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Training a carbon-nanotube/liquid crystal data classifier using evolutionary algorithms.

Vissol-Gaudin, E. and Kotsialos, A. and Massey, M. K. and Zeze, D. A. and Pearson, C. and Groves, C. and Petty, M. C. (2016) 'Training a carbon-nanotube/liquid crystal data classifier using evolutionary algorithms.', in Unconventional computation and natural computation : 15th International Conference, UCNC 2016, Manchester, UK, July 11-15, 2016 ; proceedings. Cham: Springer, pp. 130-141. Lecture notes in computer science. (9726).


Evolution-in-Materio uses evolutionary algorithms (EA) to exploit the physical properties of unconfigured, physically rich materials, in effect transforming them into information processors. The potential of this technique for machine learning problems is explored here. Results are obtained from a mixture of single walled carbon nanotubes and liquid crystals (SWCNT/LC). The complex nature of the voltage/current relationship of this material presents a potential for adaptation. Here, it is used as a computational medium evolved by two derivative-free, population-based stochastic search algorithms, particle swarm optimisation (PSO) and differential evolution (DE). The computational problem considered is data classification. A custom made electronic motherboard for interacting with the material has been developed, which allows the application of control signals on the material body. Starting with a simple binary classification problem of separable data, the material is trained with an error minimisation objective for both algorithms. Subsequently, the solution, defined as the combination of the material itself and optimal inputs, is verified and results are reported. The evolution process based on EAs has the capacity to evolve the material to a state where data classification can be performed. PSO outperforms DE in terms of results’ reproducibility due to the smoother, as opposed to more noisy, inputs applied on the material.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Publisher statement:The final publication is available at Springer via
Date accepted:25 April 2016
Date deposited:18 May 2016
Date of first online publication:15 June 2016
Date first made open access:15 June 2017

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