Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers (2018)
Conference Proceeding
Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., Petty, M., & Al-moubayed, N. (2018). Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers. In 2018 IEEE Congress on Evolutionary Computation (CEC) : 8-13 July 2018, Rio de Janeiro, Brazil ; proceedings (646-653). https://doi.org/10.1109/cec.2018.8477779

This paper focuses on a performance analysis of single-walled-carbon-nanotube / liquid crystal classifiers produced by evolution in materio. A new confidence measure is proposed in this paper. It is different from statistical tools commonly used to e... Read More about Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers.

Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms (2016)
Conference Proceeding
Vissol-Gaudin, E., Kotsialos, A., Massey, M., Zeze, D., Pearson, C., Groves, C., & Petty, M. (2016). Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms. In M. Amos, & A. Condon (Eds.), Unconventional computation and natural computation : 15th International Conference, UCNC 2016, Manchester, UK, July 11-15, 2016 ; proceedings (130-141). https://doi.org/10.1007/978-3-319-41312-9_11

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.