In fact, this is a good source of random data. However, because it is a good source, it could easily be bad for Monte-Carlo searching. As I've been suggesting in other posts, Monte-Carlo searching benefits from uniform-distributed numbers.

But truely random numbers are not statistically garunteed to be uniformly distributed (no, the law of averages does not work that way :) ), and so they cause Monte-Carlo searching to converge more slowly (but do not keep it from converging).

This is why psuedo-random numbers can, theoretically, be better than truely-random numbers, because often they are crafted to be uniformly distributed -- statistically. However, it often occurs in practice that pseudo-random numbers are not perfectly statistically uniformly distributed (what a mouthful), and so can easily lead to a mis-convergance.

Enter Quasi-random numbers. These are uniformly-distributed and have a bias towards non-repetition. This means that you still get a garunteed convergence and you get it faster (since there would be no clumps in your set).

Ok, time to go.

Ciao,
Gryn

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