A Novel Artificial Immune Network Algorithm

Author

Tian, Xiang ; Yang, Hai-dong ; Deng, Fei-qi

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2159

Lastpage

2165

Abstract

A novel artificial immune network (AINet) algorithm is proposed in this paper. In the algorithm, a new method is introduced to confirm searching radius, which is based on "the age"-the generations when the cell exists in the network. Moreover, a novel preserving method is proposed to avoid the instability and degradation of the optimal results. These two measures not only increase the local searching efficiency of the AINet algorithm, but also improve the diversity of the network. As a result, the computational complexity is significantly decreased. Simulation experiments show that, compared with the canonical AINet, the proposed algorithm enhances the correctness by 6% and the speed by 34% at least

Keywords

computational complexity; optimisation; search problems; artificial immune network algorithm; computational complexity; local search problem; Artificial neural networks; Automation; Cloning; Clustering algorithms; Constraint optimization; Cybernetics; Degradation; Educational institutions; Electronic mail; Genetic mutations; Immune system; Machine learning; Machine learning algorithms; Recruitment; Artificial Immune Algorithm; Function Optimization; Immune Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258613

Filename

4028421