Odd Rune Lykkebø

Mecobo: A Hardware and Software Platform for In Materio Evolution

Evolution in Materio (EIM) exploits properties of physical systems for computation. “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 designed especially for EIM, is described together with demonstration experiments using carbon nanotubes in a thick film placed on micro-electrode arrays.

Evolution-in-materio: Solving function optimization problems using materials

Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. In this paper, we show that using a purpose-built hardware platform called Mecobo, it is possible to evolve voltages and signals applied to physical materials to solve computational problems. We demonstrate for the first time that this methodology can be applied to function optimization. We evaluate the approach on 23 function optimization benchmarks and in some cases results come very close to the global optimum or even surpass those provided by a well-known software-based evolutionary approach. This indicates that EIM has promise and further investigations would be fruitful.

Evolution-in-materio: Solving bin packing problems using materials

Evolution-in-materio (EIM) is a form of intrinsic evolution in which evolutionary algorithms are allowed to manipulate physical variables that are applied to materials. This method aims to configure materials so that they solve computational problems without requiring a detailed understanding of the properties of the materials. The concept gained attention through the work of Adrian Thompson who in 1996 showed that evolution could be used to design circuits in FPGAS that exploited the physical properties of the underlying silicon [21]. In this paper, we show that using a purpose-built hardware platform called Mecobo, we can solve computational problems by evolving voltages, signals and the way they are applied to a microelectrode array with a chamber containing single-walled carbon nanotubes and a polymer. Here we demonstrate for the first time that this methodology can be applied to the well-known computational problem of bin packing. Results on benchmark problems show that the technique can obtain results reasonably close to the known global optima. This suggests that EIM is a promising method for configuring materials to carry out useful computation.

Evolution-in-materio: A frequency classifier using materials

Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. In this paper, we describe experiments using a purpose-built EIM platform called Mecobo to classify whether an applied square wave signal is above or below a user-defined threshold. This is the first demonstration that electrical configurations of materials (carbon nanotubes and a polymer) can be evolved to act as frequency classifiers.

An Investigation of Square Waves for Evolution in Carbon Nanotubes Material

Materials suitable to perform computation make use of evolved configuration signals which specify how the material samples are to operate. The choice of which input and configuration parameters to manipulate obviously impacts the potential of the computational device that emerges. As such, a key challenge is to understand which parameters are better suited to exploit the underlying physical properties of the chosen material. In this paper we focus on the usage of square voltage waves as such manipulation parameters for carbon nanotubes/polymer nanocomposites. The choice of input parameters influence the reachable search space, which may be critical for any kind of evolved computational task. We provide common measurements such as power spectrum and phase plots, taken with the the Mecobo platform, a custombuilt board for evolution-in-materio. In addition, an initial investigation is carried out, which links the frequency of square waves to comparability of the output from the material, while also showing differences in the material’s physical parameters. Observing the behaviour of materials under varying inputs allows macroscopic modelling of pin-to-pin characteristics with simple RC circuits. Finally, SPICE is used to provide a rudamentary simulation of the observed properties of the material. This simulation models the per-pin behaviours, and also shows that an instance of the traveling-salesmanproblem can be solved with a simple randomly generated cloud of resistors.

Comparison and evaluation of signal representations for a carbon nanotube computational device

Evolution in Materio (EIM) exploits properties of physical systems for computation. Evolution manipulates physical processes by stimulating materials by applying some sort of configuration signal. For materials such as liquid crystal and carbon nanotubes the properties of configuration data is rather open. In this work we investigate what kind of configuration data that most likely will be favourable for a carbon nanotube based device. An experimental approach targeting graph colouring is used to test three different types of signal representation: static voltages, square waves and a mixed signal representation. The results show that all signal representation was capable of producing a working device. In the experiments square wave representation produced the best result.