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2008

Automated Discovery of Local Search Heuristics for Satisfiability Testing

8 years 4 months ago
Automated Discovery of Local Search Heuristics for Satisfiability Testing
The development of successful metaheuristic algorithms such as local search for a difficult problems such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics. for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyze ...
Alex S. Fukunaga
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2008
Where EC
Authors Alex S. Fukunaga
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