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Genetic algorithms, search algorithms based on the geneticprocesses observed in natural evolution, have been used to solvedifficult problems in many different disciplines. When applied to verylarge-scale problems, genetic algorithms exhibit high computational costand degradation of the quality of the solutions because of the increasedcomplexity. One of the most relevant research trends in geneticalgorithms is the implementation of parallel genetic algorithms with thegoal of obtaining quality of solutions efficiently. This paper firstreviews the state-of-the-art in parallel genetic algorithms.Parallelization strategies and emerging implementations are reviewed andrelevant results are discussed. Second, this paper discusses importantissues regarding scalability of parallel genetic algorithms.