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State-of-the-art synthesis methods for microwave passive components suffer from the following drawbacks. They either have good efficiency but highly depend on the accuracy of the equivalent circuit models, which may fail the synthesis when the frequency is high, or they fully depend on electromagnetic (EM) simulations, with a high solution quality but are too time consuming. To address the problem of combining high solution quality and good efficiency, a new method, called memetic machine learning-based differential evolution (MMLDE), is presented. The key idea of MMLDE is the proposed online surrogate model-based memetic evolutionary optimization mechanism, whose training data are generated adaptively in the optimization process. In particular, by using the differential evolution algorithm as the optimization kernel and EM simulation as the performance evaluation method, high-quality solutions can be obtained. By using Gaussian process and artificial neural network in the proposed search mechanism, surrogate models are constructed online to predict the performances, saving a lot of expensive EM simulations. Compared with available methods with the best solution quality, MMLDE can obtain comparable results, and has approximately a tenfold improvement in computational efficiency, which makes the computational time for optimized component synthesis acceptable. Moreover, unlike many available methods, MMLDE does not need any equivalent circuit models or any coarse-mesh EM models. Experiments of 60 GHz syntheses and comparisons with the state-of-art methods provide evidence of the important advantages of MMLDE.