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 algorithms are quite different from the implementations of neural networks.

However, they both seem to be geared at determining a correlation between inputs and outputs from a potentially noisy set of training/historical data.

From a qualitative perspective, are there problem domains that are better targets for neural networks as opposed to evolutionary algorithms?

I've skimmed some articles that suggest using them in a complementary fashion. Is there a decent example of a use case for that?

Thanks