Abstract
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.
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Anton, C. (2011). An Improved Satisfiable SAT Generator Based on Random Subgraph Isomorphism. In: Butz, C., Lingras, P. (eds) Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science(), vol 6657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_5
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DOI: https://doi.org/10.1007/978-3-642-21043-3_5
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