Towards a characterization of the behaviour of stochastic local search algorithms for SAT

70Citations
Citations of this article
24Readers
Mendeley users who have this article in their library.

Abstract

Stochastic local search (SLS) algorithms have been successfully applied to hard combinatorial problems from different domains. Due to their inherent randomness, the run-time behaviour of these algorithms is characterized by a random variable. The detailed knowledge of the run-time distribution provides important information about the behaviour of SLS algorithms. In this paper we investigate the empirical run-time distributions for WalkSAT, one of the most powerful SLS algorithms for the Propositional Satisfiability Problem (SAT). Using statistical analysis techniques, we show that on hard Random-3-SAT problems, WalkSAT's run-time behaviour can be characterized by exponential distributions. This characterization can be generalized to various SLS algorithms for SAT and to encoded problems from other domains. This result also has a number of consequences which are of theoretical as well as practical interest. One of these is the fact that these algorithms can be easily parallelized such that optimal speedup is achieved for hard problem instances.

Cite

CITATION STYLE

APA

Hoos, H. H., & Stützle, T. (1999). Towards a characterization of the behaviour of stochastic local search algorithms for SAT. Artificial Intelligence, 112(1), 213–232. https://doi.org/10.1016/S0004-3702(99)00048-X

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free