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A new algorithm for solving maximum satisfiability (MAX-SAT) problems is introduced which clusters good solutions, and restarts the search from the closest feasible solution to the centroid of each cluster. This is shown to be highly efficient for finding good solutions of large MAX-SAT problems. We argue that this success is due to the population learning the large-scale structure of the fitness...
We introduce a BSP-based parallel simplex method algorithm and analyze its computational cost. Then we give some experimental results on a PC cluster and draw some conclusions.
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