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.
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Chen, J., Liang, F., Yang, D. (2005). Dynamic Negative Selection Algorithm Based on Match Range Model. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_94
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DOI: https://doi.org/10.1007/11589990_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
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