Abstract
In the past there have been several approaches to use Learning Clas sifier Systems (LCS) as a tool for modelling the functioning of the immune sys tem. In this paper we propose a modification of the classic LCS that can be used for modelling the Cognitive Immune System Theory introduced by I. Cohen. It has been pointed out before that this alternative view of the immune system and its agents provides promising functional perspectives to the field of artificial immune systems (AIS). The characteristic features of Cohen’s theory, namely degeneracy of recognition and context of immune reactions, and how they can be realized in our modified LCS are described. Moreover, we introduce the re presentations of the immune agents, the interactions that take place among them and the applied evolutionary mechanisms.
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Voigt, D., Wirth, H., Dilger, W. (2007). A Computational Model for the Cognitive Immune System Theory Based on Learning Classifier Systems. 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_23
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DOI: https://doi.org/10.1007/978-3-540-73922-7_23
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