Natural Computing

, Volume 6, Issue 1, pp 1–18 | Cite as

Artificial immune systems—today and tomorrow

Original Paper

Abstract

In this position paper, we argue that the field of artificial immune systems (AIS) has reached an impasse. For many years, immune inspired algorithms, whilst having some degree of success, have been limited by the lack of theoretical advances, the adoption of a naive immune inspired approach and the limited application of AIS to challenging problems. We review the current state of the AIS approach, and suggest a number of challenges to the AIS community that can be undertaken to help move the area forward.

Keywords

Artificial immune system Future directions Position paper 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bentley P, Greensmith J, Ujin S (2005) Two ways to grow artificial tissue. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proceedings of the 4th international conference on artificial immune systems, vol 3627 of LNCS. Springer, pp 139–152Google Scholar
  2. Berek C, Ziegner M (1993) The maturation of the immune response. Immunol Today 14:200–402CrossRefGoogle Scholar
  3. Bersini H (1991) Immune network and adaptive control. In: Proceedings of the 1st European conference on artificial life (ECAL), MIT Press, pp 217–226Google Scholar
  4. Bersini H (1992) Reinforcement and recruitment learning for adaptive process control. In: Proc int fuzzy Association Conference (IFAC/IFIP/IMACS) on artificial intelligence in real time control, pp 331–337Google Scholar
  5. Bersini H, Varela F (1994) The immune learning mechansims: recruitment, reinforcement and their applications. Chapman HallGoogle Scholar
  6. Besendovsky HO, del Ray A (1996) Immune-neuro-endocrine interactions: facts and hypotheses. Nature 249:356–358CrossRefGoogle Scholar
  7. Brzezniak Z, Zastawniak T (1999) Basic stochastic processes. SpringerGoogle Scholar
  8. Burnet F (1959) The clonal selection theory of acquired immunity. Cambridge University Press, CambridgeGoogle Scholar
  9. 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, vol 3627 of LNCS. Springer, pp 318–330Google Scholar
  10. Cohen IR (2000) Tending Adam’s Garden: evolving the cognitive immune self. Elsevier Academic PressGoogle Scholar
  11. Cooke D, Hunt J (1995) Recognising promoter sequences using an artificial immune systems. In: Proceedings of intelligent systems in molecular biology. AAAI Press, pp 89–97Google Scholar
  12. Cutello V, Nicosia G, Parvone M (2004) Exploring the capability of immune algorithms: a characterization of hypermutation operators. In: LNCS vol 3239, Springer, pp 263–276Google Scholar
  13. de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. SpringerGoogle Scholar
  14. de Castro LN, Von Zuben FJ (2001) aiNet: an artificial immune network for data analysis. Idea Group Publishing, USA, pp 231–259Google Scholar
  15. de Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evolut Comput 6(3):239–251CrossRefGoogle Scholar
  16. de Castro LN, Timmis J (2002) 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 at Canterbury, University of Kent at Canterbury Printing Unit, pp 231–240Google Scholar
  17. Esponda F, Forrest S, Helman P (2004) A formal framework for positive and negative detection schemes. IEEE Trans Systems Man Cybernet Part B 34:357–373CrossRefGoogle Scholar
  18. Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning. Physica D 22:187–204CrossRefMathSciNetGoogle Scholar
  19. Forrest S, Hofmeyr S, Somayaji A (1997) Computer immunology. Commun ACM 40(10):88–96CrossRefGoogle Scholar
  20. 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
  21. Freitas A, Timmis J (2003) Revisiting the foundations of artificial immune systems: a problem oriented perspective. In: LNCS, vol 2787, Springer, pp 229–241Google Scholar
  22. Garrett SM (2003) A paratope is not an epitope: implications for clonal selection and immune networks. In: Proceedings of the 2nd international conference on artificial immune systems, vol 2787Google Scholar
  23. Garrett S (2004) Parameter-free, adaptive clonal selection. In Congress on evolutionary computing, CEC. IEEEGoogle Scholar
  24. Garrett S (2005) How do we evaluate artificial immune systems? Evol Comput 13(2):145–177CrossRefGoogle Scholar
  25. 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, vol 3627Google Scholar
  26. Grimmett GR, Stirzaker DR (1982) Probability and random processes. Oxford University Press, OxfordGoogle Scholar
  27. Hart E (2005) Not all balls are round. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proceedings of the 4th international conference on artificial immune systems, vol 3627 of LNCS. SpringerGoogle Scholar
  28. Hart E, Ross P (2004) Studies on the implications of shape-space models for idiotypic networks. In: Proceedings of the 3rd international conference on artificial immune systems (ICARIS 2004), LNCS 3239, Springer, pp 413–426Google Scholar
  29. Hart E, Timmis J (2005) Application areas of ais: the past, the present and the future. Applied Soft Computing, In ReviewGoogle Scholar
  30. 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 2005), LNCS 3627, vol 3627 of LNCS. Springer, pp 126–138Google Scholar
  31. Hightower RR, Forrest SA, Perelson AS (1995) The evolution of emergent organization in immune system gene libraries. In: Eshelman LJ (ed) Proceedings of the 6th international conference on genetic algorithms. Morgan Kaufmann, pp 344–350Google Scholar
  32. Hone A, Kelsey J (2004) Optima, extrema and artificial immune systems. In: Lecture notes in computer science. Springer, pp 89–98Google Scholar
  33. Hunt J, Cooke D (1996) Learning using an artificial immune system. J Network Comput Appl 19:189–212CrossRefGoogle Scholar
  34. Hunt J, Timmis J, Cooke D, Neal M, King C (1998) Artificial immune systems and their applications, chapter JISYS: development of an artificial immune system for real world applications. Springer, pp 157–186Google Scholar
  35. Ishida Y (1997) Active diagnosis by self-organisation: an approach by the immune network metaphor. In: Proceedings of the international joint conference on artificial intelligence, IEEE, Nagoya, Japan, pp 1084–1089Google Scholar
  36. Janeway CA Jr, Travers P (1997) Immunobiology: the immune system in health and disease, 3rd edn. Garland Publishing, New YorkGoogle Scholar
  37. Janeway CA Jr, Medzhitov R (2002) Innate immune recognition. Ann Rev Immunol 20:197–216CrossRefGoogle Scholar
  38. Jerne NK (1974) Towards a network theory of the immune system. Ann Immunol (Inst Pasteur) 125C:373–389Google Scholar
  39. Kelsey J, Timmis J (2003) Immune inspired somatic contiguous hypermutation for function optimisation. In: Genetic and evolutionary computation conference – GECCO 2003, vol LNCS 2723. Springer, pp 207–218Google Scholar
  40. 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 at Canterbury, University of Kent at Canterbury Printing Unit, pp␣59–67Google Scholar
  41. Kim J, Bentley PJ (2002) A model of gene library evolution in the dynamic clonal selection algorithm. In: J Timmis, Bentley P (eds) Proceedings of the 1st international conferece on artificial immune systems ICARIS, University of Kent at Canterbury, University of Kent at Canterbury Printing Unit, pp 182–189Google Scholar
  42. Kuttler C, Niehren J, Blossey R (2004) Gene regulation in the pi calculus: Simulating cooperativity at the lambda switch. In: Proc concurrent models in molecular biology (Bioconcur 04)Google Scholar
  43. Langman RE, Cohn M (1986) The complete idiotype network is an absurd immune system. Immunol Today 7(4):100–101CrossRefGoogle Scholar
  44. Liu BZ, Deng GM (1991) An improved mathematical model of hormone secretion in the hypothalamo-pituitary-gonadal axis in man. J Theor Biol 150:51–58CrossRefGoogle Scholar
  45. Neal M (2002) An artificial immune system for continuous analysis of time-varying data. In: Jonathan T, Bentley PJ (eds) Proceedings of the 1st international conference on artificial immune systems ICARIS, University of Kent at Canterbury, University of Kent at Canterbury Printing Unit, pp 76–85Google Scholar
  46. Neal M, Timmis J (2004) Recent advances in biologically inspired computing, chapter: once more unto the breach... towards artificial homeostasis? IGPGoogle Scholar
  47. Newborough R, Stepney S (2005) A generic framework for population based algorithms. In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Proceedings of the 4th international conference on artificial immune systems, vol 3267 of LNCS. SpringerGoogle Scholar
  48. Perelson AS (1989) Immune network theory. Immunol Rev 110:5–36CrossRefGoogle Scholar
  49. Phillips A, Cardelli L (2004) A correct abstract machine for the stochastic pi-calculus. In: Proc concurrent models in molecular biology (Bioconcur’04). ENTCSGoogle Scholar
  50. Secker A, Freitas A, Timmis J (2003) AISEC an artificial immune system for email classification. In: Proceedings of the congress on evolutionary computation, pp 131–139Google Scholar
  51. Segal L, Cohen I (eds) (2001) Design principles for the immune system and other distributed systems. Oxford University Press, OxfordGoogle Scholar
  52. Sieburg HB, Clay OK (1991) The cellular device machine development system for modeling biology on the computer. Complex Syst 5:575–601MATHGoogle Scholar
  53. Smith WR (1983) Qualitative mathematical models of endocrine systems. Am J Physiol 245:473–477Google Scholar
  54. Stepney S, Clark JA, Tyrrell A, Johnson CG, Timmis J, Partridge D, Adamatsky A, Smith RE (2003) Journeys in non-classical computation: a grand challenge for computing research. Grand Challenge Report 7, National E-Science Centre, University of EdinburghGoogle Scholar
  55. Stepney S, Smith R, Timmis J, Tyrrell A, Neal M, Hone A (2005) Conceptual frameworks for artificial immune systems. Int J Unconvent Comput 1(3):315–338Google Scholar
  56. Stibor T, Bayarou KM, Eckert C (2004) An investigation of r-chunk detector generation on higher alphabets. In: Proceedings of the genetic and evolutionary computation conference (GECCO 2004). Springer, pp 299–307Google Scholar
  57. Stibor T, Mohr P, Timmis J, Eckert C (2005) Is negative selection appropriate for anomaly detection? In Proceedings of the genetic and evolutionary computation conference (GECCO 2005). Springer, pp 321–328Google Scholar
  58. Stibor T, Timmis J, Eckert C (2005) 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, vol 3627 of LNCS, pp 262–275Google Scholar
  59. Timmis J, de Lemos R, Ayara M, Duncan R (2002) Towards immune inspired fault tolerance in embedded systems. In: Wang L, Rajapakse J, Fukushima K, Lee S, Yao X (eds) Proceedings of 9th international conference on neural information processing. IEEE, pp 1459–1463Google Scholar
  60. Timmis J, Neal M (2001) A resource limited artificial immune system. Knowl Based Syst 14(3/4):121–130CrossRefGoogle Scholar
  61. Timmis J, Neal M, Hunt J (2000) An artificial immune system for data analysis. Biosystems 55(1/3):143–150, unknownGoogle Scholar
  62. Villalobos-Arias M, Coello Coello CA, Hernandez-Lerma O (2004) Convergence analysis of a multiobjective artificial immune system algorithm. In: Lecture notes in computer science, vol 3239, pp 226–235Google Scholar
  63. Watkins A (2001) An artificial immune recognition system. Mississippi State University. MSc Thesis.Google Scholar
  64. Watkins A, Timmis J (2004) Exploiting parallelism inherent in AIRS, an artificial immune classifier. In: Nicosia G et al (eds) Third international conference on artificial immune systems, vol 3239 in LNCS, Springer, pp 427–438Google Scholar
  65. Watkins A, Xintong B, Phadke A (2003) Parallelizing an immune-inspired algorithm for efficient pattern recognition. In: Intelligent engineering systems through ANN: smart engineering system design: neural networks, fuzzy logic, evolutionary programming, complex systems and artificial life. ASME Press, pp 224–230Google Scholar
  66. Wierzchon S, Kuzelewska U (2002) Stable clusters formation in an artificial immune system. In: Timms J, Bentley PJ (eds) Proceedings of the 1st international conference on artificial immune systems ICARIS, University of Kent at Canterbury, University of Kent at Canterbury Printing Unit, pp 68–75Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Departments of Computer Science and Electronics University of YorkHeslington, YorkUK

Personalised recommendations