DMCA
Automated Discovery of Composite SAT Variable-Selection Heuristics (2002)
Cached
Download Links
- [www.bol.ucla.edu]
- [www.aaai.org]
- [www.aaai.org]
- DBLP
Other Repositories/Bibliography
| Citations: | 33 - 1 self |
BibTeX
@MISC{Fukunaga02automateddiscovery,
author = {Alex Fukunaga},
title = {Automated Discovery of Composite SAT Variable-Selection Heuristics},
year = {2002}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Variants of GSAT and Walksat are among the most successful SAT local search algorithms. We show that several well-known SAT local search algorithms are the results of novel combinations of a set of variable selection primitives. We describe CLASS, an automated heuristic discovery system which generates new, effective variable selection heuristic functions using a simple composition operator. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty and R-Novelty . We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics recently proposed by Schuurmans and Southey.
Keyphrases
automated discovery composite sat variable-selection heuristic variable selection primitive effective variable selection heuristic function coverage metric novel combination heuristic discovery system learned heuristic walksat variant new heuristic simple composition operator successful sat local search algorithm local search behavior
