Notes on Design Through Artificial Evolution: Opportunities and Algorithms


An attempt is made to isolate a class of design problems that only evolutionary methods can tackle. A preliminary evolved design for a nano-electronic circuit is found to contain a switching element that relies on the stochastic passage of electrons due to thermal noise. Such phenomena have been exploited by natural evolution in neural systems, but not before for circuit design. There is room for an imaginative leap into areas of design space only accessible through evolution. Analysis of the evolution of a second circuit reveals that neutral evolution played a key role, and can be a natural property of evolutionary design. The developing theory of evolutionary design promises practical future benefits.


Evolutionary Algorithm Design Space Inverse Model Evolutionary Design Switching Element 
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.


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  1. 1.
    G. L. Nelson. Sonomorphs: An application of genetic algorithms to the growth and development of musical organisms. In Proc. 4th Biennial Art & Technology Symp., pages 155–169, Connecticut College, March 4-7th, 1993.
  2. 2.
    P. P. B. de Oliveira, F. M. Ramos, R. C. Gatto, et al. A research agenda for iterative approaches to inverse problems using evolutionary computation. In Proc. 3rd IEEE Int. Conf. on Evolutionary Computation, pages 55–60. IEEE Press, Piscataway NJ, 1996.CrossRefGoogle Scholar
  3. 3.
    M. Fischetti. Flight control. Scientific American, June 2001.Google Scholar
  4. 4.
    I. Rechenberg. Cybernetic solution path of an experimental problem. Royal Aircraft Establishment, Library Translation 1122, 1965. Reprinted in ‘Evolutionary Computation — The fossil record’, D. B. Fogel, ed., chap. 8, pp297–309, IEEE Press 1998.Google Scholar
  5. 5.
    I. Csurgay, W. Porod, and S. M. Goodnick, editors. Int. J. Circ. Theor. Appl. Special issues on nanoelectronic circuits. John Wiley & Sons, 2000/1. Vol. 28 Issue 6 & Vol. 29 Issue 1.Google Scholar
  6. 6.
    A. Thompson and C. Wasshuber. Design of single-electron systems through artificial evolution. Int. J. Circ. Theor. Appl., 28(6):585–599, 2000.CrossRefGoogle Scholar
  7. 7.
    A. Thompson and P. Layzell. Evolution of robustness in an electronics design. In J. Miller, A. Thompson, P. Thomson, and T. Fogarty, editors, Proc. 3rd Int. Conf. on Evolvable Systems (ICES2000): From biology to hardware, volume 1801 of LNCS, pages 218–228. Springer-Verlag, 2000.Google Scholar
  8. 8.
    L. Gammaitoni, P. Hän ggi, P. Jung, and F. Marchesoni. Stochastic resonance. Reviews of Modem Physics, 70(l):223–287, 1998.Google Scholar
  9. 9.
    J. Miller, A. Thompson, P. Thomson, and T. Fogarty, editors. Proc. 3rd Int. Conf. on Evolvable Systems (ICES2000): From Biology to Hardware, volume 1801 of LNCS. Springer-Verlag, 2000.Google Scholar
  10. 10.
    A. Thompson, P. Layzell, and R. S. Zebulum. Explorations in design space: Unconventional electronics design through artificial evolution. IEEE Trans. Evol. Comp., 3(3):167–196, 1999.CrossRefGoogle Scholar
  11. 11.
    A. LaMarca and R. E. Ladner. The influence of caches on the performance of sorting. Journal of Algorithms, 31(1):66–104, 1999.MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    F. H. Bennett III, J. R. Koza, J. Shipman, and O. Stiffelman. Building a parallel computer system for $18,000 that performs a half-petafiop per day. In W. Banzhaf, J. Daida, A. E. Eiben, et al., editors, Proc. Genetic and Evolutionary Computation conference (GECCO-99), pages 1484–1490. Morgan Kaufmann, 1999.Google Scholar
  13. 13.
    I. Harvey and A. Thompson. Through the labyrinth evolution finds a way: A silicon ridge. In T. Higuchi, M. Iwata, and L. Weixin, editors, Proc. 1st Int. Conf. on Evolvable Systems (ICES′96), volume 1259 of LNCS, pages 406–422. Springer-Verlag, 1997.Google Scholar
  14. 14.
    L. Barnett. Netcrawling — optimal evolutionary search with neutral networks. In Proc. Congress on Evolutionary Computation (CEC), pages 30–37. IEEE, 2001.Google Scholar

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© Springer-Verlag London 2002

Authors and Affiliations

  1. 1.Evolutionary & Adaptive Systems Group, University of SussexBrightonUK

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