private static void TestCrossoverVsMutation()
{
List <double> b = new List <double>();
List <double> w = new List <double>();
List <double> a = new List <double>();
var evaluation = new TspEvaluation(@"C:\Users\jbelter\source\repos\machine-learning-cvrp\data\A-n46-k7.vrp");
double[] px = { 0, .1, .8, 1, 0, 0, 0 };
double[] pw = { 0, 0, 0, 0, .2, .5, 1 };
for (int j = 0; j < 7; j++)
{
for (int i = 0; i < 20; i++)
{
var stopCondition = new IterationsStopCondition(200);
//var optimizer = new TspRandomSearch(evaluation, stopCondition);
var generator = new TspGenerator(new Random());
ASelection selection = new TournamentSelection(Convert.ToInt32(5));
var crossover = new OrderedCrossover(px[j]);
var mutation = new SwapMutation(pw[j]);
var optimizer = new GeneticAlgorithm <int>(evaluation, stopCondition, generator, selection, crossover, mutation, 100);
optimizer.Run();
b.Add(optimizer.bestSolutions.Last());
w.Add(optimizer.worstValues.Last());
a.Add(optimizer.averageValues.Last());
}
double avgB = 0.0, avgA = 0.0, avgW = 0.0;
for (int i = 0; i < b.Count; i++)
{
avgB += b[i];
avgA += a[i];
avgW += w[i];
}
avgB /= b.Count;
avgA /= b.Count;
avgW /= b.Count;
Console.WriteLine("avg(best value): " + avgB.ToString() +
" avg(average value): " + avgA.ToString() +
" avg(worst value): " + avgW.ToString());
}
}
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);
}
