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Artificial Immune Systems

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 146))

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Abstract

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.

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References

  1. de Abreu, F., Nolte ’Hoen, E., Almeida, C., Davis, D. Cellular frustration: a new conceptual framework for understanding cell-mediated immune responses. In: Proceedings of the 5th International Conference on Artificial Immune Systems (ICARIS), LNCS 4163, pp. 37–51. Lisbon, Portugal (2006)

    Google Scholar 

  2. Aickelin, U., Bentley, P., Cayzer, S., Kim, J., McLeod, J. Danger theory: the link between AIS and IDS. In: Proceedings of the 2nd International Conference on Artificial Immune Systems (ICARIS), LNCS 2787, pp. 147–155. Springer (2003)

    Google Scholar 

  3. Aickelin, U., Cayzer, S. The danger theory and its application to artificial immune systems. In: Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS), pages 141–148. University of Kent at Canterbury Printing Unit (2002)

    Google Scholar 

  4. Andrews, P., Timmis, J. A computational model of degeneracy in a lymph node a computational model of degeneracy in a lymph node a computational model of degeneracy in a lymph node. In: Proceedings of the 5th International Conference on Artificial Immune Systems (ICARIS), LNCS 4163, pp. 164–177 Springer (2006)

    Google Scholar 

  5. Andrews, P., Timmis, J. Adaptable lymphocytes for artificial immune systems. In: Proceedings of the 7th International Conference on Artificial Immune Systems (ICARIS), pp. 376–386. Phuket, Thailand (2008)

    Google Scholar 

  6. Balthrop, J., RIOT: a responsive system for mitigating computer network epidemics and attacks. Master’s thesis, University of New Mexico (2005)

    Google Scholar 

  7. Beauchemin, C., Forrest, S., Koster, F.T. Modeling influenza viral dynamics in tissue. In Proceedings of the 5th International Conference on Artificial Immune Systems (ICARIS), LNCS 4163, pp. 23–36. Springer (2006)

    Google Scholar 

  8. Bentley, P., Lee, D., Jung, S. (eds.) Artificial Immune Systems, 7th International Conference, ICARIS 2008, Phuket, Thailand, August 10-13, 2008. Proceedings, vol. 5132 of Lecture Notes in Computer Science. Springer (2008)

    Google Scholar 

  9. Bersini, H., Varela, F. Hints for adaptive problem solving gleaned from immune network. In: Parallel Problem Solving from Nature, pp. 343–354 Springer (1991)

    Google Scholar 

  10. De Boer, R., Perelson, A., Kevrekidis, I. Immune network behaviour: From stationary states to limit cycle oscilations. Bull. Math. Biol. 55(4):745–780 (1993)

    Article  Google Scholar 

  11. Mark Burgess. Computer immunology. In: LISA ’98: Proceedings of the 12th USENIX conference on System administration, pp. 283–298. USENIX Association. Berkeley, CA, USA (1998)

    Google Scholar 

  12. Burnet, F. Clonal selection and after. In: Bell, G., Perelson, A., Pimbley, G. (eds.) Theoretical Immunology, pp. 63–85. Marcel Dekker Inc., New York, NY (1978)

    Google Scholar 

  13. Cayzer, S., Aickelin, U. A recommender system based on idiotypic artificial immune networks. J. Math. Modell. Algorithms, 4:181–198 (2005)

    Article  Google Scholar 

  14. Cohen, I.R. Tending Adam’s Garden : Evolving the Cognitive Immune Self. Academic (2004)

    Google Scholar 

  15. Coico, R., Sunshine, G., Benjamini, E. Immunology: A Short Course. Wiley, New York, NY (2003)

    Google Scholar 

  16. Coutinho, A. The Le Douarin phenomenon: a shift in the paradigm of developmental self-tolerance. Int. J. Develop. Biol. 49(2–3):131–136 (2005)

    Article  Google Scholar 

  17. Cross, S., Harrison, R., Kennedy, R. Introduction to neural networks. Lancet 346(8982):1075–1079 (1995)

    Article  Google Scholar 

  18. Cutello, V., Nicosia, G., Pavone, M., Timmis, J. An immune algorithm for protein structure prediction on lattice models. IEEE Trans. Evol. Comput. 11(1):101–117 (2007)

    Article  Google Scholar 

  19. de Castro, L., Timmis, J. An artificial immune network for multimodal function optimization. In: Proceedings of the Congress on Evolutionary Computation (CEC), vol. 1, pp. 699–704, IEEE Computer Society. Los Alamitos, CA, USA (2002)

    Google Scholar 

  20. de Castro, L., Timmis, J. Artificial Immune Systems: A New Computational Approach. Springer London, UK (2002)

    Google Scholar 

  21. de Castro, L., Von Zuben, F. The clonal selection algorithm with engineering applications. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Workshop on Artificial Immune Systems, pp. 36–37. Morgan Kaufmann, Las Vegas, NV (2000)

    Google Scholar 

  22. Farmer, J., Packard, N., Perelson, A. The immune system, adaptation and machine learning. Physica D, 2(1–3):187–204 (1986)

    Article  Google Scholar 

  23. Forrest, S., Hofmeyr, S., Somayaji, A., Longstaff, T. A sense of self for unix processes. In: Proceedings of the IEEE Symposium on Research in Security and Privacy, pp. 120–128. IEEE Computer Society Press (1996)

    Google Scholar 

  24. Forrest, S., Perelson, A., Allen, L., Cherukuri, R. Self-nonself discrimination in a computer. In: Proceedings of the IEEE Symposium on Security and Privacy, pp. 202–209. IEEE Computer Society (1994)

    Google Scholar 

  25. Goldberg, D. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., (1989)

    Google Scholar 

  26. Greensmith, J. The Dendritic Cell Algorithm. Ph.D. thesis, School of Computer Science, University Of Nottingham (2007)

    Google Scholar 

  27. Greensmith, J., Aickelin, U. Dendritic cells for syn scan detection. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 49–56 (2007)

    Google Scholar 

  28. Greensmith, J., Aickelin, U. The deterministic dendritic cell algorithm. In: Proceedings of the 7th International Conference on Artificial Immune Systems (ICARIS), pp. 291–302. Springer (2008)

    Google Scholar 

  29. Greensmith, J., Aickelin, U., Feyereisl, J. The DCA-SOMe comparison: a comparative study between two biologically-inspired algorithms. Evolutionary Intelligence: Special Issue on Artificial Immune Systems, accepted for publication (2008)

    Google Scholar 

  30. Greensmith, J., Aickelin, U., Twycross, J. Articulation and clarification of the Dendritic Cell Algorithm. In: Proceedings of the 5th International Conference on Artificial Immune Systems (ICARIS), LNCS 4163, pp. 404–417 (2006)

    Google Scholar 

  31. Hofmeyr, S. An immunological model of distributed detection and its application to computer security. Ph.D. thesis, University Of New Mexico (1999)

    Google Scholar 

  32. Hofmeyr, S., Forrest, S. Immunity by design. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 1289–1296 (1999)

    Google Scholar 

  33. Hunt, J., Timmis, J., Cooke, J., King, C. Jisys: the development of an artificial immune system for real world applications. In: Dasgupta, D. (ed.) Applications of Artificial Immune Systems, pp. 157–186. Springer (1998)

    Google Scholar 

  34. Janeway, C. Approaching the asymptote? Evolution and revolution in immunology. Cold Spring Harbor Symposium on Quant Biology, 1: 1–13 (1989)

    Google Scholar 

  35. Jerne, N. Towards a network theory of the immune system. Ann. Immunol. (Inst. Pasteur), 125 C 373–389 (1974)

    Google Scholar 

  36. Ji, Z., Dasgupta, D. Revisiting negative selection algorithms. Evol. Comput. 15(2), 223–251 (2007)

    Article  Google Scholar 

  37. Kelsey, J., Timmis, J., Hone, A. Chasing chaos. In: Proceedings of the Congress on Evolutionary Computation (CEC), pp. 413–419 (2003)

    Google Scholar 

  38. Kephart, J., Sorkin, G., Swimmer, M., White, S. Blueprint for a computer immune system. In: Virus Bulletin International Conference (1999)

    Google Scholar 

  39. Kim, J., Bentley, P. Evaluating negative selection in an artificial immune system for network intrusion detection. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 1330–1337, July (2001)

    Google Scholar 

  40. Michael Krautmacher and Werner Dilger. Ais based robot navigation in a rescue scenario. In: Proceedings of the 3rd International Conference on Artificial Immune Systems (ICARIS), LNCS 3239, pp. 106–118. Springer (2004)

    Google Scholar 

  41. Luh, G., Liu, W. Reactive immune network based mobile robot navigation. In: Proceedings of the 3rd International Conference on Artificial Immune Systems (ICARIS), LNCS 3239, pp. 119–132 (2004)

    Google Scholar 

  42. Matzinger, P. Tolerance, danger and the extended family. Ann. Rev. Immunol. 12:991–1045 (1994)

    Article  Google Scholar 

  43. Murphy, K., Travers, P., Walport, M. Janeway’s Immunobiology. 7th (edn.) Garland Science (2008)

    Google Scholar 

  44. Oates, R., Greensmith, J., Aickelin, U., Garibaldi, J., Kendall, G. The application of a dendritic cell algorithm to a robotic classifier. In: Proceedings of the 6th International Conference on Artificial Immune Systems (ICARIS), LNCS 4628, pp. 204–215. Springer, Santos (2007)

    Google Scholar 

  45. Owens, N., Timmis, J., Greensted, A., Tyrrell, A. Modelling the tunability of early t cell signalling events. In: Proceedings of the 7th International Conference on Artificial Immune Systems (ICARIS), pp. 12–23. Springer, Phuket (2008)

    Google Scholar 

  46. Rajewsky, K. Clonal selection and learning in the antibody system. Nature 381(6585), 751–758 (1996)

    Article  Google Scholar 

  47. Salazar-Bañuelos, A. Immune responses: a stochastic model. In: Proceedings of the 7th International Conference on Artificial Immune Systems (ICARIS), pp. 24–35. Springer, Phuket (2008)

    Google Scholar 

  48. Silverstein, A. Cellular versus humoral immunology: a century-long dispute. Nat. Immunol. 4(5):425–428 (2003)

    Article  Google Scholar 

  49. Silverstein, A. Paul Ehrlich, archives and the history of immunology. Nat. Immunol. 6(7):639–639 (2005)

    Article  Google Scholar 

  50. Smith, R., Forrest, S., Perelson, A. Searching for diverse, cooperative subpopulations with Genetic Algorithms. Evol. Comput. 1(2):127–149 (1993)

    Article  Google Scholar 

  51. Stepney, S., Smith, R., Timmis, J., Tyrrell, A. Towards a conceptual framework for artificial immune systems. In: Proceedings of the 3rd International Conference on Artificial Immune Systems (ICARIS), LNCS 3239, pp. 53–64. Springer, Catania (2004)

    Google Scholar 

  52. Stibor, T., Timmis, J., Eckert, C. On permutation masks in hamming negative selection. In Proceedings of the 5th International Conference on Artificial Immune Systems (ICARIS), LNCS 4163, pp. 122–135 Springer, Banff (2006)

    Google Scholar 

  53. Suzuki, J., Yamamoto, Y. Building an artificial immune network for decentralisezed policy negatioation in a communication end system: Open webserver/inexus study. In: Proceedings of the 4th World Conference on Systemics, Cybernetics and Informatics (SCI). International Institute of Informatics and Systemics Online Publications, Orlando, FL (2000)

    Google Scholar 

  54. Timmis, J. Artificial immune systems: today and tomorrow. Nat. Comput. 6(1):1–18 (2007)

    Article  Google Scholar 

  55. Timmis, J., Neal, M. A resource limited artificial immune system for data analysis. In: Proceedings of ES2000 Research and Development in Intelligent Systems XVII, pp. 19–32. Springer, London (2000)

    Google Scholar 

  56. Twycross, J. Integrated Innate and Adaptive Artificial Immune Systems Applied to Process Anomaly Detection. Ph.D. thesis, University Of Nottingham (2007)

    Google Scholar 

  57. Twycross, J., Aickelin, U. libtissue–-implementing innate immunity. In: Proceedings of the Congress on Evolutionary Computation (CEC), pp. 499–506. IEEE Computer Socitey, Vancouver (2006)

    Google Scholar 

  58. Vargas, P., de Castro, L., Von Zuben, F. Mapping artificial immune systems into learning classifier systems. In: Proceedings of the 5th International Workshop on Learning Classifier Systems (IWLCS), pp. 163–186. Springer, Grenada (2002)

    Google Scholar 

  59. Watanabe, Y., Ishiguro, A., Shirai, Y., Uchikawa, Y. Emergent construction of behaviour arbitration mechanism based on the immune system. In: Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC), pp. 481–486. IEEE, Anchorage, AK (1998)

    Google Scholar 

  60. Watkins, A., Timmis, J., Boggess, L. Artificial immune recognition system (AIRS): an immune-inspired supervised learning algorithm. Genet. Prog. Evolv. Mach., 5(3), 291–317 (2004)

    Article  Google Scholar 

  61. Whitbrook, A.M., Aickelin, U., Garibaldi, J.M., Idiotypic immune networks in mobile robot control. IEEE Trans. Sys., Man Cybernetics- Part B: Cybernetics, 37(6):1581–1598 (2007)

    Article  Google Scholar 

  62. Whitbrook, A.M., Aickelin, U., Garibaldi, J.M. An idiotypic immune network as a short-term learning architecture for mobile robots. In: Proceedings of the 7th International Conference on Artificial Immune Systems (ICARIS), LNCS 5132, pp. 266–278. Springer, Phuket (2008)

    Google Scholar 

  63. Wilson, W., Birkin, P., Aickelin, U. Motif detection inspired by immune memory. In: Proceedings of the 6th International Conference on Artificial Immune Systems (ICARIS), LNCS 4628, pp. 276–287. Springer, Santos (2007)

    Google Scholar 

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Acknowledgments

This work is supported by the EPSRC (EP/D071976/1). The authors would like to thank Jon Timmis for his feedback and assistance.

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Authors and Affiliations

  1. School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK

    Julie Greensmith

  2. University of Nottingham, Nottingham, UK

    Amanda Whitbrook & Uwe Aickelin

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  1. Julie Greensmith
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  3. Uwe Aickelin
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Correspondence to Julie Greensmith .

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  1. , Département de mathématiques et génie in, École Polytechnique Montréal, Montreal, H3C 3A7, Québec, Canada

    Michel Gendreau

  2. Dépt. Informatique et Recherche, Opérationnelle, Université de Montreal, Pavillon André-Aisenstadt, Montreal, H3C 3J7, Québec, Canada

    Jean-Yves Potvin

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Greensmith, J., Whitbrook, A., Aickelin, U. (2010). Artificial Immune Systems. In: Gendreau, M., Potvin, JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 146. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1665-5_14

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