@inproceedings{ef820bd0c4db46a4a4591f743cd2527b,
title = "Parameter optimisation in the receptor density algorithm",
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.",
keywords = "Artificial Immune Systems, CUDA, Genetic Algorithms, GPGPU, NSGA-II, Receptor Density Algorithm",
author = "Hilder, {James A.} and Owens, {Nick D.L.} and Hickey, {Peter J.} and Cairns, {Stuart N.} and Kilgour, {David P.A.} and Jon Timmis and Andy Tyrrell",
year = "2011",
doi = "10.1007/978-3-642-22371-6_21",
language = "English",
isbn = "9783642223709",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "226--239",
booktitle = "Artificial Immune Systems - 10th International Conference, ICARIS 2011, Proceedings",
note = "10th International Conference on Artificial Immune Systems, ICARIS 2011 ; Conference date: 18-07-2011 Through 21-07-2011",
}