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. © 2011 Springer-Verlag.
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CITATION STYLE
Silverthorn, B., & Miikkulainen, R. (2011). Learning polarity from structure in SAT. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6695 LNCS, pp. 377–378). https://doi.org/10.1007/978-3-642-21581-0_37
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