CODEWITHSUNDEEP
rgenetic-algorithm
Tom
Tom

Reputation: 339

How to build a mutation algorithm for GA in R

I'm trying to build my mutation algorithm for GA.

It supposed to work like this: with a probability Pm the mutation passes - we draw two gens a & b. After that we change them order or a sequence between those two (if they're not neighbours). If the mutation doesn't pass - we don't do nothing.

Let's say we have an offspring [010110], if mutation starts, we choose AB = [2,5] that points at [0{1}01{1}0]. We do reverse and obtain [011010].

I build something like this:

for(i in 1 : popSize){
  genomeMutId <- which(runif(2, Dim*cel)>pMut

   for(j in 1:length(genomeMutId)){
    drawn <- runif(1,genomeMutId[j],lenght(genomeMutId))
    iter <- 0
    for(k in genomeMutId[j]:drawn) {
      tmpValue <- nextGeneration[i, k]
      nextGeneration[i, k] = nextGeneration[i, drawn-iter]
      nextGeneration[i, drawn-iter] = tmpValue 
      iter <- iter + 1
    }
   }
}

Unfortunately it doesn't work properly. Any suggestions? Maybe i use sample instead of runif?

Upvotes: 2

Views: 223

Answers (2)

digEmAll
digEmAll

Reputation: 57210

You can do in this way :

offspring <- c(0,1,0,1,1,0,1,1,0,1)

# given an offspring vector (e.g. 0,1,0,0,1,0)
# choose 2 cut points AB and invert the values between them
getNewOffspring <- function(offspring){
  AB <- sort(sample.int(length(offspring),2))
  if(AB[2] - AB[1] > 2){
    subSqIdxs <- seq.int(from=AB[1]+1,to=AB[2]-1)
    offspring[subSqIdxs] <- rev(offspring[subSqIdxs])
  }
  offspring
}

Example usage :

getNewOffspring(c(0,1,0,1,1,0,1,1,0,1))
# e.g. with AB being 3,8
> 0 1 0 1 0 1 1 1 0 1

EDIT :

Assuming the list of offsprings being stored in a list called offspringsList you can extract a random number for each offspring to decide which has to be mutated, and then call the previous function :

offspringsToMutate <- which(runif(lenght(offspringsList)) > pM)

for(offspringIndex in seq_len(length(offspringsToMutate))){
   mutated <- getNewOffspring(offspringsList[[offspringIndex]])
   offspringsToMutate[[offspringIndex]] <- mutated
}
# now the list contains the mutated offsprings

Upvotes: 3

SeGa
SeGa

Reputation: 9809

I had to implement a mutation function for my GA, which otimizes the layout of a windfarm. All functions are exported, so you can check them out:

library(windfarmGA)
## Create 4 random individuals with binary values
a <- cbind(bin=sample(c(0,1),20,replace=TRUE,prob = c(70,30)),
           bin.1=sample(c(0,1),20,replace=TRUE,prob = c(30,70)),
           bin.2=sample(c(0,1),20,replace=TRUE,prob = c(30,70)),
           bin.3=sample(c(0,1),20,replace=TRUE,prob = c(30,70)))

a

## Mutate the individuals with a low percentage
aMut <- mutation(a,0.1)
## Check which values are not like the originals
a==aMut

## Mutate the individuals with a high percentage
aMut <- mutation(a,0.4)
## Check which values are not like the originals
a==aMut

mutation

It is not totally what you want, but maybe it helps to get there.

Upvotes: 1

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