GSAT versus Simulated Annealing

The question of satissability for a given proposi-tional formula arises in many areas of AI. Especially nding a model for a satissable formula is very important though known to be NP-complete. There exist complete algorithms for satissability testing like the Davis-Putnam-Algorithm, but they often do not construct a satisfying assignment for the formula , are not practically applicable for more than 400 or 500 variable problems, or in practice take too much time to nd a solution. Recently, a (in practice) very fast, though incomplete , procedure, the model generating algorithm GSAT, has been introduced and several reened variants were created. Another method is Simulated Annealing (SA). Both approaches have already been compared with diierent results. We clarify these diierences and do a more elaborate comparison showing that the performance of an already optimized variant of GSAT and an ordinary SA algorithm are more or less the same and that the attempts to further improve GSAT have lead to a procedure very similar to SA. Moreover we investigate how these algorithms can be parallelized.