GSAT Enhanced with Learning Automata and Multilevel Paradigm
Noureddine Bouhmala, Ole‐Christoffer Granmo · 2011
A large number of problems that occur in knowledgerepresentation, learning, very large scale integration technology (VLSI-design), and other areas of artificial intelligence, are essentially satisfiability problems. The satisfiability problem refers to the task of finding a satisfying assignment that makes a Boolean expression evaluate to True. The growing need for more efficient and scalable algorithms has led to the development of a large number of SAT solvers. This paper introduces two new techniques that combine finite learning automata and multilevel paradigm with the Greedy Satisfiability Algorithm (GSAT). We present a detailed comparative analysis of the new approaches using a benchmark set containing randomized and practical engineering applications from various domains.