  As an emerging field, Automated Machine Learning (AutoML) aims to reduce or
eliminate manual operations that require expertise in machine learning. In this
paper, a graph-based architecture is employed to represent flexible
combinations of ML models, which provides a large searching space compared to
tree-based and stacking-based architectures. Based on this, an evolutionary
algorithm is proposed to search for the best architecture, where the mutation
and heredity operators are the key for architecture evolution. With Bayesian
hyper-parameter optimization, the proposed approach can automate the workflow
of machine learning. On the PMLB dataset, the proposed approach shows the
state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn.
Some of the optimized models are with complex structures which are difficult to
obtain in manual design.
