Hybrid Computational Intelligence Schemes in Complex Domains: An Extended Review
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Advanced models of cellular genetic algorithms evaluated on SAT
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolutionary dynamics on scale-free interaction networks
IEEE Transactions on Evolutionary Computation
OEA_SAT: an organizational evolutionary algorithm for solving satisfiability problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A multiagent evolutionary algorithm for combinatorial optimization problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Analyzing radar emitter signals with membrane algorithms
Mathematical and Computer Modelling: An International Journal
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A parallel hybrid method for solving the satisfiability (SAT) problem that combines cellular genetic algorithms (GAs) and the random walk SAT (WSAT) strategy of greedy SAT (GSAT) is presented. The method, called cellular genetic WSAT (CGWSAT), uses a cellular GA to perform a global search from a random initial population of candidate solutions and a local selective generation of new strings. The global search is then specialized in local search by adopting the WSAT strategy. A main characteristic of the method is that it indirectly provides a parallel implementation of WSAT when the probability of crossover is set to zero. CGWSAT has been implemented on a Meiko CS-2 parallel machine using a 2D cellular automaton as a parallel computation model. The algorithm has been tested on randomly generated problems and some classes of problems from the DIMACS and SATLIB test set