一种改进的基于遗传算法的模糊C-均值算法  

An Improved Fuzzy C-means Algorithm Based on Genetic Algorithm

作　　者：侯惠芳[1] 刘素华[1] 

机构地区：[1]河南工业大学计算机科学系

出　　处：《计算机工程》2005年第17期152-154,共3页Computer Engineering

基　　金：河南省科技攻关基金资助项目(0224010011)

摘　　要：把遗传算法搜索的随机性和并行性引入到模糊聚类中,克服了模糊C-均值聚类的局部性和对初始聚类中心的敏感性;该改进算法中采用了适合于模糊聚类的树型编码方案,且在遗传算法中采用了适合于模糊聚类的树型编码方案。同时详细设计了该方法,将该算法引入仓储物害虫的模式识别分类系统中,实验结果表明了该算法的可行性和有效性。This paper inducts genetic algorithm into fuzzy clustering by using its searches randomly and parallelism.It solves the locality and the sensitiveness of the initial condition of fuzzy C-means clustering. A tree encoding scheme which is proper for the fuzzy clustering algorithm is determined to this improved algorithm,At the same time, this algorithm is designed in detail and inducts this algorithm into the system of pattern recognition classification for storedproducted insects.The results of experiments show that the proposed method is effective and feasible.

关 键 词：模糊聚类 遗传算法 树型编码 模拟退火算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]