# gafs_initial

##### Ancillary genetic algorithm functions

Built-in functions related to genetic algorithms

These functions are used with the `functions`

argument of the
`gafsControl`

function. More information on the details of these
functions are at http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html.

Most of the `gafs_*`

functions are based on those from the GA package
by Luca Scrucca. These functions here are small re-writes to work outside of
the GA package.

The objects `caretGA`

, `rfGA`

and `treebagGA`

are example
lists that can be used with the `functions`

argument of
`gafsControl`

.

In the case of `caretGA`

, the `...`

structure of
`gafs`

passes through to the model fitting routine. As a
consequence, the `train`

function can easily be accessed by
passing important arguments belonging to `train`

to
`gafs`

. See the examples below. By default, using `caretGA`

will used the resampled performance estimates produced by
`train`

as the internal estimate of fitness.

For `rfGA`

and `treebagGA`

, the `randomForest`

and
`bagging`

functions are used directly (i.e. `train`

is not
used). Arguments to either of these functions can also be passed to them
though the `gafs`

call (see examples below). For these two
functions, the internal fitness is estimated using the out-of-bag estimates
naturally produced by those functions. While faster, this limits the user to
accuracy or Kappa (for classification) and RMSE and R-squared (for
regression).

##### Usage

`gafs_initial(vars, popSize, ...)`gafs_lrSelection(population, fitness, r = NULL, q = NULL, ...)

gafs_spCrossover(population, fitness, parents, ...)

gafs_raMutation(population, parent, ...)

gafs_nlrSelection(population, fitness, q = 0.25, ...)

gafs_rwSelection(population, fitness, ...)

gafs_tourSelection(population, fitness, k = 3, ...)

gafs_uCrossover(population, parents, ...)

##### Arguments

- vars
number of possible predictors

- popSize
the population size passed into

`gafs`

- …
not currently used

- population
a binary matrix of the current subsets with predictors in columns and individuals in rows

- fitness
a vector of fitness values

- r, q, k
tuning parameters for the specific selection operator

- parent, parents
integer(s) for which chromosomes are altered

##### Value

The return value depends on the function.

##### References

Scrucca L (2013). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1-37.

https://cran.r-project.org/package=GA

http://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html

##### See Also

##### Examples

```
# NOT RUN {
pop <- gafs_initial(vars = 10, popSize = 10)
pop
gafs_lrSelection(population = pop, fitness = 1:10)
gafs_spCrossover(population = pop, fitness = 1:10, parents = 1:2)
# }
# NOT RUN {
## Hypothetical examples
lda_ga <- gafs(x = predictors,
y = classes,
gafsControl = gafsControl(functions = caretGA),
## now pass arguments to `train`
method = "lda",
metric = "Accuracy"
trControl = trainControl(method = "cv", classProbs = TRUE))
rf_ga <- gafs(x = predictors,
y = classes,
gafsControl = gafsControl(functions = rfGA),
## these are arguments to `randomForest`
ntree = 1000,
importance = TRUE)
# }
# NOT RUN {
# }
```

*Documentation reproduced from package caret, version 6.0-86, License: GPL (>= 2)*