abstract = "For centuries, the process of formulating new
knowledge from observations has driven scientific
discoveries. With rapid advancements in machine
learning, it is natural to question the possibility of
automating knowledge discovery in the scientific field.
A benchmark task for automated knowledge discovery is
called symbolic regression. The task aims to predict a
mathematical equation that best describes the
observational data. The advancements in symbolic
regression have significant potential to aid research
in understanding unexplored systems' dynamics and
governing properties. However, the combinatorial nature
of the problem makes it an expensive and challenging
problem to solve efficiently. Several types of symbolic
regression algorithms exist, from genetic programming
and sparse regression to deep generative models.
However, no survey collates these prominent algorithms.
Therefore, this paper aims to summarize key research
works in symbolic regression and perform a comparative
study to understand the strength and limitations of
each method. Finally, we highlight the challenges in
the current methods and future research directions in
the application of machine learning in knowledge