Genetic algorithms are (and were already back in 2000) a pretty decent and -more importantly- generic solution to the problem of global optimization (as opposed to local optimization) when the problem to optimize has some sort of (maybe not-so-smooth) structure.

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.