On the run-time behaviour of stochastic local search algorithms for SAT
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
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
An adaptive noise mechanism for walkSAT
Eighteenth national conference on Artificial intelligence
Clause Weighting Local Search for SAT
Journal of Automated Reasoning
Scaling and probabilistic smoothing: dynamic local search for unweighted MAX-SAT
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in 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
Adaptive clause weight redistribution
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
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Many real-world problems are over-constrained and require search techniques adapted to optimising cost functions rather than searching for consistency. This makes theMAX-SAT problem an important area of research for the satisfiability (SAT) community. In this study we perform an empirical analysis of several of the best performing SAT local search techniques in the domain of unweighted MAX-SAT. In particular, we test two of the most recently developed SAT clause weight redistribution algorithms, DDFW and DDFW+, against three more well-known techniques (RSAPS, AdaptNovelty+ and PAWS). Based on an empirical study across a range of previously studied problems we conclude that DDFW is the most promising algorithm in terms of robust average performance.