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In this paper, a genetic algorithm (GA) for scheduling tasks onto dynamically reconfigurable devices is presented. The scheduling problem is NP-hard and more complicated than multiprocessor scheduling, because both the task allocation and the configurations need to be carefully managed. The approach has been validated with a number of random task graphs. The results show that the GA approach has good convergence and it is in average 8.6% better than a list-based scheduler for large task graphs of various sizes.