A Genetic Programming-Driven
{" b* C6 w% @0 Z% @0 XData Fitting Method " }* v4 M- Z) ^! ^! `
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Data fifitting is the process of constructing a curve, or a set of mathematical functions, that has
+ w) K& G, N. t3 b; ?the best fifit to a series of data points. Different with constructing a fifitting model from same type of function,
- [, p0 A: q& a/ U. xsuch as the polynomial model, we notice that a hybrid fifitting model with multiple types of function may have
/ `5 n' A: j% @' s R% V3 ra better fifitting result. Moreover, this also shows better interpretability. However, a perfect smooth hybrid/ L: l" d4 w3 V: ]5 {; O3 \
fifitting model depends on a reasonable combination of multiple functions and a set of effective parameters.8 y9 E; ^5 c- R& c7 \0 h: p
That is a high-dimensional multi-objective optimization problem. This paper proposes a novel data fifitting
/ S. {: }6 z& i) Gmodel construction approach. In this approach, the model is expressed by an improved tree coding expression
3 N; b0 t* w K- a' W% |4 tand constructed through an evolution search process driven by the genetic programming. In order to verify+ @! Q/ t0 w0 [$ R
the validity of generated hybrid fifitting model, 6 prediction problems are chosen for experiment studies. The& h, ^8 l: p) A2 a+ y f
experimental results show that the proposed method is superior to 7 typical methods in terms of the prediction
* e# \& _# k1 `# \1 Uaccuracy and interpretability
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