A Genetic Programming-Driven Data Fitting Method

杨利霞

A Genetic Programming-Driven

Data Fitting Method

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Data fifitting is the process of constructing a curve, or a set of mathematical functions, that has
! l1 j- h. {" z& R2 Nthe best fifit to a series of data points. Different with constructing a fifitting model from same type of function,
/ T' i) F( Y/ M! I7 nsuch as the polynomial model, we notice that a hybrid fifitting model with multiple types of function may have
a 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
9 P! j  {- P0 G5 v( Y+ S# p1 c. Tmodel construction approach. In this approach, the model is expressed by an improved tree coding expression
0 p3 h& ^. ^* N* w2 Q, dand 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
0 [$ r1 u8 Q7 y% m7 n2 Vexperimental results show that the proposed method is superior to 7 typical methods in terms of the prediction
accuracy and interpretability.
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