And if you don't have time, just go to the bullet point list at the end; that's all of the best practices, and they are fantastic.
Something often forgotten here: if your PRNG only takes e.g. a 32-bit seed, you can generate at most 2^32 unique objects. Which you might chew through in seconds of fuzzing.
Edit: this is addressed later in the article/in a reference where they talk about using an exhaustive implementation of a PRNG interface. Neat!
More specifically: if you uniformly sample from a space of size N, then in O(N log N) tries you can expect to sample every point in the space. There's a logarithmic cost to this random sampling, but that's not too bad.
The keyword to look up more details is "coupon collector's problem".
You could _implement_ non-determinism via probabilistic sampling, but you could also implement the same interface as exhaustive search.
An example is something like "pairwise testing" of arguments to a function. Just randomly generating values will hit all possible pairs of values to arguments, again with a logarithmic penalty.
