  Modeling real-world phenomena is a focus of many science and engineering
efforts, such as ecological modeling and financial forecasting, to name a few.
Building an accurate model for complex and dynamic systems improves
understanding of underlying processes and leads to resource efficiency. Towards
this goal, knowledge-driven modeling builds a model based on human expertise,
yet is often suboptimal. At the opposite extreme, data-driven modeling learns a
model directly from data, requiring extensive data and potentially generating
overfitting. We focus on an intermediate approach, model revision, in which
prior knowledge and data are combined to achieve the best of both worlds. In
this paper, we propose a genetic model revision framework based on
tree-adjoining grammar (TAG) guided genetic programming (GP), using the TAG
formalism and GP operators in an effective mechanism to incorporate prior
knowledge and make data-driven revisions in a way that complies with prior
knowledge. Our framework is designed to address the high computational cost of
evolutionary modeling of complex systems. Via a case study on the challenging
problem of river water quality modeling, we show that the framework efficiently
learns an interpretable model, with higher modeling accuracy than existing
methods.
