private static void Run()
{
var dataset = new Dataset();
var nn = new NeuralNetwork(dataset, 8); //8, 4
const int populationSize = 10;
const int iterations = 1_000_000;
var crossovers = new ICrossover[]
{
new BlxDoubleCrossover(),
new ArithmeticCrossover(),
new DiscreteUniformRecombination(),
new OnePointCrossover(),
new SimpleArithmeticRecombination()
};
var mutations = new IMutation[]
{
new GaussianAdditiveMutation(mutationProbability: 0.02, deviation: 0.2),
new GaussianAdditiveMutation(mutationProbability: 0.02, deviation: 0.4),
new GaussianSwapMutation(mutationProbability: 0.02, deviation: 0.5)
};
var desirability = new[] { 18.0, 4.0, 2.0 };
var combinedCrossover = new CombinedCrossover(crossovers);
var combinedMutation = new CombinedMutation(mutations, desirability);
var selection =
new KTournamentSelection(nn.MeanSquareError, combinedCrossover, combinedMutation, k: 3);
var parametersCount = nn.GetNumberOfRequiredParameters();
var parameters = new double[parametersCount];
var doubleGa = new DoubleGa(nn, parameters, parametersCount, populationSize, iterations);
var bestNetworkParameters = doubleGa.FindBestIndividual(speedRun: true, selection).Representation;
var success = 0;
Console.WriteLine($"\nPrediction | Actual");
foreach (var sample in dataset)
{
var prediction = nn.CalculateOutput(sample.X, sample.Y, bestNetworkParameters);
var A = prediction[0] < 0.5 ? 0 : 1;
var B = prediction[1] < 0.5 ? 0 : 1;
var C = prediction[2] < 0.5 ? 0 : 1;
Console.WriteLine($"[{A} {B} {C}] | [{sample.A} {sample.B} {sample.C}]");
if (A == sample.A && B == sample.B && C == sample.C)
{
success++;
}
}
Console.WriteLine(
$"Prediction rate: {success}/{dataset.DatasetCount()} = {(double) success / dataset.DatasetCount() * 100}%");
}
/// <summary>
/// Executes a solver procedure.
/// </summary>
/// <param name="problem"></param>
/// <returns></returns>
internal override MaxTimeSolution Solve(MaxTimeProblem problem)
{
_customers = problem.Customers;
//float[] solutions = OsmSharp.Math.VRP.Core.BestPlacement.CheapestInsertionHelper.CalculateBestValues(
// problem, _customers);
generations = 0;
_max_generations = 10000000;
// calculate one tsp solution.
//Tools.Math.TSP.ISolver tsp_solver = new OsmSharp.Math.TSP.EdgeAssemblyGenetic.EdgeAssemblyCrossOverSolver(_population, _stagnation,
// new OsmSharp.Math.TSP.Genetic.Solver.Operations.Generation._3OptGenerationOperation(),
// new OsmSharp.Math.TSP.Genetic.Solver.Operations.CrossOver.EdgeAssemblyCrossover(30,
// OsmSharp.Math.TSP.Genetic.Solver.Operations.CrossOver.EdgeAssemblyCrossover.EdgeAssemblyCrossoverSelectionStrategyEnum.SingleRandom,
// true));
//IRoute tsp_solution = tsp_solver.Solve(new OsmSharp.Routing.VRP.NoDepot.MaxTime.TSPPlacement.TSPProblem(
// problem));
// initialize the generation.
IGenerationOperation <MaxTimeSolution, MaxTimeProblem, Fitness> generation =
//new SolverGenerationOperation(new TSPPlacement.TSPPlacementSolver<ResolvedType>(
// this.Router, this.Max, this.DeliveryTime, tsp_solution));
new OsmSharp.Routing.VRP.NoDepot.MaxTime.Genetic.Generation.RandomBestPlacement();
//new SolverGenerationOperation(new CheapestInsertionSolverWithImprovements<ResolvedType>(
// this.Router, this.Max, this.DeliveryTime, 5, 0.1f, true, 0.1f, false, 1f, null, null));
//new SolverGenerationOperation(new CheapestInsertionSolverWithImprovements<ResolvedType>(
// this.Router, this.Max, this.DeliveryTime, 5, 0.1f, true, 0.1f, true, 1f));
// initialize the crossover.
ICrossOverOperation <MaxTimeSolution, MaxTimeProblem, Fitness> cross_over =
new OsmSharp.Routing.VRP.NoDepot.MaxTime.Genetic.CrossOver.RouteExchangeOperation();
// initialize the mutation.
//IMutationOperation<MaxTimeSolution, MaxTimeProblem, Fitness> mutation =
// new VehicleMutation();
List <IMutationOperation <MaxTimeSolution, MaxTimeProblem, Fitness> > mutators =
new List <IMutationOperation <MaxTimeSolution, MaxTimeProblem, Fitness> >();
mutators.Add(new VehicleMutation());
//mutators.Add(new ThreeOptMutation());
//mutators.Add(new RedivideRouteMutation());
mutators.Add(new RoutePartExchangeMutation());
if (_probabilities == null)
{
_probabilities = new List <double>();
_probabilities.Add(0.2);
_probabilities.Add(0.6);
_probabilities.Add(0.2);
}
CombinedMutation <MaxTimeSolution, MaxTimeProblem, Fitness> mutation = new CombinedMutation <MaxTimeSolution, MaxTimeProblem, Fitness>(
StaticRandomGenerator.Get(),
mutators,
_probabilities);
SolverSettings settings = new SolverSettings(_stagnation, _population, _max_generations,
_elitism_percentage, _cross_percentage, _mutation_percentage);
MaxTimeProblem genetic_problem = problem;// new MaxTimeProblem(max, problem, solutions);
Solver <MaxTimeSolution, MaxTimeProblem, Fitness> solver =
new Solver <MaxTimeSolution, MaxTimeProblem, Fitness>(genetic_problem, settings,
new TournamentBasedSelector <MaxTimeSolution, MaxTimeProblem, Fitness>(5, 0.5),
mutation, //new ThreeOptMutation(),
cross_over, // new RouteExchangeOperation(), //new RouteExchangeOperation(), //new RouteExchangeAndVehicleOperation(), // Order1CrossoverOperation()
generation, //new RandomBestPlacement(),//new RandomGeneration(), //new RandomBestPlacement(),
new FitnessCalculator());
solver.NewFittest += new Solver <MaxTimeSolution, MaxTimeProblem, Fitness> .NewFittestDelegate(solver_NewFittest);
//solver.NewGeneration += new Solver<MaxTimeSolution, Problem, Fitness>.NewGenerationDelegate(solver_NewGeneration);
Individual <MaxTimeSolution, MaxTimeProblem, Fitness> solution = solver.Start(null);
//this.solver_NewFittest(solution);
MaxTimeSolution routes = solution.Genomes;
long ticks_after = DateTime.Now.Ticks;
StringBuilder sizes = new StringBuilder();
foreach (int size in routes.Sizes)
{
sizes.Append(size);
sizes.Append(" ");
}
StringBuilder weights = new StringBuilder();
foreach (double weight in solution.Fitness.Weights)
{
weights.Append(weight.ToString(CultureInfo.InvariantCulture));
weights.Append(" ");
}
return(routes);
}
/// <summary>
/// Executes a solver procedure.
/// </summary>
/// <param name="problem"></param>
/// <returns></returns>
internal override MaxTimeSolution Solve(MaxTimeProblem problem)
{
// _customers = problem.Customers;
//float[] solutions = OsmSharp.Math.VRP.Core.BestPlacement.CheapestInsertionHelper.CalculateBestValues(
// problem, _customers);
generations = 0;
_max_generations = 10000000;
// calculate one tsp solution.
//Tools.Math.TSP.ISolver tsp_solver = new OsmSharp.Math.TSP.EdgeAssemblyGenetic.EdgeAssemblyCrossOverSolver(_population, _stagnation,
// new OsmSharp.Math.TSP.Genetic.Solver.Operations.Generation._3OptGenerationOperation(),
// new OsmSharp.Math.TSP.Genetic.Solver.Operations.CrossOver.EdgeAssemblyCrossover(30,
// OsmSharp.Math.TSP.Genetic.Solver.Operations.CrossOver.EdgeAssemblyCrossover.EdgeAssemblyCrossoverSelectionStrategyEnum.SingleRandom,
// true));
//IRoute tsp_solution = tsp_solver.Solve(new OsmSharp.Routing.VRP.NoDepot.MaxTime.TSPPlacement.TSPProblem(
// problem));
// initialize the generation.
IGenerationOperation<MaxTimeSolution, MaxTimeProblem, Fitness> generation =
//new SolverGenerationOperation(new TSPPlacement.TSPPlacementSolver<ResolvedType>(
// this.Router, this.Max, this.DeliveryTime, tsp_solution));
new OsmSharp.Routing.VRP.NoDepot.MaxTime.Genetic.Generation.RandomBestPlacement();
//new SolverGenerationOperation(new CheapestInsertionSolverWithImprovements<ResolvedType>(
// this.Router, this.Max, this.DeliveryTime, 5, 0.1f, true, 0.1f, false, 1f, null, null));
//new SolverGenerationOperation(new CheapestInsertionSolverWithImprovements<ResolvedType>(
// this.Router, this.Max, this.DeliveryTime, 5, 0.1f, true, 0.1f, true, 1f));
// initialize the crossover.
ICrossOverOperation<MaxTimeSolution, MaxTimeProblem, Fitness> cross_over =
new OsmSharp.Routing.VRP.NoDepot.MaxTime.Genetic.CrossOver.RouteExchangeOperation();
// initialize the mutation.
//IMutationOperation<MaxTimeSolution, MaxTimeProblem, Fitness> mutation =
// new VehicleMutation();
List<IMutationOperation<MaxTimeSolution, MaxTimeProblem, Fitness>> mutators =
new List<IMutationOperation<MaxTimeSolution, MaxTimeProblem, Fitness>>();
mutators.Add(new VehicleMutation());
//mutators.Add(new ThreeOptMutation());
//mutators.Add(new RedivideRouteMutation());
mutators.Add(new RoutePartExchangeMutation());
if (_probabilities == null)
{
_probabilities = new List<double>();
_probabilities.Add(0.2);
_probabilities.Add(0.6);
_probabilities.Add(0.2);
}
CombinedMutation<MaxTimeSolution, MaxTimeProblem, Fitness> mutation = new CombinedMutation<MaxTimeSolution, MaxTimeProblem, Fitness>(
StaticRandomGenerator.Get(),
mutators,
_probabilities);
SolverSettings settings = new SolverSettings(_stagnation, _population, _max_generations,
_elitism_percentage, _cross_percentage, _mutation_percentage);
MaxTimeProblem genetic_problem = problem;// new MaxTimeProblem(max, problem, solutions);
Solver<MaxTimeSolution, MaxTimeProblem, Fitness> solver =
new Solver<MaxTimeSolution, MaxTimeProblem, Fitness>(genetic_problem, settings,
new TournamentBasedSelector<MaxTimeSolution, MaxTimeProblem, Fitness>(5, 0.5),
mutation, //new ThreeOptMutation(),
cross_over, // new RouteExchangeOperation(), //new RouteExchangeOperation(), //new RouteExchangeAndVehicleOperation(), // Order1CrossoverOperation()
generation, //new RandomBestPlacement(),//new RandomGeneration(), //new RandomBestPlacement(),
new FitnessCalculator());
solver.NewFittest += new Solver<MaxTimeSolution, MaxTimeProblem, Fitness>.NewFittestDelegate(solver_NewFittest);
//solver.NewGeneration += new Solver<MaxTimeSolution, Problem, Fitness>.NewGenerationDelegate(solver_NewGeneration);
Individual<MaxTimeSolution, MaxTimeProblem, Fitness> solution = solver.Start(null);
//this.solver_NewFittest(solution);
MaxTimeSolution routes = solution.Genomes;
long ticks_after = DateTime.Now.Ticks;
StringBuilder sizes = new StringBuilder();
foreach (int size in routes.Sizes)
{
sizes.Append(size);
sizes.Append(" ");
}
StringBuilder weights = new StringBuilder();
foreach (double weight in solution.Fitness.Weights)
{
weights.Append(weight.ToString(CultureInfo.InvariantCulture));
weights.Append(" ");
}
return routes;
}
