  Symbolic regression (SR) searches for analytical expressions representing the
relationship between a set of explanatory and response variables. Current SR
methods assume a single dataset extracted from a single experiment.
Nevertheless, frequently, the researcher is confronted with multiple sets of
results obtained from experiments conducted with different setups. Traditional
SR methods may fail to find the underlying expression since the parameters of
each experiment can be different. In this work we present Multi-View Symbolic
Regression (MvSR), which takes into account multiple datasets simultaneously,
mimicking experimental environments, and outputs a general parametric solution.
This approach fits the evaluated expression to each independent dataset and
returns a parametric family of functions f(x; theta) simultaneously capable of
accurately fitting all datasets. We demonstrate the effectiveness of MvSR using
data generated from known expressions, as well as real-world data from
astronomy, chemistry and economy, for which an a priori analytical expression
is not available. Results show that MvSR obtains the correct expression more
frequently and is robust to hyperparameters change. In real-world data, it is
able to grasp the group behavior, recovering known expressions from the
literature as well as promising alternatives, thus enabling the use of SR to a
large range of experimental scenarios.
