Genetic Programming Model Regularization

被引：1

作者：

Alonso, Cesar L. ^{[1
]}

Luis Montana, Jose ^{[2
]}

Enrique Borges, Cruz ^{[3
]}

机构：

[1] Univ Oviedo, Ctr Inteligencia Artificial, Campus Gijon, Gijon 33271, Spain

[2] Univ Cantabria, Dept Matemat Estadist & Comput, E-39005 Santander, Spain

[3] Univ Deusto, DeustoTech Energy Unit, Bilbao 48007, Spain

来源：

COMPUTATIONAL INTELLIGENCE, IJCCI 2013 | 2016年 / 613卷

关键词：

Genetic Programming; Straight Line Program; Pfaffian Operator; Symbolic Regression; INDUCTIVE INFERENCE;

D O I：

10.1007/978-3-319-23392-5_6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a tool for controlling the complexity of Genetic Programming models. The tool is supported by the theory of Vapnik-Chervonekis dimension (VCD) and is combined with a novel representation of models named straight line program. Experimental results, implemented on conventional algebraic structures (such as polynomials), show that the empirical risk, penalized by suitable upper bounds for the Vapnik-Chervonenkis dimension, gives a generalization error smaller than the use of statistical conventional techniques such as Bayesian or Akaike information criteria.

引用

页码：105 / 120

页数：16

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