Are evolutionary algorithms and neural networks used in the same domains?

Question

I am trying to get a feel for the difference between the various classes of machine-learning algorithms.

I understand that the implementations of evolutionary algo

Answer 1

Look at Neuro Evolution. (NE)

The current best methods is NEAT and HyperNEAT by Kenneth Stanley.

Genetic Algorithms only find a genome of some sort; It's great to create the genome of a neural network, because you get the reactive nature of the neural network, rather than just a bunch of static genes.

There's not many limits to what it can learn. But it takes time of course. Neural topology have to be evolved through the usual mutation and crossover, as well as weights updated. There can be no back propagation.

Also you can train it with a fitness function, which is thus superior to back propagation when you do not know what the output should be. Perfect for learning complex behaviour for systems that you do not know any optimal strategies for. Only problem is that it'll learn behaviour you didn't anticipate. Often that behaviour can be very alien, although it does exactly what you rewarded it for in the fitness function. Thus you'll be using as much time deriving fitness functions as you would have creating output sets for backpropagation :P