DocumentCode
2749071
Title
Immune evolutionary algorithms
Author
Lei, Wang ; Licheng, Jiao
Author_Institution
Key Lab. for Radar Signal Process., Xidian Univ., Xi´´an, China
Volume
3
fYear
2000
fDate
2000
Firstpage
1655
Abstract
Three evolutionary algorithms, the immune genetic algorithm (IGA), the immune evolutionary programming (IEP) and the immune evolutionary strategy (IES), are presented based on the immune theory in biology, which are not only convergent but used for solving complex discrete optimization problems as well. They all construct an immune operator accomplished by two components, vaccination and immune selection. The methods for selecting vaccines and constructing an immune operator are also proposed. Simulations show that these algorithms can restrain the degenerate phenomenon and improve the searching capability of the existing algorithms, therefore increase the convergent speed greatly
Keywords
computational complexity; convergence; evolutionary computation; travelling salesman problems; degenerate phenomenon; immune evolutionary programming; immune evolutionary strategy; immune genetic algorithm; immune selection; searching capability; vaccination; Biological system modeling; Biology computing; Computational modeling; Evolution (biology); Evolutionary computation; Genetic algorithms; Genetic programming; Immune system; Power engineering and energy; Signal processing algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on
Conference_Location
Beijing
Print_ISBN
0-7803-5747-7
Type
conf
DOI
10.1109/ICOSP.2000.893419
Filename
893419
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