RNGs, quality of solutions and repeat tests

Hi guys. In my work I use random numbers a lot. All my runs start with
the same random seed (for consistency). Is this good practice? I am of
the belief that assuming your RNG is a good one, one random seed is as
good as any other (especially if you call the RNG many times). Of
course if your RNG is a bad one, the resulting sequence of random
numbers may have a significant bias depending on the seed.

Other researchers in the stochastic optimisation field sometimes
produce "averaged" results of optimisation runs with a selection of
random seeds, but seeing how my runs sometimes take a few days to
complete, obtaining a statistically significant sample is not
tractable.

I use the "Park & Miller with Bays-Durham shuffle" method.

Any comments would be welcome.

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