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. In mathematics, computing, linguistics, and related disciplines, an algorithm is a finite set of well-defined instructions for accomplishing some task which, given an initial state, will terminate in a defined end-state. ... A scanning electron microscope image of a single neutrophil (yellow), engulfing anthrax bacteria (orange). ... “Learned” redirects here. ... In psychology, memory is an organisms ability to store, retain, and subsequently recall information. ... Hondas humanoid robot AI redirects here. ... A genetic algorithm (or short GA) is a search technique used in computing to find true or approximate solutions to optimization and search problems. ...

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. Pattern recognition is a field within the area of machine learning. ... This article needs to be cleaned up to conform to a higher standard of quality. ... The clonal selection theory has become a widely accepted model for how the immune system responds to infection and how certain types of B and T lymphocytes are selected for destruction of specific antigens invading the body. ... B cells are lymphocytes that play a large role in the humoral immune response (as opposed to the cell-mediated immune response). ... In biology, negative selection is artificial selection in which negative, rather than positive traits of a species are selected. ... T cells are a subset of lymphocytes that play a large role in the immune response. ... The process by which B-cells produce antibodies with increased affinity for antigen. ...

This article covers the algorithmic implementation of these processes. For underlying biological terminology, refer to the natural immune system. In mathematics, computing, linguistics, and related disciplines, an algorithm is a finite set of well-defined instructions for accomplishing some task which, given an initial state, will terminate in a defined end-state. ... Galunggung in 1982, showing a combination of natural events. ... A scanning electron microscope image of a single neutrophil (yellow), engulfing anthrax bacteria (orange). ...

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. Each antibody binds to a specific antigen; an interaction similar to a lock and key. ... An antigen is a substance that stimulates an immune response, especially the production of antibodies. ... In mathematics, the Euclidean distance or Euclidean metric is the ordinary distance between the two points that one would measure with a ruler, which can be proven by repeated application of the Pythagorean theorem. ... Taxicab geometry, considered by Hermann Minkowski in the 19th century, is a form of geometry in which the usual metric of Euclidean geometry is replaced by a new metric in which the distance between two points is the sum of the (absolute) differences of their coordinates. ... In information theory, the Hamming distance, named after Richard Hamming, is the number of positions in two strings of equal length for which the corresponding elements are different. ...

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. The clonal selection theory has become a widely accepted model for how the immune system responds to infection and how certain types of B and T lymphocytes are selected for destruction of specific antigens invading the body. ... Each antibody binds to a specific antigen; an interaction similar to a lock and key. ... This article needs to be cleaned up to conform to a higher standard of quality. ...

History

AIS began in the mid 80's with Farmer, Packard and Perelson's (1986) and Bersini and Varela's papers on immune networks (1990). 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. In biology, negative selection is artificial selection in which negative, rather than positive traits of a species are selected. ... The clonal selection theory has become a widely accepted model for how the immune system responds to infection and how certain types of B and T lymphocytes are selected for destruction of specific antigens invading the body. ...

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. The innate immune system comprises the cells and mechanisms that defend the host from infection by other organisms, in a non-specific manner. ...

Originally AIS set out to find efficient abstrations of processes found in the immune system but, more recently, it is becoming interested in modelling the biological processes and in applying immune algorithms to bioinformatics problems. A scanning electron microscope image of a single neutrophil (yellow), engulfing anthrax bacteria (orange). ...

References

• J.D. Farmer, N. Packard and A. Perelson, (1986) "The immune system, adaptation and machine learning", Physica D, vol. 22, pp. 187--204
• H. Bersini, F.J. Varela, Hints for adaptive problem solving gleaned from immune networks. Parallel Problem Solving from Nature, First Workshop PPSW 1, Dortmund, FRG, October, 1990.
• 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).

External links

• http://ais.cs.memphis.edu/ Dipankar Dasgupta's website for AIS resources.
• http://www.artificial-immune-systems.org links to the ICARIS series of conferences that are devoted entirely to AIS.
• http://www.elec.york.ac.uk/ARTIST/ provides information about the UK AIS network, ARTIST. It provides technical and financial support for AIS in the UK and beyond, and aims to promote AIS projects.
• http://www.dca.fee.unicamp.br/~lnunes/immune.html Leandro de Castro's home page, which provides links to his papers, plus links and other AIS resources.
• http://www-users.cs.york.ac.uk/jtimmis/ Jon Timmis' home page and links.
• http://www.dmi.unict.it/~nicosia/ Giuseppe Nicosia's home page and links.
• http://ais.cs.memphis.edu/papers/ais_bibliography.pdf an extensive Bibliography on AIS is available.
• http://www.ict.swin.edu.au/personal/jbrownlee/aisthesisbib.html an extensive Ph.D. and Masters dissertation bibliography.