Evolving difficult SAT instances thanks to local search
Neural and Evolutionary Computing
2010-11-29 v1 Logic in Computer Science
Abstract
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.
Cite
@article{arxiv.1011.5866,
title = {Evolving difficult SAT instances thanks to local search},
author = {Olivier Bailleux},
journal= {arXiv preprint arXiv:1011.5866},
year = {2010}
}