摘要
针对遗传算法的K-Means聚类算法在遗传过程中容易受到适应度最大染色体的影响,存在过早收敛于局部最优值和遗传算法的局部搜索性能较差的问题,提出了结合混沌优化方法形成的混合遗传算法。仿真实验表明:该方法有效地克服了遗传算法的早熟问题,从而得到最优的聚类中心。
K-means algorithm based on genetic technique has a disadvantage that local optimal value is obtained earlier, because the largest fitness chromosome easily influences this algorithm in genetic process, and genetic algorithm possesses very poor local search performance. By combining the properties of both chaos optimization method and genetic algorithm, a new combinatorial optimization approach, the hybrid evolutional programming, is proposed in this paper. The experimental results show this algorithm avoids limitation of genetic algorithm.
出处
《华东理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2005年第2期219-222,共4页
Journal of East China University of Science and Technology
关键词
数据挖掘
遗传算法
混沌优化
聚类
data mining
genetic algorithm
chaos optimization
clustering
