public static TSPGenotype randomGenotype(TSP problem)
{
TSPGenotype g = new TSPGenotype(problem);
g.genotype = problem.randomSolutionInt();
return(g);
}
public static TSPGenotype crossover(TSPGenotype parent1, TSPGenotype parent2)
{
Random r = new Random();
int len = parent1.genotype.Length;
List <int> genotype2 = new List <int>(parent2.genotype);
TSPGenotype child = new TSPGenotype(parent1);
int position1 = r.Next(len);
int position2 = r.Next(len);
while (position1 == position2)
{
position2 = r.Next(len);
}
if (position1 > position2)
{
int temp = position1;
position1 = position2;
position2 = temp;
}
for (int i = position1; i <= position2; i++)
{
genotype2.Remove(child.genotype[i]);
}
for (int i = 0; i < position1; i++)
{
child.genotype[i] = genotype2[0];
genotype2.RemoveAt(0);
}
for (int i = position2 + 1; i < len; i++)
{
child.genotype[i] = genotype2[0];
genotype2.RemoveAt(0);
}
return(child);
}
public static void testDifferentAlgorithms(TSP[] problems, int populationSize, int generationNum, int numOfLaunch = 10)
{
string line;
var file = new System.IO.StreamWriter(@"/Users/aggami/Library/Mobile Documents/com~apple~CloudDocs/Studia/Semestr 6/SIiIW/" + "testAlgorytmow.txt");
file.AutoFlush = true;
string startLine = "ProblemName; ;Best;Worst;Avg;Std;Best;Worst;Avg;Std;Best;Worst;Avg;Std;";
file.WriteLine(startLine);
RandomSolution r = new RandomSolution();
foreach (TSP problem in problems)
{
Console.WriteLine(problem.Name);
line = problem.Name + "; ;";
Console.WriteLine("Random");
r.run(populationSize * generationNum, problem);
line += r.Best + "; " + r.Worst + "; " + r.Avg + "; " + r.Dev + "; ";
float best = Single.MaxValue;
float worst = 0;
float avg = 0;
float div = 0;
float[] solutions = new float[problem.Dimention];
Console.WriteLine("Greedy");
for (int i = 0; i < problem.Dimention; i++)
{
float temp = new TSPGenotype(TSPGenotype.solutionToGenotype(GreedyAlgorithm.solveGreedy(problem, i)), problem).fitness();
solutions[i] = temp;
if (temp > worst)
{
worst = temp;
}
if (temp < best)
{
best = temp;
}
avg += temp;
}
avg = avg / problem.Dimention;
for (int i = 0; i < problem.Dimention; i++)
{
div += (solutions[i] - avg) * (solutions[i] - avg);
}
div = div / problem.Dimention;
div = Convert.ToSingle(Math.Sqrt(div));
line += best + "; " + worst + "; " + avg + "; " + div + "; ";
best = Single.MaxValue;
worst = 0;
avg = 0;
div = 0;
Console.WriteLine("Ewolucyjny");
for (int i = 0; i < 10; i++)
{
AlgorytmEwolucyjny alg = new AlgorytmEwolucyjny(problem, populationSize, generationNum, "TNM");
alg.CrossoverProb = 0.9;
alg.MutationProb = 0.4;
if (i == 0)
{
file.WriteLine(alg.toString());
}
float temp = alg.runAlgorithm().fitness();
solutions[i] = temp;
if (temp > worst)
{
worst = temp;
}
if (temp < best)
{
best = temp;
}
avg += temp;
}
avg = avg / problem.Dimention;
for (int i = 0; i < problem.Dimention; i++)
{
div += (solutions[i] - avg) * (solutions[i] - avg);
}
div = div / problem.Dimention;
div = Convert.ToSingle(Math.Sqrt(div));
line += best + "; " + worst + "; " + avg + "; " + div + "; ";
file.WriteLine(line);
}
}
public TSPGenotype(TSPGenotype parent)
{
this.problem = parent.problem;
this.genotype = (int[])parent.genotype.Clone();
}
