private static void checkHugeProblem()
{
var hugeProblem = new TspEvaluation(@"C:\Users\jbelter\source\repos\machine-learning-cvrp\data\M-n200-k16.vrp");
List <TspEvaluation> problem = new List <TspEvaluation>();
problem.Add(hugeProblem);
//TestRandom(problem);
//TestGreedy(problem);
TestGA(hugeProblem, .8, .4, 200, 500, 20);
}
public TspGreedy(TspEvaluation evaluation, AStopCondition stopCondition, int start)
: base(evaluation, stopCondition)
{
if (start < evaluation.iSize)
{
startPosition = start;
}
else
{
throw new Exception("incorrect starting position");
}
}
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());
}
}
public TspRandomSearch(TspEvaluation evaluation, AStopCondition stopCondition)
: base(evaluation, stopCondition, new TspGenerator(new Random()))
{
}
