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

作　　者：DUHaifeng GONGMaoguo JIAOLicheng LIURuochen 

机构地区：[1]InstituteofIntelligentInformationProcessingandKeyLaboratoryofRadarSignalProcessing,XidianUniversity,Xi＇an710071,China [2]InstituteofIntelligentInformationProcessingandKeyLaboratoryofRadarSignalProcessing,XidianUniversity,Xi＇an710071,China／／SchoolofMechanicalEngineering,Xi＇anJiaotongUniversity,Xi＇an710049,China

出　　处：《Progress in Natural Science:Materials International》2005年第5期463-471,共9页自然科学进展·国际材料（英文版）

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

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.Based on the clonal selection theory and immune memory theory, a novelartificial immune system algorithm, immune memory clonal programming algorithm (IMCPA), is putforward. Using the theorem of Markov chain, it is proved that IMCPA is convergent. Compared withsome other evolutionary programming algorithms (like Breeder genetic algorithm), IMCPA is shown tobe an evolutionary strategy capable of solving complex machine learning tasks, like high-dimensionalfunction optimization, which maintains thediversity of the population and avoids prematurity tosome extent, and has a higher convergence speed.

关 键 词：clonal selection immune memory artificial immune system evolutionaryalgorithms Markov chain 

分 类 号：O211.67[理学—概率论与数理统计]