// Worker thread
void SearchSolution()
{
// create fitness function
TSPFitnessFunction fitnessFunction = new TSPFitnessFunction(map);
// create population
Population population = new Population(populationSize,
(greedyCrossover) ? new TSPChromosome(map) : new PermutationChromosome(citiesCount),
fitnessFunction,
(selectionMethod == 0) ? (ISelectionMethod) new EliteSelection() :
(selectionMethod == 1) ? (ISelectionMethod) new RankSelection() :
(ISelectionMethod) new RouletteWheelSelection()
);
// iterations
int i = 1;
// path
double[,] path = new double[citiesCount + 1, 2];
// loop
while (!needToStop)
{
// run one epoch of genetic algorithm
population.RunEpoch();
// display current path
ushort[] bestValue = ((PermutationChromosome)population.BestChromosome).Value;
for (int j = 0; j < citiesCount; j++)
{
path[j, 0] = map[bestValue[j], 0];
path[j, 1] = map[bestValue[j], 1];
}
path[citiesCount, 0] = map[bestValue[0], 0];
path[citiesCount, 1] = map[bestValue[0], 1];
mapControl.UpdateDataSeries("path", path);
// set current iteration's info
SetText(currentIterationBox, i.ToString());
SetText(pathLengthBox, fitnessFunction.PathLength(population.BestChromosome).ToString());
// increase current iteration
i++;
//
if ((iterations != 0) && (i > iterations))
{
break;
}
}
// enable settings controls
EnableControls(true);
}
// Worker thread
void SearchSolution()
{
TSPFitnessFunction fitnessFunction = new TSPFitnessFunction(map);
Population population = new Population(populationSize, new TSPChromosome(map), fitnessFunction,
(selectionMethod == 0) ? new EliteSelection() : (selectionMethod == 1) ? new RankSelection()
: (ISelectionMethod) new RouletteWheelSelection());
int i = 1;
double[,] path = new double[citiesCount + 1, 2];
while (!needToStop)
{
RILogManager.Default?.SendDebug("Running Epoch " + i);
population.RunEpoch();
ushort[] bestValue = ((PermutationChromosome)population.BestChromosome)?.Value;
for (int j = 0; j < citiesCount; j++)
{
if (bestValue != null && map != null)
{
path[j, 0] = map[bestValue[j], 0];
path[j, 1] = map[bestValue[j], 1];
}
}
if (bestValue != null && map != null)
{
path[citiesCount, 0] = map[bestValue[0], 0];
path[citiesCount, 1] = map[bestValue[0], 1];
}
mapControl?.UpdateDataSeries("path", path);
SetText(currentIterationBox, i.ToString("N0"));
i++;
if ((iterations != 0) && (i > iterations))
{
break;
}
}
EnableControls(true);
}
// Worker thread
void SearchSolution()
{
// create fitness function
TSPFitnessFunction fitnessFunction = new TSPFitnessFunction(map);
// create population
Population population = new Population(populationSize,
(greedyCrossover) ? new TSPChromosome(map) : new PermutationChromosome(citiesCount),
fitnessFunction,
(selectionMethod == 0) ? (ISelectionMethod)new EliteSelection() :
(selectionMethod == 1) ? (ISelectionMethod)new RankSelection() :
(ISelectionMethod)new RouletteWheelSelection()
);
// iterations
int i = 1;
// path
double[,] path = new double[citiesCount + 1, 2];
// loop
while (!needToStop)
{
// run one epoch of genetic algorithm
population.RunEpoch();
// display current path
ushort[] bestValue = ((PermutationChromosome)population.BestChromosome).Value;
for (int j = 0; j < citiesCount; j++)
{
path[j, 0] = map[bestValue[j], 0];
path[j, 1] = map[bestValue[j], 1];
}
path[citiesCount, 0] = map[bestValue[0], 0];
path[citiesCount, 1] = map[bestValue[0], 1];
mapControl.UpdateDataSeries("path", path);
// set current iteration's info
SetText(currentIterationBox, i.ToString());
SetText(pathLengthBox, fitnessFunction.PathLength(population.BestChromosome).ToString());
// increase current iteration
i++;
//
if ((iterations != 0) && (i > iterations))
break;
}
// enable settings controls
EnableControls(true);
}
