现代电子技术2012，Vol.35Issue(18)：80-82,3.
基于改进遗传算法的自动组卷问题研究
Test paper auto-generation based on improved genetic algorithm
张亚昕1
作者信息
- 1. 西安铁路职业技术学院,陕西西安710014
- 折叠
摘要
Abstract
In order to solve the problems prone to premature convergence and slow convergence of the traditional genetic algorithm in test paper auto-generation, a test paper auto-generation method based on an improved genetic algorithm is proposed. The segmented binary encoding strategy is adopted to carry out the initial population selection for each subspace to ensure the rich patterns containing in initial population and increase the likelihood of convergence to the global optimum search. The cross operator and mutation operator were optimized. The adaptive adjustment that the crossover probability and mutation probability vary with the change of solution was realized. The experimental results show that the improved genetic algorithm can effectively solve the problem existing in the test paper auto-generation, and improve the convergence speed and the success rate of the test paper auto-generation.关键词
遗传算法/自动组卷/适应度函数/分段二进制编码Key words
genetic algorithm/ test paper auto-generation/ fitness function/ segmented binary encoding分类
信息技术与安全科学引用本文复制引用
张亚昕..基于改进遗传算法的自动组卷问题研究[J].现代电子技术,2012,35(18):80-82,3.
