Genetic algorithm for permutations without repetition - r

Question

I am writing a Genetic Algorithm (GA) to find a certain permutation in my social network data (an iGraph object). I am using the R library GA, but the permutations it generates contain repetitions and their length varies, while I want permutations without repetitions and the same length.
I understand that the mutation and the cross-over functions cause this phenomenon, but I cannot find a way around it. I have tried to implement a fitness function that gives a low score to the "bad" permutations but this caused an error (see below).
cp_GA <- function(g, ratio = 0.2, maxiter = 1000, run = 40, pop = 200) {
library("igraph")
library("GA")
# ratio : ratio of the number of core/all vertices
# this is describing the desired size of the core group
# maxiter: max number of iterations for the GA
# run : max number of runs with the same fitness
# pop : population size for tha GA
# desired core size:
coresize <- round(vcount(g) * ratio, 0)
fitness_vertex_perm <- function(permutation) {
# this is the fitness function for the GA
# it calculates the density of the core with the current permutation
# the if-else structure prevents permutations with repetitions
if (sort(permutation) == c(1:vcount(g))) {
dens <- edge_density(
induced_subgraph(permute(g, as.numeric(permutation)), 1:coresize, impl =
"auto"))
} else {
dens <- 0
}
return(dens)
}
lowerlimit <- 1:vcount(g)
upperlimit <- vcount(g):1
hint <- order(degree(g), decreasing = TRUE)
maxfitness <- 1
GA <- ga(type = "permutation",
fitness = fitness_vertex_perm,
lower = lowerlimit,
upper = upperlimit,
maxiter = maxiter,
run = run,
maxFitness = maxfitness,
suggestions = hint,
popSize = pop
)
return(GA)
}
In the fitness function above the if else statement checks if a permutation is OK but this drops an error:
testresult <- cp_GA(g, ratio = 0.13, maxiter = 1000, run = 60, pop = 400)
Error in getComplete(control) :
argument "control" is missing, with no default
In addition: Warning message:
In if (sort(permutation) == c(1:vcount(g))) { :
Error in getComplete(control) :
argument "control" is missing, with no default
without the if-else it runs but produces a permutation result that is not useful for me.
How can I set GA to generate the right type of permutations?

Answer 1

See now you have two thigs to implement:
1.GA selection mechanism.
2.Without replacement strategy.
Theory of GA selection is that when you have parent selction you can do that randomly or alao can do that by applying some technique, you seem to have just done what is required.
Theory of without replacement is that you will have to deduct the remaining population one less as compared to the previous.
Probability(new) = 1/ (Proability(old) - 1)
So if you adjust your upper limit of the population in the looping section to one less you can achieve your result.
Hope this is all you need a hint in right direction.