the t-detectors maturation algorithm based on match range model
Negative selection algorithm is used to generate detector for change detection, anomaly detection. But it can not be adapted to the change of self data because the match threshold must be set at first. In this paper, inspired from the maturation of T-cells, a match range model is proposed. Base on the model, a novel algorithm composed of positive selection and negative selection is proposed to generate T-detectors and the match threshold is not needed. Genetic algorithm is used to evolve the detectors with self-adapted match range. The proposed algorithm is tested by simulation experiment for anomaly detection and compared with the negative selection algorithm. The results show that the proposed algorithm is more effective than the negative selection algorithm and match range is self-adapted.
match range negative selection t-cells maturation