Abstract
The immune system is highly distributed, highly adaptive, self-organising in nature, maintains a memory of past encounters and has the ability to continually learn about new encounters. From a computational point of view, the immune system has much to offer by way of inspiration to computer scientists and engineers alike. As computational problems become more complex, increasingly, people are seeking out novel approaches to these problems, often turning to nature for inspiration. A great deal of attention is now being paid to the vertebrate immune system as a potential source of inspiration, where it is thought that different insights and alternative solutions can be gleaned, over and above other biologically inspired methods.
Given this rise in attention to the immune system, it seems appropriate to explore this area in some detail. This survey explores the salient features of the immune system that are inspiring computer scientists and engineers to build Artificial Immune Systems. An extensive survey of applications is presented, ranging from network security to optimisation and machine learning. However, this is not complete, as no survey ever is, but it is hoped this will go some way to illustrate the potential of this exciting and novel area of research.
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References
De Castro, L.N and Timmis, J (2002). Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Berlin, Heidelberg, New York. ISBN 185233–594–7
Jerne, N. (1974). Towards a network theory of the immune system. Annals of Immunology (Inst.Pasteur). 125C. pp. 373–389.
Perelson, A. S. (1989). Immune Network Theory, Imm. Rev., 110, pp. 5–36.
Bersini, H and Varela, F. (1990). Hints for adaptive problem solving gleaned from immune networks. Parallel Problem Solving from Nature, 1st Workshop. PPSW1. Dortmund, Germany. Springer, Berlin, Heidelberg, New York, pp. 343–354.
Ishida, Y. (1990). Fully Distributed Diagnosis by PDP Learning Algorithm: Towards Immune Network PDP Model. Proc. of the IEEE International Joint Conference on Neural Networks. San Diego, USA, pp. 777–782.
Forrest, S, Perelson, A, Allen, L and Cherukuri, R (1994). Self-Nonself Discrimination in a Computer. Proc. of IEEE Symposium on Research in Security and Privacy. Oakland, USA, pp. 202–212.
Bersini, H. (2002). A Tribute to In proceedings of 1st International Conference on Artificial Immune Systems (ICARIS). Timmis J. and Bentley P. (Eds.) pp. 107–112.
De Castro, L.N and Timmis, J. (2003). Artificial Immune Systems as a Novel Soft Computing Paradigm. Soft Computing.
Dasgupta, D (1998b). An overview of artificial immune systems. Artificial Immune Systems and Their Applications. pp. 3–19. Springer, Berlin, Heidelberg, New York
Kepler, T and Perelson, A. (1993). Somatic Hypermutation in B-cells: An Optimal Control Treatment. Journal of Theoretical Biology. 164. pp. 37–64.
Berek, C. and Ziegner, M. (1993). The Maturation of the Immune Response, Immunol. Today, 14 (8), pp. 400–402.
Varela, F, Coutinho, A, Dupire, B and Vaz, N. (1988). Cognitive Networks: Immune and Neural and Otherwise. Theoretical Immunology: Part Two, SFI Studies in the Sciences of Complexity, 2, pp. 359–371
Janeway, C. (1993). Life, Death and the Immune System. Scientific American Special Issue. How the immune system recognises invaders, pp. 27–36.
Roitt, I. (1997). Essential Immunology: 9th Edition. Chap. Specific Acquired Immunity, pp. 22–39. Pub. Blackwell Science, Oxford
Burnet, F. M. (1959). The Clonal Selection Theory of Acquired Immunity, Cambridge University Press., Cambridge.
Smith, D. J., S. Forrest and A. S. Perelson (1998). Immunological Memory is Associative. InArtificial Immune Systems and their Applications. Ed. D. Dasgupta. Springer, Berlin Heidelberg New York
Tizzard, I. (1988a). Immunology: An Introduction. 2 nd edition. Chap. The response of B-cells to Antigen. Pp. 199–223. Saunders College.
Timmis, J. (2000). Artificial Immune Systems: A novel data analysis technique inspired by the immune network theory. Ph.D. Thesis. University of Wales, Aberystwyth. 2000.
De Castro, L. N. and Von Zuben, F. J. (1999). Artificial Immune Systems: Part I - Basic Theory and Applications, Technical Report - RT DCA01 /99, p. 95
Hunt, J. E. and Cooke, D. E. (1996). Learning Using an Artificial Immune System, Journal of Network and Computer Applications, 19, pp. 189–212.
Tizzard, I. (1988b). Immunology: An Introduction. 2 nd edition. Chap. The response of T-cell to Antigen. pp. 224–260. Pub. Saunders College.
Tew, J and Mandel, T. (1979). Prolonged antigen half-life in the lymphoid follicles of antigen-specifically immunised mice. Immunology, 37, pp. 69–76.
Tew, J, Phipps, P and Mandel, T. (1980). The maintenance and regulation of the humoral immune response. Persisting antigen and the role of follicular antigen-binding dendritic cells. Immunological Review, 53, pp. 175–211.
Ada, G. L. Nossal, G. J. V. (1987). The Clonal Selection Theory, Scientific American, 257 (2), pp. 50–57.
Matzinger, P. (1994). Immunological Memories Are Made of This?Nature, 369, pp. 605–606.
Coutinho, A. (1989). Beyond Clonal Selection and Network, Immunol. Rev., 110, pp. 63–87.
Farmer, J, Packard, N and Perelson, A. (1986). The Immune System, Adaptation and Machine Learning. Physica D. 22, pp. 187–204.
Carneiro, J Stewart, J. (1995). Self and nonself revisited: Lessons from modelling the immune network. Third European Conference on Artificial Life, Granada, Spain. pp. 405–420.
Coutinho, A. 1980. The self non-self discrimination and the nature and acquisition of the antibody repertoire. Annals of Immunology. (Inst. Past.)131D.
Bersini, H and Varela, F. (1994). The immune learning mechanisms : Reinforcement and recruitment and their applications. Computing and Biological Metaphors. Pages 166–192. Chapman Hall.
Bersini, H. (1991). Immune Network and Adaptive Control, Proc. of the First European Conference on Artificial Life, MIT Press, pp. 217–226.
Perelson, A. S., Mirmirani, M. Oster, G. F. (1978). Optimal Strategies in Immunology II. B Memory Cell Production, J. Math. Biol., 5, pp. 213–256.
Perelson, A. S. Weisbuch, G. (1997). Immunology for Physicists, Rev. of Modern Physics, 69 (4), pp. 1219–1267.
Zinkernagel, R. M. Kelly, J. (1997). How Antigen Influences Immunity, The Immunologist, 4 /5, pp. 114–120.
De Castro, L. N., Von Zuben, F. J., (2000b). An Evolutionary Immune Network for Data Clustering, Proc. of the IEEE SBRN, pp. 84–89.
De Castro, L.N and Timmis, J (2002a). An Artificial Immune Network for Multimodal Optimisation. In Proceedings of theCongress on Evolutionary Computation. Part of the 2002 IEEE World Congress on Computational Intelligence., pp. 699–704, Honolulu, Hawaii, USA. IEEE.
De Castro, L. N. Von Zuben, F. J. (2000a). Artificial Immune Systems: Part II - A Survey of Applications, Technical Report -RT DCA 02/00, p. 65.
Cooke, D and Hunt, J. (1995). Recognising Promoter Sequences Using an Artificial Immune System. Proc. of Intelligent Systems in Molecular Biology. AAAI Press, pp. 89–97.
Quinlan, J. (1993)C4.5: Programs for machine learning. Morgan Kaufmann.
Kolodner, J. (1993). Case Based Reasoning. Pub. Morgan Kaufmann.
Hunt, J, Cooke, D and Holstein, H. (1995). Case Memory and Retrieval Based on the Immune System. Case-Based Reasoning Research and Development, Lecture Notes in Artificial Intelligence. 1010. pp. 205–216
Hunt, J Fellows, A (1996). Introducing an Immune Response into a CBR system for Data Mining. BCS ESG’96 Conference and published as Research and Development in Expert Systems XIII. pp. 35–42. Springer, Berlin, Heidelberg, New York.
Hunt, J, King, C and Cooke, D. (1996). Immunising Against Fraud. Proc. Knowledge Discovery and Data Mining. IEE Colloquium. IEE, pp. 38–45.
Hunt, J, Timmis, J, Cooke, D, Neal, M and King, C. (1998). JISYS: Development of an Artificial Immune System for real world applications. InArtificial Immune Systems and theory Applications. Ed. D. Dasgupta. pp. 157–186.
Neal, M, Hunt, J and Timmis, J. (1998). Augmenting an artificial immune network. Proc. of the IEEE SMC, San Diego, Calif., pp. 3821–3826.
Timmis, J, Neal, M and Hunt, J. (2000). An Artificial Immune System for Data Analysis. Biosystems. 55 (1/3), pp. 143–150
Fisher, R (1936). The use of multiple measurements in taxonomic problems. Annual Eugenics. 7, H. pp. 179–188
Kohonen, T. (1997a). Self-Organising Maps. 2nd Edition.
Timmis, J, Neal, M and Hunt, J. (1999). Data Analysis with Artificial Immune Systems and Cluster Analysis and Kohonen Networks: Some Comparisons. Proceedings of the IEEE SMC, Tokyo, Japan. pp. 922–927.
Timmis, J and Neal, M. (2001) A Resource Limited Artificial Immune System for Data Analysis. Knowledge Based Systems, 14(3–4):121–130, June 2001.
Timmis, J (2001). aiVIS: Artificial Immune Network Visualisation. EuroGraphics UK 2001 Conference Proceedings, pp. 61–69, University College London, April 2001.
Knight, T and Timmis, J. (2001). In N Cercone, T Lin, and Xindon Wu, editors, IEEE International Conference on Data Mining, pp. 297–304, San Jose, Calif. December 2001. IEEE, New York
Neal, M. (2002). An Artificial Immune System for Continuous Analysis of Time-Varying Data. In1 st International Conference on Artificial Immune Systems (ICARIS), pages 75–86, Canterbury, UK.
Knight, T and Timmis, J. (2002). A Multi-Layered Immune Inspired Approach to Data Mining. Recent Advances in Soft Computing, Nottingham, UK. 2002
De Castro, L. N., Von Zuben, F. J., (2001). The Construction of a Boolean Competitive Neural Network Using Ideas From Immunology, submitted.
Slavov, V Nikoleav, N (1998). Immune network dynamics for inductive problem solving. Lecture Notes in Computer Science, 1498, pp. 712–721. Springer, Berlin, Heidelberg, New York.
Hart, E Ross, P. (2001). Clustering Moving Data with a Modified Immune Algorithm. EvoWorkshops 2001 - Real World Applications of Evolutionary Computing.
Hart, E Ross, P. (2002a). Exploiting the Analogy between Immunology and Sparse Distributed Memories. Proc. of ICARIS-2002, pp. 49–58.
Hart, E. (2002b)Immunology as a Metaphor for Computational Information Processing: Fact of Fiction? PhD thesis. University of Edinburgh.
Kanerva, P. (1998)Sparse Distributed Memory. MIT Press, Cambridge, Mass.
Potter, M.A. De Jong, K.A (2000)Cooperative coevolution: An architecture for evolving co adapted subcomponents. Evolutionary Computation, 8(1):1–29.
Carter, J.H. (2000). The Immune System as a Model for Pattern Recognition and Classification. Journal of the American Medical Informatics Assocation. 7/1. pp. 2841.
Wettschereck, D. Aha, D.W, and Mohri, T. 1997. A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review.11: 273–314.
Gennari, J.H. Langley, P and Fisher, D. (1989). Models of information concept formation. Artificial Intelligence; 40: 11–61.
Watkins, A. (2001). A resource limited artificial immune classifier. MS Thesis. Mississippi State University. Miss.
De Castro, L. N. Von Zuben, F. J. (2000c). The Clonal Selection Algorithm with Engineering Applications, Proc. of GECCO’00 — Workshop Proceedings, pp. 36–37.
Watkins, A and Timmis, J. (2002). Artificial Immune Recognition Systems (AIRS): Revisions and Refinements. InProceedings of the 1 st International Conference on Artificial Immune Systems. pages 173–181, University of Kent at Canterbury, September.
Mitsumoto, N, Fukuda, T and Idogaki, T. (1996). Self-Organising Multiple Robotic System. Proceedings of IEEE International Conference on Robotics and Automation. PP. 1614–1619. Minneapolis, USA. IEEE
Mitsumoto, N, Fukuda, T, Arai, F Ishihara, H (1997). Control of distributed autonomous robotic system based on the biologically inspired immunological architecture. Proceedings of IEEE International Conference on Robotics and Automation. PP. 3551–3556. Albuquerque, N M IEEE, New York
Lee, Dong-Wook and Sim, Kwee-Bo. (1997). Artificial immune network based cooperative control in collective autonomous mobile robots. Proc. of IEEE International Workshop on Robot and Human Communication. Sendai, Japan. IEEE, New York, pp. 58–63.
Watanabe, Y, Ishiguro and Uchikawa, Y. (1998). Decentralised behaviour arbitration mechanism for autonomous mobile robots using immune network. InArtificial Immune Systems and their applications. Ed. D. Dasgupta. pp. 187–209. Springer, Berlin Heidelberg New York
Kondo, T, Ishiguro, A, Watanabe, Y and Uchikawa, Y. (1998). Evolutionary construction of an immune network based behaviour arbitration mechanism for autonomous mobile robots.Electrical Engineering in Japan. 123/3. pp. 1–10
Kayama, M, Sugita, Y, Morooka, Y Fukuodka, S. (1995). Distributed diagnosis system combining the immune network and learning vector Quantization, pp. 1531–1536of Proc. IEEE 21 st International Conference on Industrial Electronics and Control and Instrumentation, Orlando, USA. 79.
Kohonen, T. (1997b). Self-Organising Maps. 2nd Edition. Chap. Learning Vector Quantization. pp. 203–217. Springer, Berlin Heidelberg New York
Ishida, Y Mizessyn, F. (1992). Learning algorithms on immune network model: application to sensor diagnosis. Proc. International Joint Conference on Neural Networks, Beijing, China, pp. 33–38.
Ishida, Y (1996). Distributed and autonomous sensing based on immune network. Proc. of Artificial Life and Robotics. Beppu. AAAI Press, pp. 214–217.
Ishida, Y Tokimasa, T. (1996). Diagnosis by a dynamic network inspired by immune network. Proc. World Congress of Neural Networks, San Diego, Calif. pp. 508–511.
Ishida, Y. (1997). Active Diagnosis by Self-Organisation: An approach by the immune network metaphor. Proceedings of the International Joint Conference on Artificial Intelligence. pp. 1084–1089. Nagoya, Japan.
Bradly, D. W. Tyrrell, A. M. (2000a), Immunotronics: harware Fault Tolerance Inspired by the Immune System, Lecture Notes in Computer Science, 1801, pp 11–20.
Timmis, J, de Lemos, R, Ayara, M and Duncan R. (2002) Towards Immune Inspired Fault Tolerance in Embedded Systems. To appear in the Proceedings of International Conference on Neural Information Processing. Singapore. November 2002.
Hajela, P., Yoo, J. Lee, J. (1997). GA Based Simulation of Immune Networks - Applications in Structural Optimization, Journal of Engineering Optimization.
Toma, N, Endo, S Yamada, K (1999). Immune algorithm with immune network and MHC for adaptive problem solving. Proc. IEEE SMC. Tokyo, Japan, IV, pp. 271–276.
Mori, K, Tsukiyama, M and Fukuda, T. (1996). Multi-optimisation by immune algorithm with diversity and learning. Proc. of the IEEE SMC, pp. 118–123.
Mori, K, Tsukiyama, M and Fukuda, T (1998). Application of an immune algorithm to multi-optimisation problems. Electrical Engineering in Japan. 122/2. pp. 30–37
Fukuda, T, Mori, K and Tsukiyama, M. (1998). Parallel Search for Multi-Modal Function Optimisation with Diversity and Learning of Immune Algorithm. Artificial Immune Systems and Their Applications. pp. 210–220. Springer, Berlin Heidelberg New York
Mori, K, Tsukiyama, M and Fukuda, T. (1994). Immune Algorithm and Its Application to Factory Load Dispatching Planning. pp. 1343–1346 ofProc. Japan-USA Symposium on Flexible Automation.
Chun, J, Kim, M Jun, H. (1997). Shape Optimisation of Electromagnetic Devices Using Immune Algorithms. IEEE Transactions on Magnetics, 33, (2).
Hart, E. Ross, P. and Nelson, T (1998). Producing robust schedules via an artificial immune system. Proc. of IEEE CEC’98, pp. 464–469. IEEE.
Hart, E. Ross, P. (1999a). The Evolution and Analysis of a Potential Antibody Library for Use in Job-Shop Scheduling, InNew Ideas in Optimisation, D. Corne, M. Dorigo F. Glover (Eds.), McGraw Hill, London, pp. 185–202.
Hart, E. Ross, P. (1999b). An Immune System Approach to Scheduling in Changing Environments, Proc. of GECCO’99, pp. 1559–1566.
Dasgupta, D (1999). Immunity based intrusion detection systems: A general framework. Proceedings of the 22nd National Information Systems Security Conference (NISSC). Pp. 147–159
D’haeseleer, P, Forrest, S and Helman, P (1996). An Immunological Approach To Change Detection: Algorithm and Analysis and Implications. Proceedings of the 1996 IEEE Symposium on Computer Security and Privacy. pp. 110–119
Forrest, S, Hofmeyr Somayaji, A Longstaff, T. (1996). A sense of self for UNIX processes. Proc. IEEE Symposium on Research in Security and Privacy. Oakland, USA, pp. 120–128.
Forrest, S, Hofmeyr, S and Somayaji, A (1997). Computer Immunology. Communications of the ACM. 40/10. pp. 88–96
Somayaji, A., Hofmeyr, S. A. Forrest, S. (1997), Principles of a Computer Immune System, Proc. of the new Security Paradigms Workshop, pp. 75–81.
Hofmeyr, S, Forrest, S Somayaji, A. (1998). Intrusion detection using a sequence of system calls. Journal of Computer Security, 6, pp. 151–180.
Hofmeyr, S and Forrest, S (1999). Immunity by Design: An artificial immune system. Proc. of GECCO’99, Pub. Morgan-Kaufman. pp. 1289–1296
Hofmeyr, S.A. and Forrest, S. (2000). Architecture for an Artificial Immune System. Evolutionary Computation7 (1): 45–68.
Kim, J and Bentley, P. (1998). The human immune system and network intrusion detection. Proc. of 7th European Congress on Intelligent Techniques - Soft Computing. Aachan and Germany
Kim, J. Bentley, P. (1999), Negative Selection and Niching by an Artificial Immune System for Network Intrusion Detection, Proc. of GECCO’99, pp. 149–158.
Dasgupta, D. (2000). An Immune Agent Architecture for Intrusion Detection, Proc. of GECCO’00, Workshop on Artificial Immune Systems and Their Applications, pp. .
Kephart, J. O. (1994). A Biologically Inspired Immune System for Computers, R. A. Brooks P. Maes (Eds.), Artificial Life IV Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems, MIT Press, Cambridge, Mass., pp. 130–139.
Kephart, J. O., Sorkin, G. B. Swimmer, M. (1997), An Immune System for Cyberspace, Proc. of the IEEE SMC’97, pp. 879–884.
Kephart, J. Sorkin, B. Swimmer, M and White, S. (1998). Blueprint for a computer immune system. InArtificial Immune Systems and their Applications. Ed. D. Dasgupta. pp. 242–260. Springer, Berlin Heidelberg New York
Marmelstein, M, Veldhuizen Lamont, G. (1998). A Distributed Architecture for an Adaptive Computer Virus System. Proc. of the IEEE SMC, San Diego, Calif.m. pp. 3838–3843.
Lamont, G. B., Marmelstein, R. E. Van Veldhuizen D. A. (1999), A Distributed Architecture for a Self-Adaptive Computer Virus Immune System, New Ideas in Optimisation, D. Come, M. Dorigo F. Glover (Eds.), McGraw Hill, London, pp. 167–183.
Harmer, P.K. and lamont, G.B. (2000). An Agent Based Architecture for a Computer Virus Immune System. In Proceedings of Artificial Immune Systems Workshops. pp. 45–46. GECCO 2000, Las Vegas, USA.
Acklien, U and Cayzer, S. (2002). The Danger Theory and its Application to Artificial Immune Systems. Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS). pp. 141–148. Canterbury, UK.
Matzinger, (1994a). Tolerance, Danger and the Extended Family. Annual Review of Immunology. 12: 991–1045.
Warrender, C, Forrest, S Pearhmutter, B. (1999). Detecting intrusions using system calls: Alternative data models. Proc. of Symposium on Security and privacy. IEEE, New York, pp. 133–145.
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Timmis, J., Knight, T., de Castro, L.N., Hart, E. (2004). An Overview of Artificial Immune Systems. In: Paton, R., Bolouri, H., Holcombe, M., Parish, J.H., Tateson, R. (eds) Computation in Cells and Tissues. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-06369-9_4
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