/* ----------------------------------------------------------------------------  ex23.C  mbwall 5jan96  Copyright (c) 1995-1996  Massachusetts Institute of Technology DESCRIPTION:   This example shows how to use max/min feature of GAlib to maximize or minimize your objective functions.---------------------------------------------------------------------------- */#include <stdio.h>#include <iostream.h>#include <fstream.h>#include <math.h>#include <ga/ga.h>#include <ga/GARealGenome.h>#include <ga/GARealGenome.C>#ifndef M_PI#define M_PI            3.14159265358979323846#endif#define MIN_VALUE -2#define MAX_VALUE 2#define INC       0.005float Objective(GAGenome &);float Comparator(const GAGenome&, const GAGenome&);intmain(int argc, char** argv){  cout << "Example 23\n\n";  cout << "This program tries to maximize or minimize, depending on the\n";  cout << "command line argument that you give it.  Use the command-line\n";  cout << "argument 'mm -1' to minimize (the default for this example), or\n";  cout << "'mm 1' to maximize.  The objective function is a simple \n";  cout << "sinusoidal.\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.  for(int jj=1; jj<argc; jj++) {    if(strcmp(argv[jj++],"seed") == 0) {      GARandomSeed((unsigned int)atoi(argv[jj]));    }  }  ofstream outfile;  GARealAlleleSet alleles(MIN_VALUE, MAX_VALUE);  GARealGenome genome(1, alleles, Objective);  GASharing scale(Comparator);  GASimpleGA ga(genome);  ga.minimize();		// by default we want to minimize the objective  ga.scaling(scale);		// set the scaling method to our sharing  ga.populationSize(50);	// how many individuals in the population  ga.nGenerations(25);		// number of generations to evolve  ga.pMutation(0.001);		// likelihood of mutating new offspring  ga.pCrossover(0.9);		// likelihood of crossing over parents  ga.scoreFilename("bog.dat");	// name of file for scores  ga.scoreFrequency(1);		// keep the scores of every generation  ga.flushFrequency(10);	// specify how often to write the score to disk  ga.selectScores(GAStatistics::AllScores);  ga.parameters(argc, argv, gaTrue); // parse commands, complain if bogus args  ga.initialize();// dump the initial population to file  cout << "printing initial population to file..." << endl;  outfile.open("popi.dat", (ios::out | ios::trunc));  for(int ii=0; ii<ga.population().size(); ii++){    genome = ga.population().individual(ii);    outfile << genome.gene(0) << "\t" << genome.score() << "\n";  }  outfile.close();  while(!ga.done()) ga.step();// dump the final population to file  cout << "printing final population to file..." << endl;  outfile.open("popf.dat", (ios::out | ios::trunc));  for(int i=0; i<ga.population().size(); i++){    genome = ga.population().individual(i);    outfile << genome.gene(0) << "\t" << genome.score() << "\n";  }  outfile.close();// dump the function to file so you can plot the population on it  cout << "printing function to file..." << endl;  outfile.open("sinusoid.dat", (ios::out | ios::trunc));  for(float x=MIN_VALUE; x<=MAX_VALUE; x+=INC){    outfile << genome.gene(0,x) << "\t" << genome.score() << "\n";  }  outfile.close();  return 0;} // This objective function returns the sin of the value in the genome.floatObjective(GAGenome& g){  GARealGenome& genome = (GARealGenome &)g;  return 1 + sin(genome.gene(0)*2*M_PI);}// The comparator returns a number in the interval [0,1] where 0 means that// the two genomes are identical (zero diversity) and 1 means they are // completely different (maximum diversity).floatComparator(const GAGenome& g1, const GAGenome& g2) {  GARealGenome& a = (GARealGenome &)g1;  GARealGenome& b = (GARealGenome &)g2;  return exp( -(a.gene(0) - b.gene(0)) * (a.gene(0) - b.gene(0)) / 1000.0);}