SAT Solving + Turing Machines

Question

I have a couple of questions based on how SAT solvers work. I understand that SAT solvers may employ any/all of the following techniques:

• Randomness
• Heuristics
• Backtracking

SAT is just one example of an NP-complete problem. Is it common to use such techniques in conjunction to solve intractable problems? I normally see approaches to solving difficult problems in sections, like probabilistic algorithms or parallelism, but what stops them from being employed simultaneously?

Further to this, how is randomness achieved on a Turing machine? I mean by this pseudo-randomness, so is this possible on a Turing machine?

This is a slightly more specific question, but heuristic algorithms will have different meanings based on the problem. So if someone were to make a polynomial-time solver for another NP-complete problem like clique, based on heuristics, how would this also make SAT in P? Basically, certain methods of solving certain NP-complete problems will undoubtedly differ. How can this all at the end of the day reduce to the same problem? Can methods of solving an NP-complete problem be reduced to others?