Genetic Algorithms with gaoptim package

Two days ago i just submitted my first R package: gaoptim. For my surprise, the next day it was already living on CRAN.

In this post i want to show you how to use gaoptim to perform a simple function maximization. This same task could be accomplished with the function optim() from the stats package, but this should serve as a simple introduction to Genetic Algorithms, which are particularly good when you have a huge search space.

The R code below defines a 'wild' function, with global maximum at about -15.81515. Then we setup a GAReal object and call the evolve() function, passing the number of generations to evolve as argument. Finally, we plot the results.

Some planned features for the next package version are:

• feature selection
• binary encoding

Enjoy it!

	# global minimum at about -15.81515
	wild.FUN = function (x)
	10*sin(0.3*x)*sin(1.3*x^2) + 0.00001*x^4 + 0.2*x+80
	# GAReal performs maximization, so transform the target function
	target.FUN = function(x)
	-wild.FUN(x)
	require(gaoptim)
	# The target function can return negative values, so use a uniform selection
	# See ?GAReal details section
	ga = GAReal(target.FUN, lb = -50, ub = 50, selection = 'uniform')
	ga$evolve(200)
	# plot the original function and results
	op <- par(no.readonly = TRUE)
	par(mfrow = c(2, 1))
	plot(wild.FUN, -50, 50, n=1000, main = "GAReal() minimising 'wild function'")
	xmin = ga$bestIndividual()
	ymin = wild.FUN(xmin)
	abline(h = ymin, v = xmin, lty = 'dashed', col = 'tomato')
	plot(ga)
	par(op)

view raw gaoptim_example.R hosted with ❤ by GitHub