All Outputs (2)

Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms (2016)
Book Chapter
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

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 o... Read More about Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms.

Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms (2016)
Presentation / Conference Contribution
Vissol-Gaudin, E., Kotsialos, A., Massey, M., Zeze, D., Pearson, C., Groves, C., & Petty, M. (2016, July). Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms. Presented at 15th International Conference on Unconventional Computation and Natural Computation, Manchester, UK

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 proble... Read More about Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms.