Genetic programming, ensemble methods and the bias/variance tradeoff – introductory investigations

Abstract

The decomposition of regression error into bias and variance terms provides insight into the generalization capability of modeling methods. The paper offers an introduction to bias/variance decomposition of mean squared error, as well as a presentation of experimental results of the application of genetic programming. Finally ensemble methods such as bagging and boosting are discussed that can reduce the generalization error in genetic programming.