Dynamic negative selection algorithm based on match range model

Authors:
Jungan Chen;Feng Liang;Dongyong Yang
Affiliations:
Zhejiang Wanli University, Ningbo, Zhejiang, China;Zhejiang Wanli University, Ningbo, Zhejiang, China;Zhejiang University of Technology, Hangzhou, Zhejiang, China
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

Dynamic Negative Selection Algorithm Based on Affinity Maturation (DNSA-AM) is proposed to generate dynamic detectors changed with nonselves. But it can not be adapted to the change of self because the match threshold is constant. In this work, a match range model inspired from T-cells maturation is proposed. Based on the model, an augmented algorithm is proposed. There is no match threshold but self-adapted match range. The proposed algorithm is tested by simulation experiment for anomaly detection and compared with DNSA-AM. The results show that the proposed algorithm is more effective than DNSA-AM with several excellent characters such as self-adapted match range and less time complexity.