Symbolic Regression Driven by Training Data and Prior Knowledge

Understand

In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples.

• However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models.
• Standard symbolic regression techniques then yield models that are partially incorrect, for instance, in terms of their steady-state characteristics or local behavior.
• If these properties were considered already during the search process, more accurate and relevant models could be produced.