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Evolution-in-materio: solving computational problems using carbon nanotube–polymer composites

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Maktuba Mohid
Maktuba Mohid
Maktuba Mohid
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Julian Francis Miller at University of York
Julian Francis Miller
Simon Lawrence Harding at Machine Intelligence Ltd.
Simon Lawrence Harding
  • Machine Intelligence Ltd.
Gunnar Tufte at Norwegian University of Science and Technology
Gunnar Tufte
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Abstract and Figures

Evolution-in-materio uses evolutionary algorithms to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. We show that using a purpose-built hardware platform called Mecobo, it is possible to solve computational problems by evolving voltages and signals applied to an electrode array covered with a carbon nanotube–polymer composite. We demonstrate for the first time that this methodology can be applied to function optimization and also to the tone discriminator problem (TDP). For function optimization, we evaluate the approach on a suite of optimization benchmarks and obtain results that in some cases come very close to the global optimum or are comparable with those obtained using well-known software-based evolutionary approach. We also obtain good results in comparison with prior work on the tone discriminator problem. In the case of the TDP we also investigated the relative merits of different mixtures of materials and organizations of electrode array.
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Soft Comput (2016) 20:3007–3022
DOI 10.1007/s00500-015-1928-6
FOCUS
Evolution-in-materio: solving computational problems using
carbon nanotube–polymer composites
Maktuba Mohid1·Julian F. Miller1·Simon L. Harding1·Gunnar Tufte2·
Mark K. Massey3·Michael C. Petty3
Published online: 12 November 2015
© Springer-Verlag Berlin Heidelberg 2015
Abstract Evolution-in-materio uses evolutionary
algorithms to exploit properties of materials to solve compu-
tational problems without requiring a detailed understanding
of such properties. We show that using a purpose-built
hardware platform called Mecobo, it is possible to solve
computational problems by evolving voltages and signals
applied to an electrode array covered with a carbon nanotube–
polymer composite. We demonstrate for the first time that this
methodology can be applied to function optimization and
also to the tone discriminator problem (TDP). For function
optimization, we evaluate the approach on a suite of opti-
mization benchmarks and obtain results that in some cases
come very close to the global optimum or are comparable
with those obtained using well-known software-based evolu-
tionary approach. We also obtain good results in comparison
with prior work on the tone discriminator problem. In the
case of the TDP we also investigated the relative merits of
different mixtures of materials and organizations of electrode
array.
Keywords Evolutionary algorithm ·Evolution-in-
materio ·Material computation ·Evolvable hardware ·
Function optimization ·Tone discriminator
Communicated by D. Neagu.
BMaktuba Mohid
mm1159@york.ac.uk
1Department of Electronics, University of York, York, UK
2Department of Computer and Information Science,
Norwegian University of Science and Technology, 7491
Trondheim, Norway
3School of Engineering and Computing Sciences and Centre
for Molecular and Nanoscale Electronics, Durham University,
Durham, UK
1 Introduction
Natural evolution can be looked at as an effective algo-
rithm which exploits the physical properties of materials.
Evolution-in-materio (EIM) aims to mimic this by manipu-
lating physical systems using computer-controlled evolution
(CCE) (Harding and Miller 2009,2007;Harding et al. 2008;
Miller and Downing 2002). In this paper, we are using EIM
to solve computational problems. It is important to note, that
one of unique features of EIM is that it aims to exploit phys-
ical processes that a designer may either be unaware of, or
not know how to utilize. This is discussed in more detail in
a recent review of EIM (Miller et al. 2014).
EIM was inspired by the work of Adrian Thompson who
investigated whether it was possible for unconstrained evo-
lution to evolve working electronic circuits using a silicon
chip called a Field Programmable Gate Array (FPGA). He
evolved a so-called tone discriminator, a digital circuit that
could discriminate between 1 or 10 kHz signal (Thompson
1998). When the evolved circuit was analysed, Thompson
discovered that artificial evolution had exploited physical
properties of the chip. Despite considerable analysis Thomp-
son and Layzell (1999) were unable to pinpoint what exactly
was going on in the evolved circuits.
In Miller and Downing (2002), it was argued that materials
with a rich physics might be more evolvable than those with
an impoverished physics, such as silicon chips.1This inspired
an attempt to see if computer-controlled evolution could uti-
lize the physical properties of liquid crystal (LCD) to help
solve a number of computational problems. The first demon-
stration showed that it was relatively easy to evolve a tone
discriminator in liquid crystal (Harding and Miller 2004).
1Digital chips are designed to emulate, as far as possible, a device that
operates using Boolean algebra.
123
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