Evolving Solutions to Computational Problems Using Carbon Nanotubes

Evolution-in-materio 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 two computational problems: classification and robot control. We have investigated many experimental factors that could affect the effectiveness of the device and the evolution-in-materio technique. On the classification problem (Iris) we show that the evolution-in-materio approach with analogue signals gives very good results that are competitive with a well-known effective software-based evolutionary approach. In the case of robot control, we were able to evolve a controller that successfully allowed a simulated Khepera robot to fully explore its environment without colliding with any obstacle.