Parameter optimisation in the receptor density algorithm

James A. Hilder*, Nick D.L. Owens, Peter J. Hickey, Stuart N. Cairns, David P.A. Kilgour, Jon Timmis, Andy Tyrrell

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

10 Dyfyniadau(SciVal)

Crynodeb

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.

Iaith wreiddiolSaesneg
TeitlArtificial Immune Systems - 10th International Conference, ICARIS 2011, Proceedings
Tudalennau226-239
Nifer y tudalennau14
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 2011
Cyhoeddwyd yn allanolIe
Digwyddiad10th International Conference on Artificial Immune Systems, ICARIS 2011 - Cambridge, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Hyd: 18 Gorff 201121 Gorff 2011

Cyfres gyhoeddiadau

EnwLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Cyfrol6825 LNCS
ISSN (Argraffiad)0302-9743
ISSN (Electronig)1611-3349

Cynhadledd

Cynhadledd10th International Conference on Artificial Immune Systems, ICARIS 2011
Gwlad/TiriogaethTeyrnas Unedig Prydain Fawr a Gogledd Iwerddon
DinasCambridge
Cyfnod18 Gorff 201121 Gorff 2011

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