Journal of Computer and System Sciences - 26th IEEE Conference on Foundations of Computer Science, October 21-23, 1985
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
On the complexity of local search
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
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)
Global Convergence of Genetic Algorithms: A Markov Chain Analysis
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Analysis of convergence properties of a stochastic evolution algorithm
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
sgen1: A generator of small but difficult satisfiability benchmarks
Journal of Experimental Algorithmics (JEA)
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Random Noise and Random Walk algorithms are local search strategies that have been used for the problem of satisfiability testing (SAT). We present a Markov-chain based analysis of the performance of these algorithms. The performance measures we consider are the probability of finding a satisfying assignment and the distribution of the best solution observed on a given SAT instance. The analysis provides exact statistics, but is restricted to small problems as it requires the storage and use of knowledge about the entire search space. We examine the effect of p, the probability of making non-greedy moves, on these algorithms and provide a justification for the practice of choosing this value empirically.