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Users can run their applications or tasks in pervasive cloud environment in order to gain better execution efficiency and performance leveraging powerful computing and storage capacities of pervasive clouds through task migration. During task migration, there are possibly a number of conflicting objectives to be considered when making migration decisions, such as less energy consumption and quick response, in order to find an optimal migration path. In this paper, we propose a genetic algorithms- (GAs-) based approach which is effective in addressing multiobjective optimization problems. We have performed some preliminary evaluations of the proposed approach which shows quite promising results, using one of the classical genetic algorithms. 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