[galib] Problem of partially intializing population

Essam Almasri masryesam at yahoo.com
Thu Mar 24 05:52:35 EST 2005

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;

// 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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