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.
@article{arxiv.1901.08013,
title = {DarwinML: A Graph-based Evolutionary Algorithm for Automated Machine Learning},
author = {Fei Qi and Zhaohui Xia and Gaoyang Tang and Hang Yang and Yu Song and Guangrui Qian and Xiong An and Chunhuan Lin and Guangming Shi},
journal= {arXiv preprint arXiv:1901.08013},
year = {2019}
}