计算机与现代化 ›› 2012, Vol. 208 ›› Issue (12): 19-22.doi: 10.3969/j.issn.1006-2475.2012.12.006

• 算法设计与分析 • 上一篇    下一篇

用于智能组卷的自适应小生境复合遗传算法

张旭涛1,张勇2,黄红萍3   

  1. 1.徐州机电工程高等职业学校电气工程系,江苏徐州221011;2.中国矿业大学信电学院,江苏徐州221011; 3.江苏师范大学,江苏徐州221116
  • 收稿日期:2012-10-12 修回日期:1900-01-01 出版日期:2012-12-22 发布日期:2012-12-22

Adaptive Niche Genetic Algorithm for Intelligent Test Generation

ZHANG Xu-tao1, ZHANG Yong2, HUANG Hong-ping3   

  1. 1. Department of Electrical Engineering, Xuzhou Mechanical and Electrical Engineering High School, Xuzhou 221011, China;2. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221011, China;3. Jiangsu Normal University, Xuzhou 221116, China
  • Received:2012-10-12 Revised:1900-01-01 Online:2012-12-22 Published:2012-12-22

摘要: 传统遗传算法的选择策略缺乏多样性保护机制,易出现早熟收敛。为解决智能组卷问题,采取小生境技术完成遗传操作中的种群进化机制。利用个体浓度的大小,设置自适应变异算子,保证种群多样性,防止种群陷入局部收敛;增加阈值以保证算法在接近最优解时回归到自适应遗传算法,简化算法计算量,加快算法的收敛速度。本文提出一种自适应与小生境技术复合遗传算法,来均衡算法的全局搜索和局部快速开发能力。最后,实例验证了所提算法的有效性。

关键词: 遗传算法, 小生境技术, 智能组卷, 自适应变异, 阈值

Abstract: The traditional genetic algorithm’s strategy of choice lacks of diversity protection mechanism, it easily appears premature convergence. To solve the problem of intelligent test generation, this paper adopts niche technology to complete the genetic operation of population’s evolution mechanism, setting the adaptive mutation operator to ensure the diversity of the population and prevent population from the local convergence by the size of the individual concentration; adding the threshold is to ensure that when the algorithm is close to the optimal solution, the algorithm returns to the adaptive genetic algorithm in order to simplify the algorithm and accelerate the convergence speed. This paper proposes an adaptive and niche technology combined genetic algorithm, in a balance of the global searching algorithm and local rapid development ability. Finally, an example verifies the effectiveness of the proposed algorithm.

Key words: genetic algorithm, niche technology, intelligent generation, adaptive mutation, threshold

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