Abstract
In most of the existing artificial immune systems, instabilities mainly stem from the empirical pre-definition of a scenario-specific model. In this paper we introduce a self-regulating algorithm into an integrated platform of artificial immune systems based on Model Library. The algorithm can dynamically configure multi-AIS-models according to the “pressure” produced during the course of training and testing, so that the system can automatically adapt to detect various objects. In addition, a novel hybrid evaluation method is proposed to improve the self-adaptability of the system. Experimental results demonstrate that the self-regulating algorithm can achieve better performance as compared with traditional artificial immune systems in terms of false positive and false negative rates.
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Wu, Z., Liang, Y. (2005). Self-regulating Method for Model Library Based Artificial Immune Systems. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds) Artificial Immune Systems. ICARIS 2005. Lecture Notes in Computer Science, vol 3627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536444_27
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DOI: https://doi.org/10.1007/11536444_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28175-7
Online ISBN: 978-3-540-31875-0
eBook Packages: Computer ScienceComputer Science (R0)