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De Castro, L. N. and F. J. Von Zuben. Learning and optimization using the clonal selec-
tion principle. IEEE T. Evolut. Comput. (special issue on artifi cial immune systems),
6(3), 239-251, 2002b.
Farmer, J. D., N. H. Packard and A. S. Perelson. h e immune system, adaptation and
machine learning. Physica , 22D, 187-204, 1986.
Farmer, J. D., S. A. Kauff man, N. H. Packard and A. S. Perelson. Adaptive dynamic
networks as models for the immune system and autocatalytic sets. Ann. NY Acad.
Sci ., 504, 118-131, 1987.
González, F., J. Galeano and A. Veloza. A comparative analysis of artifi cial immune
network models . Proceedings of the 2005 Conference on Genetic and Evolutionary
Computation (GECCO'05) , ACM Press, Washington, pp. 361-368, 2005.
Hopfi eld, J. Neural networks and physical systems with emerging collective computational
abilities. P. Natl. Acad. Sci ., 79, 2554-2558, 1982.
Hunt, J. E . and D. E . Cooke. Learning using an artifi cial immune system. J. Netw. Comput.
Appl. (special issue on Intelligent systems: Design and application), 19, 189-212,
1996.
Ishiguro, A., T. Kondo, Y. Watanabe, Y. Shirai and Y. Uchikawa. Immunoid: A robot with
a decentralized consensus-making mechanism based on the immune system. ICMAS
Workshop on Immunity-Based Systems , IEEE Computer Society Press, Washington,
DC, pp. 82-92, 1996.
Ishiguro, A., S. Ichikawa and Y. Uchikawa. A gait acquisition of six-legged robot using
immune networks. Proceedings of International Conference on Intelligent Robotics and
Systems (IROS '94) , Munich, Germany, vol. 2, pp. 1034-1041, 1994.
Jerne, N. K. Towards a network theory of the immune system. Ann. Immunol. (Paris) ,
125C, 373, 1974.
Luh, G.-C. and W.-W. Liu. Reactive immune network based mobile robot navigation.
In G. Nicosia, V. Cutello, P. J. Bentley and J. Timmis (Eds.), Proceeding of the h ird
Conference ICARIS , Springer, Edinburgh, pp. 119-132 , 2004.
Michelan, R. and F. J. Von Zuben. Decentralized control system for autonomous naviga-
tion based on an evolved artifi cial immune network. Proceedings of the IEEE Congress
on Evolutionary Computation , Honolulu, HI, vol. 2, pp. 1021-1026, 2002.
Mitsumoto, N., T. Fukuda, F. Arai, H. Tadashi and T. Idogaki. Self-organizing multiple
robotic system. Proceedings of the IEEE International Conference on Robotics and
Automation , Minneapolis, MN, pp. 1614-1619, 1996.
Nasraoui, O., C. Cardona, C. Rojas and F. González. TECNO-STREAMS: Tracking
evolving clusters in noisy data strea ms wit h a sca lable immune system lea rning model.
h ird IEEE International Conference on Data Mining , Melbourne, FL, 2003a.
Nasraoui, O., D. Dasgupta and F. Gonzalez. A novel artifi cial immune system approach to
robust data mining. Proceedings of the International Conference Genetic and Evolutionary
Computation (GECCO) , New York, July 9-13, 2002.
Nasraoui, O., F. González, C. Cardona, C. Rojas and D. Dasgupta. A scalable artifi cial
immune system model for dynamic unsupervised learning. Proceedings of the Genetic and
Evolutionary Computation Conference (GECCO) , LNCS 2723, Chicago, IL, 2003b.
Nasraoui, O., F. González and D. Dasgupta. h e fuzzy artifi cial immune system: Motiva-
tions, basic concepts and application to clustering and web profi ling. IEEE International
Conference on Fuzzy Systems , Honolulu, HI, pp. 711-716, 2002.
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