Where are we now? A large benchmark study of recent symbolic regression methods
This provides a comprehensive comparison for researchers and practitioners interested in symbolic regression's practical utility, though it is incremental as it focuses on benchmarking existing methods.
The authors benchmarked four recent symbolic regression methods against nine machine learning approaches on nearly 100 regression problems, finding that symbolic regression performs competitively with state-of-the-art gradient boosting algorithms but is significantly slower.
In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches. We use a set of nearly 100 regression benchmark problems culled from open source repositories across the web. We conduct a rigorous benchmarking of four recent symbolic regression approaches as well as nine machine learning approaches from scikit-learn. The results suggest that symbolic regression performs strongly compared to state-of-the-art gradient boosting algorithms, although in terms of running times is among the slowest of the available methodologies. We discuss the results in detail and point to future research directions that may allow symbolic regression to gain wider adoption in the machine learning community.