Evolutionary Intelligence

, Volume 1, Issue 1, pp 5–26 | Cite as

An interdisciplinary perspective on artificial immune systems

Review Article

Abstract

This review paper attempts to position the area of Artificial Immune Systems (AIS) in a broader context of interdisciplinary research. We review AIS based on an established conceptual framework that encapsulates mathematical and computational modelling of immunology, abstraction and then development of engineered systems. We argue that AIS are much more than engineered systems inspired by the immune system and that there is a great deal for both immunology and engineering to learn from each other through working in an interdisciplinary manner.

Keywords

Artificial immune systems Immunological modelling Mathematical modelling Computational modelling Applications of artificial immune systems Immune inspired computing Immunocomputing Computational immunology 

References

  1. 1.
    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 2003), LNCS 2787. Springer, Berlin, pp 147–155Google Scholar
  2. 2.
    Anderson CC, Matzinger P (2000) Danger: the view from the bottom of the cliff. Semin Immunol 12(3):231–238CrossRefGoogle Scholar
  3. 3.
    Andrews PS, Timmis J (2005) Inspiration for the next generation of artificial immune systems. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proc of the 4th International Conference on Artificial Immune Systems (ICARIS), Lecture Notes in Computer Science, vol 3627. Springer, Berlin, pp 126–138Google Scholar
  4. 4.
    Andrews PS, Timmis J (2005) 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 293–306Google Scholar
  5. 5.
    Andrews PS, Timmis J (2006) A computational model of degeneracy in a lymph node. LNCS, Springer, Berlin, pp 164–177Google Scholar
  6. 6.
    Balthrop J, Esponda F, Forrest S, Glickman M (2002) Coverage and generalisation in an artificial immune system. In: Genetic and evolutionary computation, pp 3–10Google Scholar
  7. 7.
    Bersini H (2002) Self-assertion versus self-recognition: a tribute to Francisco Varela. In: Timmis J, Bentley PJ (eds) Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS 2002). University of Kent Printing Unit, pp 107–112Google Scholar
  8. 8.
    Bersini H (2006) Immune system modeling: the OO way. In: Bersini H, Carneiro J (eds) Proceedings of the 5th International Conference on Artificial Immune Systems, LNCS, vol 4163. Springer, Berlin, pp 150–163Google Scholar
  9. 9.
    Bersini H, Carneiro J (eds) (2006) Proceedings of the 5th International Conference on Artificial Immune Systems, LNCS, vol 4163. Springer, BerlinGoogle Scholar
  10. 10.
    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) Proc of the 4th International Conference on Artificial Immune Systems (ICARIS), Lecture Notes in Computer Science, vol 3627. Springer, Berlin, pp 290–303Google Scholar
  11. 11.
    Bretscher P (2000) Contemporary models for peripheral tolerance and the classical ‘historical postulate’. Semin Immunol 12(3):221–229CrossRefGoogle Scholar
  12. 12.
    Burnet FM (1959) The clonal selection theory of acquired immunity. Cambridge University Press, CambridgeGoogle Scholar
  13. 13.
    de Castro LN, Von Zuben FJ (2000) An evolutionary immune network for data clustering. In: Proceeding of the IEEE Brazilian Symposium on Artificial Neural Networks, pp 84–89Google Scholar
  14. 14.
    de Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3):239–251CrossRefGoogle Scholar
  15. 15.
    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) Proc of the 4th International Conference on Artificial Immune Systems (ICARIS), Lecture Notes in Computer Science, vol 3627. Springer, Berlin, pp 318–330Google Scholar
  16. 16.
    Coelho G, Von Zuben FJ (2006) Omni-ainet: an immune-inspired approach for omni optimization. In: Bersini H, Carneiro J (eds) Proceedings of the 5th International Conference on Artificial Immune Systems, LNCS, vol 4163. Springer, Berlin, pp 294–308Google Scholar
  17. 17.
    Cohen IR (2000) Discrimination and dialogue in the immune system. Semin Immunol 12(3):215–219CrossRefGoogle Scholar
  18. 18.
    Cohen IR (2000) Tending Adam’s garden: evolving the cognitive immune self. Elsevier, AmsterdamGoogle Scholar
  19. 19.
    Cohen IR (2007) Real and artificial immune systems: computing the state of the body. Imm Rev 7:569–574CrossRefGoogle Scholar
  20. 20.
    Cohen IR, Harel D (2006) Explaining a complex living system: dynamics, multi-scaling and emergenceGoogle Scholar
  21. 21.
    Coutinho A (1995) The network theory: 21 years later. Scand J Immunol 42:3–8CrossRefGoogle Scholar
  22. 22.
    Cruz Cortes N, Coello Coello C (2003) Multiobjective optimisation using ideas from the clonal selection principle. In: Cantu-Paz E (ed) Genetic and Evolutionary Computation (GECCO), vol 1, pp 158–170Google Scholar
  23. 23.
    Cutello V, Nicosia G (2004) The clonal selection principle for in silico and in vitro computing. In: de Castro LN, von Zuben FJ (eds) Recent Developments in Biologically Inspired Computing. Idea Group Publishing, Hershey, PAGoogle Scholar
  24. 24.
    Cutello V, Nicosia G, Oliveto P, Romeo M (2007) On the convergence of immune algorithms. In: Proc of Foundations of Computational Intelligence, pp 409–416Google Scholar
  25. 25.
    Dasgupta D (ed) (1999) Artificial immune systems and their applications. Springer, BerlinGoogle Scholar
  26. 26.
    Dasgupta D, Krishna Kumar K, Wong D, Berry M (2004) Negative selection algorithm for aircraft fault detection. In: Nicosia G, Cutello V, Bentley PJ, Timmis J (eds) Proceedings of the 3rd International Conference on Artificial Immune Systems (ICARIS 2004), LNCS 3239. Springer, Berlin, pp 1–14Google Scholar
  27. 27.
    Davoudani D, Hart E, Paechter B (2007) An immune-inspired approach to speckled computing. In: de Castro LN, Von Zuben FJ, Knidel H (eds) Proceedings of the 6th International Conference on Artificial Immune Systems, Lecture Notes in Computer Science, vol 4628. Springer, Berlin, pp 288–299Google Scholar
  28. 28.
    de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer, BerlinGoogle Scholar
  29. 29.
    de Castro LN, Von Zuben FJ (1999) Artificial immune systems: Part I—basic theory and applications. Tech Rep DCA-RT 01/99, School of Computing and Electrical Engineering, State University of Campinas, BrazilGoogle Scholar
  30. 30.
    de Castro LN, Von Zuben FJ (2000) Artificial immune systems: Part II—a survey of applications. Tech Rep DCA-RT 02/00, School of Computing and Electrical Engineering, State University of Campinas, BrazilGoogle Scholar
  31. 31.
    de Castro LN, Von Zuben FJ, Knidel H (eds) (2007) Proceedings of the 6th International Conference on Artificial Immune Systems, Lecture Notes in Computer Science, vol 4628. Springer, Berlin, pp 119–130Google Scholar
  32. 32.
    de Lemos R, Timmis J, Forrest S, Ayara M (2007) Immune-inspired adaptable error detection for automated teller machines. IEEE Trans Syst Man Cybern C Appl Rev 37(5):873–886CrossRefGoogle Scholar
  33. 33.
    Edelstein L, Rosen R (1978) Enzyme-substrate recognition. J Theor Biol 73(1):181–204CrossRefMathSciNetGoogle Scholar
  34. 34.
    Esponda F (2005) Negative representations of information. Ph.D. thesis, University of New MexicoGoogle Scholar
  35. 35.
    Esponda F, Ackley ES, Forrest S, Helman P (2005) On-line negative databases (with experimental results). Int J Unconventional Comput 1(3):201–220Google Scholar
  36. 36.
    Farmer JD (1990) A rossetta stone for connectionism. Physica D 42:153–187CrossRefMathSciNetGoogle Scholar
  37. 37.
    Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning. Physica D 22:187–204CrossRefMathSciNetGoogle Scholar
  38. 38.
    Folwer M (2004) UML Distilled. Addison-Wesley, Reading, MAGoogle Scholar
  39. 39.
    Forrest S, Beauchemin C (2007) Computer immunology. Immunol Rev 216(1):176–197Google Scholar
  40. 40.
    Forrest S, Perelson A, Allen L, Cherukuri R (1994) Self–nonself discrimination in a computer. In: Proceedings of the IEEE Symposium on Research Security and Privacy, pp 202–212Google Scholar
  41. 41.
    Freitas A, Timmis J (2007) Revisiting the foundations of artificial immune systems for data mining. IEEE Trans Evol Comp 11(4):521–540CrossRefGoogle Scholar
  42. 42.
    Gamma E, Helm R, Johnson R, Vlissides J (1995) Design patterns. Addison-Wesley, Reading, MAGoogle Scholar
  43. 43.
    Garrett S (2005) How do we evaluate artificial immune systems? Evol Comput 13(2):145–177CrossRefGoogle Scholar
  44. 44.
    Germain RN (2004) An innately interesting decade of research in immunology. Nat Med 10:1307–1320CrossRefGoogle Scholar
  45. 45.
    Gillespie D (1977) Approximate accelerated stochastic simulation of chemically reacting systems. J Phys Chem 81(25):2340–2361CrossRefGoogle Scholar
  46. 46.
    Goldsby RA, Kindt TJ, Osborne BA, Kuby J (2003) Immunology, 5th edn. W. H. Freeman and Company, San FranciscoGoogle Scholar
  47. 47.
    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 195–206Google Scholar
  48. 48.
    Gonzalez FA, Dasgupta D (2003) Anomaly detection using real-valued negative selection. Genet Program Evolvable Machines 4(4):383–403CrossRefGoogle Scholar
  49. 49.
    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) Proc of the 4th International Conference on Artificial Immune Systems (ICARIS), Lecture Notes in Computer Science, vol 3627. Springer, BerlinGoogle Scholar
  50. 50.
    Greensmith J, Aickelin U, Twycross J (2006) Articulation and clarification of the dendritic cell algorithm. In: Bersini H, Carneiro J (eds) Proceedings of the 5th International Conference on Artificial Immune Systems, LNCS, vol 4163. Springer, Berlin, 404–417Google Scholar
  51. 51.
    Grossman Z, Paul WE (2000) Self-tolerance: context dependent tuning of T cell antigen recognition. Semin Immunol 12(3):197–203CrossRefGoogle Scholar
  52. 52.
    Guzella T, Mota-Santos T, Caminhas W (2007) Towards a novel immune inspired approach to temporal anomaly detection. In: de Castro LN, Von Zuben FJ, Knidel H (eds) Proceedings of the 6th International Conference on Artificial Immune Systems, Lecture Notes in Computer Science, vol 4628. Springer, Berlin, pp 119–130Google Scholar
  53. 53.
    Harel D (1987) Statecharts: a visual formalism for complex systems. Sci Comput Program 8:231–274Google Scholar
  54. 54.
    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) Proc of the 4th International Conference on Artificial Immune Systems (ICARIS), Lecture Notes in Computer Science, vol 3627. Springer, Berlin, pp 29–42Google Scholar
  55. 55.
    Hart E, Bersini H, Santos F (2006) Tolerance vs intolerance: how affinity defines topology in an idiotypic network. In: Bersini H, Carneiro J (eds) Proceedings of the 5th International Conference on Artificial Immune Systems, LNCS, vol 4163. Springer, Berlin, pp 109–121Google Scholar
  56. 56.
    Hart E, Ross P (2004) Studies on the implications of shape-space models for idiotypic networks. In: Nicosia G, Cutello V, Bentley PJ, Timmis J (eds) Proceedings of the 3rd International Conference on Artificial Immune Systems (ICARIS 2004), LNCS 3239. Springer, Berlin, pp 413–426Google Scholar
  57. 57.
    Hart E, Santos F, Bersini H (2007) Topological constraints in the evolution of idiotypic networks. In: de Castro LN, Von Zuben FJ, Knidel H (eds) Proceedings of the 6th International Conference on Artificial Immune Systems, Lecture Notes in Computer Science, vol 4628. Springer, Berlin, pp 252–263Google Scholar
  58. 58.
    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) Proc of the 4th International Conference on Artificial Immune Systems (ICARIS), Lecture Notes in Computer Science, vol 3627. Springer, Berlin, pp 483–497Google Scholar
  59. 59.
    Hart E, Timmis J (2007) Application areas of AIS: the past, the present and the future. To appear in Applied Soft Computing. In Press, Corrected Proof, Available online 12 February 2007Google Scholar
  60. 60.
    Hofmeyr S, Forrest S (2000) Architecture for an artificial immune system. Evol Comput 7(1):1289–1296Google Scholar
  61. 61.
    Hone A, van den Berg HA (2007) Modelling a cytokine network. In: Proc of IEEE Workshop on Computational Intelligence, pp 389–293Google Scholar
  62. 62.
    Honorio L, Leite da Silva A, Barbosa D (2007) A gradient-based artificial immune system applied to optimal power flow problems. In: de Castro LN, Von Zuben FJ, Knidel H (eds) Proceedings of the 6th International Conference on Artificial Immune Systems, Lecture Notes in Computer Science, vol 4628. Springer, Berlin, pp 1–12Google Scholar
  63. 63.
    Jacob C, Pilat M, Bentley P, Timmis J (eds) (2005) Proc of the 4th International Conference on Artificial Immune Systems (ICARIS), Lecture Notes in Computer Science, vol 3627. Springer, BerlinGoogle Scholar
  64. 64.
    Janeway C, Travers P, Walport M, Shlomchik M (2004) Immunobiology: the immune system is health and disease, 6th edn. Garland Science, New YorkGoogle Scholar
  65. 65.
    Janeway CA (1992) The immune system evolved to discriminate infectious nonself from noninfectious self. Immunol Today 13:11–16CrossRefGoogle Scholar
  66. 66.
    Janeway CA, Travers P, Walport M, Shlomchik M (2001) Immunobiology, 5th edn. Garland Publishing, New YorkGoogle Scholar
  67. 67.
    Jerne NK (1974) Towards a network theory of the immune system. Ann Immunol (Inst Pasteur) 125C:373–389Google Scholar
  68. 69.
    Ji Z, Dasgupta D (2003) Artificial immune system (AIS) research in the last five years. In: Congress on evolutionary computation, vol 1. IEEE, Canberra, Australia, pp 123–130Google Scholar
  69. 69.
    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, Piscataway, NJ, pp 528–535Google Scholar
  70. 70.
    Kelsey J, Timmis J (2003) Immune inspired somatic contiguous hypermutation for function optimisation. In: Genetic and evolutionary computation conference—GECCO 2003. Springer, Berlin, pp 207–218Google Scholar
  71. 71.
    Kim J, Bentley P, Aickelin U, Greensmith J, Tedesco G, Twycross J (2007) Immune system approaches to intrusion detection—a review. Nat Comput (in print)Google Scholar
  72. 72.
    Langman RE, Cohn M (2000) Editorial summary. Semin Immunol 12(3):343–344CrossRefGoogle Scholar
  73. 73.
    Langman RE, Cohn M (2000) A minimal model for the self–nonself discrimination: a return to basics. Semin Immunol 12(3):189–195CrossRefGoogle Scholar
  74. 74.
    Lutz MB, Schuler G (2002) Immature, semi-mature and fully mature dendritic cells: which signals induce tolerance or immunity? Trends Immunol 23(9):445–449CrossRefGoogle Scholar
  75. 75.
    Matzinger P (1994) Tolerance, danger and the extended family. Ann Rev Immunol 12:991–1045Google Scholar
  76. 76.
    Matzinger P (2002) The danger model: a renewed sense of self. Science 296:301–305CrossRefGoogle Scholar
  77. 77.
    May P, Timmis J, Mander K (2007) Immune and evolutionary approaches to software mutation testing. In: de Castro LN, Von Zuben FJ, Knidel H (eds) Proceedings of the 6th International Conference on Artificial Immune Systems, Lecture Notes in Computer Science, vol 4628. Springer, Berlin, pp 336–347Google Scholar
  78. 78.
    McEwan C, Hart E, Paechter B (2007) Revisiting the central and peripheral immune system. In: de Castro LN, Von Zuben FJ, Knidel H (eds) Proceedings of the 6th International Conference on Artificial Immune Systems, Lecture Notes in Computer Science, vol 4628. Springer, Berlin, pp 240–251Google Scholar
  79. 79.
    Medzhitov R, Janeway CA (2000) How does the immune system distinguish self from nonself? Semin Immunol 12(3):185–188CrossRefGoogle Scholar
  80. 80.
    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) Proc of foundations of computational intelligence, pp 394–400Google Scholar
  81. 81.
    Milner R (1999) Communicating and mobile systems: the π-calculus. Cambridge University Press, CambridgeGoogle Scholar
  82. 82.
    Mosmann TR, Livingstone AM (2004) Dendritic cells: the immune information management experts. Science 5(6):564–566Google Scholar
  83. 83.
    Neal M, Trapnel B (2007) In silico immuonology, chap. Go Dutch: exploit interactions and environments with artificial immune systems. Springer, Berlin, pp 313–330Google Scholar
  84. 84.
    Newborough R, Stepney S (2005) A generic framework for population based algorithms. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proc of the 4th International Conference on Artificial Immune Systems (ICARIS), Lecture Notes in Computer Science, vol 3627. Springer, BerlinGoogle Scholar
  85. 85.
    Nicosia G, Cutello V, Bentley PJ, Timmis J (eds) (2004) Proceedings of the 3rd International Conference on Artificial Immune Systems (ICARIS 2004), LNCS 3239. Springer, BerlinGoogle Scholar
  86. 86.
    Owens N, Timmis J, Greensted A, Tyrrell A (2007) On immune inspired homeostasis for electronic systems. In: de Castro LN, Von Zuben FJ, Knidel H (eds) Proceedings of the 6th International Conference on Artificial Immune Systems, Lecture Notes in Computer Science, vol 4628. Springer, Berlin, pp 216–227Google Scholar
  87. 87.
    Perelson AS, Oster GF (1979) Theoretical studies of clonal selection: minimal antibody repertoire size and reliability of self–non-self discrimination. J Theor Biol 81(4):645–670CrossRefMathSciNetGoogle Scholar
  88. 88.
    Phillips A, Cardelli L (2007) Efficient, correct simulation of biological processes in the stochastic pi-calculus. In: Proceedings of computational methods in systems biology (CMSB’07), vol 4695, pp 184–199Google Scholar
  89. 89.
    Priami C (1995) Stochastic π-calculus. Comput J 38(7):578–589CrossRefGoogle Scholar
  90. 90.
    Priami C, Regev A, Shapiro E (2001) Application of a stochastic name-passing calculus to representation for biological processes in the stochastic π-calculus. Inform Process Lett 80:25–31MATHCrossRefMathSciNetGoogle Scholar
  91. 91.
    Regev A, Silverman W, Shapiro E (2001) Representation and simulation of bio-chemical processes using the pi-calculus process algebra. In: Pacific symposium on biocomputing, vol 6, pp 459–470Google Scholar
  92. 92.
    Rudolph G (1998) Finite markov chain results in evolutionary computation: a tour d’horizon. Fundam Inf 35(1–4):67–89MATHMathSciNetGoogle Scholar
  93. 93.
    Secker A, Freitas A (2007) Wairs: improving classification accuracy by weighting attributes in the airs classifier. In: Proceedings of the congress on evolutionary computation, pp 3759–3765Google Scholar
  94. 94.
    Secker A, Freitas A, Timmis J (2003) A danger theory inspired approach to web mining. In: Timmis J, Bentley P, Hart E (eds) Proceedings of the 2nd International Conference on Artificial Immune Systems (ICARIS 2003), LNCS 2787. Springer, Berlin, pp 156–167Google Scholar
  95. 95.
    Silverstein AM, Rose NR (2000) There is only one immune system! The view from immunopathology. Semin Immunol 12(3):173–178CrossRefGoogle Scholar
  96. 96.
    Stepney S, Smith R, Timmis J, Tyrrell A, Neal M, Hone A (2006) Conceptual frameworks for artificial immune systems. Int J Unconventional Comput 1(3):315–338Google Scholar
  97. 97.
    Stepney S, Smith RE, Timmis J, Tyrrell AM (2004) Towards a conceptual framework for artificial immune systems. In: Nicosia G, Cutello V, Bentley PJ, Timmis J (eds) Proceedings of the 3rd International Conference on Artificial Immune Systems (ICARIS 2004), LNCS 3239. Springer, Berlin, pp 53–64Google Scholar
  98. 98.
    Stibor T (2006) On the appropriateness of negative selection for anomaly detection and network intrusion detection. Ph.D. thesis, Darmstadt University of TechnologyGoogle Scholar
  99. 99.
    Stibor T (2007) Phase transition and the computational complexity of generating r-contiguous detectors. In: de Castro LN, Von Zuben FJ, Knidel H (eds) Proceedings of the 6th International Conference on Artificial Immune Systems, Lecture Notes in Computer Science, vol 4628. Springer, Berlin, pp 142–155Google Scholar
  100. 100.
    Stibor T, Timmis J (2007) An investigation into the compression quality of ainet. In: Fogel D (ed) Proc of foundations of computational intelligenceGoogle Scholar
  101. 101.
    Stibor T, Timmis J, Eckert C (2006) The link between r-contiguous detectors and k-cnf satisfiability. In: Proceedings of the congress on evolutionary computation, pp 491–498Google Scholar
  102. 102.
    Tauber AI (2000) Moving beyond the immune self? Semin Immunol 12(3):241–248CrossRefGoogle Scholar
  103. 103.
    Timmis J (2007) Artificial immune systems: today and tomorow. Nat Comput 6(1):1–18MATHCrossRefMathSciNetGoogle Scholar
  104. 104.
    Timmis J, Bentley P, Hart E (eds) (2003) Proceedings of the 2nd International Conference on Artificial Immune Systems (ICARIS 2003), LNCS 2787. Springer, BerlinGoogle Scholar
  105. 105.
    Timmis J, Bentley PJ (eds) (2002) Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS 2002). University of Kent Printing UnitGoogle Scholar
  106. 106.
    Timmis J, Hone A, Stibor T, Clark E (2007) Theoretical advances in artificial immune systems. J Theor Comput Sci (submitted)Google Scholar
  107. 107.
    Timmis J, Knight T (2001) Data mining: a heuristic approach, chap. Artificial immune systems: using the immune system as inspiration for data mining. Idea Group, Hershey, PA, pp 209–230Google Scholar
  108. 108.
    Timmis J, Neal M (2001) A resource limited artificial immune system for data analysis. Knowl Based Syst 14(3–4):121–130CrossRefGoogle Scholar
  109. 109.
    Twycross J, Aickelin U (2005) Towards a conceptual framework for innate immunity. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proc of the 4th International Conference on Artificial Immune Systems (ICARIS), Lecture Notes in Computer Science, vol 3627. Springer, Berlin, pp 112–125Google Scholar
  110. 110.
    Varela FJ, Coutinho A (1991) Second generation immune networks. Immunol Today 12(5):159–166Google Scholar
  111. 111.
    Villalobos-Arias M, Coello CAC, Hernandez-Lerma O (2004) Convergence analysis of a multiobjective artificial immune system algorithm. In: Nicosia G, Cutello V, Bentley PJ, Timmis J (eds) Proceedings of the 3rd International Conference on Artificial Immune Systems (ICARIS 2004), LNCS 3239. Springer, Berlin, pp 226–235Google Scholar
  112. 112.
    Voigt D, Wirth H, Dilger W (2007) A computational models for the cognitive immune system theory based on learning classifier systems. In: de Castro LN, Von Zuben FJ, Knidel H (eds) Proceedings of the 6th International Conference on Artificial Immune Systems, Lecture Notes in Computer Science, vol 4628. Springer, Berlin, pp 264–275Google Scholar
  113. 113.
    Watkins A, Timmis J, Boggess L (2004) Artificial immune recognition system (AIRS): an immune-inspired supervised learning algorithm. Genet Program Evolvable Machines 5(1):291–317CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of Computer Science and Department of ElectronicsUniversity of YorkHeslington, YorkUK
  2. 2.Department of Computer ScienceUniversity of YorkHeslington, YorkUK
  3. 3.Department of ElectronicsUniversity of YorkHeslington, YorkUK

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