On selecting a satisfying truth assignment (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
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CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
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CHES '99 Proceedings of the First International Workshop on Cryptographic Hardware and Embedded Systems
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FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
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SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
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IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Paper: Robust taboo search for the quadratic assignment problem
Parallel Computing
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AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Tuning local search for satisfiability testing
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
UBCSAT: an implementation and experimentation environment for SLS algorithms for SAT and MAX-SAT
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
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SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
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Stochastic local search (SLS) methods are underlying some of the best-performing algorithms for certain types of SAT instances, both from an empirical as well as from a theoretical point of view. By definition and in practice, random decisions are an essential ingredient of SLS algorithms. In this paper we empirically analyse the role of randomness in these algorithms. We first study the effect of the quality of the underlying random number sequence on the behaviour of well-known algorithms such as Papadimitriou's algorithm and Adaptive Novelty+. Our results indicate that while extremely poor quality random number sequences can have a detrimental effect on the behaviour of these algorithms, there is no evidence that the use of standard pseudo-random number generators is problematic. We also investigate the amount of randomness required to achieve the typical behaviour of these algorithms using derandomisation. Our experimental results indicate that the performance of SLS algorithms for SAT is surprisingly robust with respect to the number of random decisions made by an algorithm.