ex9.cpp

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

/* ----------------------------------------------------------------------------
  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 value
of a continuous function in two variables.  This program uses a binary-to-
decimal genome.
---------------------------------------------------------------------------- */
#include <stdio.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 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)
//
float
objective(GAGenome & c)
{
  GABin2DecGenome & genome = (GABin2DecGenome &)c;

  float y;
  y = -genome.phenotype(0) * genome.phenotype(0);
  y -= genome.phenotype(1) * genome.phenotype(1);
  return y;
}