Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Guided Local Search — an Illustrative Example in Function Optimisation
BT Technology Journal
A Discrete Lagrangian-Based Global-SearchMethod for Solving Satisfiability Problems
Journal of Global Optimization
Global Optimization for Satisfiability (SAT) Problem
IEEE Transactions on Knowledge and Data Engineering
A Two Phase Algorithm for Solving a Class of Hard Satisfiability Problems
A Two Phase Algorithm for Solving a Class of Hard Satisfiability Problems
Global search methods for solving nonlinear optimization problems
Global search methods for solving nonlinear optimization problems
When gravity fails: local search topology
Journal of Artificial Intelligence Research
Ten challenges in propositional reasoning and search
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Performance test of local search algorithms using new types of random CNF formulas
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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
Adding new clauses for faster local search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Weighting for godot: learning heuristics for GSAT
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Two Level Local Search for MAX-SAT Problems with Hard and Soft Constraints
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Using weighted MAX-SAT engines to solve MPE
Eighteenth national conference on Artificial intelligence
Propositional Satisfiability and Constraint Programming: A comparative survey
ACM Computing Surveys (CSUR)
A search agent for a Max-2sat memetic algorithm approach
ACACOS'08 Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science
Local search starting from an LP solution: Fast and quite good
Journal of Experimental Algorithmics (JEA)
MAX-2-SAT: how good is Tabu search in the worst-case?
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Memory intensive AND/OR search for combinatorial optimization in graphical models
Artificial Intelligence
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
Journal of Automated Reasoning
Generalization and property analysis of GENET
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Systematic vs. non-systematic algorithms for solving the MPE task
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Random walk with continuously smoothed variable weights
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Solving weighted MAX-SAT via global equilibrium search
Operations Research Letters
Agent-based guided local search
Expert Systems with Applications: An International Journal
Global equilibrium search algorithms for combinatorial optimization problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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In this paper, we show how Guided Local Search (GLS) can be applied to the SAT problem and show how the resulting algorithm can be naturally extended to solve the weighted MAX-SAT problem. GLS is a general, penalty-based meta-heuristic, which sits on top of local search algorithms to help guide them out of local minima. GLS has been shown to be successful in solving a number of practical real-life problems, such as the traveling salesman problem, BT"s workforce scheduling problem, the radio link frequency assignment problem, and the vehicle routing problem. We present empirical results of applying GLS to instances of the SAT problem from the DIMACS archive and also a small set of weighted MAX-SAT problem instances and compare them with the results of other local search algorithms for the SAT problem.