Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
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
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
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
UnitWalk: A New SAT Solver that Uses Local Search Guided by Unit Clause Elimination
Annals of Mathematics and Artificial 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
SATzilla-07: the design and analysis of an algorithm portfolio for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
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
Using CBR to select solution strategies in constraint programming
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Generating Satisfiable SAT Instances Using Random Subgraph Isomorphism
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Exploiting historical relationships of clauses and variables in local search for satisfiability
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
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
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One way to design a local search algorithm that is effective on many types of instances is allowing this algorithm to switch among heuristics. In this paper, we refer to the way in which non-weighting algorithm adaptG2WSAT+ selects a variable to flip, as heuristic adaptG2WSAT+, the way in which clause weighting algorithm RSAPSselects a variable to flip, as heuristic RSAPS, and the way in which variable weighting algorithm VWselects a variable to flip, as heuristic VW. We propose a new switching criterion: the evenness or unevenness of the distribution of clause weights. We apply this criterion, along with another switching criterion previously proposed, to heuristic adaptG2WSAT+, heuristic RSAPS, and heuristic VW. The resulting local search algorithm, which adaptively switches among these three heuristics in every search step according to these two criteria to intensify or diversify the search when necessary, is called NCVW(Non-, Clause, and Variable Weighting). Experimental results show that NCVWis generally effective on a wide range of instances while adaptG2WSAT+, RSAPS, VW, and gNovelty+ and adaptG2WSAT0, which won the gold and silver medals in the satisfiable random category in the SAT 2007 competition, respectively, are not.