Compiling neural networks for fast neuro-evolution
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. 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).