计算机工程 ›› 2009, Vol. 35 ›› Issue (1): 165-167.doi: 10.3969/j.issn.1000-3428.2009.01.056

• 人工智能及识别技术 • 上一篇    下一篇

交互式遗传算法的改进方法及应用

巩 固1,黄永清1,郝国生2   

  1. (1. 徐州师范大学计算机科学与技术学院,徐州 221116;2. 中国矿业大学信息与电气工程学院,徐州 221008)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-01-05 发布日期:2009-01-05

Improved Method of Interactive Genetic Algorithm and Application

GONG Gu1, HUANG Yong-qing1, HAO Guo-sheng2   

  1. (1. College of Computer Science and Technology, Xuzhou Normal University, Xuzhou 221116; 2. College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou 221008)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-01-05 Published:2009-01-05

摘要: 针对交互式遗传算法中收敛速度慢和容易陷入局部收敛的缺点,提出遗传算法算子的一些改进策略,即利用定位部分优良基因方法,使这些基因较好地遗传到下一代。改进的算法能有效减少无效的交叉操作,收敛速度、全局搜索能力和局部搜索能力比交互式遗传算法均得到了较大的提高。将改进的算法应用于服装设计中,实验结果证明了改进后的算法在平均收敛代数和收敛到最优解的概率都优于遗传算法。

关键词: 交互式遗传算法, 改进策略, 早熟收敛, 搜索空间, 遗传算子

Abstract: In order to effectively solve the disadvantages of Interactive Genetic Algorithm(IGA) which converges slowly and easily runs into local extremism, some improved strategies are proposed. The improved strategies which reserve some elitist genes can reduce useless crossover effectively and thus the convergence speed and the search capability are greatly improved when the Elitist Reserved Genetic Algorithm(ERGA) that keeps best strategies compared with IGA. The efficiency of the proposed method is analyzed, at the same time the improved algorithm is applied to fashion design and the simulation validates its efficiency. Experimental results show that the rapidity of convergence and the probability of the improved algorithm can be superior to GA.

Key words: Interactive Genetic Algorithm(IGA), improved strategies, premature convergence, searching space, genetic operators

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