Abstract
Using hyperspheres as antibody recognition regions is an established abstraction which was initially proposed by theoretical immunologists for use in the modeling of antibody-antigen interactions. This abstraction is also employed in the development of many artificial immune system algorithms. Here, we show several undesirable properties of hyperspheres, especially when operating in high dimensions and discuss the problems of hyperspheres as recognition regions and how they have affected overall performance of certain algorithms in the context of real-valued negative selection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Perelson, A.S., Oster, G.F.: Theoretical studies of clonal selection: minimal antibody repertoire size and reliability of self-nonself discrimination. J. Theor. Biol. 81, 645–670 (1979)
Percus, J.K., Percus, O.E., Perelson, A.S.: Predicting the size of the t-cell receptor and antibody combining region from consideration of efficient self-nonself discrimination. Proceedings of National Academy of Sciences USA 90, 1691–1695 (1993)
de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)
González, F., Dasgupta, D., Niño, L.F.: A randomized real-valued negative selection algorithm. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 261–272. Springer, Heidelberg (2003)
Ji, Z., Dasgupta, D.: Real-valued negative selection algorithm with variable-sized detectors. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 287–298. Springer, Heidelberg (2004)
Stibor, T., Timmis, J.I., Eckert, C.: A comparative study of real-valued negative selection to statistical anomaly detection techniques. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 262–275. Springer, Heidelberg (2005)
Watkins, A., Boggess, L.: A new classifier based on resource limited artificial immune systems. In: Proceedings of the 2002 Congress on Evolutionary Computation CEC 2002, pp. 1546–1551. IEEE Press, Los Alamitos (2002)
Bezerra, G.B., Barra, T.V., de Castro, L.N., Von Zuben, F.J.: Adaptive radius immune algorithm for data clustering. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 290–303. Springer, Heidelberg (2005)
Bentley, P.J., Greensmith, J., Ujjin, S.: Two ways to grow tissue for artificial immune systems. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 139–152. Springer, Heidelberg (2005)
Hart, E., Ross, P.: Studies on the implications of shape-space models for idiotypic networks. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 413–426. Springer, Heidelberg (2004)
Leppmeier, M.: Kugelpackungen von Kepler bis heute. Vieweg Verlag (1997)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Bellman, R.: Adaptive Control Processes: A Guided Tour. Princeton University Press, Princeton (1961)
Mosegaard, K., Sambridge, M.: Monte Carlo analysis of inverse problems. Inverse problems 18, 29–54 (2002)
Fishman, G.S.: Monte Carlo Concepts, Algorithms, and Applications. Springer, Heidelberg (1995)
Stibor, T., Mohr, P.H., Timmis, J., Eckert, C.: Is negative selection appropriate for anomaly detection? In: Proceedings of Genetic and Evolutionary Computation Conference – GECCO-2005, pp. 321–328. ACM Press, New York (2005)
Hettich, S., Bay, S.D.: KDD Cup 1999 Data (1999), http://kdd.ics.uci.edu
Verleysen, M.: Learning high-dimensional data. Limitations and Future Trends in Neural Computation 186, 141–162 (2003)
Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, Heidelberg (1999)
Schölkopf, B., Platt, J.C., Shawe-Taylor, Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Technical Report MSR-TR-99-87, Microsoft Research (MSR) (1999)
Freitas, A., Timmis, J.: Revisiting the Foundations of Artificial Immune Systems: A Problem Oriented Perspective. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 229–241. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stibor, T., Timmis, J., Eckert, C. (2006). On the Use of Hyperspheres in Artificial Immune Systems as Antibody Recognition Regions. In: Bersini, H., Carneiro, J. (eds) Artificial Immune Systems. ICARIS 2006. Lecture Notes in Computer Science, vol 4163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823940_17
Download citation
DOI: https://doi.org/10.1007/11823940_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37749-8
Online ISBN: 978-3-540-37751-1
eBook Packages: Computer ScienceComputer Science (R0)