Choosing initial population of mlr genetic algorithm - r

Question

I would like to choose the initial population of the genetic algorithm for variable selection with makeFeatSelControlGA() function from mlr package.
Is it doable ?
Edit
Since it is not implemented I did a dirty editing of selectFeaturesGA function for this purpose. Here is the code, if it helps anyone.
library(R.utils)
reassignInPackage("selectFeaturesGA","mlr",function (learner, task, resampling, measures, bit.names, bits.to.features,
control, opt.path, show.info)
{
states=get("states",envir=.GlobalEnv)
mu = control$extra.args$mu
lambda = control$extra.args$lambda
yname = opt.path$y.names[1]
minimize = opt.path$minimize[1]
if (!length(states))
{
for (i in seq_len(mu)) {
while (TRUE) {
states[[i]] = rbinom(length(bit.names), 1, 0.5)
if (is.na(control$max.features) || sum(states[[i]] <=
control$max.features))
break
}
}
}
evalOptimizationStatesFeatSel(learner, task, resampling,
measures, bits.to.features, control, opt.path, show.info,
states, 0L, NA_integer_)
pop.inds = seq_len(mu)
for (i in seq_len(control$maxit)) {
pop.df = as.data.frame(opt.path)[pop.inds, , drop = FALSE]
pop.featmat = as.matrix(pop.df[, bit.names, drop = FALSE])
mode(pop.featmat) = "integer"
pop.y = pop.df[, yname]
kids.list = replicate(lambda, generateKid(pop.featmat,
control), simplify = FALSE)
kids.evals = evalOptimizationStatesFeatSel(learner, task,
resampling, measures, bits.to.features, control,
opt.path, show.info, states = kids.list, i, as.integer(NA))
kids.y = extractSubList(kids.evals, c("y", yname))
oplen = getOptPathLength(opt.path)
kids.inds = seq(oplen - lambda + 1, oplen)
if (control$extra.args$comma) {
setOptPathElEOL(opt.path, pop.inds, i - 1)
pool.inds = kids.inds
pool.y = kids.y
}
else {
pool.inds = c(pop.inds, kids.inds)
pool.y = c(pop.y, kids.y)
}
pop.inds = pool.inds[order(pool.y, decreasing = !minimize)[seq_len(mu)]]
setOptPathElEOL(opt.path, setdiff(pool.inds, pop.inds),
i)
}
makeFeatSelResultFromOptPath(learner, measures, resampling,
control, opt.path)
})
And in the global environment, you need to add a list named states with length equal to mu

Answer 1

This is currently not supported by mlr, so you would have to modify the source code to achieve that.