Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Local Search Algorithms for SAT: An Empirical Evaluation
Journal of Automated Reasoning
Parallel Execution of Stochastic Search Procedures on Reduced SAT Instances
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Criticality and Parallelism in Structured SAT Instances
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
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
Propositional Satisfiability and Constraint Programming: A comparative survey
ACM Computing Surveys (CSUR)
GridSAT: a system for solving satisfiability problems using a computational grid
Parallel Computing - Optimization on grids - Optimization for grids
Asynchronous Cooperative Local Search for the Office-Space-Allocation Problem
INFORMS Journal on Computing
Additive versus multiplicative clause weighting for SAT
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Domain-independent extensions to GSAT: solving large structured satisfiability problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Integrating systematic and local search paradigms: a new strategy for MaxSAT
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Combining adaptive noise and look-ahead in local search for SAT
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
A new method for solving hard satisfiability problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Solving the really hard problems with cooperative search
AAAI'93 Proceedings of the eleventh national conference on 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
Tradeoffs in the empirical evaluation of competing algorithm designs
Annals of Mathematics and 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
Diversification and determinism in local search for satisfiability
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
An overview of parallel SAT solving
Constraints
Towards massively parallel local search for SAT
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
From sequential to parallel local search for SAT
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
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In this work, our objective is to study the impact of knowledge sharing on the performance of portfolio-based parallel local search algorithms. Our work is motivated by the demonstrated importance of clause-sharing in the performance of complete parallel SAT solvers. Unlike complete solvers, state-of-the-art local search algorithms for SAT are not able to generate redundant clauses during their execution. In our settings, each member of the portfolio shares its best configuration (i.e., one which minimizes conflicting clauses) in a common structure. At each restart point, instead of classically generating a random configuration to start with, each algorithm aggregates the shared knowledge to carefully craft a new starting point. We present several aggregation strategies and evaluate them on a large set of problems.