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 ...

• Type: Journal
• Year: 2008
• Where: EC
• Authors: Alex S. Fukunaga
• Keywords: EC 2008, ECommerce, Local Search, Local Search Heuristics, SAT Local Search