[galib] Problem of partially intializing population

[Essam Almasri]

Hallo

 

I have the problem of initial population. I have 30 population size and want to initial only 2 individuals (not all individual) with values, e.g. the firs one is (0.2,0.2) and the second one is (0.1, 0.1); each individual has 2 control variables. How can one do that?. Here is the example, from galib, I try. GABin2DecGenome is used. 

 

/* ----------------------------------------------------------------------------

  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;

}


		
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