Tabu search for nonlinear and parametric optimization (with links to genetic algorithms)
Discrete Applied Mathematics - Special volume: viewpoints on optimization
Genetic algorithms and tabu search: hybrids for optimization
Computers and Operations Research - Special issue on genetic algorithms
An Experimental Evaluation of a Scatter Search for the Linear Ordering Problem
Journal of Global Optimization
Randomness in Heuristics: An Experimental Investigation for the Maximum Satisfiability Problem
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
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
Representations, Fitness Functions and Genetic Operators for the Satisfiability Problem
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
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
Solving weighted Max-Sat optimization problems using a Taboo Scatter Search metaheuristic
Proceedings of the 2004 ACM symposium on Applied computing
A selective approach to parallelise Bees Swarm Optimisation metaheuristic: application to MAX-W-SAT
International Journal of Innovative Computing and Applications
Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework
Genetic Programming and Evolvable Machines
Average-case analysis for the MAX-2SAT problem
Theoretical Computer Science
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Designing a phenotypic distance index for radial basis function neural networks
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
ABSO: advanced bee swarm optimization metaheuristic and application to weighted MAX-SAT problem
BI'11 Proceedings of the 2011 international conference on Brain informatics
Efficient and experimental meta-heuristics for MAX-SAT problems
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
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The recent evolutionary approach called scatter search is studied for solving the satisfiability problem designated by SAT and its weighted version MAX-W-SAT. It is a population-based meta-heuristic founded on a formulation proposed two decades ago by Fred Glover. It uses linear combination on a population subset to create new solutions while other evolutionary approaches like genetic algorithms resort to randomization.First we propose a scatter search algorithm for SAT and MAX-W-SAT, namely SS-SAT. We present a procedure to generate good scattered initial solutions, a combination operator and a technique for improving the solutions quality. The method is tested and various experimental results show that SS-SAT performs better than or as well as GRASP for most benchmark problems.Secondly, we augment scatter search with the random walk strategy and compare its performance to the standard version. It appears that the added strategy does not lead to increased performance.