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Paper detail

Evolved preambles for MAX-SAT heuristics

MAX-SAT heuristics normally operate from random initial truth assignments to the variables. We consider the use of what we call preambles, which are sequences of variables with corresponding single-variable assignment actions intended to be used to determine a more suitable initial truth assignment for a given problem instance and a given heuristic. For a number of well established MAX-SAT heuristics and benchmark instances, we demonstrate that preambles can be evolved by a genetic algorithm such that the heuristics are outperformed in a significant fraction of the cases.

preprint2011arXivOpen access
Luis O. Rigo JrValmir C. Barbosa
Neural and Evolutionary ComputingArtificial Intelligence
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Luis O. Rigo JrValmir C. Barbosa

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