I think that this is a better de-randomization: Presumably there is some set S of numbers that have been observed to pass Diehard tests reliably. The de-randomized algorithm is simply to read off S. Computationally, this is cheaper than generating your own random set and reading it off.
ETA: And, if I’m not mistaken, if S has already been observed to pass Diehard tests reliably, then it is even more likely to pass them in the future than is a newly generated random set.
I think that this is a better de-randomization: Presumably there is some set S of numbers that have been observed to pass Diehard tests reliably. The de-randomized algorithm is simply to read off S. Computationally, this is cheaper than generating your own random set and reading it off.
ETA: And, if I’m not mistaken, if S has already been observed to pass Diehard tests reliably, then it is even more likely to pass them in the future than is a newly generated random set.