Problems with GAlib

[Aureli Soria Frisch]

Hello again,
Maybe you need the code (Thanks again):

/* ----------------------------------------------------------------------------
 example_wsum.c
---------------------------------------------------------------------------- */
#include <stdio.h>
#include <iostream.h>
#include <fstream.h>
#include <math.h>

#include <ga/GASimpleGA.h>
#include <ga/GARealGenome.h>
#include <ga/GARealGenome.C>



float Objective(GAGenome &);

int main(int argc, char **argv)
{

  cout << "Example weighted sum\n\n";
  cout << "Tries to classify data with weighted sum\n";

  int i,j;

  // iris Data
  char filename[128]="iris.txt";

  // Read in the pattern from the specified file.  File format is pretty
simple:
// two integers that give the height then width of the matrix, then the matrix
// of 1's and 0's (with whitespace inbetween).

  ifstream inStream(filename, ios :: in);
  if(!inStream){
    cerr << "Cannot open " << filename << " for input.\n";
    exit(1);
  }

  int height, width;
  inStream >> height >> width;

  float **target = new float*[width];
  for(i=0; i<width; i++)
    target[i] = new float[height];

  for(j=0; j<height; j++)
    for(i=0; i<width; i++)
      inStream >> target[i][j];

  inStream.close();

  // Print out the pattern to be sure we got the right one.

  cout << "input pattern:\n";
  for(j=0; j<height; j++){
    for(i=0; i<width; i++)
      cout << (target[i][j]) << " ";
    cout << "\n";
  }
  cout << "\n"; cout.flush();




// Declare variables for the GA parameters and set them to some default values.

  int popsize  = 50;
  int ngen     = 200;
  float pmut   = 0.01;
  float pcross = 0.6;


// See if we've been given a seed to use (for testing purposes).  When you
// specify a random seed, the evolution will be exactly the same each time
// you use that seed number.

  unsigned int seed = 0;
  for(int ii=1; ii<argc; ii++) {
    if(strcmp(argv[ii++],"seed") == 0) {
      seed = atoi(argv[ii]);
    }
  }


/*
 *  // Create a phenotype then fill it with the phenotypes we will need to
map to
 *  // the values we read from the file.  The arguments to the add() method
of a
 *  // Bin2Dec phenotype are (1) number of bits, (2) min value, and (3) max
value.
 *  // The phenotype maps a floating-point number onto the number of bits that
 *  // you designate.  Here we just make everything use 8 bits and use the
max and
 *  // min that were used to generate the target values.  You can
experiment with
 *  // the number of bits and max/min values in order to make the GA work
better
 *  // or worse.
 *
 *    GABin2DecPhenotype map;
 *    for(i=0; i<n; i++)
 *      map.add(8, min[i], max[i]);
 */
/*
 *
 *  // Create the template genome using the phenotype map we just made.  The
 *  // GA will use this genome to clone the population that it uses to do the
 *  // evolution.  We pass the objective function to create the genome.  We
 *  // also use the user data function in the genome to keep track of our
 *  // target values.
 *
 *    GABin2DecGenome genome(map, Objective, (void *)target);
 */

GARealAlleleSet limits(0,1);
GARealGenome genome(4,limits,Objective, (void *)target);

 // Now create the GA using the genome, set the parameters, and run it.

   GASimpleGA ga(genome);
   ga.populationSize(popsize);
   ga.nGenerations(ngen);
   ga.pMutation(pmut);
   ga.pCrossover(pcross);
   ga.scoreFilename("bog.dat");
   ga.flushFrequency(20);        // dump scores to disk every 50th generation
   ga.evolve(seed);

 // Dump the results of the GA to the screen.  We print out first what a random
 // genome looks like (so we get a bit of a feel for how hard it is for the
 // GA to find the right values) then we print out the best genome that the
 // GA was able to find.

   genome.initialize();
   cout << "random values in the genome:\n";;
   for(i=0; i<4; i++){
     cout.width(10); cout << genome.gene(i) << " ";
   }
   cout << "\n";

   genome = ga.statistics().bestIndividual();
   cout << "the ga generated:\n";
   for(i=0; i<4; i++){
     cout.width(10); cout << genome.gene(i) << " ";
   }
   cout << "\n\n"; cout.flush();

 // We could print out the genome directly, like this:
 // cout << genome << "\n";

   cout << "best of generation data are in 'bog.dat'\n";

 // Clean up by freeing the memory we allocated.
   delete []target;

   return 0;
}

// This objective tries to maximize each element in the genome.

float Objective(GAGenome& g)
{
  GARealGenome& genome = (GARealGenome&)g;
  float value=0.0,genome_value;
  float **matrix=(float **)g.userData();

  for(int i=0; i<150; i++)
    {
	genome_value=0.0;
	for(int j=0;j<genome.length();j++)
	    {
		genome_value+=matrix[i][j]*genome.gene(j);
		}
	if (i<50)
	{
	    value+=fabs(1.0-genome_value);
	    }
	else
	{
	    value+=fabs(0.0-genome_value);
	    }
     }
  return value;
}

#################################
Aureli Soria Frisch
Fraunhofer IPK
Dept. Pattern Recognition

post: Pascalstr. 8-9, 10587 Berlin, Germany
e-mail:aureli at ipk.fhg.de
fon: +49 30 39006-150
fax: +49 30 3917517
#################################