If you want to know whether two sequences are outputs of the same computer program with different seeds, then I doubt you can do much better than enumerating all computer programs and trying them. This can't be done in practice. Some preudo-random number generators work reasonably well with one seed, and terribly with other seeds, which means the same algorithm with different seeds can produce either a sequence which looks random or Bach's suites for solo cello.

Perhaps you want the idea of a [randomness test][1], such as [Marsaglia's Diehard tests][2]. The reason there are so many, including many silly tests, is that pseudo-random numbers which appear uniform often fail catastrophically when they are used to generate a sequence of pseudo-random numbers.

Another possible interpretation is to say that you have a particular distribution, perhaps the uniform distribution. You can test whether a sample is consistent with that distribution, e.g., with the [Pearson chi-squared test][3]. If you have two samples, you can estimate a distribution from the first sample and test whether the second sample is consistent with this. Since you talk about identifying when you switch from one generator to another in a sequence, you may want to look at what is called "structural change" or ["change points"][4] in statistics. There are [automated tests][5] for this which assume that the generators are not even uniform.


  [1]: http://en.wikipedia.org/wiki/Randomness_tests
  [2]: http://en.wikipedia.org/wiki/Diehard_tests
  [3]: http://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test
  [4]: http://stats.stackexchange.com/questions/tagged/change-point
  [5]: http://cran.r-project.org/web/packages/strucchange/index.html