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Data classification using carbon-nanotubes and 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) 'Data classification using carbon-nanotubes and evolutionary algorithms.', in Parallel problem solving from nature – PPSN XIV : 14th International Conference, Edinburgh, UK, September 17-21, 2016 : proceedings. Cham: Springer, pp. 644-654. Lecture notes in computer science. (9921).


The potential of Evolution in Materio (EiM) for machine learning problems is explored here. This technique makes use of evolutionary algorithms (EAs) to influence the processing abilities of an un-configured physically rich medium, via exploitation of its physical properties. The EiM results reported are obtained using particle swarm optimisation (PSO) and differential evolution (DE) to exploit the complex voltage/current relationship of a mixture of single walled carbon nanotubes (SWCNTs) and liquid crystals (LCs). The computational problem considered is simple binary data classification. Results presented are consistent and reproducible. The evolutionary process based on EAs has the capacity to evolve the material to a state where data classification can be performed. Finally, it appears that through the use of smooth signal inputs, PSO produces classifiers out of the SWCNT/LC substrate which generalise better than those evolved with DE.

Item Type:Book chapter
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Record Created:25 Aug 2016 09:51
Last Modified:03 Sep 2017 00:47

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