| A novel algorithm of artificial immune system for high-dimensional function numerical optimization |
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| 作者姓名： | DU Haifeng GONG Maoguo JIAO Licheng LIU Ruochen |
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| 作者单位： | 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,Institute of Intelligent Information Processing and Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China,Institute of Intelligent Information Processing and Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China,Institute of Intelligent Information Processing and Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China |
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| 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.
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| 关 键 词： | clonal selection immune memory artificial immune system evolutionary algorithms Markov chain. |
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