Prime-Time: Symbolic Regression Takes
Its Place in the Real World
Sean Stijven, Ekaterina Vladislavleva, Arthur Kordon, Lander Willem,
and Mark E. Kotanchek
Abstract In this chapter we review a number of real-world applications where
symbolic regression was used recently and with great success. Industrial scale
symbolic regression armed with the power to select right variables and variable
combinations, build robust trustable predictions and guide experimentation has
undoubtedly earned its place in industrial process optimization, business forecast-
ing, product design and now complex systems modeling and policy making.
Keywords Symbolic regression • Forecasting • DataModeler • Extrapolation
- Prediction • Simulation-based optimization
1 Introduction
Symbolic regression remains the poster child for real-world application of genetic
programming and over the past quarter-century has moved from discovering the
low-order polynomials of toy data sets to extracting insights, models and profits
from data sets ranging up to millions of records and thousands of variables. The
ability to simultaneously explore the worth of different variable combinations during
S. Stijven ()
Department of Mathematics - Computer Sciences, University of Antwerp, Antwerp, Belgium
e-mail:[email protected]
E. Vladislavleva
Evolved Analytics Europe BVBA, Beerse, Belgium
e-mail:[email protected]
A. Kordon (retired)
Kordon Consulting LLC, Fort Lauderdale, FL, USA
e-mail:[email protected]
L. Willem
Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
e-mail:[email protected]
M.E. Kotanchek
Evolved Analytics LLC, Midland, MI, USA
e-mail:[email protected]
© Springer International Publishing Switzerland 2016
R. Riolo et al. (eds.),Genetic Programming Theory and Practice XIII,
Genetic and Evolutionary Computation, DOI 10.1007/978-3-319-34223-8_14
241