I'm aiming to create a neural network that can learn to predict the next state of a board using the rules of Conway's Game of Life. This is technically three questions, but I felt that they needed to be together to get the full picture and I don't want to spam the Artificial Intelligence SE with new questions.
My network will look at each cell individually (to reduce computing power needed and to increase learning speed) and its surrounding cells. 9 input nodes for the network will go into one hidden layer. The output layer will be one node for the state of that cell in the next state of the game.
Nodes have two states (alive and dead) and connections between nodes can either transfer that value, or invert it.
For the learning part, I was going to make use of mutation and natural selection. The starting network will have the input and output layers with no hidden layer and no connections. My idea was then to introduce mutation by randomly generating a number of new networks by adding nodes and randomly connecting them to inputs and to the output. The number of nodes in the middle layer will be limited to 512, since there are 512 possible inputs, however I may reduce this if it is too slow. Should I also have it randomly delete nodes and connections, in case they also make improvements?
Each network will be tested on the same board state, and their accuracy will be calculated by comparing their output to a correct output generated by a computer program. The most accurate network will then be used for the next generation.
My issue is that I don't know how to program the nodes. Should the nodes in the hidden layer perform a logical AND on all of their inputs or an OR?
I know that the network won't learn the rules within the first few turns, but how do I know if it will ever get above 90% accuracy, or even just 50%?