I've got an experiment using this exact technique running on a machine right now, but I wouldn't argue that it is a good alternative for an actual population.
The reason I attempted this is because snapshotting a candidate is an unbelievably cheap operation. You don't have to allocate memory once the snapshot buffer is in place. It's just a bunch of low-level memory block copy operations. I can achieve several million iterations per candidate per hour with this hack, but only at the expense of maintaining a large population.
My conclusions so far are that it is interesting to use a snapshot/restore approach, but it seems to only take you into local minima faster than everything else. Real population diversity is an information theory thing that will implicate memory bandwidth, et. al., and slow things down by 3-4 OOM from where we'd prefer to be.
I think I have seen research papers addressing the crossover of neural network optimization and EA, but I think direct optimization techniques have been preferred for managing the actual weights. Manipulating the input data is more along the lines of perturbation methods. If you look hard enough you can probably see whatever you want but it's a stretch.
To me, neural network training is a distinct task that draws from many areas of optimization and does not fit neatly under any one umbrella.