An Improved Satisfiable SAT Generator Based on Random Subgraph Isomorphism
An Improved Satisfiable SAT Generator Based on Random Subgraph Isomorphism
复制标题
基于随机子图同构的改进可满足SAT生成器
DOI：
--
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Calin Anton
中科院分区：
文献类型：
--
作者：
Calin Anton
本文介绍了可满足随机高次子图同构生成器（SRHD-SGI），它是可满足随机子图同构生成器（SR-SGI）的一个变种。我们使用直接编码将SRHD-SGI实例转换为可满足的SAT实例。我们提出的经验证据表明，新的模型保留了SAT编码的SR-SGI的主要特点：易-难-易模式的演变和指数增长的经验硬度。我们的实验表明，SAT编码SRHD-SGI实例的经验比他们的SR-SGI同行更难。因此，我们得出结论，SRHD-SGI是一个改进的可满足SAT实例生成器。
We introduce Satisfiable Random High Degree Subgraph Isomorphism Generator(SRHD-SGI), a variation of the Satisfiable Random Subgraph Isomorphism Generator (SR-SGI). We use the direct encoding to translate the SRHD-SGI instances into Satisfiable SAT instances. We present empirical evidence that the new model preserves the main characteristics of SAT encoded SR-SGI: easy-hard-easy pattern of evolution and exponential growth of empirical hardness. Our experiments indicate that SAT encoded SRHD-SGI instances are empirically harder than their SR-SGI counterparts. Therefore we conclude that SRHD-SGI is an improved generator of satisfiable SAT instances.