For GA, you might try writing an "ant simulator". Basically, you get a 2D map with a bunch of ants and food on it, allow the ants to lay down pheremone trails, and use a genetic algorithm to 'tune' the ants to those who are best at surviving by e.g. exploring to find new food and following other ants' pheremone trails. You can have also combine techniques, like using a neural network to control each ant and using GA to find the weights for the neurons (instead of back-propogation or whatever). Basically you can play around and make whatever you want
Thank you for your reply. You gave me a great start for my first project. Basically it's the same idea as the ant simulator, minus the chemical trails for now. It slightly works, I'm having some trouble getting the creatures to eat the food they see, but I'm sure I can work it out. I'm mostly just proud to have gotten the "mating" to work properly.
One thing I'm really just befuddled on is the whole ideas of neural networks. I loved your suggestion of giving each creature its own "brain", so to speak. The problem is, the few articles and tutorials out there really don't make it clear enough how to utilize the idea. How do you make the creatures "think"? What exactly are you programming them to do? Remember where food commonly appears? Evaluate past paths and create new ones to follow? I'm just completely confused on that whole idea, so some clarification would be greatly appreciated.
One last question which was pointed out to me by my friend. I never questioned the idea of representing the genetical data using binary. What's the purpose? The only possibility I could think of was it makes it easier in the whole crossover/mutation process. Am I correct?
Other than that, thank you again for your reply.