HAO Qinxia, WANG Yang, LI Guomin, et al. Online intelligent examination system of coal mine enterprises based on improved GAAA algorithm[J]. Industry and Mine Automation, 2015, 41(7): 25-30. doi: 10.13272/j.issn.1671-251x.2015.07.007
Citation: HAO Qinxia, WANG Yang, LI Guomin, et al. Online intelligent examination system of coal mine enterprises based on improved GAAA algorithm[J]. Industry and Mine Automation, 2015, 41(7): 25-30. doi: 10.13272/j.issn.1671-251x.2015.07.007

Online intelligent examination system of coal mine enterprises based on improved GAAA algorithm

doi: 10.13272/j.issn.1671-251x.2015.07.007
  • Publish Date: 2015-07-10
  • An online intelligent examination system of coal mining enterprises was proposed based on analysis of actual examination requirements of enterprise employees. A Web examination system is built firstly according to professional characteristics of the employees, and the system is optimized functionality. Secondly, GAAA algorithm integrated with genetic algorithm and ant algorithm is improved. The improved GAAA algorithm firstly updates ant colony pheromone, then searches test database by a comprehensive search of genetic algorithm and parallel distribution of ant algorithm for optimizing group volume of the database. At last, the improved GAAA algorithm is applied in the system to achieve examination of employees at anytime they like and anywhere they are and auto-generating examination paper. The practical application shows that speed of auto-generating examination paper of the system is high, repetition rate is low, the algorithm is feasible, and the system can meet practical requirements of online examination effectively and improve online examination efficiency of coal mine enterprise.

     

  • loading
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (83) PDF downloads(2) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return