A novel algorithm of artificial immune system for high-dimensional function numerical optimization

作     者：DU Haifeng 1,2*, GONG Maoguo1, JIAO Licheng1 and LIU Ruochen1(1. Institute of Intelligent Information Processing and Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China 2. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China) 

作者机构：Institute of Intelligent Information Processing and Key Laboratory of Radar Signal Processing Xidian University Xi''an 710071 China School of Mechanical Engineering Xi''an Jiaotong University Xi''an 710049 China 

出 版 物：《Progress in Natural Science:Materials International》 (自然科学进展·国际材料(英文))

年 卷 期：2005年第15卷第5期

页      面：463-471页

核心收录：

学科分类：08[工学] 09[农学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0901[农学-作物学] 0836[工学-生物工程] 090102[农学-作物遗传育种] 0702[理学-物理学] 

基　　金：Supported by National Natural Science Foundation of China (Grant Nos. 60133010 and 60372045) 

主　　题：clonal selection immune memory artificial immune system evolutionary algorithms Markov chain. 

摘      要：Based on the clonal selection theory and immune memory theory, a novel artificial immune system algorithm, immune memory clonal programming algorithm (IMCPA), is put forward. Using the theorem of Markov chain, it is proved that IMCPA is convergent. Compared with some other evolutionary programming algorithms (like Breeder genetic algorithm), IMCPA is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like high-dimensional function optimization, which maintains the diversity of the population and avoids prematurity to some extent, and has a higher convergence speed.