Artificial immune system

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An artificial immune system (AIS) is a type of optimisation algorithm inspired by the principles and processes of the vertebrate immune system. The algorithms typically exploit the immune system's characteristics of learning and memory to solve a problem. They are coupled to artificial intelligence and closely related to genetic algorithms.

Processes simulated in AlS include pattern recognition, hypermutation and clonal selection for B cells, negative selection of T cells, affinity maturation and immune network theory.

This article covers the algorithmic implementation of these processes. For underlying biological terminology, refer to the natural immune system.

Contents

[edit] Pattern recognition

Antibody & antigen representation is commonly implemented by strings of attributes. Attributes may be binary, integer or real-valued, although in principle any ordinal attribute could be used. Matching is done on the grounds of Euclidean distance, Manhattan distance or Hamming distance.

[edit] Hypermutation

Clonal selection algorithms are commonly used for antibody hypermutation. This allows the attribute string to be improved (as measured by a fitness function) using mutation alone.

[edit] History

AIS began in the mid 80's with Farmer, Packard and Perelson's paper on immune networks (1986). However, it was only in the mid-90's that AIS became a subject area in its own right. Forrest et al (on negative selection) began in 1994; and Dasgupta conducted extensive studies on Negative Selection Algorithms. Hunt and Cooke started the works on Immune Network models in 1995; Timmis and Neal continued this work and made some improvements. De Castro & Von Zuben's and Nicosia & Cutello's work (on clonal selection) became notable in 2002. The first book on Artificial Immune Systems was edited by Dasgupta in 1999.

New ideas, such as danger theory and algorithms inspired by the innate immune system, are also now being explored. Although some doubt that they are yet offering anything over and above existing AIS algorithms, this is hotly debated, and the debate is providing one the main driving forces for AIS development at the moment.

Originally AIS set out simply to find efficient abstrations of processes found in the immune system but, more recently, some AIS practitioners are becoming interested in modelling the immune system, and in applying AIS computation to immunological problems. This is clearly related to immunoinformatics.

[edit] References and Web Resources

  • J.D. Farmer, N. Packard and A. Perelson, (1986) "The immune system, adaptation and machine learning", Physica D, vol. 22, pp. 187--204
  • D. Dasgupta (Editor), Artificial Immune Systems and Their Applications, Springer-Verlag, Inc. Berlin, January 1999, ISBN 3-540-64390-7
  • L. DeCastro and J. Timmis (2001) "Artificial Immune Systems: A New Computational Intelligence Approach" ISBN 1-85233-594-7
  • J Timmis, M Neal and J Hunt, (2000) "An Artificial Immune System for Data Analysis" pp. 143--150, Biosystems, no. 1/3, vol. 55.
  • V. Cutello and G. Nicosia (2002) "An Immunological Approach to Combinatorial Optimization Problems" Lecture Notes in Computer Science, Springer vol. 2527, pp. 361-370.
  • L. N. de Castro and F. J. Von Zuben, (1999) "Artificial Immune Systems: Part I -Basic Theory and Applications", School of Computing and Electrical Engineering, State University of Campinas, Brazil, No. DCA-RT 01/99.
  • S. Garrett (2005) "How Do We Evaluate Artificial Immune Systems?" Evolutionary Computation, vol. 13, no. 2, pp. 145--178. http://mitpress.mit.edu/journals/pdf/EVCO_13_2_145_0.pdf
  • V. Cutello, G. Nicosia, M. Pavone, J. Timmis (2006) An Immune Algorithm for Protein Structure Prediction on Lattice Models, IEEE Transactions on Evolutionary Computation, vol. 10 (to appear).
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