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GeneticAlgorithm.cpp
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GeneticAlgorithm.cpp
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#include "GeneticAlgorithm.h"
#include <iostream>
#include <iomanip>
#include <sstream>
GeneticAlgorithm::GeneticAlgorithm(void)
{
// ustaw wartosc domyslna
bestFitness = inf;
}
GeneticAlgorithm::~GeneticAlgorithm(void)
{
}
// zainicjalizuj wartosci parametrow, generuj populacje poczatkowa
void GeneticAlgorithm::Load(const int& cratio, const int& mratio,
const int& psize, const int& gener, const int& csize, const int& ssize,
const std::string& path)
{
SetParameters(cratio, mratio, psize, gener, csize, ssize);
CreatePopulation();
record.Open(path.c_str());
}
// ustaw dane wartosci parametrow
void GeneticAlgorithm::SetParameters(const int& cratio, const int& mratio,
const int& psize, const int& gener, const int& csize, const int& ssize)
{
numberGenerations = gener;
populationSize = psize;
chromosomeSize = csize;
crossoverRatio = cratio;
mutationRatio = mratio;
selection_size = ssize;
}
void GeneticAlgorithm::CreatePopulation()
{
pop.CreateRandomPopulation(populationSize);
}
// wykonaj algorytm
void GeneticAlgorithm::Start()
{
for (int i = 0; i < numberGenerations; i++)
{
RecordResult(Evaluate(), i);
Select();
Crossover();
Mutate();
}
}
// zapisz statystyki do pliku lub wyswietl w konsoli
void GeneticAlgorithm::RecordResult(const double& result,
const int& gener)
{
// zapis do pliku
std::stringstream ss;
ss << gener << " " << best_x << " " << best_y << " " << result;
record.Write((char*)ss.str().c_str());
// wydruk w konsoli
int precision = 7;
std::cout << "generacja = " << std::setw(6) << gener
<< " x = " << std::fixed << std::setprecision(precision) << std::setw(precision + 1) << best_x
<< " y = " << std::fixed << std::setprecision(precision) << std::setw(precision + 1) << best_y
<< " f(x,y) = " << std::setw(precision + 1) << bestFitness
<< std::endl;
}
// ocen sprawnosc chromosomow w populacji
double GeneticAlgorithm::Evaluate()
{
float bx = -1;
float by = -1;
double best = pop.EvaluatePopulation(bx, by);
if (best < bestFitness)
{
bestFitness = best;
best_x = bx;
best_y = by;
}
return bestFitness;
}
// wybierz chromosomy z populacji na podstawie ich sprawnosci
void GeneticAlgorithm::Select()
{
int i = 0;
while (i < selection_size)
{
// wybierz pare chromosomow do porownania
int index1 = rand() % populationSize;
int index2 = rand() % populationSize;
while (index1 == index2)
{
index2 = rand() % populationSize;
}
double fitness1 = pop.GetChromosomeFitness(index1);
double fitness2 = pop.GetChromosomeFitness(index2);
// celem jest znalezienie takich x i y, ktore minimalizuja funkcje
// stad im wieksza zwrocona wartosc, tym mniejsza sprawnosc
if (fitness1 > fitness2)
{
// skopiuj elementy chromosomu 1 do chromosomu 2
pop.CopyChromosome(index2, index1);
}
else
{
// skopiuj elementy chromosomu 2 do chromosomu 1
pop.CopyChromosome(index1, index2);
}
i++;
}
}
// zastosuj operator krzyzowania do wyboru par chromosomow
void GeneticAlgorithm::Crossover()
{
for (int i = 0; i < populationSize; i++)
{
int r = rand() % 100;
if (r < crossoverRatio)
{
// wybierz losowo pare chromosomow
int index1 = rand() % populationSize;
int index2 = rand() % populationSize;
while (index1 == index2)
{
index2 = rand() % populationSize;
}
if (index1 > index2)
{
int tmp = index1;
index1 = index2;
index2 = tmp;
}
// uzyskaj punkt rozciecia
int point1 = rand() % chromosomeSize;
// wykonaj krzyzowanie z jednym puktem przeciecia
pop.Crossover(index1, index2, point1);
}
}
}
// przeprowadz mutacje na wybranych chromosomach
void GeneticAlgorithm::Mutate()
{
for (int i = 0; i < populationSize; i++)
{
int r = rand() % 100;
if (r < mutationRatio)//
{
pop.Mutation(i, mutationRatio);
}
}
}