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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Audemard, G., Jabbour, S., Sais, L.: SAT graph-based representation: A new perspective. J. Algorithms 63(1-3), 17–33 (2008)
Ansótegui, C., Béjar, R., Fernández, C., Mateu, C.: Generating hard SAT/CSP instances using expander graphs. In: Proc. AAAI 2008, pp. 1442–1443 (2008)
Xu, K., Boussemart, F., Hemery, F., Lecoutre, C.: A simple model to generate hard satisfiable instances. In: Proceedings of IJCAI 2005, pp. 337–342 (2005)
Achlioptas, D., Gomes, C., Kautz, H., Selman, B.: Generating satisfiable problem instances. In: Proceedings of AAAI 2000, pp. 256–261 (2000)
Anton, C., Olson, L.: Generating satisfiable SAT instances using random subgraph isomorphism. In: Gao, Y., Japkowicz, N. (eds.) AI 2009. LNCS, vol. 5549, pp. 16–26. Springer, Heidelberg (2009)
Culberson, J., Gao, Y., Anton, C.: Phase transitions of dominating clique problem and their implications to heuristics in satisfiability search. In: Proc. IJCAI 2005, pp. 78–83 (2005)
Anton, C., Neal, C.: Notes on generating satisfiable SAT instances using random subgraph isomorphism. In: Farzindar, A., Kešelj, V. (eds.) Canadian AI 2010. LNCS, vol. 6085, pp. 315–318. Springer, Heidelberg (2010)
Culberson, J.: Hidden solutions, tell-tales, heuristics and anti-heuristics. In: IJCAI 2001 Workshop on Empirical Methods in AI, pp. 9–14 (2001)
Bayardo, R., Schrag, R.: Using CSP look-back techniques to solve exceptionally hard SAT instances. In: Freuder, E.C. (ed.) CP 1996. LNCS, vol. 1118, pp. 46–60. Springer, Heidelberg (1996)
The international sat competitions web page, http://www.satcompetition.org
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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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