public AOptimizer(AEvaluation <Element> evaluation, AStopCondition stopCondition)
{
Result = null;
this.Evaluation = evaluation;
this.StopCondition = stopCondition;
bestSolutions = new List <double>();
}
public double Evaluate(AEvaluation <Element> evaluation)
{
if (!evaluated)
{
Fitness = evaluation.dEvaluate(Genotype);
evaluated = true;
}
return(Fitness);
}
public APopulationOptimizer(AEvaluation <Element> evaluation, AStopCondition stopCondition, AGenerator <Element> generator,
ASelection selection, IMutation <Element> mutation, int populationSize)
: base(evaluation, stopCondition)
{
this.generator = generator;
this.selection = selection;
this.mutation = mutation;
this.populationSize = populationSize;
population = new List <Individual <Element> >();
averageValues = new List <double>();
worstValues = new List <double>();
}
private static void TestGA(AEvaluation <int> problem, double probCrossover, double probMutation, int population, int iterations, double tournament)
{
List <String> results = new List <String>();
List <String> learningProgress = new List <String>();
int savedRun = new Random().Next(0, 10);
for (int i = 0; i < 10; i++)
{
//var evaluation = new TspEvaluation(@"C:\Users\jbelter\source\repos\machine-learning-cvrp\data\A-n32-k5.vrp");
var evaluation = problem;
var stopCondition = new IterationsStopCondition(iterations);
//var optimizer = new TspRandomSearch(evaluation, stopCondition);
var generator = new TspGenerator(new Random());
ASelection selection;
if (tournament > 1 && tournament <= population)
{
selection = new TournamentSelection(Convert.ToInt32(tournament));
}
else if (tournament > 0 && tournament <= 1)
{
selection = new TournamentSelection(tournament);
}
else
{
selection = new TournamentSelection(5);
}
var crossover = new OrderedCrossover(probCrossover);
var mutation = new SwapMutation(probMutation);
var optimizer = new GeneticAlgorithm <int>(evaluation, stopCondition, generator, selection, crossover, mutation, population);
optimizer.Run();
if (i == savedRun)
{
learningProgress = formatLearning(optimizer.worstValues, optimizer.averageValues, optimizer.bestSolutions);
}
//ReportOptimizationResult(optimizer.Result);
results.Add(String.Join(", ",
problem.iSize.ToString(),
optimizer.timeTaken.ToString(),
optimizer.bestSolutions.Last().ToString(),
optimizer.averageValues.Last().ToString(),
optimizer.worstValues.Last().ToString(),
probCrossover.ToString(),
probMutation.ToString(),
population.ToString(),
iterations.ToString(),
tournament.ToString()
));
}
SaveToFile(@"C:\Users\jbelter\source\repos\machine-learning-cvrp\resultsGA.txt", results);
SaveToFile((@"C:\Users\jbelter\source\repos\machine-learning-cvrp\progress\GA" + "-" +
problem.iSize.ToString() + "-" +
probCrossover.ToString() + "-" +
probMutation.ToString() + "-" +
population.ToString() + "-" +
iterations.ToString() + "-" +
tournament.ToString())
.Replace(".", "") + ".txt",
learningProgress);
}
public SamplingOptimizer(AEvaluation <Element> evaluation, AStopCondition stopCondition, AGenerator <Element> generator)
: base(evaluation, stopCondition)
{
this.generator = generator;
}
public GeneticAlgorithm(AEvaluation <Element> evaluation, AStopCondition stopCondition, AGenerator <Element> generator,
ASelection selection, ACrossover crossover, IMutation <Element> mutation, int populationSize)
: base(evaluation, stopCondition, generator, selection, mutation, populationSize)
{
this.crossover = crossover;
}
