Solving Binary Classification Problems with Carbon Nanotube / Liquid Crystal Composites and Evolutionary Algorithms
Solving Binary Classification Problems with Carbon Nanotube / Liquid Crystal Composites and Evolutionary Algorithms
Eleonore Vissol-Gaudin, Apostolos Kotsialos, Christopher Groves, M. Kieran Massey, Christopher Pearson, Dagou A. Zeze and Michael C. Petty
Eleonore Vissol-Gaudin, Apostolos Kotsialos, Christopher Groves, M. Kieran Massey, Christopher Pearson, Dagou A. Zeze and Michael C. Petty
School of Engineering and Computing Sciences, Durham University, United Kingdom
School of Engineering and Computing Sciences, Durham University, United Kingdom
School of Engineering and Computing Sciences, Durham University, United Kingdom
Sch
School of Engineering and Computing Sciences, Durham University, United Kingdom
School of Engineering and Computing Sciences, Durham University, United Kingdom
School of Engineering and Computing Sciences, Durham University, United Kingdom
Sch
This paper presents a series of experiments demonstrating the capacity of
single-walled carbon-nanotube (SWCNT)/liquid crystal (LC) mixtures to be trained
by evolutionary algorithms to act as classifiers on linear and nonlinear binary
datasets. The training process is formulated as an optimisation problem with
hardware in the loop. The liquid SWCNT/LC samples used here are un-configured
and with nonlinear current-voltage relationship, thus presenting a potential for
being evolved. The nature of the problem means that derivative-free stochastic
search algorithms are required. Results presented here are based on differential
evolution (DE) and particle swarm optimisation (PSO). Further investigations
using DE, suggest that a SWCNT/LC material is capable of being reconfigured for
different binary classification problems, corroborating previous research. In
addition, it is able to retain a physical memory of each of the solutions to the
problems it has been trained to solve.
