Evolving Local Search Heuristics for SAT Using Genetic Programming
نویسنده
چکیده
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).
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