Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Generating Satisfiable Problem Instances
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on 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
Local Search Characteristics of Incomplete SAT Procedures
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
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
Balance and filtering in structured satisfiable problems
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
The breakout method for escaping from local minima
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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Clause weighting local search methods are widely used for satisfiability testing. A feature of particular importance for such methods is the scheme used to maintain the clause weight distribution relevant to different areas of the search landscape. Existing methods periodically adjust clause weights either multiplicatively or additively. Tie breaking strategies are used whenever a method’s evaluation function encounters more than one optimal candidate flip, with the dominant approach being to break such ties randomly. Although this is acceptable for multiplicative methods as they rarely encounter such situations, additive methods encounter significantly more tie breaking scenarios in their landscapes, and therefore a more refined tie breaking strategy is of much greater relevance. This paper proposes a new way of handling the tie breaking situations frequently encountered in the landscapes of additive constraint weighting local search methods. We demonstrate through an empirical study that when this idea is used to modify the purely random tie breaking strategy of a state-of-the-art solver, the modified method significantly outperforms the existing one on a range of benchmarks, especially when we consider the encodings of large and structured problems.