ex15.cpp

这是一个面向对象的GA遗传算法库GAlib: A C++ Library of Genetic Algorithm Components)

/* ----------------------------------------------------------------------------
  ex15.C
  mbwall 28jul94
  Copyright (c) 1995-1996  Massachusetts Institute of Technology

 DESCRIPTION:
   This example nearly identical to example 2, but it uses convergence as the
stopping criterion for the GA rather than number-of-generations.
---------------------------------------------------------------------------- */
#include <stdio.h>
#include <math.h>
#include <ga/ga.h>
#include <ga/std_stream.h>

#define cout STD_COUT

float objective(GAGenome &);

int
main(int argc, char **argv)
{
  cout << "Example 15\n\n";
  cout << "This program generates a sequence of random numbers then uses\n";
  cout << "a simple GA and binary-to-decimal genome to match the\n";
  cout << "sequence.  It uses the convergence of the best-of-generation\n";
  cout << "as the criterion for when to stop.\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.

  unsigned int seed = 0;
  for(int ii=1; ii<argc; ii++) {
    if(strcmp(argv[ii++],"seed") == 0) {
      seed = atoi(argv[ii]);
    }
  }

// Declare variables for the GA parameters and set them to some default values.
// When we use convergence as the completion measure we have to specify both
// a convergence value (larger means more converged) and a number-of-gen
// which specifies how many generations back to look to calculate the 
// convergence.  The number of generations back defaults to 20, so you do not
// have to set that if you don't want to.

  int popsize  = 30;
  float pmut   = 0.01;
  float pcross = 0.6;
  float pconv  = 0.99;		// threshhold for when we have converged
  int nconv    = 50;		// how many generations back to look

// Generate a sequence of random numbers using the values in the min and max
// arrays.  We also set one of them to integer value to show how you can get
// explicit integer representations by choosing your number of bits
// appropriately.

  GARandomSeed(seed);
  int i;
  int n=7;
  float *target = new float[n];
  float min[] = {0, 0,   3, -5, 100,    0.001, 0};
  float max[] = {1, 100, 3, -2, 100000, 0.010, 7};
  for(i=0; i<n; i++)
    target[i] = GARandomFloat(min[i], max[i]);
  target[6] = GARandomInt((int)min[6], (int)max[6]);

// Print out the sequence to be sure we got the right one.

  cout << "input sequence:\n";
  for(i=0; i<n; i++){
    cout.width(10);
    cout << target[i] << " ";
  }
  cout << "\n"; cout.flush();

// Create a phenotype then fill it with the phenotypes we will need to map to
// the values we read from the file.  The arguments to the add() method of a
// Bin2Dec phenotype are (1) number of bits, (2) min value, and (3) max value.
// The phenotype maps a floating-point number onto the number of bits that
// you designate.  Here we just make everything use 8 bits and use the max and
// min that were used to generate the target values.  You can experiment with
// the number of bits and max/min values in order to make the GA work better
// or worse.

  GABin2DecPhenotype map;
  for(i=0; i<n; i++)
    map.add(8, min[i], max[i]);

// Create the template genome using the phenotype map we just made.  The
// GA will use this genome to clone the population that it uses to do the
// evolution.

  GABin2DecGenome genome(map, objective, (void *)target);

// Now create the GA using the genome and run it.

  GASteadyStateGA ga(genome);
  ga.scoreFrequency(1);
  ga.flushFrequency(50);
  ga.scoreFilename("bog.dat");
  ga.populationSize(popsize);
  ga.pMutation(pmut);
  ga.pCrossover(pcross);
  ga.pConvergence(pconv);
  ga.nConvergence(nconv);
  ga.terminator(GAGeneticAlgorithm::TerminateUponConvergence);
  ga.evolve();

// Dump the results of the GA to the screen.

  genome.initialize();
  cout << "random values in the genome:\n";;
  unsigned int jj=0;
  for(jj=0; jj<map.nPhenotypes(); jj++){
    cout.width(10); cout << genome.phenotype(jj) << " ";
  }
  cout << "\n";
  genome = ga.statistics().bestIndividual();
  cout << "the ga generated:\n";
  for(jj=0; jj<map.nPhenotypes(); jj++){
    cout.width(10); cout << genome.phenotype(jj) << " ";
  }
  cout << "\n\n"; cout.flush();

// Clean up by freeing the memory we allocated.

  delete [] target;
  return 0;
}
 

// For this objective function we try to match the values in the array of float
// that is passed to us as userData.  If the values in the genome map to 
// values that are close, we return a better score.  We are limited to positive
// values for the objective value (because we're using linear scaling), so we
// take the reciprocal of the absolute value of the difference between the
// value from the phenotype and the value in the sequence.
float
objective(GAGenome & c)
{
  GABin2DecGenome & genome = (GABin2DecGenome &)c;
  float *sequence = (float *)c.userData();

  float value=genome.nPhenotypes();
  for(int i=0; i<genome.nPhenotypes(); i++)
    value += 1.0 / (1.0 + fabs(genome.phenotype(i) - sequence[i]));
  return(value);
}