A recent answer mentioned the use of Fortuna or Mersenne Twister Random Number Generators (RNGs) to seed a Monte Carlo simulation. I hadn't heard of Fortuna before so I looked it up - looks like it is mainly intended for cryptographic use.I currently use a Mersenne Twister in production code to seed a K-Means algorithm.Which (Fortuna or Mersenne Twister) is considered the best for algorithmic seeding applications (eg. seeding Monte Carlo and K-Means)? Or is it a toss up - i.e. use the most convenient.From where I am sitting, best should provide the highest quality random numbers, operate quickly, and (possibly) have a low memory footprint. Of these, quality is probably the most important for most of us.
Is Fortuna or Mersenne Twister preferable as an algorithmic RNG?
algorithms;random
Well, everything is a trade-off of some sort or another. For random number generators, I group them into 3 basic categories:  Good enough for homework.  Good enough to bet your company on.   Good enough to bet your country on.  Linear congruential PRNGs (the method generally implemented in most libraries) are solidly in category 1. Both Fortuna and Mersenne Twister are solidly in category 2. For an interesting article on how messing up a shuffling algorithm can cost you your company/casino, I recommend this one from 1999. Due to link rot, the images are gone, but figure 4, the one where you plot next number out of the PRNG against the previous number generated, is a set of parallel lines. As J.M. points out, Fortuna is slow. As you've pointed out, Mersenne Twister is reasonably fast. 