Many hard examples for resolution
Journal of the ACM (JACM)
Threshold values of random K-SAT from the cavity method
Random Structures & Algorithms
Using Cost Distributions to Guide Weight Decay in Local Search for SAT
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Automatic algorithm configuration based on local search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
Towards industrial-like random SAT instances
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
SATenstein: automatically building local search SAT solvers from components
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
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
Diversification and determinism in local search for satisfiability
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Random walk with continuously smoothed variable weights
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Improving stochastic local search for SAT with a new probability distribution
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
Dynamic scoring functions with variable expressions: new SLS methods for solving SAT
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
An exploration-exploitation compromise-based adaptive operator selection for local search
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Choosing probability distributions for stochastic local search and the role of make versus break
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Off the trail: re-examining the CDCL algorithm
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Quantifying homogeneity of instance sets for algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
A method to avoid duplicative flipping in local search for SAT
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
From sequential to parallel local search for SAT
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Local search for Boolean Satisfiability with configuration checking and subscore
Artificial Intelligence
Comprehensive score: towards efficient local search for SAT with long clauses
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Weight-enhanced diversification in stochastic local search for satisfiability
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Stochastic local search (SLS) methods are well known for their ability to find models of randomly generated instances of the propositional satisfiability problem (SAT) very effectively. Two well-known SLS-based SAT solvers are SPARROW, one of the best-performing solvers for random 3-SAT instances, and VE-SAMPLER, which achieved significant performance improvements over previous SLS solvers on SAT-encoded software verification problems. Here, we introduce a new highly parametric algorithm, CAPTAIN JACK, which extends the parameter space of SPARROW to incorporate elements from VE-SAMPLER and introduces new variable selection heuristics. CAPTAIN JACK has a rich design space and can be configured automatically to perform well on various types of SAT instances. We demonstrate that the design space of CAPTAIN JACK is easy to interpret and thus facilitates valuable insight into the configurations automatically optimized for different instance sets. We provide evidence that CAPTAIN JACK can outperform well-known SLS-based SAT solvers on uniform random k-SAT and 'industrial-like' random instances.