I am developing an application to run a genetic algorithm over the input characteristics of a neural network. I am currently looking for help finding a good "genome" to use along with good example problems that might yield interesting results, problems that must have certain types of neural network characteristics to solve.
Currently my genome is pretty sparse and it only consists of altering the networks transfer function along with its hidden layer depth and the widths of each hidden layer. The transfer functions range from
Linear, ramp, step, sigmoid, tanh, guassian, trapezoid, sgn, sin, and log
and the range of the hidden layers count and widths are configurable to whatever is desired.
My current training data is simply the XOR function. I am looking for functions that will test my current genome and suggestions on possible additions to the genome.