Genetic Algorithms-based Detector Generation in Negative Selection Algorithm

This paper proposes a genetic algorithms (GA) based detector optimization scheme in the negative selection algorithm (NSA). The NSA is a natural immune response inspired pattern discrimination method. In our scheme, the NSA detectors are optimized by the GA to occupy the maximal coverage of the nonself space so that they can achieve the best anomaly detection performance. Two numerical examples including the discriminant analysis of Fisher's iris data are demonstrated to compare our new approach with a conventional detector generation method. Simulation results show that the former is more efficient than the latter for generating the NSA detectors