/* ----------------------------------------------------------------------------  ex10.C  mbwall 10apr95  Copyright (c) 1995-1996  Massachusetts Institute of Technology DESCRIPTION:   Sample program that illustrates how to use a distance function to do speciation.  This example does both gene-based and phenotype-based distancecalculations.  The differences are quite interesting.  Also, the length of thebit string (i.e. the size of the search space) is also a significant factor inthe performance of the speciation methods.   Notice that Goldberg describes fitness scaling speciation in the context ofa simple genetic algorithm.  You can try using it with a steady-state algorithm, but you'll get bogus results unless you modify the algorithm.---------------------------------------------------------------------------- */#include <stdlib.h>#include <stdio.h>#include <iostream.h>#include <fstream.h>#include <math.h>#include <ga/ga.h>#define USE_RAW_SINE#ifndef M_PI#define M_PI            3.14159265358979323846#endif#define NBITS     8#ifdef USE_RAW_SINE#define FUNCTION  Function1#define MIN_VALUE 0#define MAX_VALUE 5#else#define FUNCTION  Function2#define MIN_VALUE -100#define MAX_VALUE 100#endiffloat Function1(float);float Function2(float);float Objective(GAGenome &);float BitDistance(const GAGenome & a, const GAGenome & b);float PhenotypeDistance(const GAGenome & a, const GAGenome & b);intmain(int argc, char **argv){  cout << "Example 10\n\n";  cout << "This program uses sharing to do speciation.  The objective\n";  cout << "function has more than one optimum, so different genomes\n";  cout << "may have equally high scores.  Speciation keeps the population\n";  cout << "from clustering at one optimum.\n";  cout << "  Both gene-wise and phenotype-wise distance functions are used.\n";  cout << "  Populations from all three runs are written to the files \n";  cout << "pop.nospec.dat, pop.genespec.dat and pop.phenespec.dat.  The\n";  cout << "function is written to the file sinusoid.dat\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 ii=1; ii<argc; ii++) {    if(strcmp(argv[ii++],"seed") == 0) {      GARandomSeed((unsigned int)atoi(argv[ii]));    }  }  int i;  char filename[32] = "sinusoid.dat";  char popfilename1[32] = "pop.nospec.dat";  char popfilename2[32] = "pop.genespec.dat";  char popfilename3[32] = "pop.phenespec.dat";  ofstream outfile;// 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 the minimum to maximum value as defined by the macros.  GABin2DecPhenotype map;  map.add(NBITS, MIN_VALUE, MAX_VALUE);// Create the template genome using the phenotype map we just made.  GABin2DecGenome genome(map, Objective);// Now create the GA using the genome and set all of the parameters.// You'll get different results depending on the type of GA that you use.  The// steady-state GA tends to converge faster (depending on the type of replace-// ment method you specify).    GASimpleGA ga(genome);  ga.set(gaNpopulationSize, 200);  ga.set(gaNnGenerations, 50);  ga.set(gaNpMutation, 0.001);  ga.set(gaNpCrossover, 0.9);  ga.parameters(argc, argv);// Do the non-speciated and write to file the best-of-generation.  cout << "running with no speciation (fitness proportionate scaling)...\n";  cout.flush();  GALinearScaling lin;  ga.scaling(lin);  ga.evolve();  genome = ga.statistics().bestIndividual();  cout << "the ga found an optimum at the point "<<genome.phenotype(0)<<endl;  outfile.open(popfilename1, (ios::out | ios::trunc));  if(outfile.fail()){    cerr << "Cannot open " << popfilename1 << " for output.\n";    exit(1);  }  for(i=0; i<ga.population().size(); i++){    outfile<<((GABin2DecGenome&)(ga.population().individual(i))).phenotype(0);    outfile << "\t";    outfile << ga.population().individual(i).score() << "\n";  }  outfile.close();// Now do speciation using the gene-wise distance function  cout << "running the ga with speciation (sharing using bit-wise)...\n";  cout.flush();  GASharing bitSharing(BitDistance);  ga.scaling(bitSharing);  ga.evolve();  genome = ga.statistics().bestIndividual();  cout << "the ga found an optimum at the point "<<genome.phenotype(0)<<endl;  outfile.open(popfilename2, (ios::out | ios::trunc));  if(outfile.fail()){    cerr << "Cannot open " << popfilename2 << " for output.\n";    exit(1);  }  for(i=0; i<ga.population().size(); i++){    outfile<<((GABin2DecGenome&)(ga.population().individual(i))).phenotype(0);    outfile << "\t";    outfile << ga.population().individual(i).score() << "\n";  }  outfile.close();// Now do speciation using the phenotype-wise distance function  cout << "running the ga with speciation (sharing using phenotype-wise)...\n";  cout.flush();  GASharing pheneSharing(PhenotypeDistance);  ga.scaling(pheneSharing);  ga.evolve();  genome = ga.statistics().bestIndividual();  cout << "the ga found an optimum at the point "<<genome.phenotype(0)<<endl;  outfile.open(popfilename3, (ios::out | ios::trunc));  if(outfile.fail()){    cerr << "Cannot open " << popfilename3 << " for output.\n";    exit(1);  }  for(i=0; i<ga.population().size(); i++){    outfile<<((GABin2DecGenome&)(ga.population().individual(i))).phenotype(0);    outfile << "\t";    outfile << ga.population().individual(i).score() << "\n";  }  outfile.close();// Now dump the function to file for comparisons  cout << "dumping the function to file..." << endl;  outfile.open(filename, (ios::out | ios::trunc));  if(outfile.fail()){    cerr << "Cannot open " << filename << " for output.\n";    exit(1);  }  float inc = MAX_VALUE - MIN_VALUE;  inc /= pow(2.0,NBITS);  for(float x=MIN_VALUE; x<=MAX_VALUE; x+=inc){    outfile << x << "\t" << FUNCTION (x) << "\n";  }  outfile << "\n";  outfile.close();  return 0;} // You can choose between one of two sinusoidal functions.  The first one has// peaks of equal amplitude.  The second is modulated.floatObjective(GAGenome & c){  GABin2DecGenome & genome = (GABin2DecGenome &)c;  return FUNCTION (genome.phenotype(0));}floatFunction1(float v) {  return 1 + sin(v*2*M_PI);}floatFunction2(float v) {  float y;  y = 100.0 * exp(-fabs(v) / 50.0) * (1.0 - cos(v * M_PI * 2.0 / 25.0));  if(v < -100 || v > 100) y = 0;  if(y < 0) y = 0;  return y+0.00001;}// Here are a couple of possible distance functions for this problem.  One of// them uses the genes to determine the same-ness, the other uses the// phenotypes to determine same-ness.  If the genomes are the same, then// we return a 0.  If they are completely different then we return a 1.//   In either case, you should be sure that the distance function will return// values only between 0 and 1 inclusive.  If your function returns values// outside these limits, the GA will produce bogus results and it WILL NOT warn// you that your distance function is brain-dead!// This distance function uses the genes to determine same-ness.  All we do // is check to see if the bit strings are identical.floatBitDistance(const GAGenome & c1, const GAGenome & c2){  GABin2DecGenome & a = (GABin2DecGenome &)c1;  GABin2DecGenome & b = (GABin2DecGenome &)c2;  float x=0;  for(int i=a.length()-1; i>=0; i--)    x += (a[i] != b[i] ? 1 : 0);  return x/a.length();}// This distance function looks at the phenotypes rather than the genes of the// genome.  This distance function will try to drive them to extremes.floatPhenotypeDistance(const GAGenome & c1, const GAGenome & c2){  GABin2DecGenome & a = (GABin2DecGenome &)c1;  GABin2DecGenome & b = (GABin2DecGenome &)c2;  return fabs(a.phenotype(0) - b.phenotype(0)) / (MAX_VALUE-MIN_VALUE);}