abstract = "Symbolic regression via genetic programming is a
branch of empirical modeling that evolves summary
expressions for available data. Although intrinsically
difficult (the search space is infinite), recent
algorithmic advances coupled with faster computers have
enabled application of symbolic regression to a wide
variety of industrial data sets. Unique benefits of
symbolic regression include human insight and
interpretability of model results, identification of
key variables and variable combinations, and the
generation of computationally simple models for
deployment into operational models. In this
presentation, we review the symbolic regression
evolution process, practical issues, and approaches to
managing, reviewing, and refining modeling results. A
Mathematica symbolic regression package implementation
will be demonstrated that stresses quality model
development and a user-centric approach for model
development, assessment, exploitation, and
management.",