[galib] CONSTRAINS DEFINITION

[Markku Laukkanen]

On Sun, 9 Nov 2003, Carlos Andres wrote:

> Hi everyone, I saw the example 9 that comes witth galib, and I need to do something similar, my problem is that in this example they uses  constrains that have only one variable and I need to represent constrains that uses two or more variables, like this:
>  
>                 X1+6*X2-13*X3 = 20
>  
> How can I do that?
>  
> using the GABin2DecPhenotype?
>  
> Please be the clearest as you can, Im new. Thanks
>  
> Carlos 
>  
>              
>  

Not really a nice solution, but works maybe like you want
	PKY                                                                                

-------------- next part --------------
/* ----------------------------------------------------------------------------
  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 <iostream.h>
#include <fstream.h>
#include <ga/ga.h>

float objective(GAGenome &);

int
main(int argc, char **argv)
{
  /*
    This program tries to calculate 
    X1+6*X2-13*X3 - 20 = 0;
  */


// 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  = 1000;
  int ngen     = 100;
  float pmut   = 0.1;
  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);
  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) << ", " << genome.phenotype(2) << ")\n\n";
  cout << "the value is " << genome.phenotype(0) + 6 * genome.phenotype(1) - 13 * genome.phenotype(2) - 20 << endl;
  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;

  /* So the function was X1+6*X2-13*X3 - 20 = 0;*/
  float y = -20;
   y += genome.phenotype(0);
   y += 6 * genome.phenotype(1);
   y -= 13 * genome.phenotype(2);   
   if (y == 0) {
     cout << "Exact match = " << genome.phenotype(0) << ", " << genome.phenotype(1) << ", " << genome.phenotype(2) << ")\n\n";
     cout << "the value is " << genome.phenotype(0) + 6 * genome.phenotype(1) - 13 * genome.phenotype(2) - 20 << endl;
     exit(0);
   } else 
     return 1 / y;
}