private void InitializeGeneticAlgorithm()
{
fitnessFunction = new FitnessFunction(cities);
selectionOperator = new TournamentSelection(Convert.ToInt32(inputTournamentSize.Value));
elitistSelection = new ElitistSelection();
crossoverOperator = GetSelectedCrossoverOperator();
mutationOperator = GetSelectedMutationOperator();
initialPopulationProvider = new InitialPopulationProvider();
settings = new Settings
{
Cities = cities,
NumberOfElites = Convert.ToInt32(inputElites.Value),
PopulationSize = Convert.ToInt32(inputMaxPopulation.Value),
CrossoverRate = Convert.ToDouble(inputCrossoverRate.Value),
MutationRate = Convert.ToDouble(inputMutationRate.Value)
};
selectionOperator = new TournamentSelection(Convert.ToInt32(inputTournamentSize.Value));
geneticAlgorithm = new GeneticAlgorithm(settings, initialPopulationProvider, fitnessFunction, selectionOperator, elitistSelection, crossoverOperator, mutationOperator);
generationsPlotData = new List <double>();
averageScorePlotData = new List <double>();
bestScorePlotData = new List <double>();
UpdatePlotData();
Plot();
DrawSolution(geneticAlgorithm.CurrentBestSolution, panelBestSolution, bestSolutionGraphics);
buttonNextGeneration.Enabled = true;
buttonRun.Enabled = true;
buttonReset.Enabled = true;
}
private void InitializeGeneticAlgorithm()
{
fitnessFunction = new FitnessFunction(inventory);
selectionOperator = new RouletteWheelSelection();
elitistSelection = new ElitistSelection();
crossoverOperator = new ContinuousUniformCrossover();
mutationOperator = new WeightsMutation();
initialPopulationProvider = new InitialPopulationProvider();
settings = new Settings
{
Inventory = inventory,
NumberOfCriterias = 3,
NumberOfElites = Convert.ToInt32(inputElites.Value),
PopulationSize = Convert.ToInt32(inputMaxPopulation.Value),
CrossoverRate = Convert.ToDouble(inputCrossoverRate.Value),
MutationRate = Convert.ToDouble(inputMutationRate.Value)
};
selectionOperator = new TournamentSelection(Convert.ToInt32(inputTournamentSize.Value));
geneticAlgorithm = new GeneticAlgorithm(settings, initialPopulationProvider, fitnessFunction, selectionOperator, elitistSelection, crossoverOperator, mutationOperator);
generationsPlotData = new List <double>();
averageScorePlotData = new List <double>();
bestScorePlotData = new List <double>();
UpdatePlotData();
Plot();
buttonNextGeneration.Enabled = true;
buttonRun.Enabled = true;
buttonReset.Enabled = true;
}
private void Benchmark()
{
fitnessFunction = new FitnessFunction(cities);
selectionOperator = new TournamentSelection(Convert.ToInt32(inputTournamentSize.Value));
elitistSelection = new ElitistSelection();
initialPopulationProvider = new InitialPopulationProvider();
selectionOperator = new TournamentSelection(Convert.ToInt32(inputTournamentSize.Value));
var csv = "Crossover Operator,Mutation Operator,Avg. Best Score,Avg. Computation Time";
var result = RunBenchmark(new CycleCrossover(), new InsertMutation());
csv += $"\nCycle,Insert,{result.Item1},{result.Item2}";
result = RunBenchmark(new CycleCrossover(), new InversionMutation());
csv += $"\nCycle,Inversion,{result.Item1},{result.Item2}";
result = RunBenchmark(new CycleCrossover(), new ScrambleMutation());
csv += $"\nCycle,Scramble,{result.Item1},{result.Item2}";
result = RunBenchmark(new CycleCrossover(), new SwapMutation());
csv += $"\nCycle,Swap,{result.Item1},{result.Item2}";
result = RunBenchmark(new OrderOneCrossover(), new InsertMutation());
csv += $"\nOrder1,Insert,{result.Item1},{result.Item2}";
result = RunBenchmark(new OrderOneCrossover(), new InversionMutation());
csv += $"\nOrder1,Inversion,{result.Item1},{result.Item2}";
result = RunBenchmark(new OrderOneCrossover(), new ScrambleMutation());
csv += $"\nOrder1,Scramble,{result.Item1},{result.Item2}";
result = RunBenchmark(new OrderOneCrossover(), new SwapMutation());
csv += $"\nOrder1,Swap,{result.Item1},{result.Item2}";
result = RunBenchmark(new PMXCrossover(), new InsertMutation());
csv += $"\nPMX,Insert,{result.Item1},{result.Item2}";
result = RunBenchmark(new CycleCrossover(), new InversionMutation());
csv += $"\nPMX,Inversion,{result.Item1},{result.Item2}";
result = RunBenchmark(new CycleCrossover(), new ScrambleMutation());
csv += $"\nPMX,Scramble,{result.Item1},{result.Item2}";
result = RunBenchmark(new CycleCrossover(), new SwapMutation());
csv += $"\nPMX,Swap,{result.Item1},{result.Item2}";
File.WriteAllText("simulations.csv", csv);
}
public GeneticAlgorithm(Settings settings,
IInitialPopulationProvider initialPopulationProvider,
IFitnessFunction fitnessFunction,
ISelectionOperator selectionOperator,
IElitistSelection elitistSelection,
ICrossoverOperator crossoverOperator,
IMutationOperator mutationOperator)
{
this.settings = settings;
this.fitnessFunction = fitnessFunction;
this.selectionOperator = selectionOperator;
this.elitistSelection = elitistSelection;
this.crossoverOperator = crossoverOperator;
this.mutationOperator = mutationOperator;
currentPopulation = initialPopulationProvider.GetInitialPopulation(settings.PopulationSize, settings.NumberOfCriterias);
ComputeCurrentGenerationData();
}
public void Setup()
{
initialPopulationProvider = new InitialPopulationProvider();
}
