Parameter optimisation in the receptor density algorithm

Authors:
James A. Hilder;Nick D. L. Owens;Peter J. Hickey;Stuart N. Cairns;David P. A. Kilgour;Jon Timmis;Andy Tyrrell
Affiliations:
Department of Electronics, University of York, Heslington, UK;Department of Electronics, University of York, Heslington, UK;Department of Electronics, University of York, Heslington, UK;Department of Electronics, University of York, Heslington, UK;Department of Electronics, University of York, Heslington, UK;Department of Electronics, University of York, Heslington, UK;Department of Electronics, University of York, Heslington, UK
Venue:
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Year:
2011

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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.