DarwinML: A Graph-based Evolutionary Algorithm for Automated Machine Learning

Abstract--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 graphbased architectureis 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. I. INTRODUCTION Various models have been thoroughly investigated by the machine learning (ML) community. In theory, these models are general and applicable to both academia and industry. However, it could be time-consuming to build a solution on a specific ML task, even for a ML expert.