A Genetic Programming-Driven Data Fitting Method

杨利霞

A Genetic Programming-Driven

Data Fitting Method

Data fifitting is the process of constructing a curve, or a set of mathematical functions, that has
$ Y4 }) T$ G: T) H, V1 Y4 o& Z7 Cthe best fifit to a series of data points. Different with constructing a fifitting model from same type of function,
/ h" Y; ~* O  r" c0 C( gsuch as the polynomial model, we notice that a hybrid fifitting model with multiple types of function may have
% T7 N; K5 g, B' {+ P6 v( T: o: `8 Z4 Ya better fifitting result. Moreover, this also shows better interpretability. However, a perfect smooth hybrid
fifitting model depends on a reasonable combination of multiple functions and a set of effective parameters.
That is a high-dimensional multi-objective optimization problem. This paper proposes a novel data fifitting
, |6 S0 e* G. m, ~2 \  Emodel construction approach. In this approach, the model is expressed by an improved tree coding expression
2 G! ?) ^7 T" J0 K2 o) |and constructed through an evolution search process driven by the genetic programming. In order to verify
the validity of generated hybrid fifitting model, 6 prediction problems are chosen for experiment studies. The
, E* v9 y  O6 q& _3 c' u% [experimental results show that the proposed method is superior to 7 typical methods in terms of the prediction
accuracy and interpretability
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