TY - JOUR
T1 - Novel algorithm of artificial immune system for high-dimensional function numerical optimization
AU - Du, Haifeng
AU - Gong, Maoguo
AU - Jiao, Licheng
AU - Liu, Ruochen
PY - 2005/5
Y1 - 2005/5
N2 - 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.
AB - 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.
KW - Artificial immune system
KW - Clonal selection
KW - Evolutionary algorithms
KW - Immune memory
KW - Markov chain
UR - https://www.scopus.com/pages/publications/19644388894
U2 - 10.1080/10020070512331342410
DO - 10.1080/10020070512331342410
M3 - 文章
AN - SCOPUS:19644388894
SN - 1002-0071
VL - 15
SP - 463
EP - 471
JO - Progress in Natural Science
JF - Progress in Natural Science
IS - 5
ER -