Genetic algorithms, heuristic hill-climbing, simulated annealing, etc, etc, etc. These are all basically equivalent ways of tackling an optimization function that can only be point-wise queried efficiently, and they are all subject to same problems (most notably local optima, curse of dimensionality, etc.).

I've seen these used rather sporadically in academia, where people generally prefer to reformulate a difficult optimization problem as a convex (or similar) relaxation that is provably solvable, and then throwing solvers at it.

However, I get the impression that when you just need to get some reasonable solution quick and dirty in practice (especially with discrete domains), one of these is the method of choice. I'm not aware of some particularly good arguments for GA's specifically though.