means a practicable set of hyper-parameters requires researchers to have experience in training neural networks. However, the credibility of empirical values is weakened because of the lack of logical reasoning. In addition, experiences generally provide “workable” instead of “optimal” hyper-parameter sets.\
The “no free lunch theorem” suggests that the computational cost for any optimization problem is the same for all problems and no solution offers a shortcut [@wolpert97; @igel14].A feasible alternative for computational resources is the preliminary knowledge of experts, which is efficient in selecting influential parameters and narrowing down the search space. To save the rare resource of experts’ experience, automated machine learning (AutoML) has been proposed as a burgeoning technology to design and train neural networks automatically, at the cost of computational resources [@feurer15; @katz16; @bello17; @zoph16; @jin18].Hyper-parameter optimization (HPO) is an important component of AutoML in searching for optimum hyper-parameters for neural network structures and the model training process.\
Hyper-parameter refers to parameters that cannot be updated during the training of machine learning. They can be involved in building the structure of the model, such as the number of hidden layers and the activation function, or in determining the efficiency and accuracy of model training, such as 