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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Adamatzky, A.: Computing in nonlinear media and automata collectives. IOP Publishing Ltd., Bristol (2001)
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)
Averbeck, B.B., Latham, P.E., Pouget, A.: Neural correlations, population coding and computation. Nature Reviews Neuroscience 7(5), 358–366 (2006)
Banâtre, J.-P., Fradet, P., Giavitto, J.-L., Michel, O. (eds.): UPP 2004. LNCS, vol. 3566. Springer, Heidelberg (2005)
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)
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)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing Series. Springer (2008)
Gorecki, J., Yoshikawa, K., Igarashi, Y.: On chemical reactors that can count. The Journal of Physical Chemistry A 107(10), 1664–1669 (2003)
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)
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)
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)
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)
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:1010016313373
Pouget, A., Dayan, P., Zemel, R.: Information processing with population codes. Nature Reviews Neuroscience 1(2), 125–132 (2000)
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)
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)
Weicker, K.: Evolutionäre Algorithmen. Vieweg+Teubner (2002)
Zitzler, E., Laumanns, M., Bleuler, S.: A tutorial on evolutionary multiobjective optimization. Metaheuristics for Multiobjective Optimisation, 3–37 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gruenert, G., Escuela, G., Dittrich, P. (2012). Symbol Representations in Evolving Droplet Computers. In: Durand-Lose, J., Jonoska, N. (eds) Unconventional Computation and Natural Computation. UCNC 2012. Lecture Notes in Computer Science, vol 7445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32894-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-32894-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32893-0
Online ISBN: 978-3-642-32894-7
eBook Packages: Computer ScienceComputer Science (R0)