Evolving difficult SAT instances thanks to local search

Details Evolving difficult SAT instances thanks to local search Evolving difficult SAT instances thanks to local search

2010-11-26

arxiv.org/abs/1011.5866v1

Computer Science

Neural and Evolutionary Computing

Scientific paper

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.

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