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Network morphism, which keeps the\nfunctionality of a neural network while changing its neural architecture, could\nbe helpful for NAS by enabling more efficient training during the search. In\nthis paper, we propose a novel framework enabling Bayesian optimization to\nguide the network morphism for efficient neural architecture search. The\nframework develops a neural network kernel and a tree-structured acquisition\nfunction optimization algorithm to efficiently explores the search space.\nIntensive experiments on real-world benchmark datasets have been done to\ndemonstrate the superior performance of the developed framework over the\nstate-of-the-art methods. Moreover, we build an open-source AutoML system based\non our method, namely Auto-Keras. 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