Question

I’m working on an assignment and I need to evolve the weights of my neural network.
My neural network is work but I’m unsure of how to evolve my network in a way that will get me good results.

I know my AI teacher said I need to use a sigmoid function and add up my weights*inputs but I’m not exactly sure on the rest.

Thanks.

Edit:
I need to use a GA to train the weights. Sorry I didn’t make it clear.

Answer 1

There are any number of ways to do this, and generally one is not (for homework) just told to go make it happen without being given an algorithm to implement.

One of the common methods taught in an AI or neural networks class is backpropagation:

http://en.wikipedia.org/wiki/Backpropagation

UPDATE: Oh, I see. Now I can at least point you in the proper direction. The discussion is a bit long to provide in the answer space on stackoverflow, but the basic idea is to generate a bunch of random neural networks to (very badly!) solve your problem, then apply genetic algorithms to the networks (i.e., convert the neural networks to chromosomes that can be mutated, crossed-over/recombined, etc, according to their fitness) and let the whole system bootstrap itself out of the primordial ooze. So to speak.

There is a very good paper about one particular application (chess) written by Fogel, et al, here: http://www.aics-research.com/ieee-chess-fogel.pdf