Is there a rule of thumb (or set of examples) to determine when to use genetic algorithms as opposed to neural networks (and vice-versa) to solve a problem?
I know there are cases in which you can have both methods mixed, but I am looking for a high-level comparison between the two methods.
GAs generate new patterns in a structure that you define.
NNs classify (or recognize) existing patterns based on training data that you provide.
GAs perform well at efficiently searching a large state-space of solutions, and converging on one or more good solutions, but not necessarily the 'best' solution.
NNs can learn to recognize patterns (via training), but it is notoriously difficult to figure out what they have learned, i.e. to extract the knowledge from them once trained, and reuse the knowledge in some other (non-NN).