Permutation optimization with gaoptim package

My recent update of gaoptim package brings up a new function, GAPerm, which can be used to perform combinatorial optimization using the Genetic Algorithm approach.

The example below solves a TSP instance with 10 points around a circumference, then plots the results.
Enjoy it!

	# TSP with 10 cities around a circular pattern
	require(gaoptim)
	op <- par(mfrow = c(2, 1))
	n = 10
	R = 10
	angs = seq(0, 2*pi, length = n)
	xp = R * cos(angs) + rnorm(n)
	yp = R * sin(angs) + rnorm(n)
	xp = c(xp, xp[1])
	yp = c(yp, yp[1])
	base.M = matrix(c(xp, yp), ncol = 2)
	dist.FUN = function(p)
	{
	p = c(p, p[1])
	M.diff = diff(base.M[p, ])
	dists = apply(M.diff, 1, function(x)x[1]^2 + x[2]^2)
	1/sum(dists)
	}
	ga1 = GAPerm(dist.FUN, n, popSize = 100, mutRate = 0.3)
	ga1$evolve(100)
	plot(ga1)
	plot(xp, yp, type = 'n', xlab = '', ylab = '', main = 'Best Tour')
	res = ga1$bestIndividual()
	res = c(res, res[1])
	i = 1:n
	xi = base.M[res[i], 1]
	yi = base.M[res[i], 2]
	xf = base.M[res[i + 1], 1]
	yf = base.M[res[i + 1], 2]
	arrows(xi, yi, xf, yf, col = 'red', angle = 10)
	text(base.M[res, 1], base.M[res, 2], 1:n, cex = 0.9, col = 'gray20')
	par(op)

view raw ga_perm.R hosted with ❤ by GitHub