Evolution-In-Materio: Solving Machine Learning Classification Problems Using Materials

Mohid, Maktuba; Miller, Julian F.; Harding, Simon; Tufte, Gunnar; Lykkebø, Odd Rune; Massey, Mark K.; Petty, Michael C.

doi:10.1007/978-3-319-10762-2_71

Cited by 19 publications

(16 citation statements)

References 11 publications

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“…Different organic and inorganic media have been used as materials, such as slime moulds [7], bacterial consortia [1], cells (neurons) [18], liquid crystals (LC) panels [6] and nano-particles [3]. Single walled carbon nanotubes (SWCNT) based materials have shown the potential to solve computational problems [8,11,22,14,15]. In [19] it is argued that inorganic materials make a better medium for unconventional computing exploration.…”

Section: Introductionmentioning

confidence: 99%

Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms

Vissol-Gaudin

Kotsialos

Massey

et al. 2016

Parallel Problem Solving From Nature – PPSN XIV

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The nal publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-45823-660Additional information: Use policyThe 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-pro t 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. 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 unconfigured physically rich medium, via exploitation of its physical properties. The EiM results reported are obtained using particle swarm optimisation (PSO) and differential evolution (DE) to exploit the complex voltage/current relationship of a mixture of single walled carbon nanotubes (SWCNTs) and liquid crystals (LCs). The computational problem considered is simple binary data classification. Results presented are consistent and reproducible. The evolutionary process based on EAs has the capacity to evolve the material to a state where data classification can be performed. Finally, it appears that through the use of smooth signal inputs, PSO produces classifiers out of the SWCNT/LC substrate which generalise better than those evolved with DE.

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Section: Introductionmentioning

confidence: 99%

Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms

Vissol-Gaudin

Kotsialos

Massey

et al. 2016

Parallel Problem Solving From Nature – PPSN XIV

View full text Add to dashboard Cite

show abstract

“…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

Self Cite

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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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“…Different materials that have been followed include biological material like slime moulds [10], [11], bacterial consortia [12] and biological cells (neurons) [13] as well as non-biological materials such as, liquid crystals [14], single-walled carbon nanotubes (SWCNT) [4], nano-particles [15]. SWCNT based materials have shown the potential to solve variety of computational problems [4] [16] [17] [18] [19].…”

Section: Methodsmentioning

confidence: 99%