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Genetic Network Programming (GNP), one of the evolutionary computational methods, can generate behavior sequences of agents. In this paper, a new method named parallel GNP has been proposed and applied to functionally distributed systems consisted of several tasks. GNPs corresponding to several tasks in parallel GNP operate separately and independently but concurrently, dealing with the conflicts in task execution. Parallel GNP converges faster and has better fitness results than conventional GNP, which was shown by simulations comparing with conventional GNP on dynamic problems.