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Efficient initial solution to extremal optimization algorithm for weighted MAXSAT problem
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
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Phase Transition in the Bandwidth Minimization Problem
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
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MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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In this paper, tabu search for SAT is investigated from an experimental point of view. To this end, TSAT, a basic tabu search algonthm for SAT, is introduced and compared With Selman et al. Random Walk Strategy GSAT procedure, in short RWS-GSAT. TSAT does not involve the additional stochastic process of RWS-GSAT. This should facilitate the understanding of why simple local search methods for SAT work. It is shown that the length of the tabu list plays a critical role in the performance of the algorithm. Moreover surprising properties about the (experimental) optimal length of the tabu list are exhibited, raising interesting issues about the nature of hard random SAT problems.