetic Algorithms to solve NP complete problems. In: Processings of the Third International Conference on Genetic Algorithms, pp. 124–132 (1989)Google Scholar
  5. 5.
    Wei, J.: Intelligent Test Paper Composition Systems Based on SOM. Journal of Liaoning Normal University (Natural Science Edition) 28(3), 283–284 (2005)Google Scholar
  6. 6.
    Luo, X., Sun, M., Tsou, B.K.: Covering Ambiguity Resolution in Chinese Word Segmentation Based on Contextual Information. In: Proceedings of the 19th COLING, pp. 598–604 (2002)Google Scholar
  7. 7.
    Li, J., Kwan, R.S.K.: A fuzzy genetic algorithm for driver scheduling. European Journal of Operational Research 147(2), 334–344 (2003)MATHCrossRefGoogle Scholar
  8. 8.
    Andre, J., Siarry, P., Dognon, T.: An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization. Advances in Engineering Software 32, 49–60 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Lushan CollegeGuangXi University of TechnologyLiuZhouChina
  2. 2.Department of Computer EngineeringGuangXi University of TechnologyLiuZhouChina

Personalised recommendations