Systematic vs. local search for SAT

被引:0
|
作者
Hoos, HH
Stützle, T
机构
[1] Univ British Columbia, Dept Comp Sci, Vancouver, BC V6T 1Z4, Canada
[2] Free Univ Brussels, IRIDIA, B-1050 Brussels, Belgium
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Traditionally, the propositional satisfiability problem (SAT) was attacked with systematic search algorithms, but more recently, local search methods were shown to be very effective for solving large and hard SAT instances. Generally, it is not well understood which type of algorithm performs best on a specific type of SAT instances. Here, we present results of a comprehensive empirical study, comparing the performance of some of the best performing stochastic local search and systematic search algorithms for SAT on a wide range of problem instances. Our experimental results suggest that, considering the specific strengths and rveaknesses of both approaches, hybrid algorithms or portfolio combinations might be most effective for solving SAT problems in practice.
引用
收藏
页码:289 / 293
页数:5
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