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.
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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
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DOI: https://doi.org/10.1007/978-3-642-32894-7_13
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