Abstract
In this paper a system which optimises parameter values for the Receptor Density Algorithm (RDA), an algorithm inspired by T-cell signalling, is described. The parameter values are optimised using a genetic algorithm. This system is used to optimise the RDA parameters to obtain the best results when finding anomalies within a large prerecorded dataset, in terms of maximising detection of anomalies and minimising false-positive detections. A trade-off front between the objectives is extracted using NSGA-II as a base for the algorithm. To improve the run-time of the optimisation algorithm with the goal of achieving real-time performance, the system exploits the inherent parallelism of GPGPU programming techniques, making use of the CUDA language and tools developed by NVidia to allow multiple evaluations of a given data set in parallel.
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Hilder, J.A. et al. (2011). Parameter Optimisation in the Receptor Density Algorithm. In: Liò, P., Nicosia, G., Stibor, T. (eds) Artificial Immune Systems. ICARIS 2011. Lecture Notes in Computer Science, vol 6825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22371-6_21
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DOI: https://doi.org/10.1007/978-3-642-22371-6_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22370-9
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