  Models can be built directly from input and output data trough a process
known as system identification. The Nonlinear AutoRegressive with eXogenous
inputs (NARMAX) models are among the most used mathematical representations in
the area and has many successful applications on data-driven modeling in
different fields. Such models become extremely large when they have high degree
of non-linearity and long-term dependencies. Hence, a structure selection
process must be performed to make them parsimonious. In the present paper, it
is introduced a toolbox in Python that performs the structure selection process
using the evolutionary algorithm named Multi-Gene Genetic Programming (MGGP).
The toolbox encapsulates basic tools for parameter estimation, simulation and
validation, and it allows the users to customize their evaluation function
including prior knowledge and constraints in the individual structure (gray-box
identification).
