Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
On the hardness of approximate reasoning
Artificial Intelligence
Approximating MAPs for belief networks is NP-hard and other theorems
Artificial Intelligence
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
A Markov Chain Analysis on A Genetic Algorithm
Proceedings of the 5th International Conference on Genetic Algorithms
Approximating MAP using Local Search
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A Bayesian Approach to Tackling Hard Computational Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Restart Policies with Dependence among Runs: A Dynamic Programming Approach
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
A mixture-model for the behaviour of SLS algorithms for SAT
Eighteenth national conference on Artificial intelligence
Using weighted MAX-SAT engines to solve MPE
Eighteenth national conference on Artificial intelligence
Stochastic Greedy Search: Efficiently Computing a Most Probable Explanation in Bayesian Networks
Stochastic Greedy Search: Efficiently Computing a Most Probable Explanation in Bayesian Networks
Efficient bayesian network inference: genetic algorithms, stochastic local search, and abstraction
Efficient bayesian network inference: genetic algorithms, stochastic local search, and abstraction
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Macroscopic models of clique tree growth for Bayesian networks
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Complexity results and approximation strategies for MAP explanations
Journal of Artificial Intelligence Research
Efficient stochastic local search for MPE solving
IJCAI'05 Proceedings of the 19th international joint 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
Tuning local search for satisfiability testing
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
IEEE Transactions on Knowledge and Data Engineering
Markov chain models of parallel genetic algorithms
IEEE Transactions on Evolutionary Computation
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
The crowding approach to niching in genetic algorithms
Evolutionary Computation
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Fundamenta Informaticae - Methodologies for Intelligent Systems
Understanding the scalability of Bayesian network inference using clique tree growth curves
Artificial Intelligence
Towards software health management with bayesian networks
Proceedings of the FSE/SDP workshop on Future of software engineering research
Journal of Automated Reasoning
An Empirical Analysis of Some Heuristic Features for Planning through Local Search and Action Graphs
Fundamenta Informaticae - RCRA 2009 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
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Stochastic local search (SLS) algorithms have recently been proven to be among the best approaches to solving computationally hard problems. SLS algorithms typically have a number of parameters, optimized empirically, that characterize and determine their performance. In this article, we focus on the noise parameter. The theoretical foundation of SLS, including an understanding of how to the optimal noise varies with problem difficulty, is lagging compared to the strong empirical results obtained using these algorithms. A purely empirical approach to understanding and optimizing SLS noise, as problem instances vary, can be very computationally intensive. To complement existing experimental results, we formulate and analyze several Markov chain models of SLS in this article. In particular, we compute expected hitting times and show that they are rational functions for individual problem instances as well as their mixtures. Expected hitting time curves are analytical counterparts to noise response curves reported in the experimental literature. Hitting time analysis using polynomials and convex functions is also discussed. In addition, we present examples and experimental results illustrating the impact of varying noise probability on SLS run time. In experiments, where most probable explanations in Bayesian networks are computed, we use synthetic problem instances as well as problem instances from applications. We believe that our results provide an improved theoretical understanding of the role of noise in stochastic local search, thereby providing a foundation for further progress in this area.