Bias-variance decomposition in Genetic Programming

Bias-variance decomposition in Genetic Programming

We study properties of Linear Genetic Programming (LGP) through several regression and classification benchmarks. In each problem, we decompose the results into bias and variance components, and explore the effect of varying certain key parameters on the overall error and its decomposed contribution...

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Bibliographic Details
Main Authors: Kowaliw Taras, Doursat René
Format: Article
Language:English
Published: De Gruyter 2016-01-01
Series:Open Mathematics
Subjects:
analysis of algorithms
bias-variance decomposition
classification
computational learning theory
evolutionary computation
genetic programming
learning and adaptive systems
non-parametric inference
regression
62g08
62j10
68q32
68t05
68w40
Online Access:https://doi.org/10.1515/math-2016-0005

Internet

https://doi.org/10.1515/math-2016-0005

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