Definition of the Subject
Immunecomputing, or artificial immune systems (AIS), has recently emerged as a computational intelligence approach that shows great promise. Inspired by the complexity of the immune system, computer scientists and engineers have created systems that in some way mimic or capture certain computationally appealing properties of the immune system, with the aim of building more robust and adaptable solutions. AIS have been defined by de Castro and Timmis (2002b) as:
adaptive systems, inspired by theoretical immunology and observed immune functions, principle and models, which are applied to problem solving.
However, in order to build AIS, an interdisciplinary approach is required that employs modeling of immunology (both mathematical and computational) in order to understand the underlying complexity inherent within the immune system. AIS do not rival their natural counterparts; they do not exhibit the same level of complexity or even perform the same function, but...
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Abbreviations
- Affinity :
-
Measure or tightness of the binding between an antigen-combining site and an antigenic determinant; the stronger the binding, the higher the affinity.
- Antibody :
-
A soluble protein molecule produced and secreted by B cells in response to an antigen. Antibodies are usually defined in terms of their specific binding to an antigen.
- Antigen :
-
Any substance that when introduced into the body is capable of inducing an immune response.
- Antigen-presenting cells (APCs) :
-
B cells, cells of the monocyte lineage (including macrophages as well as dendritic cells), and various other body cells that present antigen in a form that B and T cells can recognize.
- B cell :
-
White blood cells expressing immunoglobulin molecules on its surface. Also known as B lymphocytes, they are derived from the bone marrow and develop into plasma cells that are the main antibody secretors.
- Clonal selection theory :
-
A theory that states that the specificity and diversity of an immune response are the result of selection by antigen of specifically reactive clones from a large repertoire of preformed lymphocytes, each with individual specificities.
- Complex (MHC) :
-
Cell-surface molecules (MHC class I and II) that are involved in controlling several aspects of the immune response. MHC genes code for self-markers on all body cells and play a major role in transplantation rejection.
- Dendritic cell :
-
Set of antigen-presenting cells (APCs) present in lymph nodes and the spleen and at low levels in blood, which are particularly active in stimulating T cells.
- Lymph node :
-
Small organs of the immune system, widely distributed throughout the body and linked by lymphatic vessels.
- Lymphocyte :
-
White blood cell found in the blood, tissue, and lymphoid organs.
- Major histocompatibility :
-
A group of genes encoding polymorphic.
- Pathogen :
-
A microorganism that causes disease.
- T cell :
-
White blood cell that orchestrates and/or directly participates in the immune defenses.
Bibliography
Primary Literature
Aickelin U, Bentley P, Cayzer S, Kim J, McLeod J (2003) Danger theory: the link between AIS and IDS? In: Timmis J, Bentley P, Hart E (eds) Proceedings of the 2nd international conference on artificial immune systems (ICARIS). LNCS, vol 2787. Springer, Berlin, pp 147–155
Andrews PS, Timmis J (2005a) Inspiration for the next generation of artificial immune systems. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proceedings of the 4th international conference on artificial immune systems (ICARIS). LNCS, vol 3627. Springer, Berlin, pp 126–138
Andrews PS, Timmis J (2005b) On diversity and artificial immune systems: incorporating a diversity operator into aiNet. In: Proceedings of the international conference on natural and artificial immune systems (NAIS05). LNCS, vol 391. Springer, Berlin, pp 293–306
Andrews PS, Timmis J (2006) A computational model of degeneracy in a lymph node. In: Bersini H, Carneiro J (eds) Proceedings of 5th international conference on artificial immune systems. LNCS. Springer, Berlin, pp 164–177
Ayara M (2005) An immune inspired solution for adaptable error detection in embedded systems. PhD thesis, University of Kent
Ayara M, Timmis J, de Lemos R, de Castro L, Duncan R (2002) Negative selection: how to generate detectors. In: Proceedings of the 1st international conference on artificial immune systems (ICARIS-2002). University of Kent, Canterbury, pp 89–98
Balthrop J, Forrest S, Glickman M (2002) Revisiting lisys: parameters and normal behavior. In: Proceedings of congress on evolutionary computation (CEC). IEEE Press, pp 1045–1050
Bentley PJ, Greensmith J, Ujjin S (2005) Two ways to grow tissue for artificial immune systems. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proceedings of the 4th international conference on artificial immune systems (ICARIS). LNCS, vol 3627. Springer, Berlin, pp 139–152
Berek C, Ziegner M (1993) The maturation of the immune response. Immunol Today 14:200–402
Bersini H (1991) Immune network and adaptive control. In: Proceedings of the 1st European conference on artificial life (ECAL). MIT Press, Cambridge, pp 217–226
Bersini H (1992) Reinforcement and recruitment learning for adaptive process control. In: Proceedings of the international Fuzzy association conference (IFAC/IFIP/IMACS) on artificial intelligence in real time control, pp 331–337
Bersini H, Carneiro J (eds) (2006) Proceedings of 5th international conference on artificial immune systems. LNCS, vol 4163. Springer, Berlin
Bersini H, Varela F (1994) The immune learning mechanisms: recruitment, reinforcement and their applications. Chapman Hall, Austin
Bezerra G, Barra T, de Castro LN, Von Zuben F (2005) Adaptive radius immune algorithm for data clustering. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proceedings of the 4th international conference on artificial immune systems (ICARIS). LNCS, vol 3627. Springer, Berlin, pp 290–303
Burnet FM (1959) The clonal selection theory of acquired immunity. Cambridge University Press, Cambridge
Canham RO, Tyrrell AM (2002) A multilayered immune system for hardware fault tolerance within an embryonic array. In: Timmis J, Bentley P (eds) Proceedings of the 1st international conference on artificial immune systems (ICARIS). University of Kent, Canterbury, pp 3–11
Clark E, Hone A, Timmis J (2005) A Markov chain model of the B-cell algorithm. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proceedings of the 4th international conference on artificial immune systems (ICARIS). LNCS, vol 3627. Springer, Berlin, pp 318–330
Cohen IR (2000) Tending Adam’s garden: evolving the cognitive immune self. Elsevier Academic, London
Cooke D, Hunt J (1995) Recognising promoter sequences using an artificial immune system. In: Proceedings of intelligent systems in molecular biology. AAAI Press, pp 89–97
Cutello V, Nicosia G, Parvone M (2004) Exploring the capability of immune algorithms: a characterisation of hypermutation operators. In: Nicosia G, Cutello V, Bentley P, Timmis J (eds) Proceedings of the 3rd international conference on artificial immune systems (ICARIS). LNCS, vol 3239. Springer, Berlin, pp 263–276
Cutello V, Nicosia G, Pavone M, Timmis J (2007a) An immune algorithm for protein structure prediction on lattice models. IEEE Trans Evol Comput 11(1):101–117
Cutello V, Nicosia G, Oliveto P, Romeo M (2007b) On the convergence of immune algorithms. In: Proceedings of foundations of computational intelligence. IEEE Press, pp 409–416
Dasgupta D (1999) Artificial immune systems and their applications. Springer, Berlin
Dasgupta D, Forrest S (1995) Tool breakage detection in milling operations using a negative selection algorithm. Technical report no CS95-5. Department of Computer Science, University of New Mexico
Dasgupta D, Majumdar NS (2002) Anomaly detection in multidimensional data using negative selection algorithm. In: Proceedings of congress on evolutionary computation (CEC). IEEE Press, Honolulu, pp 1039–1044
Dasgupta D, Nino F (2000) A comparison of negative and positive selection algorithms in novel pattern detection. In: Proceedings of the IEEE international conference on systems, man and cybernetics (SMC), Nashville, 8–11 Oct
de Castro LN, Timmis J (2002a) An artificial immune network for multi modal optimisation. In: Proceedings of the world congress on computational intelligence WCCI. IEEE Press, Honolulu, pp 699–704
de Castro LN, Timmis J (2002b) Artificial immune systems: a new computational intelligence approach. Springer, Berlin
de Castro LN, Timmis J (2002c) Hierarchy and convergence of immune networks: basic ideas and preliminary results. In: Timmis J, Bentley P (eds) Proceedings of the 1st international conference on artificial immune systems (ICARIS). University of Kent, Canterbury, pp 231–240
de Castro LN, Von Zuben FJ (2000) The clonal selection algorithm with engineering applications. In: GECCO workshop on artificial immune systems and their applications, pp 36–37
de Castro LN, Von Zuben FJ (2001) aiNet: an artificial immune network for data analysis. Idea Group Publishing, pp 231–259
de Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3):239–251
Ebner M, Breunig H-G, Albert J (2002) On the use of negative selection in an artificial immune system. In: Proceedings of genetic and evolutionary computation conference (GECCO). Morgan Kaufman Publishers, San Francisco, pp 957–964
Esponda F, Forrest S, Helman P (2004) A formal framework for positive and negative detection schemes. IEEE Trans Syst Man Cybern B 34(1):357–373
Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning. Physica D 22:187–204
Forrest S, Perelson AS, Allen L, Cherukuri R (1994) Self-nonself discrimination in a computer. In: Proceedings of the IEEE symposium on research security and privacy. IEEE Press, pp 202–212
Forrest S, Hofmeyr S, Somayaji A (1997) Computer immunology. Commun ACM 40(10):88–96
Freitas A, Timmis J (2003) Revisiting the foundations of artificial immune systems: a problem oriented perspective. In: Timmis J, Bentley P, Hart E (eds) Proceedings of the 2nd international conference on artificial immune systems (ICARIS). LNCS, vol 2787. Springer, Berlin, pp 229–241
Garrett SM (2005) How do we evaluate artificial immune systems? Evol Comput 13(2):145–177
Gaspar A, Hirsbrunner B (2002) From optimization to learning in learning in changing environments: The pittsburgh immune classifier system. In: Timmis J, Bentley P (eds) Proceedings of the 1st international conference on artificial immune systems (ICARIS). University of Kent, Canterbury, pp 190–199
Germain RN (2004) An innately interesting decade of research in immunology. Nat Med 10:1307–1320
González F, Dagupta D (2003) Anomaly detection using real-valued negative selection. Genet Program Evolvable Mach 4(4):383–403
González F, Dasgupta D, Kozma R (2002) Combining negative selection and classification techniques for anomaly detection. In: IEEE congress on evolutionary computation. IEEE, pp 705–710
González F, Dasgupta D, Gómez J (2003) The effect of binary matching rules in negative selection. In: Genetic and evolutionary computation – GECCO-2003. Lecture notes in computer science, vol 2723. Springer, Chicago, pp 195–206
Goodman D, Boggess L, Watkins A (2002) Artificial immune system classification of multiple-class problems. In: Proceedings of intelligent engineering systems. ASME, pp 179–184
Goodman D, Boggess L, Watkins A (2003) An investigation into the source of power for AIRS, an artificial immune classification system. In: Proceedings of the international joint conference on neural networks. IEEE, pp 1678–1683
Greensmith J, Aickelin U, Cayzer S (2005) Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proceedings of the 4th international conference on artificial immune systems (ICARIS). LNCS, vol 3627. Springer, Berlin, pp 153–167
Greensmith J, Aickelin U, Twycross J (2006) Articulation and clarification of the dendritic cell algorithm. In: Bersini H, Coutinho A (eds) Proceedings of the 5th international conference on artificial immune systems. LNCS, vol 4163. Springer, Berlin
Hart E (2005) Not all balls are round: an investigation of alternative recognition-region shapes. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proceedings of the 4th international conference on artificial immune systems (ICARIS). LNCS, vol 3627. Springer, Berlin, pp 29–42
Hart E, Ross P (2002) Exploiting the analogy between immunology and sparse distributed memories: a system for clustering non-stationary data. In: Timmis J, Bentley P (eds) Proceedings of the 1st international conference on artificial immune systems (ICARIS). University of Kent, Canterbury, pp 49–58
Hart E, Ross P (2004) Studies on the implications of shape-space models for idiotypic networks. In: Nicosia G, Cutello V, Bentley P, Timmis J (eds) Proceedings of the 3rd international conference on artificial immune systems (ICARIS). LNCS, vol 3239. Springer, Berlin, pp 413–426
Hart E, Timmis J (2005) Application areas of AIS: the past, the present and the future. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proceedings of the 4th international conference on artificial immune systems (ICARIS). LNCS, vol 3627. Springer, Berlin, pp 483–497
Hightower RR, Forrest SA, Perelson AS (1995) The evolution of emergent organization in immune system gene libraries. In: Proceedings of the 6th international conference on genetic algorithms. Morgan Kaufmann, pp 344–350
Hofmeyr S, Forrest S (1999) Immunity by design: an artificial immune system. In: Proceedings of genetic and evolutionary computation conference (GECCO), pp 1289–1296
Hofmeyr S, Forrest S (2000) Architecture for an artificial immune system. Evol Comput 7(1):1289–1296
Hunt J, Cooke D (1996) Learning using an artificial immune system. J Netw Comput Appl 19:189–212
Hunt J, Timmis J, Cooke D, Neal M, King C (1998) JISYS: development of an artificial immune system for real-world applications. In: Dasgupta D (ed) Artificial immune systems and their applications. Springer, Berlin, pp 157–186
Jacob C, Pilat M, Bentley P, Timmis J (eds) (2005) Proceedings of the 4th international conference on artificial immune systems (ICARIS). LNCS, vol 3627. Springer, Berlin
Jerne NK (1974) Towards a network theory of the immune system. Ann Immunol (Inst Pasteur) 125C:373–389
Ji Z, Dasgupta D (2004a) Augmented negative selection algorithm with variable-coverage detectors. In: IEEE congress on evolutionary computation. IEEE, pp 1081–1088
Ji Z, Dasgupta D (2004b) Real-valued negative selection algorithm with variable-sized detectors. In: Genetic and evolutionary computation – GECCO-2004, part I. Lecture notes in computer science, vol 3102. Springer, Seattle, pp 287–298
Ji Z, Dasgupta D (2005) Estimating the detector coverage in a negative selection algorithm. In: Proceedings of genetic and evolutionary computation conference (GECCO). ACM Press, pp 281–288
Ji Z, Dasgupta D (2006) Applicability issues of the real-valued negative selection algorithms. In: Proceedings of genetic and evolutionary computation conference (GECCO). ACM Press, pp 111–118
Ji Z, Dasgupta D, Yang Z, Teng H (2006) Analysis of dental images using artificial immune systems. In: Proceedings of congress on evolutionary computation (CEC). IEEE Press, pp 528–535
Kelsey J, Timmis J (2003) Immune inspired somatic contiguous hypermutation for function optimisation. In: Proceedings of genetic and evolutionary computation conference (GECCO). LNCS, vol 2723. Springer, Berlin, pp 207–218
Kelsey J, Timmis J, Hone A (2003) Chasing chaos. In: Proceedings of congress on evolutionary computation (CEC). IEEE, Canberra, pp 89–98. http://www.cs.ukc.ac.uk/pubs/2002/1504
Kim J (2002) Integrating artificial immune algorithms for intrusion detection. PhD thesis, UCL
Kim J, Bentley PJ (2001a) An evaluation of negative selection in an artificial immune system for network intrusion detection. In: Proceedings of genetic and evolutionary computation conference (GECCO). Morgan Kaufmann, San Francisco, pp 1330–1337
Kim J, Bentley PJ (2001b) Towards an artificial immune system for network intrusion detection: an investigation of clonal selection with negative selection operator. In: Proceedings of congress on evolutionary computation (CEC). Morgan Kaufmann, Seoul, pp 1244–1252
Kim J, Bentley PJ (2002) Immune memory in the dynamic clonal selection algorithm. In: Timmis J, Bentley P (eds) Proceedings of the 1st international conference on artificial immune systems (ICARIS). University of Kent, Canterbury, pp 59–67
Knight T, Timmis J (2003) A multi-layered immune inspired machine learning algorithm. In: Lotfi A, Garibaldi M (eds) Applications and science in soft computing. Springer, Berlin, pp 195–202. http://www.cs.kent.ac.uk/pubs/2003/1760
Krohling R, Zhou Y, Tyrrell A (2002) Evolving FPGA-based robot controllers using an evolutionary algorithm. In: Timmis J, Bentley P (eds) (2002) Proceedings of the 1st international conference on artificial immune systems (ICARIS). University of Kent, Canterbury, pp 41–46
Matzinger P (1997) An innate sense of danger. Semin Immunol 10(5):399–415
Matzinger P (2002) The danger model: a renewed sense of self. Science 296:301–305
Mendao M, Timmis J, Andrews PS, Davies M (2007) The immune system in pieces: computational lessons from degeneracy in the immune system. In: Fogel DB (ed) Proceedings of foundations of computational intelligence. IEEE Press, pp 394–400
Neal M (2002) An artificial immune system for continuous analysis of time-varying data. In: Timmis J, Bentley P (eds) Proceedings of the 1st international conference on artificial immune systems (ICARIS). University of Kent, Canterbury, pp 76–85
Nicosia G (2004) Immune algorithms for optimization and protein structure prediction. PhD thesis, University of Catania
Nicosia G, Cutello V, Bentley P, Timmis J (eds) (2004) Proceedings of the 3rd international conference on artificial immune systems (ICARIS). LNCS, vol 3239. Springer, Berlin
Perelson AS (1989) Immune network theory. Immunol Rev 110:5–36
Secker A, Freitas A, Timmis J (2003) AISEC: an artificial immune system for email classification. In: Proceedings of congress on evolutionary computation (CEC). IEEE Press, pp 131–139
Singh S (2002) Anomaly detection using negative selection based on the r-contiguous matching rule. In: Timmis J, Bentley PJ (eds) Proceedings of the 1st international conference on artificial immune systems ICARIS. University of Kent at Canterbury Printing Unit, University of Kent at Canterbury, pp 99–106. http://www.aber.ac.uk/icaris-2002
Stepney S, Smith R, Timmis J, Tyrrell A, Neal M, Hone A (2006) Conceptual frameworks for artificial immune systems. Int J Unconv Comput 1(3):315–338
Stibor T, Timmis J (2007) An investigation into the compression quality of ainet. In: Fogel D (ed) Proceedings of foundations of computational intelligence. IEEE Press
Stibor T, Bayarou KM, Eckert C (2004) An investigation of R-chunk detector generation on higher alphabets. In: Proceedings of genetic and evolutionary computation conference (GECCO). LNCS, vol 3102. Springer, Berlin, pp 299–307
Stibor T, Timmis J, Eckert C (2005a) A comparative study of real-valued negative selection to statistical anomaly detection techniques. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proceedings of the 4th international conference on artificial immune systems (ICARIS). LNCS, vol 3627. Springer, Berlin, pp 262–275
Stibor T, Mohr P, Timmis J, Eckert C (2005b) Is negative selection appropriate for anomaly detection? In: Proceedings of genetic and evolutionary computation conference (GECCO). ACM Press
Stibor T, Timmis J, Eckert C (2006) Generalization regions in hamming negative selection. In: Intelligent information processing and web mining. Advances in soft computing. Springer, Berlin, pp 447–456
Tarakanov AO, Skormin VA, Sokolova SP (2003) Immunocomputing: principles and applications. Springer, New York
Tarakanov AO, Goncharova LB, Tarakanov OA (2005a) A cytokine formal immune network. In: Advances in artificial life, 8th European conference, ECAL 2005, Canterbury, 5–9 Sept 2005, pp 510–519
Tarakanov AO, Kvachev SV, Sukhorukov AV (2005b) A formal immune network and its implementation for on-line intrusion detection. In: MMM-ACNS, pp 394–405
Timmis J (2000) Artificial immune systems: a novel data analysis technique inspired by the immune system. PhD thesis, University of Wales
Timmis J (2007) Artificial immune systems: today and tomorrow. Nat Comput 6(1):1–18
Timmis J, Bentley P (eds) (2002) Proceedings of the 1st international conference on artificial immune systems (ICARIS). University of Kent, Canterbury
Timmis J, Edmonds C (2004) A comment on opt-AINet: an immune network algorithm for optimisation. In: Proceedings of genetic and evolutionary computation conference (GECCO). LNCS, vol 3102. Springer, Berlin, pp 308–317
Timmis J, Knight T (2001) Artificial immune systems: using the immune system as inspiration for data mining. In: Abbas H, Ruhul A, Sarker A, Newton S (eds) Data mining: a heuristic approach. Idea Group, pp 209–230
Timmis J, Neal M (2001) A resource limited artificial immune system for data analysis. Knowl Based Syst 14(3–4):121–130
Timmis J, Neal M, Hunt J (2000) An artificial immune system for data analysis. Biosystems 55(1/3):143–150
Timmis J, Bentley P, Hart E (eds) (2003) Proceedings of the 2nd international conference on artificial immune systems (ICARIS). LNCS, vol 2787. Springer, Berlin
Timmis J, Edmonds C, Kelsey J (2004) Assessing the performance of two immune inspired algorithms and a hybrid genetic algorithm for function optimisation. In: Proceedings of congress on evolutionary computation (CEC), vol 1. IEEE, pp 1044–1051
Twycross J, Aickelin U (2006) Libtissue: implementing innate immunity. In: Proceedings of the congress on evolutionary computation. IEEE Press, pp 499–506
Varela F, Coutinho A, Dupire B, Vaz N (1988) Cognitive networks: immune, neural and otherwise. J Theor Immunol 2:359–375
Watkins A (2001) AIRS: a resource limited artificial immune classifier. Master’s thesis, Mississippi State University
Watkins A (2005) Exploiting immunological metaphors in the development of serial, parallel and distributed learning algorithms. PhD thesis, University of Kent
Watkins A, Timmis J (2004) Exploiting parallelism inherent in AIRS, an artificial immune classifier. In: Nicosia G, Cutello V, Bentley P, Timmis J (eds) Proceedings of the 3rd international conference on artificial immune systems (ICARIS). LNCS, vol 3239. Springer, Berlin, pp 427–438
Watkins A, Xintong B, Phadke A (2003) Parallelizing an immune-inspired algorithm for efficient pattern recognition. In: Intelligent engineering systems through artificial neural networks: smart engineering system design: neural networks, fuzzy logic, evolutionary programming, complex systems and artificial life. ASME Press, pp 224–230
Watkins A, Timmis J, Boggess L (2004) Artificial immune recognition system (AIRS): an immune inspired supervised machine learning algorithm. Genet Program Evolvable 5(3):291–318. http://www.cs.kent.ac.uk/pubs/2004/1634
Whitesides GM, Boncheva M (2002) Beyond molecules: self-assembly of mesoscopic and macroscopic components. Proc Natl Acad Sci U S A 99(8):4769–4774
Wierzchon S, Kuzelewska U (2002) Stable clusters formation in an artificial immune system. In: Timmis J, Bentley P (eds) Proceedings of the 1st international conference on artificial immune systems (ICARIS). University of Kent, Canterbury, pp 68–75
Books and Reviews
Cohen I, Segal L (2001) Design principles for the immune system and other distributed autonomous systems. SFT. Oxford University Press, New York
Ishida Y (2004) Immunity-based systems: a design perspective. Springer, New York
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media New York
About this entry
Cite this entry
Timmis, J. (2015). Immunecomputing. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_282-3
Download citation
DOI: https://doi.org/10.1007/978-3-642-27737-5_282-3
Received:
Accepted:
Published:
Publisher Name: Springer, Berlin, Heidelberg
Online ISBN: 978-3-642-27737-5
eBook Packages: Springer Reference Physics and AstronomyReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics