GeneticAlgorithm/GeneticAlgorithm.cpp at main · annakepowicz/GeneticAlgorithm · GitHub

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#include "GeneticAlgorithm.h"
#include <sstream>
#include <iostream>

using namespace std;
using namespace NGroupingChallenge;

GeneticAlgorithm::GeneticAlgorithm(int popSize, double crossProb, double mutProb,  CGroupingEvaluator& evaluator)
	: crossProb(crossProb), mutProb(mutProb),  evaluator(evaluator)
{
	if (popSize % 2 != 0) this->popSize = popSize - 1;
	genotypeLen = evaluator.iGetNumberOfPoints();
	maxValue = evaluator.iGetUpperBound();
	population = generatePop(popSize, maxValue);
}

//GeneticAlgorithm::~GeneticAlgorithm()
//{
//	for (int i = 0; i < genotypeLen; i++)
//	{
//		delete population[i];
//	}
//	//population.clear();
//}

vector<Individual*> GeneticAlgorithm::generatePop(int popSize, int maxValue)
{
	vector<Individual*> pop;

	for (int i = 0; i < popSize; i++)
	{
		pop.push_back(new Individual(genotypeLen, maxValue));
	}

	return pop;
}

void GeneticAlgorithm::evaluateFitnesses()
{
	for (int i = 0; i < popSize; i++)
	{
		population[i]->evaluateFitness(evaluator);

	}
}

void GeneticAlgorithm::crossPop()
{
	vector<Individual*> newPop;
	uniform_real_distribution<> dist(0, 1);

	for (int i = 0; i < popSize/2; i++)
	{
		if (newPop.size() < popSize) {
			Individual* fstToCross = chooseInd();
			Individual* sndToCross = chooseInd();

			double crossRand = dist(gen);

			if (crossRand < crossProb) {
				vector<Individual*> afterCross = fstToCross->cross(*sndToCross, NUM_OF_CHILDREN);

				int iter = 0;
				while (newPop.size() < popSize) {
					newPop.push_back(afterCross[i]);
					iter++;
				}

				// ---BEFORE MODIFICATION---

				/*newPop.push_back(afterCross.first);
				newPop.push_back(afterCross.second);*/

			}
			else
			{
				newPop.push_back(new Individual(*fstToCross));
				newPop.push_back(new Individual(*sndToCross));
			}
		}

	}

	population = newPop;

}

void GeneticAlgorithm::mutatePop()
{
	for (int i = 0; i < popSize; i++)
	{
		population[i]->mutate(mutProb, maxValue);
	}
}

Individual* GeneticAlgorithm::chooseInd()
{
	uniform_int_distribution<> dist(0, popSize-1);

	int first = dist(gen);
	int second = dist(gen);

	if (population[first]->getFitness() < population[second]->getFitness()) { return population[first]; }
	else { return population[second]; }

}

string GeneticAlgorithm::popStr()
{

	ostringstream oss;
	for (int i = 0; i < popSize; ++i) {
		oss << "Ind" << i << population[i]->genotypeString() << "\n";
	}

	string result = oss.str();

	return result;

}

double GeneticAlgorithm::getOverallBestFit()
{
	return evaluator.getBestFit();
}

void GeneticAlgorithm::start(int numOfIters)
{
	for (int i = 0; i < numOfIters; i++)
	{
		evaluateFitnesses();
		crossPop();
		mutatePop();
	}
}