An Artificial Immune Univariate Marginal Distribution Algorithm

被引：1
|
作者：
Zhang, Qingbin [1 ]
Kang, Shuo [2 ]
Gao, Junxiang [2 ]
Wu, Song [1 ]
Tian, Yanping [1 ]
机构：
[1] Shijiazhuang Inst Railway Technol, Shijiazhuang 050041, Peoples R China
[2] Hebei Med Univ, Second Hosp, Shijiazhuang 050000, Peoples R China
来源：
COMPUTATIONAL INTELLIGENCE AND INTELLIGENT SYSTEMS | 2009年 / 51卷
关键词：
UMDA; artificial immune algorithm; hybridization;
D O I：
10.1007/978-3-642-04962-0_8
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Hybridization is an extremely effective way of improving the performance of the Univariate Marginal Distribution Algorithm (UMDA). Owing to its diversity and memory mechanisms, artificial immune algorithm has been widely used to construct hybrid algorithms with other optimization algorithms. This paper proposes a hybrid algorithm which combines the UMDA with the principle of general artificial immune algorithm. Experimental results on deceptive function of order 3 show that the proposed hybrid algorithm can get more building blocks (BBs) than the UMDA.
引用
收藏
页码：66 / +
页数：2
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