DocumentCode :
2168326
Title :
Immunity clonal strategies
Author :
Ruochen, Liu ; Haifeng, Du ; Licheng, Jiao
Author_Institution :
Natinal Key Lab. for Radar Signal Process., Xidian Univ., Xi´´an, China
fYear :
2003
fDate :
27-30 Sept. 2003
Firstpage :
290
Lastpage :
295
Abstract :
Based on the clonal selection theory, the main mechanisms of clone, which will be explored in the field of artificial intelligence, are analyzed in this paper. An improved evolutionary strategy algorithm, immunity clonal strategy algorithm (ICS), which includes immunity monoclonal strategy algorithm (IMSA) and immunity polyclonal strategy algorithm (IPSA), is put forward. Compared with the classical evolutionary strategy algorithm (CES), ICS is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like multi-objective optimization, and the results are better. Using the theories of Markov chain, it is proved that ICS algorithm is convergent.
Keywords :
Markov processes; artificial life; evolutionary computation; learning (artificial intelligence); Markov chain; artificial intelligence; classical evolutionary strategy algorithm; clonal selection theory; clone; complex machine learning task; immunity clonal strategy algorithm; immunity monocolonal strategy algorithm; immunity polyclonal strategy algorithm; multiobjective optimization; Artificial intelligence; Cloning; Computational modeling; Computer simulation; Evolution (biology); Genetic mutations; Immune system; Machine learning; Machine learning algorithms; Signal analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Multimedia Applications, 2003. ICCIMA 2003. Proceedings. Fifth International Conference on
Print_ISBN :
0-7695-1957-1
Type :
conf
DOI :
10.1109/ICCIMA.2003.1238140
Filename :
1238140
Link To Document :
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