A Negative Selection Algorithm Base on the Self R-Tree

Abstract:

In this article, we present a new negative selection algorithm which the self-data is organized as a R-Tree structure. And the negative selection process could be transformed into the data query process in the self-R-Tree, if a new detector is indexed in any leaf node it will be dropped. As the time complexity of data query process in the tree is in the log level, the negative selection process of our algorithm is superior to the linearly comparation procedure in the traditional negative selection algorithms.

• Periodical: Applied Mechanics and Materials (Volumes 411-414)
• Pages: 2007-2012
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.411-414.2007
• Online since: September 2013
• Authors: Kun Peng Wang
• Keywords: Artificial Immune, Immune Detector, Negative Selection, R-Tree

References

[1] C. Andrzej, W.T. Slawomir, An immune approach to classifying the high-dimensional datasets, Proceedings of the Interational Multiconference on Computer Science and Information Technology pp.91-96.

[2] S. L. Stephen, T. Jon, An immune network inspired evolutionary algorithm for the diagnosis of Parkinson's disease, Biosystems 94(1-2): 34-46 (2008).

[3] K.C. Tan, C.K. Goh, A.A. Mamun, E.Z. Ei, An evolutionary artificial immune system for multi-objective optimization, European Journal of Operational Research, Volume 187, Issue 2, 1 June 2008, pp.371-392.

DOI: 10.1016/j.ejor.2007.02.047

[4] C.A. Laurentys, G. Ronacher, R.M. Palhares, W.M. Caminhas, Design of an Artificial Immune System for fault detection: A Negative Selection Approach, Expert Systems with Applications, Volume 37, Issue 7, July 2010, pp.5507-5513.

DOI: 10.1016/j.eswa.2010.02.004

[5] Chien-Cheng Chang, Hwai-En Tseng, Ling-Peng Meng, Artificial immune systems for assembly sequence planning exploration, Engineering Applications of Artificial Intelligence, Volume 22, Issue 8, December 2009, pp.1218-1232.

DOI: 10.1016/j.engappai.2009.04.007

[6] Bo Chen, Chuanzhi Zang, Artificial, immune pattern recognition for structure damage classification, Computers & Structures, Volume 87, Issues 21-22, November 2009, pp.1394-1407.

DOI: 10.1016/j.compstruc.2009.08.012

[7] S. Forrest, A. S. Perelson, L. Allen, and R. Cherukuri, Self-nonself discrimination in a computer, in Proceedings of the IEEE Symposium on Research in Security and Privacy. IEEE Computer Society Press, April 1994, p.202–212.

DOI: 10.1109/risp.1994.296580

[8] Gonzalez,F., D. Dasgupta, L.F. Nino, A randomized real-valued negative selection algorithm, in Proceedings of the 2nd International Conference on Artificial Immune Systems (ICARIS). LNCS, Edinburgh, UK, Springer - Verlag, 2003, pp.261-272.

DOI: 10.1007/978-3-540-45192-1_25

[9] Zhou Ji, D. Dasgupta. Real-valued negative selection algorithm with variable-sized detectors, in Proceedings Genetic and Evolutionary Computation Conference(GECCO), June , 2004, pp.287-298.

DOI: 10.1007/978-3-540-24854-5_30

[10] Zhou Ji, D. Dasgupta. V-detector: An efficient negative selection algorithm with probably adequate detector coverage , Information sciences, 2009, pp.1390-1406.

DOI: 10.1016/j.ins.2008.12.015

[11] Thomas Stibor, Philipp Mohr, Jonathan Timmis. Is negative selection appropriate for anomaly detection? , In Proceedings Geneticand Evolutionary Computation Conference(GECCO) , DC, USA, June (2005).

DOI: 10.1145/1068009.1068061

[12] G. antonin, R-trees: A dynamic index structure for spatial searching. In Proceedings of the International Conference of Management of Data (ACM SIGMOD), pages 47-57.

[13] http: /archive. ics. uci. edu/ml/datasets/Page+Blocks+Classification.