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Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms

Vissol-Gaudin, E.; Kotsialos, A.; Massey, M.K.; Zeze, D.A.; Pearson, C.; Groves, C.; Petty, M.C.

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Authors

A. Kotsialos

M.K. Massey

D.A. Zeze

C. Pearson

C. Groves

M.C. Petty



Contributors

J. Handl
Editor

E. Hart
Editor

P.R. Lewis
Editor

M. López-Ibáñez
Editor

G. Ochoa
Editor

B. Paechter
Editor

Abstract

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.

Citation

Vissol-Gaudin, E., Kotsialos, A., Massey, M., Zeze, D., Pearson, C., Groves, C., & Petty, M. (2016). Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms. In J. Handl, E. Hart, P. Lewis, M. López-Ibáñez, G. Ochoa, & B. Paechter (Eds.), Parallel problem solving from nature – PPSN XIV : 14th International Conference, Edinburgh, UK, September 17-21, 2016 : proceedings (644-654). Springer Verlag. https://doi.org/10.1007/978-3-319-45823-6_60

Acceptance Date May 30, 2016
Online Publication Date Aug 31, 2016
Publication Date Aug 31, 2016
Deposit Date Aug 24, 2016
Publicly Available Date Aug 31, 2017
Publisher Springer Verlag
Pages 644-654
Series Title Lecture notes in computer science
Book Title Parallel problem solving from nature – PPSN XIV : 14th International Conference, Edinburgh, UK, September 17-21, 2016 : proceedings.
ISBN 9783319458229
DOI https://doi.org/10.1007/978-3-319-45823-6_60

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