abstract = "In this chapter, we explore how Genetic Programming
can assist and augment the expert-driven process of
developing data-driven models. In our use case,
modellers must develop hundreds of models that
represent individual properties of a part, components,
assets, systems and meta-systems like a power plant.
Each of these models is developed with an objective in
mind, like estimating the useful remaining life or
anomaly detection. As such, the modeller uses their
expert judgement as well as available data to select
the most appropriate method. In this initial paper, we
examine the most basic example of when the expert
selects a kind of regression modelling approach and
develops a model from data. We then use that captured
domain knowledge from their process as well as end
model to determine if Genetic Programming can augment,
assist and improve their final result. We show that
while Genetic Programming can indeed find improved
solutions according to an error metric, it is much
harder for Genetic Programming to find models that do
not increase complexity. Also, we find that one
approach in particular shows promise as a way to
incorporate domain knowledge.",