A study of cross-validation and bootstrap as objective functions for genetic algorithms

Abstract

This article addresses the problem of finding the adjustable parameters of a learning algorithm using genetic algorithms. This problem is also known as the model selection problem. Some model selection techniques (e.g., cross-validation and bootstrap) are combined with the genetic algorithms of different ways. Those combinations explore features of the genetic algorithms such as the ability for handling multiple and noise objective functions. The proposed multiobjective GA is quite general and can be applied to a large range of learning algorithms.