Not that relevant, but wanted to elaborate a little: I exhaustively solved a toy version of my problem on a cluster, but found no discernable gradients. Good solutions were isolated spikes, with no elevations adjacent that could lead to them. So, random sampling would be as good as you'd get.
The thing to do is change the problem, transform the space/dimensions, so better solutions were spatially proximate. But then, I'd be solving the problem.
Another approach is to have the computer do this, seach the space of search spaces. But this higher-level space is even less likely to have informative gradients.
OTOH, the data was in terms of a language, which would have introduced its own artefacts. A better search space would compensate for those, and might have been easy to find.
The thing to do is change the problem, transform the space/dimensions, so better solutions were spatially proximate. But then, I'd be solving the problem.
Another approach is to have the computer do this, seach the space of search spaces. But this higher-level space is even less likely to have informative gradients.
OTOH, the data was in terms of a language, which would have introduced its own artefacts. A better search space would compensate for those, and might have been easy to find.