A Genetic Programming-Driven Data Fitting Method

杨利霞

A Genetic Programming-Driven

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Data Fitting Method

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% x; x; h7 o0 M0 w: YData fifitting is the process of constructing a curve, or a set of mathematical functions, that has
$ n3 {6 q" l% B9 c' |) S: jthe best fifit to a series of data points. Different with constructing a fifitting model from same type of function,
: g4 Z" B. R$ t' v5 q5 l2 p8 Osuch as the polynomial model, we notice that a hybrid fifitting model with multiple types of function may have
5 U& F! L" {, s+ C. x; W7 x& na better fifitting result. Moreover, this also shows better interpretability. However, a perfect smooth hybrid
( ?: q! |8 {( u' J# n6 Y, R; u; Mfifitting model depends on a reasonable combination of multiple functions and a set of effective parameters.
5 s& A* @- S8 n3 U# I# DThat is a high-dimensional multi-objective optimization problem. This paper proposes a novel data fifitting
model construction approach. In this approach, the model is expressed by an improved tree coding expression
# R6 @, T0 ?& [  ^% F" Eand 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
3 V6 U" w2 l$ {6 n9 q$ U1 w( I9 |' Qexperimental results show that the proposed method is superior to 7 typical methods in terms of the prediction
0 B* o0 @" z; F  c: `1 v1 r9 raccuracy and interpretability
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