General-purpose hierarchical optimisation of machine learning pipelines with grammatical evolution

This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt has been designed to provide a flexible framework where a researcher can define the space of pos-sible pipelines to solve a specific machine learning problem, which can range from highlevel decisions about representation and features to low-level hyper-parameter values. The evaluation of HML-Opt is presented via two different case studies, both of which demonstrate that it is competitive with existing AutoML tools on a variety of benchmarks. Furthermore, HML-Opt can be applied to novel problems, such as knowledge extraction from natural language text, whereas other techniques are insufficiently flexible to capture the complexity of these scenarios. The source code for HML-Opt is available online for the research community. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

HML-Opt is an AutoML framework based on probabilistic grammatical evolution that provides a flexible solution space for machine learning problems. Two case studies demonstrate its competitiveness in various benchmarks and its applicability to novel problems such as knowledge extraction from natural language text.