/* ----------------------------------------------------------------------------  ex19.C  mbwall 5sep95  Copyright (c) 1995-1996  Massachusetts Institute of Technology DESCRIPTION:   This example runs all of the DeJong functions.  You can specify which function you want at the command line.  We use SigmaTruncation scaling to makecertain that negative objective scores won't be a problem for us.   The DeJong functions in this code were pinched from the eval.c file in thepga-2.8 genetic algorithm package (under the terms of the GNU General PublicLicense).---------------------------------------------------------------------------- */#include <stdio.h>#include <stdlib.h>#include <iostream.h>#include <fstream.h>#include <math.h>#include <ga/ga.h>double Gauss(double mean, double variance);float DeJong1(GAGenome &);float DeJong2(GAGenome &);float DeJong3(GAGenome &);float DeJong4(GAGenome &);float DeJong5(GAGenome &);GAGenome::Evaluator objective[5] = {DeJong1,DeJong2,DeJong3,DeJong4,DeJong5};intmain(int argc, char *argv[]){  cout << "Example 19\n\n";  cout << "This program runs the DeJong test problems.\n\n";  cout.flush();// 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]);    }  }  GAParameterList params;  GASteadyStateGA::registerDefaultParameters(params);  params.set(gaNpopulationSize, 30);	// population size  params.set(gaNpCrossover, 0.9);	// probability of crossover  params.set(gaNpMutation, 0.001);	// probability of mutation  params.set(gaNnGenerations, 400);	// number of generations  params.set(gaNpReplacement, 0.25);	// how much of pop to replace each gen  params.set(gaNscoreFrequency, 10);	// how often to record scores  params.set(gaNflushFrequency, 50);	// how often to dump scores to file  params.set(gaNscoreFilename, "bog.dat");  params.parse(argc, argv, gaFalse);    // parse command line for GAlib args  int whichFunction = 0;  for(int i=1; i<argc; i++){    if(strcmp("function", argv[i]) == 0 || strcmp("f", argv[i]) == 0){      if(++i >= argc){        cerr << argv[0] << ": you must specify a function (1-5)\n";        exit(1);      }      else{	whichFunction = atoi(argv[i]) - 1;	if(whichFunction < 0 || whichFunction > 4){	  cerr << argv[0] << ": the function must be in the range [1,5]\n";	  exit(1);	}        continue;      }    }    else if(strcmp("seed", argv[i]) == 0){      if(++i < argc) continue;      continue;    }    else {      cerr << argv[0] << ":  unrecognized arguement: " << argv[i] << "\n\n";      cerr << "valid arguements include standard GAlib arguments plus:\n";      cerr << "  f\twhich function to evaluate (all)\n";      cerr << "parameters are:\n\n" << params << "\n\n";      exit(1);    }  }// Create the phenotype map depending on which dejong function we are going// to be running.  GABin2DecPhenotype map;  switch(whichFunction){  case 0:    map.add(16, -5.12, 5.12);    map.add(16, -5.12, 5.12);    map.add(16, -5.12, 5.12);    break;  case 1:    map.add(16, -2.048, 2.048);    map.add(16, -2.048, 2.048);    break;  case 2:    map.add(16, -5.12, 5.12);    map.add(16, -5.12, 5.12);    map.add(16, -5.12, 5.12);    map.add(16, -5.12, 5.12);    map.add(16, -5.12, 5.12);    break;  case 3:    {      for(int j=0; j<30; j++)	map.add(16, -1.28, 1.28);    }    break;  case 4:    map.add(16, -65.536, 65.536);    map.add(16, -65.536, 65.536);    break;  default:    map.add(16, 0, 0);    break;  }// Now create the sample genome and run the GA.  GABin2DecGenome genome(map, objective[whichFunction]);  GAStatistics stats;  GASteadyStateGA ga(genome);  ga.parameters(params);  GASigmaTruncationScaling scaling;  ga.scaling(scaling);  cout << "running DeJong function number " << (whichFunction + 1) << " ...\n";  ga.evolve(seed);  cout << "the ga generated:\n" << ga.statistics().bestIndividual() << "\n";  cout << "\nthe statistics for the run are:\n" << ga.statistics();  cout << "\nbest-of-generation data are in 'bog.dat'\n";  cout.flush();  return 0;} // DeJong's first function is://// f1(x1,x2,x3) = x1*x1 + x2*x2 + x3*x3//// where each x is in the range [-5.12,5.12]floatDeJong1(GAGenome & c){  GABin2DecGenome & genome = (GABin2DecGenome &)c;  float value=0;  value += genome.phenotype(0) * genome.phenotype(0);  value += genome.phenotype(1) * genome.phenotype(1);  value += genome.phenotype(2) * genome.phenotype(2);  return(value);}// DeJong's second function is://// f2(x1,x2) = 100 * (x1*x1 - x2)^2 + (1 - x1)^2//// where each x is in the range [-2.048,2.048]floatDeJong2(GAGenome & c){  GABin2DecGenome & genome = (GABin2DecGenome &)c;  float value=100.0;  value *= genome.phenotype(0) * genome.phenotype(0) - genome.phenotype(1);  value *= genome.phenotype(0) * genome.phenotype(0) - genome.phenotype(1);  value += (1 - genome.phenotype(0))*(1 - genome.phenotype(0));  return(value);}// DeJong's third function is://// f3(x1,x2,x3,x4,x5) = //      25 + floor(x1) + floor(x2) + floor(x3) + floor(x4) + floor(x5)//// where each x is in the range [-5.12,5.12]floatDeJong3(GAGenome & c){  GABin2DecGenome & genome = (GABin2DecGenome &)c;  float value=25.0;  value -= floor(genome.phenotype(0));  value -= floor(genome.phenotype(1));  value -= floor(genome.phenotype(2));  value -= floor(genome.phenotype(3));  value -= floor(genome.phenotype(4));  return(value);}// DeJong's fourth function is:////             30//            ___// f4(xi) =   \   { i * xi^4 + Gauss(0,1) }//            /__//            i=1//// where each x is in the range [-1.28,1.28]floatDeJong4(GAGenome & c){  GABin2DecGenome & genome = (GABin2DecGenome &)c;  float value = 0;  for(int i=0; i<30; i++){    float v = genome.phenotype(i);    v *= v;			// xi^2    v *= v;			// xi^4    v *= i;    v += Gauss(0,1);    value += v;  }  return(value);}// DeJong's fifth function is (Shekel's foxholes)://////               25                 1//               ___    ---------------------------// f5(x1,x2) =   \              2//               /__           ___ //               j=1      j +  \   (x[i] - a[i][j])^6//                             /__//                             i=1//// where each x is in the range [-65.536,65.536]//static int a[2][25] ={  {-32, -16, 0, 16, 32, -32, -16, 0, 16, 32, -32, -16, 0, 16, 32,   -32, -16, 0, 16, 32, -32, -16, 0, 16, 32        },  {-32, -32, -32, -32, -32, -16, -16, -16, -16, -16,   16, 16, 16, 16, 16, 32, 32, 32, 32, 32  }};floatDeJong5(GAGenome & c){  GABin2DecGenome & genome = (GABin2DecGenome &)c;  float lowtot,prod,total=0.002;  for(int j=0; j<25; j+=1) {    lowtot=1.0 + (double)j;    for(int i=0; i<2; i+=1) {      prod=1.0;      for(int power=0; power<6; power+=1)        prod*=genome.phenotype(i)-a[i][j];      lowtot+=prod;    }    total+=1.0/lowtot;  }  return(500.0 - (1.0/total));}// Return a number from gaussian distribution.  This code was pinched from the// GNU libg++ Normal.cc implementation of a normal distribution.  That is, in// turn, based on Simulation, Modelling & Analysis by Law & Kelton, pp259.//// My random number generator (actually just the system's) isn't as good as // that in the libg++, but this should be OK for this purpose.doubleGauss(double mean, double variance){  for(;;) {    double u1 = GARandomDouble();    double u2 = GARandomDouble();    double v1 = 2 * u1 - 1;    double v2 = 2 * u2 - 1;    double w = (v1 * v1) + (v2 * v2);    if (w <= 1) {      double y = sqrt( (-2 * log(w)) / w);      double x1 = v1 * y;//      double x2 = v2 * y;       // we don't use this one      return(x1 * sqrt(variance) + mean);    }  }}