Accelerating neuro-evolution by compilation to native machine code

Any neuro-evolutionary algorithm that solves complex problems needs to deal with the issue of computational complexity. We show how a neural network (feed-forward, recurrent or RBF) can be transformed and then compiled in order to achieve fast execution speeds without requiring dedicated hardware like FPGAs. The compiled network uses a simple external data structure-a vector-for its parameters. This allows the weights of the neural network to be optimised by the evolutionary process without the need to re-compile the structure. In an experimental comparison our method effects a speedup of factor 5-10 compared to the standard method of evaluation (i.e., traversing a data structure with optimised C++ code).