Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
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An introduction to genetic algorithms
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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
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Towards a characterisation of the behaviour of stochastic local search algorithms for SAT
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A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A Lagrangian reconstruction of GENET
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Chaff: engineering an efficient SAT solver
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An algorithm based on tabu search for satisfiability problem
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CVC: A Cooperating Validity Checker
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Enhancing Davis Putnam with extended binary clause reasoning
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Solving Hard Satisfiability Problems: A Unified Algorithm Based on Discrete Lagrange Multipliers
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
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A new incomplete method for CSP inconsistency checking
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A new clause learning scheme for efficient unsatisfiability proofs
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SATzilla: portfolio-based algorithm selection for SAT
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Probabilistic consistency boosts MAC and SAC
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A dynamic approach to MPE and weighted MAX-SAT
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
GUNSAT: a greedy local search algorithm for unsatisfiability
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The exponentiated subgradient algorithm for heuristic Boolean programming
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Artificial Intelligence: A Modern Approach
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CAV'07 Proceedings of the 19th international conference on Computer aided verification
Advances in local search for satisfiability
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
On multi-threaded satisfiability solving with OpenMP
IWOMP'08 Proceedings of the 4th international conference on OpenMP in a new era of parallelism
Speeding up Local Search By Using the Island Confinement Method
Speeding up Local Search By Using the Island Confinement Method
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
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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
UBCSAT: an implementation and experimentation environment for SLS algorithms for SAT and MAX-SAT
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
Effective preprocessing in SAT through variable and clause elimination
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Diversification and determinism in local search for satisfiability
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
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The satisfiability problem (SAT), as one of the six basic core NP-complete problems, has been the deserving object of many studies in the last two decades (Lardeux et al. 2005, 2006). GASAT (Lardeux et al. 2005, 2006; Hao et al. 2002) is one of the current state-of-the-art genetic algorithms for solving SATs. Besides, the discrete lagrange-multiplier (DLM) (Wu and Wah 1999a, b) is one of the current state-of-the-art local search algorithms for solving SATs. GASAT is a hybrid algorithm of the genetic and tabu search techniques. GASAT uses tabu search to avoid restarting the search once it converges. In this paper, we improve GASAT by replacing the tabu search by the DLM algorithm. We show that the performance of the new algorithm, DGASAT, is far better than the performance of GASAT in solving most of the benchmark instances. We further improve DGASAT by introducing the notion of improving one of the best members in the current population at a time. We show through experimentation that DGASAT + is far better than DGASAT in solving nearly all the benchmark instances.