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A New Immunotronic Approach to Hardware Fault Detection Using Symbiotic Evolution

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Book cover Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach (IWINAC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3562))

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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.

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References

  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)

    Article  Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  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)

    MATH  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  8. Lala, P.K.: Digital Circuit Testing and Testablilty. Academic, New York (1997)

    Google Scholar 

  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. 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)

    MathSciNet  Google Scholar 

  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. 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. Moriarty, D.E., Miikkulanien, R.: Efficient reinforcement learning through symbiotic evolution. Mach. Learn 22, 11–32 (1996)

    Google Scholar 

  14. Smith, R.E., Forrest, S., Perelson, A.S.: Searching for diverse, cooperative populations with genetic algorithms. Evol. Comput. 1(2), 127–149 (1993)

    Article  Google Scholar 

  15. Yang, S.: Logic Synthesis and Optimization Benchmarks User Guide Version 3.0, Technical Report, Microelectronics Center of North Carolina (1991)

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Lee, S., Kim, E., Song, E., Park, a.M. (2005). A New Immunotronic Approach to Hardware Fault Detection Using Symbiotic Evolution. In: Mira, J., Álvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499305_14

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  • DOI: https://doi.org/10.1007/11499305_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26319-7

  • Online ISBN: 978-3-540-31673-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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