Automated discovery of composite SAT variable-selection heuristics

  • Authors:

  • Alex Fukunaga

  • Affiliations:

  • Computer Science Department, University of California, Los Angeles

  • Venue:
  • Eighteenth national conference on Artificial intelligence
  • Year:
  • 2002

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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 result of novel combininations 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 analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics recently proposed by Schuurmans and Southey.