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A Study of Crossvalidation and Bootstrap as Objective Functions for Genetic Algorithms

E. G. M. de LacerdaAndré Carlos Ponce de Leon Ferreira de CarvalhoTeresa Bernarda Ludermir

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 Genetics Algorithms such as the ability for handling multiple and noise objective functions.

http://csdl.computer.org/comp/proceedings/sbrn/2002/1709/00/17090118abs.htm

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