title: Random number generation with C++ or Python
body: <p>I heard that computation results can be very sensitive to choice of random number generator.</p>
  
  <p>1   I wonder whether it is relevant to program own Mersenne-Twister or other pseudo-random routines to get a good number generator. Also, I don't see why I should not trust native or library generators as random.uniform() in numpy, rand() in C++. I understand that I can build generators on my own for distributions other than uniform (inverse repartition function methor, polar method). But is it evil to use one built-in generator for uniform sampling?</p>
  
  <p>2   What is wrong with the default 'time' seed? Should one re-seed and how frequently in a code sample (and why)?</p>
  
  <p>3   Maybe you have some good links on these topics!</p>
  
  <p>--edit More precisely, I need random numbers for multistart optimization routines, and for uniform space sample to initialize some other optimization routine parameters. I also need random numbers for Monte Carlo methods (sensibility analysis). I hope the precisions help figure out the scope of question.</p>