public void PMXTest2()
{
int populationSize = 120;
file = root + "\\bays29.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
PMXCrossover crossover = new PMXCrossover((float)(0.80));
TournamentSelection selector = new TournamentSelection(5);
InversionMutation inv = new InversionMutation((float)0.05);
GeneticSolver solver = new GeneticSolver(testMatrix, inv, crossover, selector, populationSize, 10);
List <Candidate> listCand = new List <Candidate>();
List <int> GenX = new List <int>()
{
5, 20, 28, 18, 14, 2, 27, 25, 8, 4, 19, 13, 12, 17, 11, 15, 16, 9, 3, 10, 22, 21, 1, 7, 24, 6, 23, 26
};
List <int> GenY = new List <int>()
{
28, 19, 11, 27, 3, 18, 17, 14, 10, 23, 8, 12, 6, 2, 22, 7, 25, 20, 4, 1, 26, 9, 5, 15, 24, 21, 16, 13
};
Candidate parentX = new Candidate(1, GenX, solver, solver.time.ElapsedMilliseconds.ToString());
Candidate parentY = new Candidate(1, GenY, solver, solver.time.ElapsedMilliseconds.ToString());
listCand.Add(parentX);
listCand.Add(parentY);
crossover.CrossoverPopulation(listCand, 10);
}
public void TransportMutationTest1()
{
int populationSize = 2000;
file = root + "\\bays29.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
PMXCrossover crossover = new PMXCrossover((float)(0.80));
TournamentSelection selector = new TournamentSelection((int)populationSize / 2);
TranspositionMutation mutation = new TranspositionMutation((float)1);
GeneticSolver solver = new GeneticSolver(testMatrix, mutation, crossover, selector, populationSize, 10);
List <Candidate> listCand = solver.randomPopulation();
Candidate parentX = listCand[0];
Candidate parentY = listCand[1];
parentX.chromoson = new List <int>()
{
1, 2, 3, 4, 5, 6, 7, 8, 9
};
parentY.chromoson = new List <int>()
{
5, 3, 6, 7, 8, 1, 2, 9, 4,
};
mutation.Mutate(parentX);
}
private void btnStart_Click(object sender, EventArgs e)
{
Clear();
int pop_size_factor = Int32.Parse(cmbPopulationSize.Items[cmbPopulationSize.SelectedIndex].ToString());
CrossoverType crossover_type = CrossoverType.OnePointCrossover;
switch (cmbCrossoverType.SelectedIndex)
{
case 0: crossover_type = CrossoverType.OnePointCrossover; break;
case 1: crossover_type = CrossoverType.UniformCrossover; break;
case 2: crossover_type = CrossoverType.PMX; break;
}
double elitism_rate = Double.Parse(cmbElitismRate.SelectedItem.ToString());
gs = new GeneticSolver(1024 * pop_size_factor, 1000, elitism_rate / 10, 0.01, crossover_type);
gs.GeneratorFunction = PhraseGenerator;
gs.FitnessFunction = CalculateFitness;
gs.IterationCompleted += Gs_IterationCompleted;
gs.SolutionFound += Gs_SolutionFound;
gs.Evolve();
}
public void OXTest1()
{
int populationSize = 120;
file = root + "\\bays29.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
OXCrossover crossover = new OXCrossover((float)(1));
TournamentSelection selector = new TournamentSelection(5);
InversionMutation inv = new InversionMutation((float)0.05);
GeneticSolver solver = new GeneticSolver(testMatrix, inv, crossover, selector, populationSize, 10);
List <Candidate> listCand = solver.randomPopulation();
Candidate parentX = listCand[0];
Candidate parentY = listCand[1];
parentX.chromoson = new List <int>()
{
1, 2, 3, 4, 5, 6, 7, 8, 9
};
parentY.chromoson = new List <int>()
{
5, 3, 6, 7, 8, 1, 2, 9, 4,
};
crossover.Crossover(parentX, parentY);
}
public void SimpleTest()
{
KnapsackProblemModel problem = new KnapsackProblemModel(9000, 100, new List <Item>
{
new Item(18, 114, 0), new Item(42, 136, 1), new Item(88, 192, 2), new Item(3, 223, 3)
});
IKnapsackSolver solver = new BruteForceSolver();
int solve = solver.Solve(problem);
Assert.AreEqual(473, solve);
IKnapsackSolver solver2 = new BranchAndBoundSolver();
int solve2 = solver2.Solve(problem);
Assert.AreEqual(473, solve2);
IKnapsackSolver solver4 = new GeneticSolver(100, 100, new RouletteWheelSelection(), 0.01, 0.8, 0, false, false);
solver4.Solve(problem);
IKnapsackSolver solver5 = new FPTASSolver(0.9);
solver5.Solve(problem);
}
public Candidate(int generation, List <int> genotype, GeneticSolver solver, string time)
{
this.time = time;
this.generation = generation;
this.solver = solver;
chromoson = genotype;
CountFitness();
}
public Candidate(Candidate cand)
{
this.time = cand.time;
this.generation = cand.generation;
this.solver = cand.solver;
this.chromoson = new List <int>(cand.chromoson);
this.CountFitness();
}
public void algo_genetique_test()
{
FlightDatabase db = new FlightDatabase(GetFlightDataPath());
Meeting m = new Meeting(5023, db);
GeneticSolver solver = new GeneticSolver(m, true, 0.4, 150);
solver.Solve((bestSolution, population) => bestSolution != null && bestSolution.Cost < 100, 1000, 100);
}
public void run_szenario(int times, String sz, String s)
{
int render_every = 5120; //model evaluations
//n mal laufen lassen für empirische Auswertung
//(bei den direkten Heuristiken (greedy,insertion,savings nicht nötig, daher n = 1)
for (int i = 0; i < times; i++)
{
//solange ein Prozess läuft mache nichts!
while (optimizer_running)
{
Thread.Sleep(5000);
}
String problemInstance = File.ReadAllText(sz + ".json");
model = new MEPModel.MEP();
model.LoadModelString(problemInstance);
scenario = sz + "_" + s + "_" + i;
Console.WriteLine("Starting Szenario:" + scenario);
//setze Kontext
model.AllowTripContinuation = true;
model.PermutateOptions = false;
model.AllowUnprofitableRequests = true;
model.AvgTravelSpeed = 400;
model.HotelCostPerNight = -100;
model.HourlyWage = -60;
model.MilageAllowance = -0.1;
model.RevenuePerDayOnsite = 2000;
Solver.Solver so = null;
switch (s)
{
case "Greedy":
so = new GreedySolver(model);
break;
case "SA":
so = new SimulatedAnnealingSolver(model, 10000, 1600, render_every, 64); //somit maximal 10.000*16 = 160.000 modell evaluationen!
break;
case "GA":
so = new GeneticSolver(model, 19, 5120, 5, render_every); //somit maximal 100 * 1.600 = 160.000 modell evaluationen!
break;
case "Insertion":
so = new NearestInsertionSolver(model);
break;
case "Savings":
so = new SavingsSolver(model);
break;
}
start = DateTime.Now;
Start_Optimizer(so);
}
}
static void Main(string[] args)
{
var g = new GeneticSolver(1024 * 8, 1000, 0.1, 0.1, CrossoverType.UniformCrossover);
g.GeneratorFunction = PhraseGenerator;
g.FitnessFunction = CalculateFitness;
g.IterationCompleted += G_IterationCompleted;
g.SolutionFound += G_SolutionFound;
g.Evolve();
}
static void Main(string[] args)
{
var g = new GeneticSolver();
g.GeneratorFunction = VariablesGenerator;
g.FitnessFunction = CalculateFitness;
g.IterationCompleted += G_IterationCompleted;
g.SolutionFound += G_SolutionFound;
g.Evolve();
}
private void btnRunAsync_Click(object sender, RoutedEventArgs e)
{
output_to_file = false;
tokensource = new CancellationTokenSource();
if (model != null && model.Requests.Count > 0)
{
btnRunAsync.IsEnabled = false;
set_model_parameter();
//clear optimizer Output:
txtOptimizerOutput.Text = "";
Solver.Solver s = null;
if (rbGeneticAlgorithm.IsChecked == true)
{
s = new GeneticSolver(model, Convert.ToInt32(txtGenerations.Text), Convert.ToInt32(txtPopulationSize.Text), Convert.ToInt32(txtMutationProbability.Text), Convert.ToInt32(txtReportProgress.Text));
}
if (rbBruteForce.IsChecked == true)
{
if (model.GetNumberOfElements() > 11)
{
MessageBox.Show("I´m sorry Dave, I`m afraid I can`t do that!");
}
else
{
s = new BruteForceSolver(model);
}
}
if (rbSimulatedAnnealing.IsChecked == true)
{
s = new SimulatedAnnealingSolver(model, Convert.ToInt32(txtStartTemp.Text), Convert.ToInt32(txtSteps.Text), Convert.ToInt32(txtReportProgress.Text), Convert.ToInt32(txtParallelAnnealings.Text));
}
if (rbGreedy.IsChecked == true)
{
s = new GreedySolver(model);
}
if (rbInsertion.IsChecked == true)
{
s = new NearestInsertionSolver(model);
}
if (rbSavings.IsChecked == true)
{
s = new SavingsSolver(model);
}
if (s != null)
{
Start_Optimizer(s);
}
}
}
public IPredictionResult Predict <TDistribution>(IHiddenMarkovModel <TDistribution> model, IPredictionRequest request) where TDistribution : IDistribution
{
var selectionMethod = new TournamentSelection(TournamentSize);
var crossoverAlgorithm = new Crossover(CrossoverProbability);
var mutationAlgorithm = new Mutator(MutationProbability);
var evaluator = new HmmEvaluator <TDistribution>(model, new ForwardBackward(true));
var parameters = new GeneticSolverParameters
{
CrossOverProbability = CrossoverProbability,
MutationProbability = MutationProbability,
NumberOfGenerations = NumberOfGenerations,
PopulationSize = request.NumberOfDays * 10,
TournamentSize = TournamentSize
};
var predictions = new PredictionResult {
Predicted = new double[request.NumberOfDays][]
};
var solver = new GeneticSolver(parameters, mutationAlgorithm, crossoverAlgorithm, evaluator, selectionMethod);
var populationInitializer = new FromTimeSeriesRandomInitializer(request.TrainingSet);
var population = populationInitializer.Initialize <decimal>(parameters.PopulationSize, request.NumberOfDays, MutationProbability);
for (int i = 0; i < population.Count; i++)
{
var chromosome = population[i];
population[i].FintnessValue = evaluator.Evaluate(chromosome);
}
var result = solver.Solve(population);
// Get best fitted chromosome
var maximumFitness = result[0].FintnessValue;
var solution = result[0];
foreach (var chromosome in result)
{
if (maximumFitness <= chromosome.FintnessValue)
{
solution = chromosome;
maximumFitness = chromosome.FintnessValue;
}
}
// Convert it to array
for (int i = 0; i < solution.Representation.Length; i++)
{
predictions.Predicted[i] = Array.ConvertAll(solution.Representation[i].Representation, x => (double)Convert.ChangeType(x, typeof(double)));
}
return(predictions);
}
public void TournamentTest1()
{
int populationSize = 120;
file = root + "\\bays29.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
PMXCrossover crossover = new PMXCrossover((float)(0.80));
TournamentSelection selector = new TournamentSelection(10);
InversionMutation inv = new InversionMutation((float)0.05);
GeneticSolver solver = new GeneticSolver(testMatrix, inv, crossover, selector, populationSize, 10);
// listCand = selector.generateBreedingPool(listCand);
}
/// <summary>
/// Default Constructor
/// </summary>
public ProjectFourViewModel()
{
Cities = new List <City>();
PlotInfo = new PlotModel();
ChangeInfo = new PlotModel();
CitySeries = new ScatterSeries();
PathSeries = new LineSeries();
ChangeSeries = new LineSeries();
PlotInfo.Series.Add(PathSeries);
PlotInfo.Series.Add(CitySeries);
ChangeInfo.Series.Add(ChangeSeries);
GSolver = new GeneticSolver();
GSolver.NewBestTrip += OnNewBestTrip;
GSolver.DataCleared += OnDataCleared;
}
private GeneticSolver getSolver()
{
int populationSize = 3000;
file = root + "\\rbg403.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
PMXCrossover crossover = new PMXCrossover((float)(0.80));
TournamentSelection selector = new TournamentSelection((int)(5));
InversionMutation inv = new InversionMutation((float)0.05);
GeneticSolver solver = new GeneticSolver(
testMatrix, inv, crossover, selector, populationSize, 60);
return(solver);
}
public void RouletteTests1()
{
int populationSize = 1000;
file = root + "\\bays29.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
PMXCrossover crossover = new PMXCrossover((float)(0.80));
RouletteSelection selector = new RouletteSelection(100);
InversionMutation inv = new InversionMutation((float)0.05);
GeneticSolver solver = new GeneticSolver(testMatrix, inv, crossover, selector, populationSize, 10);
List <Candidate> listCand = solver.randomPopulation();
listCand = selector.generateBreedingPool(listCand);
}
public Result(GeneticSolver solver)
{
mutationName = solver.mutation.MutationName;
selectionName = solver.selector.SelectionName;
crossoverName = solver.crossover.CrossoverName;
mutationChance = solver.mutation.mutationChance;
tspFileName = solver.matrix.tspFileName;
overCrossChance = solver.crossover.CrossoverChance;
time = solver.time.ElapsedMilliseconds.ToString();
populationSize = solver.maxPopulationSize;
selectionSize = solver.selector.selectionSize;
measureName = tspFileName + "Size" + populationSize + mutationName + mutationChance + selectionName + selectionSize + crossoverName + overCrossChance + "TIME" + solver.MaxTime + "s";
bestResult = solver.bestCandidate;
results = solver.results;
TimeResults = solver.bestPerTwoMinutes;
}
public void solverTest()
{
int populationSize = 3000;
file = root + "\\rbg403.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
PMXCrossover crossover = new PMXCrossover((float)(0.80));
TournamentSelection selector = new TournamentSelection((int)(5));
InversionMutation inv = new InversionMutation((float)0.05);
GeneticSolver solver = new GeneticSolver(
testMatrix, inv, crossover, selector, populationSize, 480);
var result = solver.Solve();
result.resultToXML();
result.ToFile();
}
// Use this for initialization
private void Start()
{
m_GeneticSolver = FindObjectOfType <GeneticSolver>();
if (m_GeneticSolver == null)
{
gameObject.SetActive(false);
}
m_Dropdown = GetComponent <Dropdown>();
m_Dropdown.ClearOptions();
m_Dropdown.AddOptions(Enum.GetNames(typeof(GeneticSolver.PopulationGenerationType)).ToList());
m_Dropdown.value = (int)m_GeneticSolver.populationGenerationType;
m_Dropdown.onValueChanged.AddListener(OnDropdownValueChanged);
}
public void Solve_PopulationSizeZero_ZeroSizePopulationReturned()
{
var selectionMethod = new TournamentSelection(TournamentSize);
var crossoverAlgorithm = new Crossover(CrossoverProbability);
var mutationAlgorithm = new Mutator(MutationProbability);
var model = (HiddenMarkovModelMixtureDistribution)HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <Mixture <IMultivariateDistribution> > {
Pi = new double[3], TransitionProbabilityMatrix = new double[3][], Emissions = new Mixture <IMultivariateDistribution> [3]
});
var evaluator = new HmmEvaluator <Mixture <IMultivariateDistribution> >(model, new ForwardBackward(true));
var parameters = new GeneticSolverParameters();
var solver = new GeneticSolver(parameters, mutationAlgorithm, crossoverAlgorithm, evaluator, selectionMethod);
var result = solver.Solve(new List <IChromosome <decimal> >());
Assert.AreEqual(result.Count, 0);
}
public void GeneticSolver_AllDependencies_SolverCreatedWithAllDependecnies()
{
var selectionMethod = new TournamentSelection(TournamentSize);
var crossoverAlgorithm = new Crossover(CrossoverProbability);
var mutationAlgorithm = new Mutator(MutationProbability);
var model = (HiddenMarkovModelMixtureDistribution)HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <Mixture <IMultivariateDistribution> > {
Pi = new double[3], TransitionProbabilityMatrix = new double[3][], Emissions = new Mixture <IMultivariateDistribution> [3]
});
var evaluator = new HmmEvaluator <Mixture <IMultivariateDistribution> >(model, new ForwardBackward(true));
var parameters = new GeneticSolverParameters();
var solver = new GeneticSolver(parameters, mutationAlgorithm, crossoverAlgorithm, evaluator, selectionMethod);
Assert.IsNotNull(solver.MutationAlgorithm);
Assert.IsNotNull(solver.CrossoverAlgorithm);
Assert.IsNotNull(solver.EvaluationMethod);
Assert.IsNotNull(solver.SelectionMethod);
}
static void Main(string[] args)
{
int[] initial_grid = new int[] { 0, 6, 0, 0, 5, 0, 0, 2, 0,
0, 0, 0, 3, 0, 0, 0, 9, 0,
7, 0, 0, 6, 0, 0, 0, 1, 0,
0, 0, 6, 0, 3, 0, 4, 0, 0,
0, 0, 4, 0, 7, 0, 1, 0, 0,
0, 0, 5, 0, 9, 0, 8, 0, 0,
0, 4, 0, 0, 0, 1, 0, 0, 6,
0, 3, 0, 0, 0, 8, 0, 0, 0,
0, 2, 0, 0, 4, 0, 0, 5, 0 };
Noyau.Sudoku s = new Noyau.Sudoku(initial_grid);
Noyau.Sudoku s_1 = new Noyau.Sudoku();
Noyau.Sudoku s_2 = new Noyau.Sudoku();
Console.WriteLine(s.ToString());
GeneticSolver gs = new GeneticSolver();
s_1 = gs.Solve(s);
OrToolsSolver ots = new OrToolsSolver();
s_2 = ots.Solve(s);
Console.WriteLine("Genetic Solver");
Console.WriteLine(s_1.ToString());
Console.WriteLine("ORToolsSolver");
Console.WriteLine(s_2.ToString());
Console.ReadLine();
}
public void Solve_InitialPopulationFromTimeSeriesAndTrainedHmmEvaluator_SolvedPopulation()
{
var util = new TestDataUtils();
var series = util.GetSvcData(util.GOOGFilePath, new DateTime(2010, 12, 18), new DateTime(2011, 12, 18));
var test = util.GetSvcData(util.GOOGFilePath, new DateTime(2011, 12, 18), new DateTime(2012, 01, 18));
var model = (HiddenMarkovModelMixtureDistribution)HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <Mixture <IMultivariateDistribution> >()
{
NumberOfComponents = NumberOfComponents, NumberOfStates = NumberOfStates
});
model.Normalized = true;
model.Train(series, NumberOfIterations, LikelihoodTolerance);
var selectionMethod = new TournamentSelection(TournamentSize);
var crossoverAlgorithm = new Crossover(CrossoverProbability);
var mutationAlgorithm = new Mutator(MutationProbability);
var evaluator = new HmmEvaluator <Mixture <IMultivariateDistribution> >(model, new ForwardBackward(true));
var parameters = new GeneticSolverParameters
{
CrossOverProbability = CrossoverProbability,
MutationProbability = MutationProbability,
NumberOfGenerations = 10,
PopulationSize = PopulationSize,
TournamentSize = TournamentSize
};
var populationInitializer = new FromTimeSeriesRandomInitializer(series);
var population = populationInitializer.Initialize <decimal>(parameters.PopulationSize, test.Length, MutationProbability);
for (int i = 0; i < population.Count; i++)
{
var chromosome = population[i];
population[i].FintnessValue = evaluator.Evaluate(chromosome);
}
var solver = new GeneticSolver(parameters, mutationAlgorithm, crossoverAlgorithm, evaluator, selectionMethod);
var result = solver.Solve(population);
Assert.AreEqual(result.Count, parameters.PopulationSize);
}
private void btnEvolve_Click(object sender, EventArgs e)
{
Clear();
int pop_size_factor = 8;
int elitism_rate = 2;
var crossover_type = CrossoverType.OnePointCrossover;
queen_positions = new List <Point>();
gs = new GeneticSolver(1024 * pop_size_factor, 1000, elitism_rate / 10, 0.01, crossover_type)
{
GeneratorFunction = SolutionGenerator,
FitnessFunction = CalculateFitness
};
gs.IterationCompleted += Gs_IterationCompleted;
gs.SolutionFound += Gs_SolutionFound;
gs.Evolve();
}
public void RunSolution()
{
if (isMultiThreadChecked)
{
solverMultiThread = createNewSolver(true);
listTask.Add(solverMultiThread);
}
if (isSigleThreadChecked)
{
solverSingleThread = createNewSolver(false);
listTask.Add(solverSingleThread);
}
var tasks = new List <Task <Result> >();
foreach (var solver in listTask)
{
tasks.Add(Task.Factory.StartNew <Result>(() => solver.Solve()));
}
View.solvers = listTask.ToArray();
View.RunChart();
}
/// <summary>
/// Processes a TSP and reports errors.
/// </summary>
/// <param name="file">File name of TSP being processed.</param>
private void ProcessTsp(string file)
{
System.Diagnostics.Debug.WriteLine($"Loading TSP at path {file} ...\n");
// Try convert text to TSP
var success = Interface.TryTextToTsp(file, out var tsp);
// If conversion was a success
if (success)
{
// Print TSP info to debug console
tsp.Print();
// Update name of problem on graph
this.problemName.Text = tsp.Name;
// Create solver and event handlers
Solver solver = null;
ProgressChangedEventHandler updateHandler = null;
RunWorkerCompletedEventHandler completionHandler = null;
int drawDelay = 0;
// Set solvers and event handlers based on the chosen setting
if (bruteForceRadioButton.IsChecked == true)
{
solver = new BruteForceSolver();
MessageBox.Show("Solution method not yet implemented after refactoring.");
return;
}
else if (bfsRadioButton.IsChecked == true || dfsRadioButton.IsChecked == true)
{
var goal = Convert.ToInt32(searchGoal.Text);
if (bfsRadioButton.IsChecked == true)
{
solver = new BfsSolver();
}
else
{
solver = new DfsSolver();
}
MessageBox.Show("Solution method not yet implemented after refactoring.");
return;
}
else if (greedyRadioButton.IsChecked == true)
{
solver = new GreedySolver();
updateHandler = GreedyProgressChanged;
completionHandler = GreedyCompletion;
drawDelay = GREEDY_DRAW_DELAY;
}
else if (geneticRadioButton.IsChecked == true)
{
solver = new GeneticSolver();
updateHandler = GeneticProgressChanged;
completionHandler = GreedyCompletion;
drawDelay = GREEDY_DRAW_DELAY;
}
else
{
MessageBox.Show("No solution method was selected.");
return;
}
if (progressCheckBox.IsChecked == true)
{
// Update continously
solver.ProgressChanged += updateHandler;
}
else
{
// Update only at the end
solver.RunWorkerCompleted += completionHandler;
}
var workerArgs = new Tuple <Tsp, int>(tsp, progressCheckBox.IsChecked == true ? drawDelay : 0);
// Run solver and draw output
solver.RunWorkerAsync(workerArgs);
}
// If conversion failed
else
{
MessageBox.Show($"Could not convert TSP from text file: \n\n{file}");
}
}
public void StopTest()
{
GeneticSolver solver = new GeneticSolver();
solver.Solve();
}
internal GeneticSolver createNewSolver(bool isMultiThread)
{
MutationType mutation = null;
CrossoverType crossover = null;
SelectionType selection = null;
GeneticSolver solver = null;//add parameters TO DO
AdjacencyMatrix matrix = new AdjacencyMatrix(tspXmlFile);
switch (mutationIndex)
{
case 0:
{
if (!isMultiThread)
{
mutation = new InversionMutation(mutationChance);
}
else
{
mutation = new MultiThreadInversionMutation(mutationChance);
}
break;
}
case 1:
{
if (!isMultiThread)
{
mutation = new TranspositionMutation(mutationChance);
}
else
{
mutation = new MultiThreadTranspositionMutation(mutationChance);
}
break;
}
}
switch (crossoverIndex)
{
case 0:
{
if (!isMultiThread)
{
crossover = new PMXCrossover(crossoverChance);
}
else
{
crossover = new MultiThreadPMXCrossover(crossoverChance); //new PMXCrossover(crossoverChance); //
}
break;
}
case 1:
{
if (!isMultiThread)
{
crossover = new OXCrossover(crossoverChance);
}
else
{
crossover = new MultiThreadOXCrossover(crossoverChance);
}
break;
}
}
switch (selectorIndex)
{
case 0:
{
if (!isMultiThread)
{
selection = new TournamentSelection(selectorSize);
}
else
{
selection = new MultiThreadTournamentSelection(selectorSize);
}
break;
}
case 1:
{
if (!isMultiThread)
{
selection = new RouletteSelection(selectorSize);
}
else
{
selection = new MultiThreadRouletteSelection(selectorSize);
}
break;
}
}
if (mutation != null && selection != null && crossover != null)
{
if (isMultiThread)
{
MultiTaskOptions.parallelOptCrossover.MaxDegreeOfParallelism = int.Parse(View.tbxLvlCrossover.Text);
MultiTaskOptions.parallelOptMutation.MaxDegreeOfParallelism = int.Parse(View.tbxLvlMutation.Text);
MultiTaskOptions.parallelOptSelection.MaxDegreeOfParallelism = int.Parse(View.tbxLvlSelector.Text);
solver = new MultiTaskGeneticSolver(matrix, mutation, crossover, selection, populationSize, timeMS);
}
else
{
solver = new SingleTaskGeneticSolver(matrix, mutation, crossover, selection, populationSize, timeMS);
}
}
return(solver);
}
static void Main(string[] args)
{
Stopwatch stopwatch = new Stopwatch();
decimal frequency = Stopwatch.Frequency;
if (CommandLine.Parser.Default.ParseArguments(args, Options))
{
TextWriter reportWriter = Options.OutputFilePath == null ? Console.Out : new StreamWriter(Options.OutputFilePath);
VerboseLog("Data parsing ...");
DataParser parser = new DataParser();
List <KnapsackProblemModel> knapsackProblemModels = parser.ParseProblem(Options.InputFiles);
Dictionary <int, int> knownResults = null;
Dictionary <int, Tuple <int, long> > bruteForceResults = new Dictionary <int, Tuple <int, long> >();
Dictionary <int, Tuple <int, long> > costToRatioHeuristicsResults = new Dictionary <int, Tuple <int, long> >();
Dictionary <int, Tuple <int, long> > branchAndBoundResults = new Dictionary <int, Tuple <int, long> >();
Dictionary <int, Tuple <int, long> > dynamicByCostResults = new Dictionary <int, Tuple <int, long> >();
Dictionary <int, Tuple <int, long> > fptasResults = new Dictionary <int, Tuple <int, long> >();
Dictionary <int, Tuple <int, long> > geneticResults = new Dictionary <int, Tuple <int, long> >();
if (Options.ResultFiles != null)
{
knownResults = parser.ParseResults(Options.ResultFiles);
}
VerboseLog("Done.");
IKnapsackSolver bruteForceSolver = new BruteForceSolver();
IKnapsackSolver ratioHeuristicSolver = new RatioHeuristicSolver();
IKnapsackSolver branchAndBoundSolver = new BranchAndBoundSolver();
IKnapsackSolver dynamicByCostSolve = new DynamicByCostSolver();
IKnapsackSolver fptasSolver = null;
IKnapsackSolver geneticSolver = null;
if (Options.FPTAS)
{
fptasSolver = new FPTASSolver(Options.FPTASAccuracy);
}
if (Options.Genetics)
{
ISelectionMethod selectionMethod = null;
switch (Options.SelectionMethod)
{
case "roulette": selectionMethod = new RouletteWheelSelection();
break;
case "rank": selectionMethod = new RankSelection();
break;
case "elitary": selectionMethod = new EliteSelection();
break;
default: Console.WriteLine("Wrong selection method for genetics");
break;
}
if (selectionMethod == null)
{
return;
}
if (Options.GeneticMetaoptimization)
{
//Random selection portion
for (int i = 0; i < 100; i++)
{
double randomSelectionPortion = (i * 0.001) + 0;
//Crossover rate
for (int j = 0; j < 1; j++)
{
double crossoverRate = (j * 0.03) + 0.22;
//Mutation rate
for (int k = 0; k < 1; k++)
{
double mutationRate = (k * 0.04) + 0.87;
geneticSolver = new GeneticSolver(Options.PopulationSize, Options.IterationsCount, selectionMethod, mutationRate, crossoverRate, randomSelectionPortion, false, false);
geneticResults.Clear();
foreach (KnapsackProblemModel problem in knapsackProblemModels)
{
int result = 0;
try
{
stopwatch.Restart();
result = geneticSolver.Solve(problem);
stopwatch.Stop();
}
catch (Exception ex)
{
}
geneticResults.Add(problem.ProblemId, new Tuple <int, long>(result, stopwatch.ElapsedTicks));
}
decimal totalTime = 0;
decimal totalError = 0;
foreach (KnapsackProblemModel problem in knapsackProblemModels)
{
int problemId = problem.ProblemId;
Tuple <int, long> result = geneticResults[problemId];
totalTime += (result.Item2 / frequency);
totalError += CalculateRelativeError(knownResults[problemId], result.Item1);
}
decimal averageError = totalError / knapsackProblemModels.Count;
reportWriter.WriteLine(randomSelectionPortion + "," + crossoverRate + "," + mutationRate + "," + totalTime + "," + averageError);
}
}
}
}
geneticSolver = new GeneticSolver(Options.PopulationSize, Options.IterationsCount, selectionMethod, Options.MutationRate, Options.CrossoverRate, Options.RandomSelectionPortion, Options.DiversityCheck, true);
}
VerboseLog("Solving JIT instance");
KnapsackProblemModel jitProblem = new KnapsackProblemModel(-1, 100, new List <Item>
{
new Item(18, 114, 0), new Item(42, 136, 1), new Item(88, 192, 2), new Item(3, 223, 3)
});
bruteForceSolver.Solve(jitProblem);
ratioHeuristicSolver.Solve(jitProblem);
branchAndBoundSolver.Solve(jitProblem);
dynamicByCostSolve.Solve(jitProblem);
if (fptasSolver != null)
{
fptasSolver.Solve(jitProblem);
}
if (geneticSolver != null)
{
geneticSolver.Solve(jitProblem);
}
VerboseLog("Calculation started");
foreach (KnapsackProblemModel problem in knapsackProblemModels)
{
VerboseLog("Solving problem:");
VerboseLog(problem);
int knownResult = -1;
if (knownResults != null)
{
knownResult = knownResults[problem.ProblemId];
VerboseLog("Result should be: " + knownResult);
}
if (Options.BruteForce)
{
VerboseLog("Brute force solver ...");
stopwatch.Restart();
int result = bruteForceSolver.Solve(problem);
stopwatch.Stop();
bruteForceResults.Add(problem.ProblemId, new Tuple <int, long>(result, stopwatch.ElapsedTicks));
if (knownResult != -1 && result != knownResult)
{
Console.WriteLine("ERROR - Brute force algorithm not accurate for problem " + problem);
Environment.Exit(1);
}
}
if (Options.BranchAndBound)
{
VerboseLog("Branch and bound solver ...");
stopwatch.Restart();
int result = branchAndBoundSolver.Solve(problem);
stopwatch.Stop();
branchAndBoundResults.Add(problem.ProblemId, new Tuple <int, long>(result, stopwatch.ElapsedTicks));
if (knownResult != -1 && result != knownResult)
{
Console.WriteLine("ERROR - Branch and bound algorithm not accurate for problem " + problem);
Environment.Exit(1);
}
}
if (Options.DynamicByCost)
{
VerboseLog("Dynamic by cost solver ...");
stopwatch.Restart();
int result = dynamicByCostSolve.Solve(problem);
stopwatch.Stop();
dynamicByCostResults.Add(problem.ProblemId, new Tuple <int, long>(result, stopwatch.ElapsedTicks));
if (knownResult != -1 && result != knownResult)
{
Console.WriteLine("ERROR - Dynamic by cost algorithm not accurate for problem " + problem);
Environment.Exit(1);
}
}
if (Options.CostToRatioHeuristics)
{
VerboseLog("Ratio heuristics solver ...");
stopwatch.Restart();
int result = ratioHeuristicSolver.Solve(problem);
stopwatch.Stop();
costToRatioHeuristicsResults.Add(problem.ProblemId, new Tuple <int, long>(result, stopwatch.ElapsedTicks));
}
if (Options.FPTAS)
{
VerboseLog("FPTAS solver ...");
if (fptasSolver != null)
{
stopwatch.Restart();
int result = fptasSolver.Solve(problem);
stopwatch.Stop();
fptasResults.Add(problem.ProblemId, new Tuple <int, long>(result, stopwatch.ElapsedTicks));
}
}
if (Options.Genetics)
{
VerboseLog("Genetics solver ...");
if (geneticSolver != null)
{
stopwatch.Restart();
int result = geneticSolver.Solve(problem);
stopwatch.Stop();
geneticResults.Add(problem.ProblemId, new Tuple <int, long>(result, stopwatch.ElapsedTicks));
}
}
VerboseLog("Problem solved.");
}
reportWriter.Write("Problem ID;Items count");
if (knownResults != null)
{
reportWriter.Write(";Known result");
}
if (Options.BruteForce)
{
reportWriter.Write(";Brute force result;Time [s]");
if (knownResults != null)
{
reportWriter.Write(";Relative error");
}
}
if (Options.CostToRatioHeuristics)
{
reportWriter.Write(";Cost to weight ration heuristics result;Time [s]");
if (knownResults != null)
{
reportWriter.Write(";Relative error");
}
}
if (Options.BranchAndBound)
{
reportWriter.Write(";Branch and bound result;Time [s]");
if (knownResults != null)
{
reportWriter.Write(";Relative error");
}
}
if (Options.DynamicByCost)
{
reportWriter.Write(";Dynamic programming by cost result;Time [s]");
if (knownResults != null)
{
reportWriter.Write(";Relative error");
}
}
if (Options.FPTAS)
{
reportWriter.Write(";FPTAS result;Time [s]");
if (knownResults != null)
{
reportWriter.Write(";Relative error;Max possible error");
}
}
if (Options.Genetics)
{
reportWriter.Write(";Genetics result;Time [s]");
if (knownResults != null)
{
reportWriter.Write(";Relative error");
}
}
reportWriter.WriteLine();
foreach (KnapsackProblemModel problem in knapsackProblemModels)
{
var problemId = problem.ProblemId;
reportWriter.Write(problemId);
reportWriter.Write(";" + problem.Items.Count);
if (knownResults != null)
{
reportWriter.Write(";" + knownResults[problemId]);
}
if (Options.BruteForce)
{
Tuple <int, long> bruteForceResult = bruteForceResults[problemId];
reportWriter.Write(";" + bruteForceResult.Item1 + ";" + bruteForceResult.Item2 / frequency);
if (knownResults != null)
{
reportWriter.Write(";" + CalculateRelativeError(knownResults[problemId], bruteForceResult.Item1));
}
}
if (Options.CostToRatioHeuristics)
{
Tuple <int, long> heuristicsResult = costToRatioHeuristicsResults[problemId];
reportWriter.Write(";" + heuristicsResult.Item1 + ";" + heuristicsResult.Item2 / frequency);
if (knownResults != null)
{
reportWriter.Write(";" + CalculateRelativeError(knownResults[problemId], heuristicsResult.Item1));
}
}
if (Options.BranchAndBound)
{
Tuple <int, long> heuristicsResult = branchAndBoundResults[problemId];
reportWriter.Write(";" + heuristicsResult.Item1 + ";" + heuristicsResult.Item2 / frequency);
if (knownResults != null)
{
reportWriter.Write(";" + CalculateRelativeError(knownResults[problemId], heuristicsResult.Item1));
}
}
if (Options.DynamicByCost)
{
Tuple <int, long> heuristicsResult = dynamicByCostResults[problemId];
reportWriter.Write(";" + heuristicsResult.Item1 + ";" + heuristicsResult.Item2 / frequency);
if (knownResults != null)
{
reportWriter.Write(";" + CalculateRelativeError(knownResults[problemId], heuristicsResult.Item1));
}
}
if (Options.FPTAS)
{
Tuple <int, long> heuristicsResult = fptasResults[problemId];
reportWriter.Write(";" + heuristicsResult.Item1 + ";" + heuristicsResult.Item2 / frequency);
if (knownResults != null)
{
reportWriter.Write(";" + CalculateRelativeError(knownResults[problemId], heuristicsResult.Item1));
reportWriter.Write(";" + ((FPTASSolver)fptasSolver).GetMaximumError(problem));
}
}
if (Options.Genetics)
{
Tuple <int, long> heuristicsResult = geneticResults[problemId];
reportWriter.Write(";" + heuristicsResult.Item1 + ";" + heuristicsResult.Item2 / frequency);
if (knownResults != null)
{
reportWriter.Write(";" + CalculateRelativeError(knownResults[problemId], heuristicsResult.Item1));
}
}
reportWriter.WriteLine();
}
if (Options.Genetics)
{
decimal totalTime = 0;
decimal totalError = 0;
foreach (KnapsackProblemModel problem in knapsackProblemModels)
{
int problemId = problem.ProblemId;
Tuple <int, long> result = geneticResults[problemId];
totalTime += (result.Item2 / frequency);
totalError += CalculateRelativeError(knownResults[problemId], result.Item1);
}
decimal averageError = totalError / knapsackProblemModels.Count;
reportWriter.WriteLine("Aggregate results");
reportWriter.WriteLine("Aggregate time");
reportWriter.WriteLine(totalTime);
reportWriter.WriteLine("Average error");
reportWriter.WriteLine(averageError);
}
}
else
{
Environment.Exit(1);
}
Environment.Exit(0);
}
