A genetic algorithm (GA) approach to the calculation of canonical variates (CVs)

Kemsley, E. K. ORCID: https://orcid.org/0000-0003-0669-3883 (1998) A genetic algorithm (GA) approach to the calculation of canonical variates (CVs). TrAC - Trends in Analytical Chemistry, 17 (1). pp. 24-34. ISSN 0165-9936

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Genetic algorithms (GAs) are numerical search routines that mimic the evolutionary processes in nature. The GA's equivalents of chromosomes and genes are the unknown parameters in a mathematical model, and just as in biological evolution, these breed and mutate to produce improved solutions with each successive generation. In this paper, GAs are disclosed for tackling multivariate classification problems, using an approach derived from the dimension-reduction method of canonical variates analysis. These algorithms can be applied directly to high-dimensional data (where the number of variates exceeds the number of observations). The Incorporation of cross- validation guards against model overfitting. The algorithms are presented in the Matlab matrix programming language.

Item Type: Article
Additional Information: Funding Information: The author thanks the Biotechnology and Biological Sciences Research Council (BBSRC) for funding this work.
Uncontrolled Keywords: canonical variates analysis,genetic algorithms,multivariate classification problems,natural computation methods,analytical chemistry,spectroscopy ,/dk/atira/pure/subjectarea/asjc/1600/1602
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Depositing User: LivePure Connector
Date Deposited: 06 Feb 2023 10:31
Last Modified: 06 Feb 2023 10:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/91006
DOI: 10.1016/S0165-9936(97)00085-X

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