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A New Immunotronic Approach to Hardware Fault Detection Using Symbiotic Evolution

  • Sanghyung Lee
  • Euntai Kim
  • Eunjoo Song
  • and Mignon Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3562)

Abstract

A novel immunotronic approach to fault detection in hardware based on symbiotic evolution is proposed in this paper. In the immunotronic system, the generation of tolerance conditions corresponds to the generation of antibodies in the biological immune system. In this paper, the principle of antibody diversity, one of the most important concepts in the biological immune system, is employed and it is realized through symbiotic evolution. Symbiotic evolution imitates the generation of antibodies in the biological immune system more than the standard genetic algorithm(SGA) does. It is demonstrated that the suggested method outperforms the previous immunotronic methods with less running time. The suggested method is applied to fault detection in a decade counter (typical example of finite state machines) and MCNC finite state machines and its effectiveness is demonstrated by the computer simulation.

Keywords

immunotronic system hardware fault detection tolerance conditions antibody diversity symbiotic evolution 

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References

  1. 1.
    Forrest, S., Javornik, B., Smith, R.E., Perelson, A.S.: Using Genetic Algorithms to Explore Pattern Recognition in the Immune System. Evolutionary Computation 1(3), 191–211 (1993)CrossRefGoogle Scholar
  2. 2.
    Dasgupta, D., Forrest, S.: An anomaly detection algorithm inspired by the immune system. In: Dasgupta, D. (ed.) Artificial Immune System and Their Applications, pp. 262–277. Springer, Berlin (1998)Google Scholar
  3. 3.
    Timmis, J., Neal, M., Hunt, J.: Data analysis using artificial immune systems, cluster analysis and Kohonen networks: some comparisons. In: Proc. of IEEE SMC 1999 Conference, vol. 3(12-15), pp. 922–927 (1999)Google Scholar
  4. 4.
    Xiao, R., Wang, L., Liu, Y.: A framework of AIS based pattern classification and matching for engineering creative design. In: Proc. of International Conference on Machine Learning and Cybernetics, vol. 3(4-5), pp. 1554–1558 (November 2002)Google Scholar
  5. 5.
    Bradley, D.W., Tyrrell, A.M.: Immunotronics-Novel Finite-State-Machine Architectures With Built-In Self-Test Using Self-Nonself Differentiation. IEEE Trans. on Evolutionary Computation 6(3), 227–238 (2002)CrossRefGoogle Scholar
  6. 6.
    Chen, Y., Chen, T.: Implementing fault-tolerance via modular redundancy with comparison. IEEE Trans. on Reliability, Volume: 39 Issue 39(2), 217–225 (1990)zbMATHGoogle Scholar
  7. 7.
    Dutt, S., Mahapatra, N.R.: Node-covering, error-correcting codes and multiprocessors with very high average fault tolerance. IEEE Trans. Comput. 46, 997–1914 (1997)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Lala, P.K.: Digital Circuit Testing and Testablilty. Academic, New York (1997)Google Scholar
  9. 9.
    Forrest, S., Allen, L., Perelson, A.S., Cherukuri, R.: Self-Nonself Discrimination In A Computer. In: Proc. of IEEE Symposium on Research in Security and Privacy, pp. 202–212 (1994)Google Scholar
  10. 10.
    Lee, S., Kim, E., Park, M.: A Biologically Inspired New Hardware Fault Detection: immunotronic and Genetic Algorithm-Based Approach. International Journal of Fuzzy Logic and Intelligent Systems 4(1), 7–11 (2004)MathSciNetGoogle Scholar
  11. 11.
    Goldsby, R.A., Kindt, T.J., Osborne, B.A.: Kuby Immunology, 4th edn. W.H Freeman and Company, New York (2000)Google Scholar
  12. 12.
    Juang, C., Lin, J., Lin, C.: Genetic Reinforcement Learning through Symbiotic Evolution for Fuzzy Controller Design. IEEE Trans. on Systems, Man And Cybernetics-Part B Cybernetics 30(2) (April 2000)Google Scholar
  13. 13.
    Moriarty, D.E., Miikkulanien, R.: Efficient reinforcement learning through symbiotic evolution. Mach. Learn 22, 11–32 (1996)Google Scholar
  14. 14.
    Smith, R.E., Forrest, S., Perelson, A.S.: Searching for diverse, cooperative populations with genetic algorithms. Evol. Comput. 1(2), 127–149 (1993)CrossRefGoogle Scholar
  15. 15.
    Yang, S.: Logic Synthesis and Optimization Benchmarks User Guide Version 3.0, Technical Report, Microelectronics Center of North Carolina (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sanghyung Lee
    • 1
  • Euntai Kim
    • 1
  • Eunjoo Song
    • 2
  • and Mignon Park
    • 1
  1. 1.Dept. of Electrical and Electronic Engr.Yonsei Univ.SeoulKorea
  2. 2.Bioanalysis and Biotransformation Research CenterKorea Institute of Science and TechnologySeoulKorea

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