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.

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

• 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
• Public URL: https://durham-repository.worktribe.com/output/1641862
• Contract Date: May 30, 2016