Evolution in materio: a tone discriminator in liquid crystal

Harding, Simon; Miller, Julian F.

doi:10.1109/cec.2004.1331114

Cited by 52 publications

(48 citation statements)

References 6 publications

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“…Nano-particles were used in [2] for developing a reconfigurable Boolean logic network. In [5] and [4] liquid crystals (LC) panels were used for evolving logic gates, a tone discriminator and a robot controller. Single walled carbon nanotubes (SWCNT) based materials have shown the potential to solve variety of computational problems [7], [9], [23], [12], [13], [14] and [15].…”

Section: Introductionmentioning

confidence: 99%

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

Vissol-Gaudin

Kotsialos

Massey

et al. 2016

Unconventional Computation and Natural Computation

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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.

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Section: Introductionmentioning

confidence: 99%

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

Vissol-Gaudin

Kotsialos

Massey

et al. 2016

Unconventional Computation and Natural Computation

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“…For instance, the underlying (electrical) network might be rearranged, or the nanoscale elements could self-organise to a desired state so that the target computational functionality is obtained. An example of the latter is provided by earlier work with liquid crystal of Harding and Miller [16]. In this case, applied configuration signals caused liquid crystal molecules to twist, thus there was a physical change in the material when configuration signals were applied.…”

Section: Other Examples From the Nascence Projectmentioning

confidence: 97%

Evolution in Nanomaterio: The NASCENCE Project

Broersma

2017

Inspired by Nature

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This chapter describes some of the work that was carried out by members of the NASCENCE project, an FP7 project sponsored by the European Community. Apart from some historical notes and background material, the chapter explains how nanoscale material systems have been configured to perform computational tasks by finding appropriate configuration signals using artificial evolution. Most of this exposition is centred around the work that has been carried out at the MESA+ Institute for Nanotechnology at the University of Twente using disordered networks of nanoparticles. The interested reader will also find many pointers to references that contain more details on work that has been carried out by other members of the NASCENCE consortium on composite materials based on single-walled carbon nanotubes.

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“…Even if Thompson exploited the physical properties of the chip, the configuration vector was digital. Harding and Millers [9] did a similar experiment with liquid crystal as material. In contrast to Thompson's experiment the configuration data signal property for the liquid crystal was evolved, the configuration data was unconstrained with regards to signal type, e.g.…”

Section: Introductionmentioning

confidence: 99%

Mecobo: A Hardware and Software Platform for In Materio Evolution

Lykkebø

Harding

Tufte

et al. 2014

Unconventional Computation and Natural Computation

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Abstract. Evolution in Materio (EIM) exploits properties of physical systems to compute. "Designs" are evolved instead of a traditional top down design approach. Computation is a product of the state(s) of the material and input data. Evolution manipulates physical processes by stimulating materials assessed in situ. A hardware software platform designed for EIM experimentation is presented. The platform with features especially for EIM is described together with demonstration experiments using carbon nanotubes in a thick film placed on micro electrode arrays.

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Evolution in materio: a tone discriminator in liquid crystal

Cited by 52 publications

References 6 publications

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

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

Evolution in Nanomaterio: The NASCENCE Project

Mecobo: A Hardware and Software Platform for In Materio Evolution

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