/* ----------------------------------------------------------------------------  ex7.C  mbwall 19jan95  Copyright 1995-1996  Massachusetts Institute of Technology DESCRIPTION:   This example functions the same way as example 3, but this example showshow to use some of the other member functions in the GA library.  We also doa few more fancy things with the genome (ie use the read/write methods).---------------------------------------------------------------------------- */#include <stdio.h>#include <stdlib.h>#include <iostream.h>#include <fstream.h>#include <ga/ga.h>float objective(GAGenome &);intmain(int argc, char *argv[]){  cout << "Example 7\n\n";  cout << "This program reads in a data file then runs a steady-state GA \n";  cout << "whose objective function tries to match the pattern of bits that\n";  cout << "are in the data file.\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.  for(int ii=1; ii<argc; ii++) {    if(strcmp(argv[ii++],"seed") == 0) {      GARandomSeed((unsigned int)atoi(argv[ii]));    }  }// Set the default values of the parameters.  int i,j;  GAParameterList params;  GASteadyStateGA::registerDefaultParameters(params);  params.set(gaNpopulationSize, 50);    // number of individuals in population  params.set(gaNpCrossover, 0.8);       // likelihood of doing crossover  params.set(gaNpMutation, 0.001);	// probability of mutation  params.set(gaNnGenerations, 200);	// number of generations  params.set(gaNscoreFrequency, 20);	// how often to record scores  params.set(gaNflushFrequency, 50);    // how often to flush scores to file  params.set(gaNscoreFilename, "bog.dat");  params.parse(argc, argv, gaFalse);  char datafile[128] = "smiley.txt";  char parmfile[128] = "";// Parse the command line for arguments.  We look for two possible arguments// (after the parameter list has grabbed everything it recognizes).  One is the// name of a data file from which to read, the other is the name of a // parameters file from which to read.  Notice that any parameters in the // parameters file will override the defaults above AND any entered on the// command line.  for(i=1; i<argc; i++){    if(strcmp("dfile", argv[i]) == 0){      if(++i >= argc){        cerr << argv[0] << ": the data file option needs a filename.\n";        exit(1);      }      else{	sprintf(datafile, argv[i]);        continue;      }    }    else if(strcmp("pfile", argv[i]) == 0){      if(++i >= argc){        cerr << argv[0] << ": the parameters file option needs a filename.\n";        exit(1);      }      else{	sprintf(parmfile, argv[i]);	params.read(parmfile);        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 GAlib arguments plus:\n";      cerr << "  dfile\tdata file from which to read (" << datafile << ")\n";      cerr << "  pfile\tparameters file (" << parmfile << ")\n\n";      cerr << "default parameters are:\n" << params << "\n\n";      exit(1);    }  }// Read in the pattern from the specified file.  File format is pretty simple:// two integers that give the height then width of the matrix, then the matrix// of 1's and 0's (with whitespace inbetween).//   Here we use a binary string genome to store the desired pattern.  This// shows how you can read in directly from a stream into a genome.  (This can // be useful in a population initializer when you want to bias your population)  ifstream inStream(datafile, ios :: in);  if(!inStream){    cerr << "Cannot open " << datafile << " for input.\n";    exit(1);  }  int height, width;  inStream >> height >> width;  GA2DBinaryStringGenome target(width, height);  inStream >> target;  inStream.close();// Print out the pattern to be sure we got the right one.  cout << "input pattern:\n";  for(j=0; j<height; j++){    for(i=0; i<width; i++)      cout << (target.gene(i,j) == 1 ? '*' : ' ') << " ";    cout << "\n";  }  cout << "\n"; cout.flush();// Now create the first genome and the GA.  When we create the genome, we give// it not only the objective function but also 'user data'.  In this case, the// user data is a pointer to our target pattern.  From a C++ point of view it// would be better to derive a new genome class with its own data, but here we// just want a quick-and-dirty implementation, so we use the user-data.  GA2DBinaryStringGenome genome(width, height, objective, (void *)&target);  GASteadyStateGA ga(genome);// When you use a GA with overlapping populations, the default score// frequency (how often the best of generation score is recorded) defaults// to 100.  We use the parameters member function to change this value (along// with all of the other parameters we set above).  You can also change the // score frequency using the scoreFrequency member function of the GA.  Each of// the parameters can be set individually if you like.//   Here we just use the values that were set in the parameter list.  ga.parameters(params);// The default selection method is RouletteWheel.  Here we set the selection// method to TournamentSelection.  GATournamentSelector selector;  ga.selector(selector);// The following member functions override the values that were set using the// parameter list.  They are commented out here so that you can see how they // would be used.// We can control the amount of overlap from generation to generation using the// pReplacement member function.  If we specify a value of 1 (100%) then the// entire population is replaced each generation.  Notice that the percentage// must be high enough to have at least one individual produced in each // generation.  If not, the GA will post a warning message.//  ga.pReplacement(0.3);// Often we use the number of generations as the criterion for terminating the// GA run.  Here we override that and tell the GA to use convergence as a // termination criterion.  Note that you can pass ANY function as the stopping// criterion (as long as it has the correct signature).//   Notice that the values we set here for p- and n-convergence override those// that we set in the parameters object.  ga.terminator(GAGeneticAlgorithm::TerminateUponConvergence);//  ga.pConvergence(0.99);	        // converge to within 1%//  ga.nConvergence(100);		// within the last 100 generations// Evolve the GA 'by hand'.  When you use this method, be sure to initialize// the GA before you start to evolve it.  You can print out the status of the// current population by using the ga.population() member function.  This is// also how you would print the status of the GA to disk along the way (rather// than waiting to the end then printing the scores, for example).  ga.initialize();  while(!ga.done()){    ++ga;  }  ga.flushScores();// Now that the GA is finished, we set our default genome to equal the contents// of the best genome that the GA found.  Then we print it out.  genome = ga.statistics().bestIndividual();  cout << "the ga generated:\n";  for(j=0; j<height; j++){    for(i=0; i<width; i++)      cout << (genome.gene(i,j) == 1 ? '*' : ' ') << " ";    cout << "\n";  }  cout << "\n"; cout.flush();  cout << "best of generation data are in '" << ga.scoreFilename() << "'\n";  return 0;} // This objective function just tries to match the genome to the pattern in the// user data.floatobjective(GAGenome & c){  GA2DBinaryStringGenome & genome = (GA2DBinaryStringGenome &)c;  GA2DBinaryStringGenome * pattern =     (GA2DBinaryStringGenome *)c.userData();  float value=0.0;  for(int i=0; i<genome.width(); i++)    for(int j=0; j<genome.height(); j++)      value += (float)(genome.gene(i,j) == pattern->gene(i,j));  return(value);}