/* ----------------------------------------------------------------------------  ex9.C  mbwall 10apr95  Copyright 1995-1996 Massachusetts Institute of Technology DESCRIPTION:   Sample program that illustrates how to use a GA to find the maximum valueof a continuous function in two variables.  This program uses a binary-to-decimal genome.---------------------------------------------------------------------------- */#include <stdio.h>#include <iostream.h>#include <fstream.h>#include <ga/ga.h>float objective(GAGenome &);intmain(int argc, char **argv){  cout << "Example 9\n\n";  cout << "This program finds the maximum value in the function\n";  cout << "  y = - x1^2 - x2^2\n";  cout << "with the constraints\n";  cout << "     -5 <= x1 <= 5\n";  cout << "     -5 <= x2 <= 5\n";  cout << "\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 i=1; i<argc; i++) {    if(strcmp(argv[i++],"seed") == 0) {      seed = atoi(argv[i]);    }  }// Declare variables for the GA parameters and set them to some default values.  int popsize  = 30;  int ngen     = 100;  float pmut   = 0.01;  float pcross = 0.6;// Create a phenotype for two variables.  The number of bits you can use to// represent any number is limited by the type of computer you are using.  In// this case, we use 16 bits to represent a floating point number whose value// can range from -5 to 5, inclusive.  The bounds on x1 and x2 can be applied// here and/or in the objective function.  GABin2DecPhenotype map;  map.add(16, -5, 5);  map.add(16, -5, 5);// Create the template genome using the phenotype map we just made.  GABin2DecGenome genome(map, objective);// Now create the GA using the genome and run it.  We'll use sigma truncation// scaling so that we can handle negative objective scores.  GASimpleGA ga(genome);  GASigmaTruncationScaling scaling;  ga.populationSize(popsize);  ga.nGenerations(ngen);  ga.pMutation(pmut);  ga.pCrossover(pcross);  ga.scaling(scaling);  ga.scoreFilename("bog.dat");  ga.scoreFrequency(10);  ga.flushFrequency(50);  ga.evolve(seed);// Dump the results of the GA to the screen.  genome = ga.statistics().bestIndividual();  cout << "the ga found an optimum at the point (";  cout << genome.phenotype(0) << ", " << genome.phenotype(1) << ")\n\n";  cout << "best of generation data are in '" << ga.scoreFilename() << "'\n";  return 0;} // This objective function tries to maximize the value of the function////                  y = -(x1*x1 + x2*x2)//floatobjective(GAGenome & c){  GABin2DecGenome & genome = (GABin2DecGenome &)c;  float y;  y = -genome.phenotype(0) * genome.phenotype(0);  y -= genome.phenotype(1) * genome.phenotype(1);  return y;}