Abstract
Satisfiability testing (SAT) is a very active area of research today, with numerous real-world applications. We describe CLASS2.0, a genetic programming system for semi-automatically designing SAT local search heuristics. An empirical comparison shows that that the heuristics generated by our GP system outperform the state of the art human-designed local search algorithms, as well as previously proposed evolutionary approaches, with respect to both runtime as well as search efficiency (number of variable flips to solve a problem). © Springer-Verlag Berlin Heidelberg 2004.
Cite
CITATION STYLE
Fukunaga, A. S. (2004). Evolving local search heuristics for SAT using genetic programming. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3103, 483–494. https://doi.org/10.1007/978-3-540-24855-2_59
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