Abstract
Abstract 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 IMPCA is convergent. Compared with some other evolutionary programming algorithms (like Breeder genetic algorithm), IMPCA 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. ∗Supported by National Natural Science Foundation of China (Grant Nos. 60133010 and 60372045)
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