/* ----------------------------------------------------------------------------  ex15.C  mbwall 28jul94  Copyright (c) 1995-1996  Massachusetts Institute of Technology DESCRIPTION:   This example nearly identical to example 2, but it uses convergence as thestopping criterion for the GA rather than number-of-generations.---------------------------------------------------------------------------- */#include <stdio.h>#include <iostream.h>#include <fstream.h>#include <math.h>#include <ga/ga.h>float objective(GAGenome &);intmain(int argc, char **argv){  cout << "Example 15\n\n";  cout << "This program generates a sequence of random numbers then uses\n";  cout << "a simple GA and binary-to-decimal genome to match the\n";  cout << "sequence.  It uses the convergence of the best-of-generation\n";  cout << "as the criterion for when to stop.\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.// When we use convergence as the completion measure we have to specify both// a convergence value (larger means more converged) and a number-of-gen// which specifies how many generations back to look to calculate the // convergence.  The number of generations back defaults to 20, so you do not// have to set that if you don't want to.  int popsize  = 30;  float pmut   = 0.01;  float pcross = 0.6;  float pconv  = 0.99;		// threshhold for when we have converged  int nconv    = 50;		// how many generations back to look// Generate a sequence of random numbers using the values in the min and max// arrays.  We also set one of them to integer value to show how you can get// explicit integer representations by choosing your number of bits// appropriately.  GARandomSeed(seed);  int i;  int n=7;  float *target = new float[n];  float min[] = {0, 0,   3, -5, 100,    0.001, 0};  float max[] = {1, 100, 3, -2, 100000, 0.010, 7};  for(i=0; i<n; i++)    target[i] = GARandomFloat(min[i], max[i]);  target[6] = GARandomInt((int)min[6], (int)max[6]);// Print out the sequence to be sure we got the right one.  cout << "input sequence:\n";  for(i=0; i<n; i++){    cout.width(10);    cout << target[i] << " ";  }  cout << "\n"; cout.flush();// 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.  GABin2DecGenome genome(map, objective, (void *)target);// Now create the GA using the genome and run it.  GASteadyStateGA ga(genome);  ga.scoreFrequency(1);  ga.flushFrequency(50);  ga.scoreFilename("bog.dat");  ga.populationSize(popsize);  ga.pMutation(pmut);  ga.pCrossover(pcross);  ga.pConvergence(pconv);  ga.nConvergence(nconv);  ga.terminator(GAGeneticAlgorithm::TerminateUponConvergence);  ga.evolve();// Dump the results of the GA to the screen.  genome.initialize();  cout << "random values in the genome:\n";;  for(i=0; i<map.nPhenotypes(); i++){    cout.width(10); cout << genome.phenotype(i) << " ";  }  cout << "\n";  genome = ga.statistics().bestIndividual();  cout << "the ga generated:\n";  for(i=0; i<map.nPhenotypes(); i++){    cout.width(10); cout << genome.phenotype(i) << " ";  }  cout << "\n\n"; cout.flush();// Clean up by freeing the memory we allocated.  delete [] target;  return 0;} // For this objective function we try to match the values in the array of float// that is passed to us as userData.  If the values in the genome map to // values that are close, we return a better score.  We are limited to positive// values for the objective value (because we're using linear scaling), so we// take the reciprocal of the absolute value of the difference between the// value from the phenotype and the value in the sequence.floatobjective(GAGenome & c){  GABin2DecGenome & genome = (GABin2DecGenome &)c;  float *sequence = (float *)c.userData();  float value=genome.nPhenotypes();  for(int i=0; i<genome.nPhenotypes(); i++)    value += 1.0 / (1.0 + fabs(genome.phenotype(i) - sequence[i]));  return(value);}