Many of the recent "AI" development very often boil down to finding a local extremum using some sort of ski down the slope optimization program (aka "training"). These techniques very rarely tackles global optimization, or when they do, bundle it up under the moniker "hyper-parameter tuning".

A good example of something that falls under "global optimization" and isn't often tackled in deep learning would be finding the correct deep net architecture for a given problem.

The problem doesn't lend itself very well to local optimization, but might yield to GA-type optimization.

*a very limited one, imagine 2d, no inertia, 5 sensors at 15° each other each reporting distance from the nearest "grass", with the road 20px wide, drawn by hand gray on green and fitting the screen 13 space. network was 5x5 neuron, with weights all part of the gneetic code. car never made past the fourth corner, let alone did a lap.