#include <stdio.h>
#include <iostream.h>
#include <fstream.h>
#include <math.h>

#include <ga/GASimpleGA.h>
#include <ga/GABin2DecGenome.h>

float Objective(GAGenome &);

int
main(int argc, char **argv)
{
  cout << "This program finds the optimum of the function\n";
  cout << "f(x1,x2)=1.9*(1.35+exp(x1)*sin(13*(x1-0.6)^2)*exp(-x2)*sin(7*x2))\n";
  cout << "in the domain (x1,x2) in ([0,1],[0,1]) using a SimpleGA \n\n";

// 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]);
    }
  }

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

  int popsize  = 25;
  int ngen     = 100;
  float pmut   = 0.01;
  float pcross = 0.6;

// 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.  You can experiment with
// the number of bits and max/min values in order to make the GA work better
// or worse.

  GABin2DecPhenotype pheno;
  pheno.add(12, 0, 1); // add 12 bits to encode x1 in [0,1]
  pheno.add(12, 0, 1); // add 12 bits to encode x2 in [0,1]

// Create the template genome using the phenotype pheno 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.  

  GABin2DecGenome genome(pheno, Objective);

// 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("ga.dat");
  ga.flushFrequency(50);	// 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();
 
  genome = ga.statistics().bestIndividual();
  cout << "the ga generated:\n";
  cout << "genotype=" << ((GA1DBinaryStringGenome) genome) << endl;
  cout << "phenotype=" << genome << endl;

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

  return 0;
}
 

// Here goes our objective function

float
Objective(GAGenome& g)
{
  GABin2DecGenome & genome = (GABin2DecGenome &)g;

  return 1.9*(1.35+exp(genome.phenotype(0))*sin(13*(genome.phenotype(0)-0.6)*(genome.phenotype(0)-0.6))*exp(-genome.phenotype(1))*sin(7*genome.phenotype(1)));

}