GA: Genetic Algorithms for variable selection in classification
In ChemometricsWithR: Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences

Description Usage Arguments Details Value Author(s) References See Also Examples

A set of functions implementing simple variable selection in classification applications using genetic algorithms.

GAfun(X, C, eval.fun, kmin, kmax, popsize = 20, niter = 50,
      mut.prob = 0.05, ...)
GAfun2(X, C, eval.fun, kmin, kmax, popsize = 20, niter = 50,
       mut.prob = 0.05, ...)

GA.init.pop(popsize, nvar, kmin, kmax)
GA.select(pop, number, qlts, min.qlt = 0.4, qlt.exp = 1)
GA.mut(subset, maxvar, mut.prob = 0.01)
GA.XO(subset1, subset2)

`X`	Data matrix: independent variables used by `eval.fun`
`C`	Class vector, used by `eval.fun`
`eval.fun`	evaluation function. Should take a data matrix, a class vector (or factor), and a subset argument
`kmin`	Minimal number of variables to retain
`kmax`	Maximal number of variables to retain
`popsize`	Size of the GA population
`niter`	Number of iterations
`mut.prob`	Mutation probability
`...`	Further arguments to the evaluation function
`nvar`	The total number of variables to choose from
`pop, subset, subset1, subset2`	A (part of a) population of trial solutions
`number`	The number of trial solutions that may produce offspring
`qlts`	Vector of quality measures for members in a population
`min.qlt`	Minimal quality of a trial solution to be considered as a future parent
`qlt.exp`	Quality scaling parameter: the larger this number, the more discrimination between good and bad solutions, and the more greedy the search characteristics
`maxvar`	Number of variables to choose from

The function generates a population of trial solutions, each containing a number of variables to be retained. For every member of the population, the evaluation function calculates a quality measure, which determines the chance of that member to create offspring. In a process of "survival of the fittest", this leads to subsets for which the evaluation function has a maximal value.

The initialization is done randomly. Selection is simple threshold selection. Mutation swaps variables in or out of the subset; the cross-over type is uniform. Functions GA.init.pop, GA.select, GA.mut and GA.XO are auxiliary functions, not meant to be called directly by the user.

Functions GAfun and GAfun2 both return a list containing the following fields:

`best`	The best subset
`best.q`	The quality of the best subset
`n.iter`	The number of iterations

In addition, the outcome of GAfun2 also contains

qualities

A matrix containing the best, median and worst quality value throughout the optimization

Ron Wehrens

R. Wehrens. "Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences". Springer, Heidelberg, 2011.

Evaluation, SA

if (require("pls")) {
  data(gasoline, package = "pls")
  ## Usually more iterations are needed
  GAobj <- GAfun(gasoline$NIR, gasoline$octane,
                 eval.fun = pls.cvfun, niter = 20,
                 kmin = 3, kmax = 25, ncomp = 2)
  GAobj
} else {
  cat("Package pls not available.\nInstall it by typing 'install.packages(\"pls\")'")
}

Attaching package: 'ChemometricsWithR'

The following objects are masked from 'package:stats':

    loadings, screeplot

Loading required package: pls

Attaching package: 'pls'

The following objects are masked from 'package:ChemometricsWithR':

    loadingplot, loadings, scoreplot

The following object is masked from 'package:stats':

    loadings

$best
 [1] 242  45 130  38 290  19 176 298  83 257 223 110 239 335 124 172 374 222 162

$best.q
[1] -0.03915816

$n.iter
[1] 20