Ok, we do have to make the training set somewhat similar to the kind of problems the optimizer will encounter in the future. But if we have enough variety in the training set, then the only way to score well should be to use very general optimization techniques. It is not meant to work on "any set of algorithms"; it's specialized for real-world practical problems, which should be good enough.