• Main Page
  • Modules
  • Classes
  • Files
  • File List

D:/Projekt/ECF_trunk/ECF/floatingpoint/FloatingPointCrsBga.cpp

00001 #include "../ECF_base.h"
00002 #include "FloatingPoint.h"
00003 #include <math.h>
00004 
00005 namespace FloatingPoint
00006 {
00007 
00008 void FloatingPointCrsBga::registerParameters(StateP state)
00009 {
00010     myGenotype_->registerParameter(state, "crx.bga", (voidP) new double(0), ECF::DOUBLE);
00011 }
00012 
00013 
00014 bool FloatingPointCrsBga::initialize(StateP state)
00015 {
00016     voidP sptr = myGenotype_->getParameterValue(state, "crx.bga");
00017     probability_ = *((double*)sptr.get());
00018 
00019     return true;
00020 }
00021 
00022 
00023 bool FloatingPointCrsBga::mate(GenotypeP gen1, GenotypeP gen2, GenotypeP child)
00024 {
00025     FloatingPoint* p1 = (FloatingPoint*) (gen1.get());
00026     FloatingPoint* p2 = (FloatingPoint*) (gen2.get());
00027     FloatingPoint* ch = (FloatingPoint*) (child.get());
00028 
00029     int a;
00030     uint size = (uint) p1->realValue.size();
00031     double rang = 0.5 * (p1->getUBound() - p1->getLBound()); // gornja - donja granica
00032     double gama = 0, lambda = 0, b;
00033 
00034     for (int i = 0; i <= 15; i++) {
00035         a = state_->getRandomizer()->getRandomInteger(1, 16);
00036         if (a == 16)  //sansa da bude jedan treba biti 1/16
00037             a = 1;
00038         else
00039             a = 0;
00040         gama = gama + a * pow((double) 2., -i);
00041     }
00042 
00043     double norm = 0;
00044     for(uint i = 0; i < size; i++)
00045         norm += pow(p1->realValue[i] - p2->realValue[i], 2);
00046     norm = sqrt(norm);
00047 
00048     // scaling safeguard 
00049     if(norm < 10e-9)
00050         norm = 1;
00051 
00052     FloatingPoint *better, *worse;
00053     FitnessP parent2 = state_->getContext()->secondParent->fitness;
00054     if(state_->getContext()->firstParent->fitness->isBetterThan(parent2)) {
00055         better = p1;
00056         worse = p2;
00057     } else {
00058         better = p2;
00059         worse = p1;
00060     }
00061 
00062     for (uint i = 0; i < size; i++) {
00063         // worse gene minus better gene divided with norm
00064         lambda = (worse->realValue[i] - better->realValue[i]) / norm;
00065 
00066         b = state_->getRandomizer()->getRandomDouble();
00067         if (b <= 0.9) // minus with probability 0.9
00068             ch->realValue[i] = better->realValue[i] - rang * gama * lambda;
00069         else
00070             ch->realValue[i] = better->realValue[i] + rang * gama * lambda;
00071     }
00072 
00073     return true;
00074 }
00075 
00076 }

Generated on Fri Jul 5 2013 09:34:23 for ECF by  doxygen 1.7.1