Introduction

When artificial evolution is used to automatically design a structure, that structure usually exists in a software simulation, to make it easily manipulable. When evolving control systems for autonomous mobile robots [1], even when the real robot is used instead of a simulation, the actual structure undergoing evolution -- often an artificial neural network (ANN) -- is usually simulated in software.

Recently, however, technology has become available which allows artificial evolution to manipulate the configuration of a silicon chip directly: electronic circuits can be evolved without the use of simulation, with every fitness measurement being the evaluation of a physically real electronic circuit's performance at the desired task. But why should one be interested in this? After all, we can easily simulate ANNs on a standard desktop PC that are larger than the current capabilities of artificial evolution, so we do not need to resort to hardware implementations because of software being too slow (pace de Garis [2]). The answer is that evolution of reconfigurable hardware need not be just a high speed implementation of what could easily be done in software: evolution is crafting a physical object that exists in real time and space, and behaves according to semiconductor physics. This raises a set of opportunities for science and engineering that are not normally addressed by simulation work:

1. Evolution can exploit real-world physics that is difficult to analyse or model in simulation or theoretical studies. Once the simplifying constraints of conventional design methodologies have been dropped, this can allow highly efficient circuits to be evolved, which exploit the natural behaviour of the electronic medium.
2. The physical components have a size, shape and location, and these are crucial in determining the interactions between them. This can make the interactions richer, but in some ways more constrained, than the perfectly controllable point-to-point topological interconnections normally used when evolving in simulation.
3. The characteristics of the components and their interactions are not exactly predictable or constant over time. Evolution must find ways of coping with this.

The first point above provides the engineering motivation: extremely efficient (small, low-power) circuits can be produced. The penalty for the engineer is that to do this, the second two points must also be considered. For the scientist, all three are of great interest, as they apply as much to evolution in nature -- and attempts to draw inspiration from it -- as to electronics. As we shall see, they have implications for the organisation of a physical `nervous system', whether it be natural or artificial. This paper summarises some results from the author's work on the evolutionary engineering of electronics in general, with the intention of showing its relevance to the Evolutionary Robotics (ER) enterprise.

In the next section, I describe the technology making the direct evolution of electronics possible. The later sections then consider the three points above in turn, showing experimental results. Only an overview is given -- see the references for full details. Finally, the implications for ER are summarised.