A logical framework for depiction and image interpretation
Artificial Intelligence
Efficient local search for very large-scale satisfiability problems
ACM SIGART Bulletin
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
EVIDENCE FOR A SATISFIABILITY THRESHOLD FOR RANDOM 3CNF FORMULAS
EVIDENCE FOR A SATISFIABILITY THRESHOLD FOR RANDOM 3CNF FORMULAS
Quantum cooperative search algorithm for 3-SAT
Journal of Computer and System Sciences
Local search with edge weighting and configuration checking heuristics for minimum vertex cover
Artificial Intelligence
Adaptive iterated local search for cross-domain optimisation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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
Resolution tunnels for improved SAT solver performance
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
Random walk with continuously smoothed variable weights
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Constraint metrics for local search
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Using bees to solve a data-mining problem expressed as a max-sat one
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Longer-Term memory in clause weighting local search for SAT
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
GRASP with path-relinking for the weighted maximum satisfiability problem
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
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
On the quality and quantity of random decisions in stochastic local search for SAT
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
A multilevel memetic algorithm for large sat-encoded problems
Evolutionary Computation
Stochastic Learning for SAT-Encoded Graph Coloring Problems
International Journal of Applied Metaheuristic Computing
Using cross-entropy for satisfiability
Proceedings of the 28th Annual ACM Symposium on Applied Computing
A survey of the satisfiability-problems solving algorithms
International Journal of Advanced Intelligence Paradigms
Local search for Boolean Satisfiability with configuration checking and subscore
Artificial Intelligence
Using automatic programming to generate state-of-the-art algorithms for random 3-SAT
Journal of Heuristics
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Recently several local hill-climbing procedures for propositional satisfiability have been proposed which are able to solve large and difficult problems beyond the reach of conventional algorithms like Davis-Putnam. By the introduction of some new variants of these procedures, we provide strong experimental evidence to support our conjecture that neither greediness nor randomness is important in these procedures. One of the variants introduced seems to offer significant improvements over earlier procedures. In addition, we investigate experimentally how performance depends on their parameters. Our results suggest that runtime scales less than simply exponentially in the problem size.