Evolvable Hardware and its application to pattern recognition and fault-tolerant systems

  • Tetsuya Higuchi
  • Masaya Iwata
  • Isamu Kajitani
  • Hitoshi Iba
  • Yuji Hirao
  • Tatsumi Furuya
  • Bernard Manderick
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1062)


This paper describes Evolvable Hardware (EHW) and its applications to pattern recognition and fault-torelant systems. EHW can change its own hardware structure to adapt to the environment whenever environmental changes (including hardware malfunction) occur. EHW is implemented on a PLD(Programmable Logic Device)-like device whose architecture can be altered by re-programming the architecture bits. Through genetic algorithms, EHW finds the architecture bits which adapt best to the environment, and changes its hardware structure accordingly.

Two applications are described: the the pattern recognitionsystem and the V-shape ditch tracer with fault-tolerant circuit. First we show the exclusive-OR circuit can be learned by EHW successfully. Then the pattern recognition system with EHW is described. The objective is to take the place of neural networks, solving its weakness such as readability of learned results and the execution speed. The results show that EHW works as a hard-wired pattern recognizer with such the robustness as neural nets. The second application is the V-shape ditch tracer as part of a prototypical welding robot. EHW works as the backup of the control logic circuit for the tracing, although the EHW is not given any information about the circuit. Once a hardware error occurs, EHW takes over the malfunctioning circuit.


Genetic Algorithms Evolvable Hardware Genetic Learning Adaptative Machine Artificial Life Programable Logic Devices Classifier Systems Neural Networks Field Programmable Gate Arrays Adaptive Logic Network Robot Pattern Recognition Exclusive-OR problem 


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  1. [Aleksander84]
    Aleksander I., “Reinventing Man” Penguin Books, EnNgland, 1984.Google Scholar
  2. [Armstrong79]
    Armstrong W. and Gecsei J. “Adaptation Algorithms for Binary Tree Network” IEEE Trans. on SMC, Vol. SMC-9, No.5, 1979.Google Scholar
  3. [de Garis94]
    de Garis, H. “An Artificial Brain — ATR's CAM-Brain Project Aims to Build/Evolve an Artificial Brain with a Million Neural Net Modules Inside a Trillion Cell Cellular Atutomata Machine” New Genreration Computing, OHMSHA.LTD and Springer-Verlag, 12, pp.215–221 (1994).Google Scholar
  4. [Goldberg89]
    Goldberg D., “Genetic Algorithms in Search, Optimization, and Machine Learning” Addison Wesley, 1989.Google Scholar
  5. [Henmi94]
    Henmi H. et al “Development and Evolution of Hardware Behaviors” Proc. of Artificial Life IV, MIT Press, 1994.Google Scholar
  6. [Higuchi93]
    Higuchi T. et al., “Evolvable Hardware with Genetic Learning” in Proc. of Simulated Adaptive Behavir, MIT Press, 1993.Google Scholar
  7. [Higuchi94]
    Higuchi T. et al., “Evolvable Hardware with Genetic Learning” in Massively Parallel Artificial Intelligence(eds. H. Kitano), MIT Press, 1994.Google Scholar
  8. [Itoh92]
    Itoh, S. “Application of MDL principle to pattern classification problems” (in Japanese),JSAI journal, Vol.7, No.4, 1992.Google Scholar
  9. [Iwata95]
    Iwata M. et al. “Consideration on implementation of pattern recognition system based on evolvable hardware” ETL technical report, Oct. 1995.Google Scholar
  10. [Kajitani95]
    Kajitani I. et al. “Variable length genetic algorithms for evolvable hardware” ETL technical report, Oct. 1995.Google Scholar
  11. [Kitano95]
    Kitano H. “Evolvable Hardware with Development” in this proceedings (EVOLVE95), Oct. 1995.Google Scholar
  12. [Koza89]
    Koza J., “Genetic Programming: On the Programming of Computers by means of Natural Selection” MIT Press, 1992.Google Scholar
  13. [Lattice90]
    Lattice Semiconductor Corporation, “GAL Data Book” 1990Google Scholar
  14. [Marchal94]
    P.Marchal,C.Piguet,D.Mange,A.Stauffer,S.Durand “Embryological development on silicon” Artificial Life IV, MIT Press, 1994.Google Scholar
  15. [Rissanen89]
    Rissanen, J. Stochastic complexity in statistical inquiry World Scientific Series in COmputer Science, Vol.15, 1989.Google Scholar
  16. [Rosenblatt62]
    Rosenblatt F., “Principles of Neurodynamics” Spartan Books, New York, 1962.Google Scholar
  17. [Thompson95]
    Thompson A., “Evolving electronic robot controllers that exploit hardware resources” Proc. of 3rd European Conf. on Artificial Life, 1995.Google Scholar
  18. [Wilson87]
    Wilson S., “Classifier Systems and the Animat Problem” Machine Learning 2, 199–228, 1987.Google Scholar
  19. [Xilinx94]
    Xilinx Semiconductor Corporation, “LCA Data Book” 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Tetsuya Higuchi
    • 1
  • Masaya Iwata
    • 1
  • Isamu Kajitani
    • 5
  • Hitoshi Iba
    • 1
  • Yuji Hirao
    • 2
  • Tatsumi Furuya
    • 4
  • Bernard Manderick
    • 3
  1. 1.Electrotechnical Laboratory (ETL)IbarakiJapan
  2. 2.Tokushima Prefectural Industrial Technology CenterTokushimaJapan
  3. 3.AI-LaboratoryFree University BrusselBrusselBelgium
  4. 4.Toho UniversityChibaJapan
  5. 5.Tsukuba UniversityIbarakiJapan

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