Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms

Abstract

The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract. 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 problems is explored here. Results are obtained from a mixture of single walled carbon nanotubes and liquid crystals (SWCNT/LC). The complex nature of the voltage/current relationship of this material presents a potential for adaptation. Here, it is used as a computational medium evolved by two derivative-free, population-based stochastic search algorithms, particle swarm optimisation and differential evolution. The computational problem considered is data classification. A custom made electronic motherboard for interacting with the material has been developed, which allows the application of control signals on the material body. Starting with a simple binary classification problem of separable data, the material is trained with an error minimisation objective for both algorithms. Subsequently, the solution, defined as the combination of the material itself and optimal inputs, is verified and results are reported. The evolution process based on EAs has the capacity to evolve the material to a state where data classification can be performed. PSO outperforms EA in terms of results' reproducibility due to the smoother, as opposed to more noisy, inputs applied on the material.

Computing Based on Material Training: Application to Binary Classification Problems

“…These two contributions continue the work reported in [12], [20], [21] and [22]. The hardware platform and material are discussed in section II.…”

“…This had not been observed for simpler problems discussed in earlier papers [21,22]. In addition, the material retains a memory of previous trainings.…”

“…Previous papers reported results for simple, proof-ofconcept datasets [12,21,22]. The first part of our investigations focus on the training of un-configured SWCNT/LC mixture for medical datasets selected from the UCI repository.…”

“…It has been observed in our previous work [21,22] that an un-configured SWCNT/LC mixture can be trained to solve these binary classification problems. Current investigations focus on whether the SWCNT structures formed in the material during training are stable, a non-negligible contribution to the solution and finally if they can be retained, to some extent, when the material has been retrained for a new problem.…”

“…In [9], [11], and [17] a dry mix of Single-Walled Carbon Nanotubes (SWCNT) with a polymer were used as the computational material and its electrical conductance was used as the manipulated property for solving the problem of calculating Boolean functions using a threshold interpretation scheme; the same material is used in [14] and [15] for solving optimisation problems. In [12], [22], [21] and [20] the material used is a solution of SWCNT with LC in liquid rather than in dry solid state. The physical property used for evolving the material is its electrical conductivity and the ability of the SWCNT to form percolation paths within the LC.…”