Advertisement

Steps Forward to Evolve Bio-inspired Embryonic Cell-Based Electronic Systems

  • Elhadj Benkhelifa
  • Anthony Pipe
  • Mokhtar Nibouche
  • Gabriel Dragffy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4684)

Abstract

EHW is the acronym used to denote an emerging and relatively new research field in digital hardware design; it stands for Evolvable Hardware. This technique has attracted many researchers since the 1990’s. EHW aims at an automatic design and optimisation of a reconfigurable hardware system using Evolutionary Algorithms (EAs), such as Genetic Algorithms, Genetic programming etc. This article is published as part of a three years research project. The objective of this project is to employ the above method on a target specific hardware, the Embryonics Hardware System. The latter requires large hardware resources. Thus, in this project, EAs will be used to evolve the Embryonics Hardware System to discover novel design with reduced complexity. The new design must first ensure the correct functionality. Hence to verify the concept of Evolvable Hardware, the authors, in this paper, focus on the design of relatively simple combinatorial logic circuits using Genetic Algorithms with multi-objective fitness.

Keywords

Genetic Algorithm Evolutionary Algorithm Digital Circuit Hardware System Human Designer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhang, X., Dragffy, G., Pipe, A.G., Zhu, Q.M.: Ontogenetic Cellular Hardware for Fault Tolerant Systems. In: The proceeding of ESA 2003. The 2003 International Conference on Embedded Systems and Applications, June 2003, Las Vegas, USA (2003)Google Scholar
  2. 2.
    Thompson, A.: Silicon Evolution. In: Koza, J.R., et al. (eds.) GP 1996. Proceedings of Genetic Programming, pp. 444–452. MIT press, Cambridge (1996)Google Scholar
  3. 3.
    Zhang, X.: PhD Thesis, Biologically Inspired Highly Reliable Electronic Systems With Self- Healing Cellular Architecture, University of the West of England Bristol, UK (2005)Google Scholar
  4. 4.
    Mange, D., Sipper, M., Stauffer, A., Tempesti, G.: Towards Robust Integrated Circuits: The Embryonics approach. Proceeding of the IEEE 88(4) (2000)Google Scholar
  5. 5.
    Miller, J.F., Thomson, P., Fogarty, T.C.: Designing Electronic Circuits Using Evolutionary Algorithms. Arithmetic Circuits. In: Quagliarella, D., Periaux, J., Poloni, C., Winter, G. (eds.) Genetic and Evolution Strategies in Engineering and Computer Science:Recent Advancements and Industrial Applications, ch. 6, Wiley, Chichester (1997)Google Scholar
  6. 6.
    Zhang, X., Dragffy, G., Pipe, A.G., Zhu, Q.M.: A Reconfigurable Self-healing Embryonic Cell Architecture. In: The proceeding of ERSA 2003. The 2003 International Conference on Engineering of Reconfigurable Systems and Algorithms, June 2003, Las Vegas, USA (2003)Google Scholar
  7. 7.
    Miller, J.F., Vesselin, K.V.: Scalability Problems of digital Circuit Evolution Evolvability and Efficient Designs. In: Lohn, J., Stoica, A., Keymeulen, D. (eds.) The Second NASA/DoD workshop on Evolvable Hardware, pp. 55–64. IEEE Computer Society, Los Alamitos (2000)Google Scholar
  8. 8.
    Tomassini, M.: Evolutionary Algorithms. In: International workshop on Towards Evolvable Hardware, The Evolutionary Engineering Approach, Logic Systems Laboratory, Switzerland (1995)Google Scholar
  9. 9.
    Yao, X., Higuchi, T.: Promises and Challenges of Evolvable Hardware. IEEE Translations on Systems, Man, and Cybernetics-Part c: Applications and Reviews 29(1) (1999)Google Scholar
  10. 10.
    Haddow, P.C., Tufte, G.: Evolving a robot controller in hardware. In: NIK 1999. Norwegian Computer Science Conference, The Norwegian University of Science and Technology (1999)Google Scholar
  11. 11.
    Stoica, A.: Toward evolvable hardware chips: experiments with a programmable transistor array. In: Proceedings of 7th International Conference on Microelectronics for Neural, Fuzzyand Bio-Inspired Systems, Granada, Spain, April 7-9, 1999, IEEEComp Sci. Press (1999)Google Scholar
  12. 12.
    Teuscher, C., Mange, D., Stauffer, A., Tempesti, G.: Bio-Inspired Computing Tissues: Towards Machines that Evolve, Grow, and Learn. Biosystems 68(2-3), 235–249 (2003)CrossRefGoogle Scholar
  13. 13.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  14. 14.
    Goldberg, D.E.: Genetic Algorithms in Search. In: Optimization and Machine Learning, Kluwer Academic Publishers, Boston, MA (1989)Google Scholar
  15. 15.
    Stoica, A., Zebulum, R., Keymeulen, D., Tawel, R., Daud, T., Thakoor, A.: Reconfigurable VLSI Architectures for Evolvable Hardware: from Experimental  Field Programmable Transistor Arrays to Evolution-Oriented Chips. IEEE Transactions on VLSI Systems, Special Issue on Reconfigurable and Adaptive VLSI Systems 9(1), 227–232 (2001)Google Scholar
  16. 16.
    Stoica, A., Zebulum, R.S., Ferguson, M.I., Keymeulen, D., Duong, V., Daud, T., Guo, X.: Rapid evolution of analog circuits configured on a Field Programmable Transistor Array. In: The Proceedings of Smart Engineering System DesignGoogle Scholar
  17. 17.
    Higuchi, T., et al.: Evolvable Hardware with Genetic Learning. In: Proc. of Simulated Adaptive Behaviour, MIT Press, Cambridge (1993)Google Scholar
  18. 18.
    Higuchi, T., et al.: Evolvable Hardware at Functional Level. In: IEEE International Conference on Evolutionary Computation (1997)Google Scholar
  19. 19.
    Lohn, J.D., Hornby, G.S.: Evolvable HardwareUsing Evolutionary Computation to Design and Optimize Hardware Systems. IEEE Computational Intelligence Magazine (February 2006)Google Scholar
  20. 20.
    Stoica, A., Zebulum, R., Keymeulen, D.: Mixtrinsic Evolution. In: Miller, J.F., Thompson, A., Thompson, P., Fogarty, T.C. (eds.) ICES 2000. LNCS, vol. 1801, Springer, Heidelberg (2000)CrossRefGoogle Scholar
  21. 21.
    Corcoran, A.L., Wainwright, R.L.: Using LibGA to Develop Genetic Algorithms for Solving Combinatorial Optimization Problems. In: Practical Handbook of Genetic Algorithms, Applications. Lance Chambers editor pages, vol. I, CRC Press (1995)Google Scholar
  22. 22.
    Soliman, A.T., Abbas, H.M.: Combinational Circuit Design Using Evolutionary Algorithms. In: CCECE, May 2003, Montreal (2003)Google Scholar
  23. 23.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  24. 24.
    Rechenberg, I.: Evolution Strategy. In: Zurada, J., Marks, R. (eds.) Computational Intelligence: Imitating Life, pp. 147–159 (1994)Google Scholar
  25. 25.
    Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI (1975)Google Scholar
  26. 26.
    Gordon, T.G.W., Bentley, P.J.: On Evolvable hardware. In: Soft Computing in Industrial Electronics, University College London, UK (2001)Google Scholar
  27. 27.
    Von Neumann, J.: Theory of self-reproducing Automata. In: Burks, A.W. (ed.) University of Illinois Press, Urbana (1966)Google Scholar
  28. 28.
    Ortega-Sanchez, C.A.: PhD Thesis Embryonics: A Bio-Inspired Fault-Tolerant MultiCellular System, The University of York, UK (May 2000)Google Scholar
  29. 29.
    Torresen, J.: A Divide-and-Conquer Approach To Evolvable Hardware, University of Oslo, NorwayGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Elhadj Benkhelifa
    • 1
  • Anthony Pipe
    • 1
  • Mokhtar Nibouche
    • 2
  • Gabriel Dragffy
    • 2
  1. 1.Bristol Robotics Laboratory, University of the West of England (UWE), Frenchay Campus, Coldharbour Lane, Bristol, BS16 1QYUK
  2. 2.Bristol Institute of Technology , University of the West of England (UWE), Frenchay Campus, Coldharbour Lane, Bristol, BS16 1QYUK

Personalised recommendations