  Symbolic regression is a machine learning method with the goal to produce
interpretable results. Unlike other machine learning methods such as, e.g.
random forests or neural networks, which are opaque, symbolic regression aims
to model and map data in a way that can be understood by scientists. Recent
advancements, have attempted to bridge the gap between these two fields; new
methodologies attempt to fuse the mapping power of neural networks and deep
learning techniques with the explanatory power of symbolic regression. In this
paper, we examine these new emerging systems and test the performance of an
end-to-end transformer model for symbolic regression versus the reigning
traditional methods based on genetic programming that have spearheaded symbolic
regression throughout the years. We compare these systems on novel datasets to
avoid bias to older methods who were improved on well-known benchmark datasets.
Our results show that traditional GP methods as implemented e.g., by Operon
still remain superior to two recently published symbolic regression methods.
