Abstract
In this paper we present an empirical study of the Negative Slope Coefficient (NSC) hardness statistic to characterize the difficulty of 3-SAT fitness landscapes for randomly generated problem instances. NSC correctly classifies problem instances with a low ratio of clauses to variables as easy, while instances with a ratio close to the critical point are classified as hard, as expected. Together with previous results on many different problems and fitness landscapes, the present results confirm that NSC is a useful and reliable indicator of problem difficulty. © 2008 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Tomassini, M., & Vanneschi, L. (2008). Negative Slope coefficient and the difficulty of random 3-SAT instances. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4974 LNCS, pp. 643–648). https://doi.org/10.1007/978-3-540-78761-7_70
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