Efficient local search for very large-scale satisfiability problems
ACM SIGART Bulletin
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Towards a characterisation of the behaviour of stochastic local search algorithms for SAT
Artificial Intelligence
Local search characteristics of incomplete SAT procedures
Artificial Intelligence
Local Search Algorithms for SAT: An Empirical Evaluation
Journal of Automated Reasoning
SAT-Encodings, Search Space Structure, and Local Search Performance
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Counting Models Using Connected Components
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Backbone fragility and the local search cost peak
Journal of Artificial Intelligence Research
When gravity fails: local search topology
Journal of Artificial Intelligence Research
The exponentiated subgradient algorithm for heuristic Boolean programming
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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
Clustering at the phase transition
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Understanding the role of noise in stochastic local search: Analysis and experiments
Artificial Intelligence
The crowding approach to niching in genetic algorithms
Evolutionary Computation
A generative power-law search tree model
Computers and Operations Research
Journal of Artificial Intelligence Research
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Journal of Automated Reasoning
Impact of censored sampling on the performance of restart strategies
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
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Stochastic Local Search (SLS) algorithms are amongst the most effective approaches for solving hard and large propositional satisfiability (SAT) problems. Prominent and successful SLS algorithms for SAT, including many members of the WalkSAT and GSAT families of algorithms, tend to show highly regular behaviour when applied to hard SAT instances: The run-time distributions (RTDs) of these algorithms are closely approximated by exponential distributions. The deeper reasons for this regular behaviour are, however, essentially unknown. In this study we show that there are hard problem instances, e.g., from the phase transition region of the widely studied class of Uniform Random 3-SAT instances, for which the RTDs for well-known SLS algorithms such as GWSAT or WalkSAT/SKC deviate substantially from exponential distributions. We investigate these irregular instances and show that the respective RTDs can be modelled using mixtures of exponential distributions. We present evidence that such mixture distributions reflect stagnation behaviour in the search process caused by "traps" in the underlying search spaces. This leads to the formulation of a new model of SLS behaviour as a simple Markov process. This model subsumes and extends earlier characterisations of SLS behaviour and provides plausible explanations for many empirical observations.