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.
- Canonical variates analysis
- Genetic algorithms
- Multivariate classification problems
- Natural computation methods