evolutionary-alg-clusterization/Optimizer.cpp at benchmark · 0stam/evolutionary-alg-clusterization · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
#include "Optimizer.h"

#include <iomanip>

using namespace NGroupingChallenge;

COptimizer::COptimizer(CGroupingEvaluator& cEvaluator)
	: c_evaluator(cEvaluator)
{
	random_device c_seed_generator;
	c_random_engine.seed(c_seed_generator());
}

COptimizer::~COptimizer() {
	delete populationManager;
}

void COptimizer::vInitialize()
{
	/*numeric_limits<double> c_double_limits;
	d_current_best_fitness = c_double_limits.max();

	v_current_best.clear();
	v_current_best.resize(c_evaluator.iGetNumberOfPoints());*/

	populationManager = new PopulationManager(c_evaluator, c_evaluator.iGetNumberOfPoints(), c_evaluator.iGetUpperBound());

	v_current_best = populationManager->getBest();
	d_current_best_fitness = populationManager->getBestScore();
}

void COptimizer::vRunIteration()
{
	/*vector<int> v_candidate(c_evaluator.iGetNumberOfPoints());

	uniform_int_distribution<int> c_candidate_distribution(c_evaluator.iGetLowerBound(), c_evaluator.iGetUpperBound());

	for (size_t i = 0; i < v_candidate.size(); i++)
	{
		v_candidate[i] = c_candidate_distribution(c_random_engine);
	}

	double d_candidate_fitness = c_evaluator.dEvaluate(v_candidate);

	if (d_candidate_fitness < d_current_best_fitness)
	{
		v_current_best = v_candidate;
		d_current_best_fitness = d_candidate_fitness;
	}

	cout << d_current_best_fitness << endl;*/

	populationManager->iteration();
	v_current_best = populationManager->getBest();
	d_current_best_fitness = populationManager->getBestScore();

	std::cout << std::setprecision(15) << populationManager->getBestScore() << "\n";
}