A Genetic Programming-Driven Data Fitting Method

杨利霞

A Genetic Programming-Driven

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Data Fitting Method

+ j% r5 z4 X6 A+ {0 I+ l8 a6 MData fifitting is the process of constructing a curve, or a set of mathematical functions, that has
5 t' c& R* M3 g- F5 `the best fifit to a series of data points. Different with constructing a fifitting model from same type of function,
% ^& E1 U" F$ ^) d5 J* p3 W3 wsuch 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
5 v0 s- i6 `+ I+ M: g+ Cmodel construction approach. In this approach, the model is expressed by an improved tree coding expression
8 F0 I6 \( W4 `, v0 Q4 Aand 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
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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