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
Link To Document :
بازگشت