Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
A machine program for theorem-proving
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Parallel GRASP for MAX-SAT Problems
PARA '96 Proceedings of the Third International Workshop on Applied Parallel Computing, Industrial Computation and Optimization
Scatter Search with Random Walk Strategy for SAT and MAX-W-SAT Problems
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Solving weighted Max-Sat optimization problems using a Taboo Scatter Search metaheuristic
Proceedings of the 2004 ACM symposium on Applied computing
Approximation algorithms for combinatorial problems
Journal of Computer and System Sciences
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
The impact of parametrization in memetic evolutionary algorithms
Theoretical Computer Science
Average-case analysis for the MAX-2SAT problem
Theoretical Computer Science
A multilevel memetic algorithm for large sat-encoded problems
Evolutionary Computation
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Many problems in combinatorial optimization are NP-Hard. This has forced researchers to explore meta-heuristic techniques for dealing with this class of complex problems and finding an acceptable solution in reasonable time. The satisfiability problem, SAT, is studied by a great number of researchers the three last decades. Its wide application to the domain of AI in automatic reasoning and problem solving for instance and other domains like VLSI and graph theory motivates the huge interest shown for this problem. In this paper, tabu search, scatter search, genetic algorithms and memetic evolutionary meta-heuristics are studied for the NP-Complete satisfiability problems, in particular for its optimization version namely MAX-SAT. Experiments comparing the proposed approaches for solving MAX-SAT problems are represented. The empirical tests are performed on DIMACS benchmark instances.