Abstract
Evolution-in-materio uses evolutionary algorithms to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. We show that using a purpose-built hardware platform called Mecobo, it is possible to solve computational problems by evolving voltages and signals applied to an electrode array covered with a carbon nanotube–polymer composite. We demonstrate for the first time that this methodology can be applied to function optimization and also to the tone discriminator problem (TDP). For function optimization, we evaluate the approach on a suite of optimization benchmarks and obtain results that in some cases come very close to the global optimum or are comparable with those obtained using well-known software-based evolutionary approach. We also obtain good results in comparison with prior work on the tone discriminator problem. In the case of the TDP we also investigated the relative merits of different mixtures of materials and organizations of electrode array.
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Notes
Digital chips are designed to emulate, as far as possible, a device that operates using Boolean algebra.
Mark K. Massey and Michael C. Petty prepared the materials used as substrates and the electrode masks for our experiments.
In both cases of experimental material and CGP, offspring replaced parents if their fitness was greater than or equal to the parent.
Based on average results over 30 independent runs, and 500,000 evaluations for each run.
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Acknowledgments
The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under Grant Agreement Number 317662.
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Communicated by D. Neagu.
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Mohid, M., Miller, J.F., Harding, S.L. et al. Evolution-in-materio: solving computational problems using carbon nanotube–polymer composites. Soft Comput 20, 3007–3022 (2016). https://doi.org/10.1007/s00500-015-1928-6
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DOI: https://doi.org/10.1007/s00500-015-1928-6