Artificial immune system

Artificial Immune Systems (AIS) are computer Algorithms inspired by the principles and processes of the vertebrates immune system.The algorithms typically exploit the immune system s characteristics of learning and memory to solve a problem.

At present, artificial immune algorithms can be broadly categorised into three subgroups: those using the clonal selection theory, those using negative selection and those using the immune network theory as their main inspiration.

=History=

The first works in the field of AIS came in 1986 when Farmer et al proposed a model for the immune network theory [1] and Hoffmann, motivated by a problem regarding neural networks, explored the similarities and differences between the nervous and immune systems [2]. Research into AIS began in earnest in the mid 90s in the realm of computer security for computer virus and computer network intrusion detection systems [3].

=Applications=

Artificial Immune Systems have been used to solve problems in a huge variety of domains. Some examples include: *E-mail classification ([http://www.cs.kent.ac.uk/people/rpg/ads3/cec2003.pdf link]) *Hardware fault tolerance ([http://www.cs.kent.ac.uk/pubs/2002/1562/content.pdfm link]) *Software testing ([http://www.cs.kent.ac.uk/people/rpg/psm4/mtais.pdf link]) *Memory for context aware systems ([http://www.cs.kent.ac.uk/pubs/2004/1974/content.pdf link])

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[1] Farmer, J.D., N.H. Packard, and A.S.Perelson, The immune System, Adaptation and machine learning. Phisica, 1986. 22D: p. 187-204. [2] Hoffman, G.W., A Neural Network Model Based on the Analogy with the Immune System. Journal of Theoretical Biology, 1986. 122: p. 33-67. [3] S. Forrest, A.S. Perelson, L. Allen, R. and Cherukuri. Self-Nonself Discrimination in a Computer. In Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy, Los Alamitos, CA: IEEE Computer Society Press (1994)