Structural Risk Minimization-Driven Genetic Programming for Enhancing Generalization in Symbolic Regression

Generalization ability, which reflects the prediction ability of a learned model, is an important property in genetic programming (GP) for symbolic regression. Structural risk minimization (SRM) is a framework providing a reliable estimation of the generalization performance of prediction models. In...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on evolutionary computation Ročník 23; číslo 4; s. 703 - 717
Hlavní autori: Chen, Qi, Zhang, Mengjie, Xue, Bing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Complexity theory
Computational modeling
Data models
Estimation
Generalization
Genetic algorithms
Genetic programming
genetic programming (GP)
Minimization
Optimization
Regression analysis
Regression models
Research methodology
structural risk minimization (SRM)
symbolic regression
Training
Vapnik–Chervonenkis (VC) dimension
ISSN:1089-778X, 1941-0026
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Generalization ability, which reflects the prediction ability of a learned model, is an important property in genetic programming (GP) for symbolic regression. Structural risk minimization (SRM) is a framework providing a reliable estimation of the generalization performance of prediction models. Introducing the framework into GP has the potential to drive the evolutionary process toward models with good generalization performance. However, this is tough due to the difficulty in obtaining the Vapnik-Chervonenkis (VC) dimension of nonlinear models. To address this difficulty, this paper proposes an SRM-driven GP approach, which uses an experimental method (instead of theoretical estimation) to measure the VC dimension of a mixture of linear and nonlinear regression models for the first time. The experimental method has been conducted using uniform and nonuniform settings. The results show that our method has impressive generalization gains over standard GP and GP with the 0.632 bootstrap, and that the proposed method using the nonuniform setting has further improvement than its counterpart using the uniform setting. Further analyzes reveal that the proposed method can evolve more compact models, and that the behavioral difference between these compact models and the target models is much smaller than their counterparts evolved by the other GP methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2018.2881392