IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Local search for statistical counting
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Encoding domain knowledge for prositional planning
Logic-based artificial intelligence
Guided Local Search for Solving SAT and Weighted MAX-SAT Problems
Journal of Automated Reasoning
Local Search Algorithms for SAT: An Empirical Evaluation
Journal of Automated Reasoning
IEEE Intelligent Systems
Problem difficulty for tabu search in job-shop scheduling
Artificial Intelligence
Measuring the Spatial Dispersion of Evolutionary Search Processes: Application to Walksat
Selected Papers from the 5th European Conference on Artificial Evolution
SAT, Local Search Dynamics and Density of States
Selected Papers from the 5th European Conference on Artificial Evolution
An adaptive noise mechanism for walkSAT
Eighteenth national conference on Artificial intelligence
A mixture-model for the behaviour of SLS algorithms for SAT
Eighteenth national conference on Artificial intelligence
SAT problems with chains of dependent variables
Discrete Applied Mathematics - The renesse issue on satisfiability
Bridging the gap between planning and scheduling
The Knowledge Engineering Review
Simulated annealing applied to test generation: landscape characterization and stopping criteria
Empirical Software Engineering
Discrete Applied Mathematics
Understanding elementary landscapes
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Estimating Bounds on Expected Plateau Size in MAXSAT Problems
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
A Theoretical Analysis of the k-Satisfiability Search Space
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Backbone fragility and the local search cost peak
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Where "Ignoring delete lists" works: local search topology in planning benchmarks
Journal of Artificial Intelligence Research
Engineering benchmarks for planning: the domains used in the deterministic part of IPC-4
Journal of Artificial Intelligence Research
Understanding algorithm performance on an oversubscribed scheduling application
Journal of Artificial Intelligence Research
SAT-encodings, search space structure, and local search performance
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Local search topology in planning benchmarks: an empirical analysis
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
The COMPSET algorithm for subset selection
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The road to VEGAS: guiding the search over neutral networks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Hill-climbing strategies on various landscapes: an empirical comparison
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Local search algorithms for combinatorial search problems frequently encounter a sequence of states in which it is impossible to improve the value of the objective function; moves through these regions, called plateau moves, dominate the time spent in local search. We analyze and characterize plateaus for three different classes of randomly generated Boolean Satisfiability problems. We identify several interesting features of plateaus that impact the performance of local search algorithms. We show that local minima tend to be small but occasionally may be very large. We also show that local minima can be escaped without unsatisfying a large number of clauses, but that systematically searching for an escape route may be computationally expensive if the local minimum is large. We show that plateaus with exits, called benches, tend to be much larger than minima, and that some benches have very few exit states which local search can use to escape. We show that the solutions (i.e., global minima) of randomly generated problem instances form clusters, which behave similarly to local minima. We revisit several enhancements of local search algorithms and explain their performance in light of our results. Finally we discuss strategies for creating the next generation of local search algorithms.