Hi there,

I am bechmarking three different GA libraries (ECJ, JGAP, and GALib 
(C++)). The parameter file, the fitness function, and the my mutation 
function are at the end of this email. The length of chromosome is 600 and 
subpopulation size is 20. I am using default crossover function (source 0 
and source 1 --> FitProportionateSelection). Fitness function is 
Kozafitness (it is easier for me to solve minimization problems).
The other two libraries converge around 20,000 iterations. However, ECJ 
converges after 90,000 iterations. If I 

I wanna learn your instincts about convergence rate. Isn't it too slow? 
(Whether, I am coding something wrong or someone has the similar 
convergence rate problem for fitness value) 

Note: I am using my own mutation function because the bit selected for 
mutation should be changed from a value in the predefined vector.

Thanks in advance,

Safak
-----------FITNESS FUNCTION-----------------------------------
fitness = 0;
for (i<genome.length){
    fitness += (value of genome – (1/genome.length x i))2
}

----------PARAMETERS FILE---------------------------------------

parent.0     = simple.params

verbosity     = 0

stat       = ec.GALIB.ES1.MyStat

eval.problem               = ec.GALIB.ES1.MyGAs

pop.subpop.0.breeder      = ec.Simple.SimpleBreeder

pop.subpop.0.species.pipe     = 
ec.GALIB.ES1.MyMutation 

pop.subpop.0.species.pipe.source.0    = 
ec.vector.breed.VectorCrossoverPipeline
pop.subpop.0.species.pipe.source.0.source.0   = 
ec.select.FitProportionateSelection
pop.subpop.0.species.pipe.source.0.source.1   = 
ec.select.FitProportionateSelection
pop.subpop.0.species.pipe.source.0.toss = false

pop.subpop.0.species    = 
ec.vector.FloatVectorSpecies
pop.subpop.0.species.min-gene   = 0.0f
pop.subpop.0.species.max-gene   = 1.0f

pop.subpop.0.species.ind   = 
ec.vector.FloatVectorIndividual
pop.subpop.0.species.genome-size  = 600
pop.subpop.0.species.crossover-type  = one
pop.subpop.0.species.crossover-prob  = 0.9

pop.subpop.0.species.fitness  = ec.gp.koza.KozaFitness

breed.elite.0     = 1

pop.subpop.0.size     = 20

pop.subpop.0.file = initPop.params

generations      = 100000

function.delay     = false
#true

pop.subpop.0.species.mutation-prob  = 0.002

seed.0      = time

pop.subpop.0.duplicate-retries  = 0


---------------------- MY MUTATION----------------------
package ec.GALIB.ES1;

import ec.BreedingPipeline;
import ec.EvolutionState;
import ec.Individual;
import ec.util.Parameter;
import ec.vector.FloatVectorIndividual;
import ec.vector.FloatVectorSpecies;
import ec.vector.VectorDefaults;


public class MyMutation extends BreedingPipeline{

 public static final String P_MUTATION = "mutate";
 public static final int NUM_SOURCES = 1;
 public RandomGenerator rg = new RandomGenerator();
  
 @Override
 public int numSources() {
  // TODO Auto-generated method stub
  return NUM_SOURCES;
 }
 
 @Override
 public int produce(final int min, final int max, final int start, 
final int subpopulation,
   final Individual[] inds, final EvolutionState 
state, final int thread){
  // TODO Auto-generated method stub
  
  int n=sources[0].produce(min, max, start, subpopulation, 
inds, state, thread);
  Parameter myBase = new Parameter("function");
   
  if (!(sources[0] instanceof BreedingPipeline)){
   for (int q=start;q<n+start;q++){
    inds[q]=(Individual) inds[q].clone();
    }  
  }
  //initialize random number generator
  rg.SetState(state);
  rg.SetThread(thread);
  
  FloatVectorSpecies s = (FloatVectorSpecies)(inds
[0].species);
  FloatVectorIndividual ind;
  for(int q=start;q<start+n;q++){
   ind = (FloatVectorIndividual)inds[q];
   if (s.mutationProbability>0.0){
    if (state.random[thread].nextBoolean
(s.mutationProbability)){
    int a = state.random[0].nextInt(600);

     ind.genome[a]=(float)(rg.GetRandom
(a));
    }
   }   
   (inds[q]).evaluated=false;  
  }
  
  return n;
 }

 @Override
 public Parameter defaultBase() {
  // TODO Auto-generated method stub
  return VectorDefaults.base().push(P_MUTATION);
 }
 
 public class RandomGenerator{
  int type = 0; // 0-->Gaussian, 1-->Cauchy
  int thread;
  EvolutionState state;
  float GetRandom(float a){
   float rnum = 0.0f;
   int k = state.random[thread].nextInt(9);
   rnum = ES_Java_1._chromosomes[(int)a][k];
   /*if (type == 0){
    rnum = err*(float)state.random
[thread].nextGaussian();
   }
   else{
    rnum=(float)Math.tan(3.1415926536f*
(state.random[thread].nextFloat()-0.5f));
   }*/
   return rnum;
  }
  
  void SetState(final EvolutionState s){
   state =s;
  }
  
  void SetThread(final int t){
   thread = t;
  }
  
 }



}