I studied the basics of learning ANNs with a genetic algorithm. I found out that there are 2 things you can do:

Use GA to design the structure of the net (determine whether there should be an edge between two neurons or not). I guess we assume we can only use a certain amount of neuron-to-neuron connections.

Use GA to calculate optimal weights.

I also learned that GA makes sense only in the case of irregular networks. If the net consists of layers it's suggested to use backpropagation as it's faster.

If backpropagation is faster and requires a network made of layers, why would I bother to choose GA for learning or designing the network?