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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Parameter Optimisation in the Receptor Density Algorithm
verfasst von : James A. Hilder, Nick D. L. Owens, Peter J. Hickey, Stuart N. Cairns, David P. A. Kilgour, Jon Timmis, Andy Tyrrell
Erschienen in: Artificial Immune Systems
Verlag: Springer Berlin Heidelberg
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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.