An evolutionary approach to hardware design makes possible the relaxation of several constraints which other more orthodox techniques require. Human designers conventionally need a prior rigorous analysis of the problem, and a decomposition of a complex system into separate parts of manageable size; using artificial evolution this is not necessary. Simplifying design constraints are often applied to hardware so as to make it behave in an easily analysable fashion -- for instance, strict synchronisation to a global clock. This is no longer necessary with evolution, and such constraints can be relaxed.
However, this freedom comes at some cost; there are a whole new set of issues relating to evolution that must be considered, and many of these will be unfamiliar to those schooled in conventional design methods. Evolution of hardware systems often cannot be fitted into the constrained optimisation framework which standard genetic algorithms assume. This means that these algorithms need some crucial changes.
The main cost of an evolutionary approach is the large number of trials that are required. Adequate simulations may take time comparable to doing the trials for real, or may not be feasible; e.g. when vision in complex environments, or the modelling of detailed semiconductor physics is involved, as will be shown later. Under many circumstances robustness in the presence of noise or hardware faults is a crucial factor, which can increase the number of trials needed. Noise is not always a problem, indeed it may have advantageous evolutionary effects.
We discuss the constraints that can be relaxed and the hard consequences that must be recognised, initially at a theoretical level. Then a real example of evolved hardware will be presented in the light of these discussions. A simple asynchronous digital circuit directly takes echo pulses from a pair of left/right sonars, and drives the two motors of a real robot, so that it exhibits a wall-avoidance behaviour in the real world. The complete sensorimotor control system (no pre- or post-processing) consists of just 32 bits of RAM and a few flip-flops, and is even tolerant to single-stuck-at faults in the RAM. The remarkable efficiency of this circuit can be attributed to the facts that it was evolved as a physical piece of hardware in the real world, and that many of the constraints on its dynamics were under evolutionary control. The rationale behind this experiment applies to many other kinds of system, including Field-Programmable Gate Arrays (FPGA's) [2].
The paper proceeds thus: Sections 2-11 discuss various aspects of artificial evolution. Sections 12-14 cover issues of noise, the relationship between simulation and reality, and fault tolerance. Sections 15-17 discuss the theory of Intrinsic Hardware Evolution. Sections 18 and 19 give a case study of a physical piece of hardware, intrinsically evolved in the real world as a robot controller. A final section summarises the discussion.