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Genetic algorithm applied to the selection of principal components

✍ Scribed by A.S. Barros; D.N. Rutledge

Publisher: Elsevier Science
Year: 1998
Tongue: English
Weight: 664 KB
Volume: 40
Category: Article
ISSN: 0169-7439
DOI: 10.1016/s0169-7439(98)00002-1

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✦ Synopsis

Ž .

Ž . The application of a genetic algorithm GA to the selection of principal components PCs is proposed as an efficient method to determine the optimal multivariate regression model. This stochastic method was compared with other determinis-Ž . tic methods such as: exhaustive search here taken as a validation procedure , forward and backward-stepwise variable selec-Ž . tion and correlation principal components regression CPCR . It is shown that for the range of data sets used, the GA gives the same result as the those obtained by an exhaustive search and by CPCR whereas the stepwise procedures do not. These Ž . results also show that in order to build optimal predictive models using principal components regression PCR one needs to select the best subset of PCs rather than simply use those with the highest eigenvalues.

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