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

被引：21
作者：
Kemsley, EK [1 ]
机构：
[1] Inst Food Res, Norwich NR4 7UA, Norfolk, England
来源：
TRAC-TRENDS IN ANALYTICAL CHEMISTRY | 1998年 / 17卷 / 01期
基金：
英国生物技术与生命科学研究理事会;
关键词：
genetic algorithms; canonical variates analysis; multivariate classification problems; natural computation methods;
D O I：
10.1016/S0165-9936(97)00085-X
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
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 ave 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. (C) 1998 Elsevier Science B.V.
引用
收藏
页码：24 / 34
页数：11
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