Abstract
A new scheme for detector generation for the Real-Valued Negative Selection Algorithm (RNSA) is presented. The proposed method makes use of genetic algorithms and Quasi-Monte Carlo Integration to automatically generate a small number of very efficient detectors. Results have demonstrated that a fault detection system with detectors generated by the proposed scheme is able to detect faults in analog circuits and in a ball bearing dataset.
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Amaral, J.L.M., Amaral, J.F.M., Tanscheit, R. (2007). Real-Valued Negative Selection Algorithm with a Quasi-Monte Carlo Genetic Detector Generation. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds) Artificial Immune Systems. ICARIS 2007. Lecture Notes in Computer Science, vol 4628. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73922-7_14
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DOI: https://doi.org/10.1007/978-3-540-73922-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73921-0
Online ISBN: 978-3-540-73922-7
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