Abstract
In this paper a relation between artificial immune network algorithms and coevolutionary algorithms is established. Such relation shows that these kind of algorithms present several similarities, but also remarks features which are unique from artificial immune networks. The main contribution of this paper is to use such relation to apply a formalism from coevolutionary algorithms called solution concept to artificial immune networks. Preliminary experiments performed using the aiNet algorithm over three datasets showed that the proposed solution concept is useful to monitor algorithm progress and to devise stopping criteria.
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
Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, Oxford, UK (1996)
de Castro, L.N., Von Zuben, F.J.: An evolutionary immune network for data clustering. In: França, F.M.G., Ribeiro, C.H.C. (eds.) SBRN, pp. 84–89. IEEE Computer Society, Los Alamitos (2000)
de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Approach, September 2002. Springer, London, UK (2002)
Ficici, S.G.: Solution Concepts in Coevolutionary Algorithms. PhD thesis, Computer Science Department. Brandeis University, USA (May 2004)
Galeano, J.C., Veloza-Suan, A., González, F.A.: A comparative analysis of artificial immune network models. In: GECCO 2005. Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 361–368. ACM Press, New York, NY, USA (2005)
Jerne, N.K.: Towards a network theory of the immune system. Annals of Immunology 125C, 373–389 (1974)
Knight, T., Timmis, J.: Aine: An immunological approach to data mining. In: Cercone, N., Lin, T., Wu, X. (eds.) IEEE International Conference on Data Mining, pp. 297–304. IEEE, San Jose, CA, USA (2001)
Mitchell, M., Thomure, M.D., Williams, N.L.: The role of space in the success of coevolutionary learning. In: Artificial Life X: Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems. International Society for Artificial Life, pp. 118–124. The MIT Press (Bradford Books), Cambridge (2006)
Perelson, A.S., Weisbuch, G.: Immunology for physicists. Reviews of Modern Physics 69, 1219 (1997)
Potter, M.A., De Jong, K.A.: The coevolution of antibodies for concept learning. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN V. LNCS, vol. 1498, pp. 530–539. Springer, Heidelberg (1998)
Wiegand, R.P., Sarma, J.: Spatial embedding and loss of gradient in cooperative coevolutionary algorithms. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN VIII. LNCS, vol. 3242, pp. 912–921. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alonso, O., Gonzalez, F.A., Niño, F., Galeano, J. (2007). A Solution Concept for Artificial Immune Networks: A Coevolutionary Perspective. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds) Artificial Immune Systems. ICARIS 2007. Lecture Notes in Computer Science, vol 4628. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73922-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-73922-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73921-0
Online ISBN: 978-3-540-73922-7
eBook Packages: Computer ScienceComputer Science (R0)