An Intelligent Test Paper Generation Method Based on Genetic Particle Swarm Optimization

摘要：

　　This paper mainly studies an intelligent test paper generation method based on genetic particle swarm optimization.Firstly, this paper introduces the index system and the constraint condition of the intelligent test paper generation.Moreover, this paper proposes the intelligent test paper generation method that combines an improved adaptive genetic algorithm with particle swarm optimization.In the algorithm, the initial population is generated using the particle swarm optimization by designing proper chromosome coding and then determining the fitness function.Next, new population are obtained by processing the initial population using the genetic algorithm.As a result, operation results are output.At last, this paper analyzes the factors that affect the result of intelligent test paper generation through experimental verification.The research shows that the genetic particle swarm optimizations operation speed isvery fast and its operation results are much accurate.

关键词： intelligent test paper generation genetic algorithm particle swarm optimization multi-objective optimization

作者: Zhenfeng Hu Chunxiao Xing

作者单位: Research Institution of Information Technology Tsinghua University Beijing, China

会议类型: 国际会议

会议名称: The 13th Web Information Systems and Applications Conference(第十三届全国web信息系统及其应用学术会议)(WISA2016)、The 1st Symposium on Big Data Processing and Analysis)( BDPA 2016)第一届全国大数据处理与分析学术研讨会、The 1st Workshop on Information System Security)(ISS2016)(第一届全国信息系统安全研讨会

会议地点: 武汉

会议语种:英文

页码: 188-194

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）