Advertisement

Symbol Representations in Evolving Droplet Computers

  • Gerd Gruenert
  • Gabi Escuela
  • Peter Dittrich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7445)

Abstract

We investigate evolutionary computation approaches as a mechanism to program networks of excitable chemical droplets. For this kind of systems, we assigned a specific task and concentrated on the characteristics of signals representing symbols. Given a Boolean function like Identity, OR, AND, NAND, XOR, XNOR or the half-adder as the target functionality, 2D networks composed of 10×10 droplets were considered in our simulations. Three different setups were tested: Evolving network structures with fixed on/off rate coding signals, coevolution of networks and signals, and network evolution with fixed but pre-evolved signals. Evolutionary computation served in this work not only for designing droplet networks and input signals but also to estimate the quality of a symbol representation: We assume that a signal leading to faster evolution of a successful network for a given task is better suited for the droplet computing infrastructure. Results show that complicated functions like XOR can evolve using only rate coding and simple droplet types, while other functions involving negations like the NAND or the XNOR function evolved slower using rate coding. Furthermore we discovered symbol representations that performed better than the straight forward on/off rate coding signals for the XNOR and AND Boolean functions. We conclude that our approach is suitable for the exploration of signal encoding in networks of excitable droplets.

Keywords

excitable system droplet network signal encoding logic gate evolutionary algorithm chemical computer symbol representation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adamatzky, A.: Computing in nonlinear media and automata collectives. IOP Publishing Ltd., Bristol (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Aghdaei, S., Sandison, M., Zagnoni, M., Green, N., Morgan, H.: Formation of artificial lipid bilayers using droplet dielectrophoresis. Lab Chip 8(10), 1617–1620 (2008)CrossRefGoogle Scholar
  3. 3.
    Averbeck, B.B., Latham, P.E., Pouget, A.: Neural correlations, population coding and computation. Nature Reviews Neuroscience 7(5), 358–366 (2006)CrossRefGoogle Scholar
  4. 4.
    Banâtre, J.-P., Fradet, P., Giavitto, J.-L., Michel, O. (eds.): UPP 2004. LNCS, vol. 3566. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Brown, E.N., Kass, R.E., Mitra, P.P.: Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat. Neurosci. 7(5), 456–461 (2004)CrossRefGoogle Scholar
  6. 6.
    Dauwels, J., Vialatte, F., Weber, T., Cichocki, A.: On Similarity Measures for Spike Trains. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008, Part I. LNCS, vol. 5506, pp. 177–185. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing Series. Springer (2008)Google Scholar
  8. 8.
    Gorecki, J., Yoshikawa, K., Igarashi, Y.: On chemical reactors that can count. The Journal of Physical Chemistry A 107(10), 1664–1669 (2003)CrossRefGoogle Scholar
  9. 9.
    Gruenert, G., Szymanski, J., Holley, J., Escuela, G., Diem, A., Ibrahim, B., Adamatzky, A., Gorecki, J., Dittrich, P.: Multi-scale modelling of computers made from excitable chemical droplets. NEUNEU Technical Report (2012)Google Scholar
  10. 10.
    Holley, J., Jahan, I., Costello, B., Bull, L., Adamatzky, A.: Logical and arithmetic circuits in Belousov Zhabotinsky encapsulated discs. Physical Review E 84(5), 056110 (2011)CrossRefGoogle Scholar
  11. 11.
    Koza, J.R.: Hierarchical genetic algorithms operating on populations of computer programs. In: Sridharan, N.S. (ed.) Proceedings of the Eleventh International Joint Conference on Artificial Intelligence IJCAI 1989, Detroit, MI, USA, August 20-25, vol. 1, pp. 768–774 (1989)Google Scholar
  12. 12.
    Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Miller, J.F., Job, D., Vassilev, V.K.: Principles in the evolutionary design of digital circuits part i. Genetic Programming and Evolvable Machines 1, 7–35 (2000), 10.1023/A:1010016313373CrossRefzbMATHGoogle Scholar
  14. 14.
    Pouget, A., Dayan, P., Zemel, R.: Information processing with population codes. Nature Reviews Neuroscience 1(2), 125–132 (2000)CrossRefGoogle Scholar
  15. 15.
    Schaffer, J.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100. L. Erlbaum Associates Inc. (1985)Google Scholar
  16. 16.
    Szymanski, J., Gorecka, J.N., Igarashi, Y., Gizynski, K., Gorecki, J., Zauner, K.-P., Planque, M.D.: Droplets with information processing ability. International Journal of Unconventional Computing 7(3), 185–200 (2011)Google Scholar
  17. 17.
    Weicker, K.: Evolutionäre Algorithmen. Vieweg+Teubner (2002)Google Scholar
  18. 18.
    Zitzler, E., Laumanns, M., Bleuler, S.: A tutorial on evolutionary multiobjective optimization. Metaheuristics for Multiobjective Optimisation, 3–37 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gerd Gruenert
    • 1
  • Gabi Escuela
    • 1
  • Peter Dittrich
    • 1
  1. 1.Department of Computer Science, Bio Systems Analysis GroupFriedrich Schiller University JenaJenaGermany

Personalised recommendations