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Evolving difficult SAT instances thanks to local search

We propose to use local search algorithms to produce SAT instances which are harder to solve than randomly generated k-CNF formulae. The first results, obtained with rudimentary search algorithms, show that the approach deserves further study. It could be used as a test of robustness for SAT solvers, and could help to investigate how branching heuristics, learning strategies, and other aspects of solvers impact there robustness.

preprint2010arXivOpen access
Olivier Bailleux
Logic in Computer ScienceNeural and Evolutionary Computing
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Olivier Bailleux

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