An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML

An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML Benjamin Patrick Evans, Bing Xue, and Mengjie Zhang School of Engineering and Computer Science Victoria University of Wellington New Zealand { benjamin.evans,bing.xue,mengjie.zhang}@ecs.vuw.ac.nz Abstract A common claim of evolutionary computation methods is that they can achieve good results without the need for human intervention. However, one criticism of this is that there are still hyperparameters which must be tuned in order to achieve good performance. In this work, we propose a near "parameter-free" genetic programming approach, which adapts the hyperparameter values throughout evolution without ever needing to be specified manually. We apply this to the area of automated machine learning (by extending TPOT), to produce pipelines which can effectively be claimed to be free from human input, and show that the results are competitive with existing state-of-the-art which use hand-selected hyper-parameter values. Pipelines begin with a randomly chosen estimator and evolve to competitive pipelines automatically. This work moves towards a truly automatic approach to AutoML. 1 Introduction In recent years, machine learning has made its way into many application areas, which has attracted a wide variety of interest from many users from outside the machine learning world. This demand for machine learning has spurred the area of automated machine learning (AutoML), which aims to make machine learning accessible to non-experts [1], or allows experts to focus on other aspects of the machine learning process rather than pipeline design [2]. However, while two of the goals of AutoML are automation and ease of use, most current state-of-the-art methods become a new optimisation problem themselves: rather than searching for pipelines, one must search for appropriate 1 arXiv:2001.10178v1 Granted, this is a simpler search space than the original one, but is still an undesirable property and prevents true human-free automation.