A dynamic approach to artificial immune systems utilizing neural networks

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A dynamic approach to artificial immune systems utilizing neural networks

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 8th annual conference on Genetic and evolutionary computation table of contents

Seattle, Washington, USA

POSTER SESSION: Artificial immune systems: posters table of contents

Pages: 131 - 132

Year of Publication: 2006

ISBN:1-59593-186-4

Authors

Stefan Schadwinkel	Chemnitz University of Technology, Chemnitz, Germany
Werner Dilger	Chemnitz University of Technology, Chemnitz, Germany

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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ABSTRACT

The purpose of this work is to propose an immune-inspired setup to use a self-organizing map as a computational model for the interaction of antigens and antibodies. The proposed approach may be used as a part in other immune algorithms, or can possibly be used to detect anomalies in time series data.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Teuvo Kohonen, Self-organizing maps, Springer-Verlag New York, Inc., Secaucus, NJ, 1997
	2	Bersini, Hugues (2002): Self-Assertion versus Self-Recognition: A Tribute to Francisco Varela, Université Libre des Bruxelles, IRIDIA-CP 194/6, ICARIS 2002.
	3	Aickelin, Uwe, Cayzer, Steve (2002): The Danger Theory and Its Application to Artificial Immune Systems, HP Laboratories Bristol, HPL-2002-244.
	4	González, Fabio A., Dasgupta, Dipankar (2002): Neuro-Immune and Self-Organizing Map Approaches to Anomaly Detection: A comparison., In the proceedings of the First International Conference on Artificial Immune Systems UK, September 9-11, 2002.