"feature selection loop"

I'm new in rapidminer , so maybe that is why, I couldn't find a way :

lets suppose,  in term of significance, rank of features is known:

t100,t12,t25,t16,.....

now, I want to make an iteration such that :


t(i)=features(1:i):

t(1)=1st element of ordered features    -> t100
t(2)=2 first elements of ordered  features  -> t100,t12


repeat ( there is no any improvement in  classification performance) {
          performance of classification t(i)=p1
          performance of classification t(i-1)=p2
          if (p1<p2
                      t(i-1)=t(i+1)
                      i=i+1
                          }
}


here, there is three problems :

1- making a loop
2- whereas my classifier is Neural network and it's sensitive to initial conditions so each above iteration should be checked several times (say 100 times) and if mean of performances violate the  condition, loop should be break.
3- how can I have a table of accuracy of mean of performance at the end


please help me as much as possible

thanks