Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Morphing: combining structure and randomness
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Local Search Algorithms for SAT: An Empirical Evaluation
Journal of Automated Reasoning
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Algorithm portfolio design: theory vs. practice
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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Traditionally, the propositional satisfiability problem (SAT) was attacked with systematic search algorithms, but more recently, local search methods were shown to be very effective for solving large and hard SAT instances. Generally, it is not well understood which type of algorithm performs best on a specific type of SAT instances. Here, we present results of a comprehensive empirical study, comparing the performance of some of the best performing stochastic local search and systematic search algorithms for SAT on a wide range of problem instances. Our experimental results suggest that, considering the specific strengths and weaknesses of both approaches, hybrid algorithms or portfolio combinations might be most effective for solving SAT problems in practice.