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Ji, Z. and D. Dasgupta. Augmented negative selection algorithm with variable-coverage
detectors. Proceedings of 2004 Congress on Evolutionary Computation (CEC 2004) ,
pp. 1081-1088, Portland, OR, June 2004a.
Ji, Z. and D. Dasgupta. Real-valued negative selection algorithm with variable-sized
detectors. Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO) , LNCS 3102, pp. 287-298, Portland, OR, 2004b.
Ji, Z. and D. Dasgupta. Applicability issues of the real valued negative selection algorithms.
Genetic and Evolutionary Computation Conference (GECCO) , (Received Best Paper
Award). Seattle, Washington, July 2006.
Ji, Z. and D. Dasgupta. Revisiting negative selection algorithms. Evolutionary Computation
Journal , Issue 15.2, July 2007.
Kaers, J., R. Wheeler and H. Verrelst. h e eff ect of antibody morphology on non-self
detection. Proceedings of Second International Conference on Artifi cial Immune System
(ICARIS 2003) , Edinburgh, U.K., 2003.
Kappler, J., N. Roehm and P. Marrack. T Cell tolerance by clonal elimination in the
h ymus. Cell , (49), 273-280, 1987.
Kim, J. and P. J. Bentley. An evaluation of negative selection in an artifi cial immune
system for network intrusion detection. Proceedings of the Genetice and Evolutionary
Computation Conference (GECCO 2001) , San Francisco, CA, 2001.
Kim, J. and P. Bentley. Immune memory in the dynamic clonal selection algorithm.
Proceedings of the 1st International Conference on Artifi cial Immune Systems (ICARIS) ,
pp. 59-67, Canterbury, U.K., September 2002.
Liu, J. S. Monte Carlo Strategies in Scientifi c Computing . Springer, 2001.
Monte, Computational Science Education project. Introduction to Monte Carlo Methods.
Available at http://en.wikipedia.org/wiki/Monte_Carlo_method, 1995.
Shapiro, J. M., G. B. Lamont, and G. L. Peterson. An evolutionary algorithm to generate
hyper-ellipsoid detectors for negative selection. Proceedings of the Conference on Genetic
and Evolutionary Computation , vol. 1, ACM Press, Washington, pp. 337-344, 2005 .
Singh, S. P. N. Anomaly detection using negative selection based on the r-contiguous
matching rule. 1st International Conference on Artifi cial Immune Systems (ICARIS) ,
University of Kent at Canterbury, September 9-11, 2002.
Stibor, T., K. M. Bayarou C. Eckert. An investigation of R-Chunk detector generation
on higher alphabets. Proceedings of the Conference on Genetic and Evolutionary
Computation (GEECO) , vol. 1, pp. 299-307, Seattle, Washington, 2004.
Stibor, T., P. Mohr, J. Timmis and C. Eckert. Is Negative Selection Appropriate for Anomaly
Detection? Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO) , Washington, June 25-29, 2005.
Stibor, T., J. Timmis and E. Claudia. h e Link between r-contiguous Detectors and k-
CNF Satisfi ability. Proceedings of IEEE World Congress on Computational Intelligence
(Special Session on Recent Development In Artifi cial Immune Systems) in Congress on
Evolutionary Computation , Vancouver, July 17-21, 2006.
Tax, D. M. J. One-Class Classifi cation . PhD thesis, Te c h n i s c he Un i ve r s it e it , D e l f t ,
2001.
Wierzchon, S. Discriminative power of the receptors activated by k-contiguous bits rule.
J. Comput. Sci. Technol. , 1(3), 1-13, 2000.
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