Abstract
Few instances of a computational problem are sui generis; most instead belong to some distribution of related instances, and information gained from solving past instances from the distribution may be leveraged to solve future instances more efficiently. Algorithm portfolio methods and algorithm synthesis systems are two examples of this idea. This paper proposes and demonstrates a third approach.
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References
Hoos, H.H., Stützle, T.: SATLIB: An online resource for research on SAT (2000), http://www.cs.ubc.ca/~hoos/SATLIB/benchm.html
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: An automatic algorithm configuration framework. JAIR (2009)
KhudaBukhsh, A., Xu, L., Hoos, H., Leyton-Brown, K.: SATenstein: Automatically building local search sat solvers from components. In: IJCAI (2009)
Wei, W., Li, C.M.: Switching between two adaptive noise mechanisms in local search for SAT. In: SAT 2009 Competitive Events Booklet (2009)
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Silverthorn, B., Miikkulainen, R. (2011). Learning Polarity from Structure in SAT. In: Sakallah, K.A., Simon, L. (eds) Theory and Applications of Satisfiability Testing - SAT 2011. SAT 2011. Lecture Notes in Computer Science, vol 6695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21581-0_37
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DOI: https://doi.org/10.1007/978-3-642-21581-0_37
Publisher Name: Springer, Berlin, Heidelberg
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