I'd never heard of GJK before, but as I remember it, there's a lot of mileage to be gained in computational geometry from applying these kinds of "shape algebra" options in different combinations. And there are plenty of problems that can be adapted to computational geometry formulations by working in the state space.

I think deep learning draws on the same pool of concepts as well.