I am developing a NEAT flappy bird game, and it doesn't work, the system stays stupid for 300 generations. I chose tanh() for activation, just because it's included in JS.
I can't find a good discussion on the internet of activation functions in the context of neuroevolution, most of what I see is about derivative and other gradient descent issues which I suspect are irrelevant to forward only networks.
If you need a fixed point to answer, I have 8 inputs, one output and the problem is a classification ("jump", "don't jump"). But please explain your answer. I currently use tanh() for all the hidden and output nodes, and the output is considered "jump" if the output neuron value is >0.85