Automated Discovery of Composite SAT Variable Selection Heuristics

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{fukunaga:2002:AAAI,

• author = "Alex Fukunaga",
• title = "Automated Discovery of Composite SAT Variable Selection Heuristics",
• booktitle = "Proceedings of the National Conference on Artificial Intelligence (AAAI)",
• year = "2002",
• pages = "641--648",
• keywords = "genetic algorithms, genetic programming, satisfiability, constraint satisfaction, local search",
• URL = "http://citeseer.nj.nec.com/506523.html",
• URL = "http://www.bol.ucla.edu/~fukunaga/AAAI02.pdf",
• abstract = "Variants of GSAT and Walksat are among the most successful SAT local search algorithms. We show that several well-known SAT local search algorithms are the results of novel combinations of a set of variable selection primitives. We describe CLASS, an automated heuristic discovery system which generates new, effective variable selection heuristic functions using a simple composition operator. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty and R-Novelty . We also analyse the local search behaviour of the learned heuristics using the depth, mobility, and coverage metrics recently proposed by Schuurmans and Southey.",

}

Genetic Programming entries for Alex S Fukunaga

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