Representations, Fitness Functions and Genetic Operators for the Satisfiability Problem

Two genetic algorithms for the satisfiability problem (SAT) are presented which mainly differ in the solution representation. We investigate these representations - the classical bit string representation and the path representation - with respect to their performance. We develop fitness functions which transform the traditional fitness landscape of SAT into more distinguishable ones. Furthermore, new genetic operators (mutation and crossover) are introduced. These genetic operators incorporate problem specific knowledge and thus, lead to increased performance in comparison to standard operators.