void
Algorithm<T>::Init(int pCountOfIndividuals,
int pCountOfChromosomes,
const std::vector<T>& pAllPossibleIndividuals,
TaskDefinition<T>* pTask)
{
if (!m_isInitialized) {
assert (pAllPossibleIndividuals.size() != 0);
assert (m_countOfIndividuals <= pAllPossibleIndividuals.size());
m_countOfIndividuals = pCountOfIndividuals;
m_countOfChromosomes = pCountOfChromosomes;
m_allPossibleIndividuals = pAllPossibleIndividuals;
m_task = pTask;
// 1. Randomly initialize population.
RandomInitPopulation();
while (true) {
// 2. Calculate fitness values.
CalculateObjectiveFunction();
// 3. Calculate fitness value.
CalculateFitnessValue();
// 4. Calculate fitness probability.
CalculateFitnessProbability();
// 5. Calculate cumulative probability.
CalculateCumulativeProbability();
// 6. Selection
Selection();
// 7. Extract Parents
ExtractParents();
// 8. Crossover
Crossover();
// 9. Mutation
Mutation();
}
m_isInitialized = true;
} else {
std::cout << "Algorithm is already initialized." << std::endl;
}
}
bool GeneticAlgorithm::CreateTimetable(int stop, float infeasibilitiesWeight, float didacticDissatisfactionWeight,
float organizationalDissatisfactionWeight, int mutationGenSize, float crossoverProbability)
{
finished = false;
if(!CheckData())
{
std::cout << "Bledne dane wejsciowe!" << std::endl;
return false;
}
float min = std::numeric_limits<float>::max();
char *** solution = new char **[teachers_count];
for (int i = 0; i < teachers_count; i++)
{
solution[i] = new char*[days];
for (int j = 0; j < days; j++)
{
solution[i][j] = new char[hours_per_day];
}
}
int it = 0;
int seed = time(NULL);
while(it < stop)
{
std::cout << "New population cycle " << it << std::endl;
srand(seed++);
int iteration = 0;
Initialize();
for(int i = 0; i < population_size; ++i)
{
Filter(timetables[i]);
}
for(; it < stop; ++it)
{
std::cout << it << std::endl;
float *initial_fitness = CalculateObjectiveFunction(timetables, population_size, infeasibilitiesWeight, didacticDissatisfactionWeight,
organizationalDissatisfactionWeight);
std::vector<char***> new_population = Reproduction(initial_fitness);
Crossover(new_population, crossoverProbability, infeasibilitiesWeight,
didacticDissatisfactionWeight, organizationalDissatisfactionWeight);
float *new_fitness = CalculateObjectiveFunction(new_population.data(), new_population.size(), infeasibilitiesWeight, didacticDissatisfactionWeight,
organizationalDissatisfactionWeight);
Succession(initial_fitness, new_fitness, new_population);
delete [] initial_fitness;
delete [] new_fitness;
for (int individual = 0; individual < population_size; individual++)
{
ApplyMutationOfOrder(individual,mutationGenSize);
ApplyDayMutation(individual);
Filter(timetables[individual]);
}
float * results = CalculateObjectiveFunction(timetables, population_size, infeasibilitiesWeight, didacticDissatisfactionWeight,
organizationalDissatisfactionWeight);
int idx = -1;
for (int i = 0; i < population_size; i++)
{
if(results[i] < min)
{
min = results[i];
idx = i;
}
}
if(idx > -1)
{
for (int i = 0; i < teachers_count; i++)
{
for (int j = 0; j < days; j++)
{
for (int k = 0; k < hours_per_day; k++)
{
solution[i][j][k] = timetables[idx][i][j][k];
}
}
}
std::cout << "Actual minimum: " << min << std::endl;
iteration = 0;
}
else
iteration++;
if(iteration > max_iterations)
break;
}
}
std::ofstream result;
result.open("result.pout");
std::cout << "Final minimum: " << std::endl << min << std::endl;
std::cout << "Final solution:" << std::endl;
PrintTimetable(solution, std::cout);
PrintTimetable(solution, result);
// put minimum data into file
result << "Minimum: " << min;
result.close();
finished = true;
return true;
}
