An empirical comparison of two evolutionary methods for satisfiability problems

The paper compares two evolutionary methods for model finding in the satisfiability problem (SAT): genetic algorithms (GAs) and the mask method (MASK). The main characteristics of these two methods are that both of them are population-based, and use binary representation. Great care is taken to make sure that the same SAT instances and the same criteria are used in the comparison. Results indicate that MASK greatly outperforms GAs in the sense that MASK manages to deal with harder SAT instances at a lower cost