Evolving Local Search Heuristics for SAT Using Genetic Programming

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).

• Type: Conference
• Year: 2004
• Where: GECCO
• Authors: Alex S. Fukunaga
• Keywords: GECCO 2004, Local Search Algorithms, Local Search Heuristics, Numerous Real-world Applications