In symbolic regression, we try to find a simple formula to predict the values of a dependent variable from known values of independent variables. Machine learning methods for symbolic regression search the space of possible expressions which can be constructed using basic mathematical operators and functions and optimize numerical parameters of expression based on given input data. Most commonly genetic programming is used to solve this task. However, when we try to apply this method in real-world applications we find that this approach is limited in several ways (non-determinism, high computational effort, bloated formulas, bad interpolation and even worse extrapolation). In this talk I will discuss possible solutions to these issues which we have developed in our recent research.

1. Real-world Applications of Symbolic Regression
Gabriel Kronberger
University of Applied Sciences Upper Austria

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10. x
y

13. G(Expr):
Expr  Term | Term + Expr
Term  var | var * Term
var  X | Y
Tony Worm, Kenneth Chiu:
Prioritized grammar enumeration: symbolic regression by
dynamic programming. GECCO 2013: 1021-1028

20. Modelling Dynamical Systems
𝑑 Temp
𝑑𝑡
= 𝑓1 Power, Temp, Env
SymReg

21. Blood Glucose Concentration Prediction for
Artificial Pancreas

26. ሶ𝐺 = −0.29𝜆2 + 0.7
ሶ𝜆1 = 0
ሶ𝜆2 = 𝜆1 + 𝑐ℎ

29. Interpolation and Extrapolation

30. Interpolation and Extrapolation

31. Interpolation and Extrapolation

32. Interpolation and Extrapolation

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Feb. 2018