A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set
In this article, we proposed a negative selection algorithm which based on hierarchical level cluster of self dataset CB-RNSA. First the self data set is clustered by different cluster radius, and then the self data are substituted by cluster centers to compare with candidate detectors to reduce the number of distance counting. In the detector creating process, the value of each detector property was restricted to a given value range so as to decrease the redundancy of detectors. The stimulation result shows that CB-RNSA is an effective algorithm for the creation of artificial immune detectors.
Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo
W. Chen et al., "A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set", Advanced Materials Research, Vols. 121-122, pp. 486-489, 2010