Evolvable Hardware: From Applications to Implications for the Theory of Computation

  • Lukáš Sekanina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5715)


The paper surveys the fundamental principles of evolvable hardware, introduces main problems of the field and briefly describes the most successful applications. Although evolvable hardware is typically interpreted from the point of view of electrical engineering, the paper discusses the implications of evolvable hardware for the theory of computation. In particular, it is shown that it is not always possible to understand the evolved system as a computing mechanism if the evolution is conducted with real hardware in a loop. Moreover, it is impossible to describe a continuously evolving system using the computational scenario of a standard Turing machine.


Genetic Program Turing Machine Evolutionary Design Evolvable Hardware Prosthetic Hand 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin (2003)CrossRefzbMATHGoogle Scholar
  2. 2.
    Bentley, P.J.: Evolutionary Design by Computers. Morgan Kaufmann, San Francisco (1999)zbMATHGoogle Scholar
  3. 3.
    Higuchi, T., Niwa, T., Tanaka, T., Iba, H., de Garis, H., Furuya, T.: Evolving Hardware with Genetic Learning: A First Step Towards Building a Darwin Machine. In: Proc. of the 2nd International Conference on Simulated Adaptive Behaviour, pp. 417–424. MIT Press, Cambridge (1993)Google Scholar
  4. 4.
    de Garis, H.: Evolvable hardware – genetic programming of a darwin. In: International Conference on Artificial Neural Networks and Genetic Algorithms, Innsbruck, Austria. Springer, Heidelberg (1993)Google Scholar
  5. 5.
    Lohn, J.D., Hornby, G.S.: Evolvable hardware: Using evolutionary computation to design and optimize hardware systems. IEEE Computational Intelligence Magazine 1(1), 19–27 (2006)CrossRefGoogle Scholar
  6. 6.
    Greensted, A., Tyrrell, A.: RISA: A hardware platform for evolutionary design. In: Proceedings of 2007 IEEE Workshop on Evolvable and Adaptive Hardware, pp. 1–7. IEEE, Los Alamitos (2007)Google Scholar
  7. 7.
    Zebulum, R., Pacheco, M., Vellasco, M.: Evolutionary Electronics – Automatic Design of Electronic Circuits and Systems by Genetic Algorithms. The CRC Press International Series on Computational Intelligence (2002)Google Scholar
  8. 8.
    Layzell, P.J.: A new research tool for intrinsic hardware evolution. In: Sipper, M., Mange, D., Pérez-Uribe, A. (eds.) ICES 1998. LNCS, vol. 1478, pp. 47–56. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  9. 9.
    Sekanina, L., Ruzicka, R., Vasicek, Z., Prokop, R., Fujcik, L.: Repomo32 – new reconfigurable polymorphic integrated circuit for adaptive hardware. In: Proceedings of 2009 IEEE Workshop on Evolvable and Adaptive Hardware, pp. 39–46. IEEE CIS, Los Alamitos (2009)CrossRefGoogle Scholar
  10. 10.
    Loktev, M., Soloviev, O., Vdovin, G.: Adaptive Optics – Product Guide. OKO Technologies, Delft (2003)Google Scholar
  11. 11.
    Tour, J.M.: Molecular Electronics. World Scientific, Singapore (2003)CrossRefGoogle Scholar
  12. 12.
    Linden, D.S.: A system for evolving antennas in-situ. In: EH 2001: Proceedings of the The 3rd NASA/DoD Workshop on Evolvable Hardware, Washington, DC, USA, pp. 249–255. IEEE Computer Society, Los Alamitos (2001)CrossRefGoogle Scholar
  13. 13.
    Harding, S.L., Miller, J.F., Rietman, E.A.: Evolution in materio: Exploiting the physics of materials for computation. Journal of Unconventional Computing 4(2), 155–194 (2008)Google Scholar
  14. 14.
    Thompson, A.: Silicon Evolution. In: Proc. of Genetic Programming GP 1996, pp. 444–452. MIT Press, Cambridge (1996)Google Scholar
  15. 15.
    Thompson, A., Layzell, P., Zebulum, S.: Explorations in Design Space: Unconventional Electronics Design Through Artificial Evolution. IEEE Transactions on Evolutionary Computation 3(3), 167–196 (1999)CrossRefGoogle Scholar
  16. 16.
    Stoica, A., Zebulum, R.S., Keymeulen, D., Ferguson, M.I., Duong, V., Guo, X.: Evolvable hardware techniques for on-chip automated reconfiguration of programmable devices. Soft Computing 8(5), 354–365 (2004)CrossRefGoogle Scholar
  17. 17.
    Murakawa, M., Yoshizawa, S., Kajitani, I., Furuya, T., Iwata, M., Higuchi, T.: Evolvable Hardware at Function Level. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 62–71. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  18. 18.
    Torresen, J.: A Divide-and-Conquer Approach to Evolvable Hardware. In: Sipper, M., Mange, D., Pérez-Uribe, A. (eds.) ICES 1998. LNCS, vol. 1478, pp. 57–65. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  19. 19.
    Torresen, J.: A scalable approach to evolvable hardware. Genetic Programming and Evolvable Machines 3(3), 259–282 (2002)CrossRefzbMATHGoogle Scholar
  20. 20.
    Kitano, H.: Morphogenesis for evolvable systems. In: Sanchez, E., Tomassini, M. (eds.) Towards Evolvable Hardware 1995. LNCS, vol. 1062, pp. 99–117. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  21. 21.
    Koza, J.R., Bennett, F.H., Andre, D., Keane, M.A.: Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann Publishers, San Francisco (1999)zbMATHGoogle Scholar
  22. 22.
    Walker, J.A., Miller, J.: The Automatic Acquisition, Evolution and Re-use of Modules in Cartesian Genetic Programming. IEEE Transactions on Evolutionary Computation 12(4), 397–417 (2008)CrossRefGoogle Scholar
  23. 23.
    Higuchi, T., Liu, Y., Yao, X.: Evolvable hardware. Springer, Berlin (2006)CrossRefzbMATHGoogle Scholar
  24. 24.
    Pecenka, T., Sekanina, L., Kotasek, Z.: Evolution of synthetic rtl benchmark circuits with predefined testability. ACM Transactions on Design Automation of Electronic Systems 13(3), 1–21 (2008)CrossRefGoogle Scholar
  25. 25.
    Vasicek, Z., Zadnik, M., Sekanina, L., Tobola, J.: On evolutionary synthesis of linear transforms in FPGA. In: Hornby, G.S., Sekanina, L., Haddow, P.C. (eds.) ICES 2008. LNCS, vol. 5216, pp. 141–152. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  26. 26.
    Miller, J., Job, D., Vassilev, V.: Principles in the Evolutionary Design of Digital Circuits – Part I. Genetic Programming and Evolvable Machines 1(1), 8–35 (2000)zbMATHGoogle Scholar
  27. 27.
    Hounsell, B.I., Arslan, T., Thomson, R.: Evolutionary design and adaptation of high performance digital filters within an embedded reconfigurable fault tolerant hardware platform. Soft Computing 8(5), 307–317 (2004)CrossRefGoogle Scholar
  28. 28.
    Sekanina, L.: Evolvable components: From Theory to Hardware Implementations. Natural Computing. Springer, Berlin (2004)CrossRefzbMATHGoogle Scholar
  29. 29.
    Glette, K., Torresen, J., Gruber, T., Sick, B., Kaufmann, P., Platzner, M.: Comparing evolvable hardware to conventional classifiers for electromyographic prosthetic hand control. In: Proc. of the 2008 NASA/ESA Conference on Adaptive Hardware and Systems, pp. 32–39. IEEE Computer Society, Los Alamitos (2008)CrossRefGoogle Scholar
  30. 30.
    Koza, J.R., Keane, M.A., Streeter, M.J., Mydlowec, W., Yu, J., Lanza, G.: Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers, Dordrecht (2003)zbMATHGoogle Scholar
  31. 31.
    Keymeulen, D., Zebulum, R., Jin, Y., Stoica, A.: Fault-tolerant evolvable hardware using field-programmable transistor arrays. IEEE Transactions on Reliability 49(3), 305–316 (2000)CrossRefGoogle Scholar
  32. 32.
    Stoica, A., Keymeulen, D., Zebulum, R.S., Guo, X.: Reconfigurable electronics for extreme environments. In: Higuchi, T., Liu, Y., Yao, X. (eds.) Evolvable Hardware, pp. 145–160. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  33. 33.
    Stoica, A., Wang, X., Keymeulen, D., Zebulum, R.S., Ferguson, M., Guo, X.: Characterization and Recovery of Deep Sub Micron (DSM) Technologies Behavior Under Radiation. In: 2005 IEEE Aerospace Conference, pp. 1–9. IEEE, Los Alamitos (2005)Google Scholar
  34. 34.
    Stoica, A., Keymeulen, D., Zebulum, R.S., Katkoori, S., Fernando, P., Sankaran, H., Mojarradi, M., Daud, T.: Self-reconfigurable mixed-signal integrated circuits architecture comprising a field programmable analog array and a general purpose genetic algorithm ip core. In: Hornby, G.S., Sekanina, L., Haddow, P.C. (eds.) ICES 2008. LNCS, vol. 5216, pp. 225–236. Springer, Heidelberg (2008)Google Scholar
  35. 35.
    Sakanashi, H., Iwata, M., Higuchi, T.: A lossless compression method for halftone images using evolvable hardware. In: Liu, Y., Tanaka, K., Iwata, M., Higuchi, T., Yasunaga, M. (eds.) ICES 2001. LNCS, vol. 2210, pp. 314–326. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  36. 36.
    Kajitani, I., Iwata, M., Higuchi, T.: A ga hardware engine and its applications. In: Higuchi, T., Liu, Y., Yao, X. (eds.) Evolvable Hardware, pp. 41–63. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  37. 37.
    Murakawa, M., Yoshizawa, S., Adachi, T., Suzuki, S., Takasuka, K., Iwata, M., Higuchi, T.: Analogue EHW chip for intermediate frequency filters. In: Sipper, M., Mange, D., Pérez-Uribe, A. (eds.) ICES 1998. LNCS, vol. 1478, pp. 134–143. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  38. 38.
    Nofli, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press/Bradford Books (2000)Google Scholar
  39. 39.
    Lipson, H., Pollack, J.B.: Automatic design and manufacture of robotic lifeforms. Nature 406, 974–978 (2000)CrossRefGoogle Scholar
  40. 40.
    Sekanina, L.: Evolved computing devices and the implementation problem. Minds and Machines 17(3), 311–329 (2007)CrossRefGoogle Scholar
  41. 41.
    Copeland, B.J.: What is computation? Synthese 108, 335–359 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Johnson, C.G.: What kinds of natural processes can be regarded as computations? In: Paton, R. (ed.) Computation in Cells and Tissues: Perspectives and Tools of Thought, pp. 327–336. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  43. 43.
    Bartels, R.A., Murnane, M.M., Kapteyn, H.C., Christov, I., Rabitz, H.: Learning from Learning Algorithms: Applications to attosecond dynamics of high-harmonic generation. Physical Review A 70(4), 1–5 (2004)CrossRefGoogle Scholar
  44. 44.
    van Leeuwen, J., Wiedermann, J.: The Turing Machine Paradigm in Contemporary Computing. In: Mathematics Unlimited - 2001 and Beyond, pp. 1139–1155. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lukáš Sekanina
    • 1
  1. 1.Faculty of Information TechnologyBrno University of TechnologyBrnoCzech Republic

Personalised recommendations