Abstract
The evolutionary circuit design is an approach allowing engineers to realize computational devices. The evolved computational devices represent a distinctive class of devices that exhibits a specific combination of properties, not visible and studied in the scope of all computational devices up till now. Devices that belong to this class show the required behavior; however, in general, we do not understand how and why they perform the required computation. The reason is that the evolution can utilize, in addition to the “understandable composition of elementary components”, material-dependent constructions and properties of environment (such as temperature, electromagnetic field etc.) and, furthermore, unknown physical behaviors to establish the required functionality. Therefore, nothing is known about the mapping between an abstract computational model and its physical implementation. The standard notion of computation and implementation developed in computer science as well as in cognitive science has become very problematic with the existence of evolved computational devices. According to the common understanding, the evolved devices cannot be classified as computing mechanisms.
Similar content being viewed by others
Notes
Bartels used an evolutionary optimization algorithm to shape laser pulses to distort molecules in specific ways to catalyze chemical reactions, with the ultimate goal of manipulating large molecules, for example proteins and enzymes, in a biological cell. The method has also been used to create new quantum behaviors at the atomic level. Human can probe quantum systems, but are not capable of exploring different quantum behaviors in a fast automated fashion. The results are completely unexpected and amazing from a physical point of view: behaviors are being evolved that were not known to be physically possible. This includes anti-correlated attosecond harmonics in quantum systems. The ability to move beyond the nanoscale to the attoscale is a major breakthrough, and the potential applications of controlling the behavior of materials at atomic level are enormous (Bartels et al. 2004).
References
Alberts, B., et al. (1998). Essential cell biology – An introduction to the molecular biology of the cell. New York: Garland Publishing.
Bartels, R., et al. (2004). Learning from learning algorithms: Applications to attosecond dynamics of high-harmonic generation. Physical Review A, 70(1), 1–5.
Bentley, P. (Ed.) (1999). Evolutionary design by computers. San Francisco CA: Morgan Kaufmann Publishers.
Brooks, R. (2001). The relationship between matter and life. Nature, 409(6818), 409–411.
Copeland, B. J. (1996). What is computation? Synthese, 108, 335–359.
Copeland, B. J. (1998). Super-Turing machines. Complexity, 4(1), 30–32.
Copeland, B. J., & Sylvan, R. (1999). Beyond the universal Turing machine. Australasian Journal of Philosophy, 77(1), 46–66.
Dawkins, R. (1991). The blind watchmaker. London: Penguin Books.
Eberbach, E., Goldin, D., & Wegner, P. (2004). Turing’s ideas and models of computation. In Ch. Teuscher (Eds.), Alang Turing: Life and Legacy of a Great Thinker (pp. 166–173). Berlin Heidelberg New York: Springer.
Gandy, R. (1978). Church’s thesis and principles for mechanisms. In K. J. Barwise, H. J. Keisler, & K. Kunen (Eds.), The Kleene symposium (pp. 123–148). New York: North Holland.
de Garis, H. (1993). Evolvable hardware – Genetic programming of a Darwin machine. In ICANNGA’93: International Conference on Artificial Neural Networks and Genetic Algorithms, Innsbruck, Austria. Berlin Heidelberg New York: Springer.
Gruska, J. (1997). Foundations of Computing. International Thomson Publishing Computer Press.
Harding, S., & Miller, J. (2005). Evolution in materio: Evolving logic gates in liquid crystal. In Proceedings of the workshop on unconventional computing at ECAL 2005 VIIIth European conference on artificial life. To appear in International Journal of Unconventional Computing, pp 12.
Higuchi, T., et al. (1993). Evolving hardware with genetic learning: A first step towards building a Darwin machine. In SAB’92: Proceedings of the 2nd International Conference on Simulated Adaptive Behaviour (pp. 417–424). Cambridge MA: MIT Press.
Higuchi, T., et al. (1999). Real-world applications of analog and digital evolvable hardware. IEEE Transactions on Evolutionary Computation, 3(3), 220–235.
Johnson, C. G. (2004). What kinds of natural processes can be regarded as computations? In T. Paton (Eds.), Computation in cells and tissues: Perspectives and tools of thought. Berlin Heidelberg New York: Springer.
Keymeulen, D., Stoica, A., & Zebulum, R. (2000). Fault-tolerant evolvable hardware using field programmable transistor arrays. IEEE Transactions on Reliability. Special Issue on Fault-Tolerant VLSI Systems, 49(3), 305–316.
van Leeuwen, J., & Wiedermann, J. (2001). The turing machine paradigm in contemporary computing. In Mathematics unlimited – 2001 and beyond (pp. 1139–1155). Berlin Heidelberg New York: Springer.
Lloyd, S. (2000). Ultimate physical limits to computation. Nature, 406, 1047–1054.
Maclennan, B. J. (2003). Transcending turing computability. Minds and Machines, 13(1), 3–22.
Michalewicz, Z., & Fogel, D. B. (2000). How to Solve It – modern heuristics. Berlin Heidelberg New York: Springer.
Miller, J., Job, D., & Vassilev, V. (2000). Principles in the evolutionary design of digital circuits – Part I. Genetic Programming and Evolvable Machines, 1(1), 8–35.
Miller, J., & Downing, K. (2002). Evolution in Materio: Looking beyond the silicon box. In Stoica, A. et al. (Ed.), EH’02: Proceedings of the 4th NASA/DoD conference on evolvable hardware. Alexandria, Virginia, USA, 2002 (IEEE Computer Society, Los Alamitos 2002) pp 167–176.
Piccinini, G. (2003). Computations and Computers in the Sciences of Mind and Brain. PhD thesis, University of Pittsburgh, p 323.
Scheutz, M. (1999). When physical systems realize functions. Minds and Machines, 9(2), 161–196.
Searle, J. (1990). Is the brain a digital computer? Proceedings and Addresses of the American Philosophical Association, 64, 21–37.
Sekanina, L. (2004). Evolvable Components: From Theory to Hardware Implementations. Natural Computing Series. Berlin Heidelberg New York: Springer.
Sekanina, L. (2004). Evolvable computing by means of evolvable components. Natural Computing, 3(3), 323–355.
Sekanina, L., & Zebulum, R. (2005). Evolutionary discovering of the concept of the discrete state at the transistor level. In EH’05: Proc. of the 2005 NASA/DoD Workshop on Evolvable Hardware, Lohn, J. et al. (Eds.), Washington DC, USA, 2005. IEEE Computer Society, Los Alamitos, pp. 73–78.
Stannett, M. (2003). Computation and hypercomputation. Minds and Machines, 13(1), 115–153.
Stoica, A., Keymeulen, D., Arslan, T., Duong, V., Zebulum, R., Ferguson I., & Guo, X. (2004). Circuit Self-Recovery Experiments in Extreme Environments. In Zebulum, R. et al. (Eds.), EH’04: Proc. of the 2004 NASA/DoD Workshop on Evolvable Hardware (pp. 142–145). Seattle USA, 2004. IEEE Computer Society, Los Alamitos.
Stoica, A., Zebulum, R., Keymeulen, D., Ferguson, I., Duong, V., & Guo, X. (2004). Evolvable hardware techniques for on-chip automated reconfiguration of programmable devices. Soft Computing – Spec. Issue on Evolvable Hardware, 8(5), 354–365.
Stoica, A. (2004). Evolvable Hardware for Autonomous Systems. Tutorial at IEEE Congress on Evolutionary Computation. http://ehw.jpl.nasa.gov/Content/Public/TutorialCEC2004/TutorialCEC2004.pdf
Thompson, A. (1998). Hardware evolution: Automatic design of electronic circuits in reconfigurable hardware by artificial evolution. Distinguished Dissertation Series. Springer, London.
Thompson, A., Layzell, P., & Zebulum, R. S. (1999). Explorations in design space: unconventional electronics design through artificial evolution. IEEE Trans. on Evolutionary Computation, 3(3), 167–196.
Tour, J. M. (2003). Molecular electronics. World Scientific.
Turing, A. M. (1937). On computable numbers, with an application to the Entscheidungsproblem. In Proceedings of the London Mathematical Society, 42(2) (1936–37), 230–265 (with corrections from Proceedings of the London Mathematical Society, Series 2, 43 (1937), 544–546).
Wiedermann, J., & van Leeuwen, J. (2002) Relativistic Computers and Non-uniform Complexity Theory. In Calude C. S. (Ed.), UMC’02: Proceedings of 3rd Conference on Unconventional Models of Computation. Kobe, Japan, 2002. Lecture Notes in Computer Science, vol 2509. Springer, Berlin Heidelberg New York, pp. 287–299.
Wegner, P., & Goldin, D. (2003). Computation beyond Turing machines. Communications of the ACM, 46(4), 100–102.
Zebulum, R., Pacheco, M., & Vellasco, M. (2002). Evolutionary electronics – Automatic design of electronic circuits and systems by genetic algorithms. Boca Raton: CRC Press.
Acknowledgements
This research was partially supported by the Grant Agency of the Czech Republic under No. 102/07/0850 Design and hardware implementation of a patent-invention machine and the Research Plan No. MSM 0021630528 Security-Oriented Research in Information Technology.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sekanina, L. Evolved Computing Devices and the Implementation Problem. Minds & Machines 17, 311–329 (2007). https://doi.org/10.1007/s11023-007-9071-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11023-007-9071-5