public static void testHistoryGenerationsRou(TSP problem, int populationSize, int numOfGenerations)
{
string line;
TSPGenotype genotype;
var file = new System.IO.StreamWriter(@"/Users/aggami/Library/Mobile Documents/com~apple~CloudDocs/Studia/Semestr 6/SIiIW/" + problem.Name + "testHistoriiGeneracjiPs=" + populationSize + ".txt");
file.AutoFlush = true;
string startLine = "Generation; Avg; Min; Max;";
AlgorytmEwolucyjny algorytmT = new AlgorytmEwolucyjny(problem, populationSize, numOfGenerations, "ROU");
algorytmT.CrossoverProb = 0.9;
algorytmT.MutationProb = 0.4;
file.WriteLine(algorytmT.toString());
file.WriteLine(startLine);
genotype = algorytmT.runAlgorithmWithHistory();
for (int i = 0; i < numOfGenerations; i++)
{
line = i + "; " + algorytmT.AvgFitnessGen[i] + "; " + algorytmT.BestFitnessGen[i] + "; " + algorytmT.WorstFitnessGen[i];
file.WriteLine(line);
}
file.Close();
}
public static void finalTestAfter(TSP[] problems, int[] populationSizes, int[] generationNums)
{
string line;
var file = new System.IO.StreamWriter(@"/Users/aggami/Library/Mobile Documents/com~apple~CloudDocs/Studia/Semestr 6/SIiIW/" + "finalTestAfter2.txt");
file.AutoFlush = true;
string startLine = "ProblemName; Popsize; numOfGen; TNMSize; Crossover Prob; Mutation Prob;Best;Worst;Avg;Std;";
file.WriteLine(startLine);
for (int i = 0; i < problems.Length; i++)
{
Console.WriteLine(problems[i].Name);
float best = Single.MaxValue;
float worst = 0;
float avg = 0;
float div = 0;
float[] solutions = new float[10];
double cp = 0.8;
double mp = 0.4;
int ts = 20;
for (int j = 0; j < 10; j++)
{
Console.WriteLine("Run " + j);
AlgorytmEwolucyjny alg = new AlgorytmEwolucyjny(problems[i], populationSizes[i], generationNums[i], "TNM");
alg.CrossoverProb = cp;
alg.MutationProb = mp;
alg.TournamentSize = ts;
float temp = alg.runAlgorithmWithGreedy().fitness();
solutions[j] = temp;
if (temp > worst)
{
worst = temp;
}
if (temp < best)
{
best = temp;
}
avg += temp;
}
avg = avg / 10;
for (int j = 0; j < 10; j++)
{
div += (solutions[i] - avg) * (solutions[j] - avg);
}
div = div / 10;
div = Convert.ToSingle(Math.Sqrt(div));
line = problems[i].Name + "; " + populationSizes[i] + "; " + generationNums[i] + "; " + ts + "; " + cp + "; " + mp + "; " + best + "; " + worst + "; " + avg + "; " + div + "; ";
file.WriteLine(line);
}
}
public static void testGenerations(TSP problem, int populationSize, int[] numOfGenerations)
{
string line;
TSPGenotype genotype;
var file = new System.IO.StreamWriter(@"/Users/aggami/Library/Mobile Documents/com~apple~CloudDocs/Studia/Semestr 6/SIiIW/" + problem.Name + "testGeneracjiPop=" + populationSize + ".txt");
file.AutoFlush = true;
string startLine = "Popsize; Avg; Min; Max; Stand.dev; AvgTime; ";
foreach (int ng in numOfGenerations)
{
Console.WriteLine("Start for value: " + ng);
float[] results = new float[10];
float min = Single.MaxValue;
float max = 0;
float sum = 0;
float avg = 0;
float dev = 0;
float temp;
var watch = System.Diagnostics.Stopwatch.StartNew();
line = ng.ToString() + "; ";
for (int i = 0; i < 10; i++)
{
Console.WriteLine("Run " + i);
AlgorytmEwolucyjny algorytmT = new AlgorytmEwolucyjny(problem, populationSize, ng, "TNM");
if (i == 0)
{
file.WriteLine(algorytmT.toString());
file.WriteLine(startLine);
}
genotype = algorytmT.runAlgorithm();
temp = genotype.fitness();
sum += temp;
results[i] = genotype.fitness();
if (temp > max)
{
max = temp;
}
if (temp < min)
{
min = temp;
}
}
watch.Stop();
avg = sum / 10;
for (int i = 0; i < 10; i++)
{
dev += (results[i] - avg) * (results[i] - avg);
}
dev = Convert.ToSingle(Math.Sqrt(dev / 10));
var elapsedTime = watch.ElapsedMilliseconds;
elapsedTime = elapsedTime / 10000;
line = ng + "; " + avg + "; " + min + "; " + max + "; " + dev + "; " + elapsedTime + ";";
file.WriteLine(line);
}
file.Close();
}
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 static void testRoulette(TSP problem, int populationSize, int generationNum)
{
string line;
TSPGenotype genotype;
var file = new System.IO.StreamWriter(@"/Users/aggami/Library/Mobile Documents/com~apple~CloudDocs/Studia/Semestr 6/SIiIW/" + problem.Name + "testRuletki pop=" + populationSize + " gen=" + generationNum + ".txt");
AlgorytmEwolucyjny algorytmProb = new AlgorytmEwolucyjny(problem, populationSize, generationNum, "TNM");
algorytmProb.CrossoverProb = 0.9;
algorytmProb.MutationProb = 0.4;
string infoLine = algorytmProb.toString();
string startLine = "Method; Avg; Min; Max; Stand.dev; AvgTime; ";
file.WriteLine(startLine);
Console.WriteLine("Start ROU");
float[] results = new float[10];
float min = Single.MaxValue;
float max = 0;
float sum = 0;
float avg = 0;
float dev = 0;
float temp;
var watch = System.Diagnostics.Stopwatch.StartNew();
for (int i = 0; i < 10; i++)
{
Console.WriteLine("Run " + i);
AlgorytmEwolucyjny algorytmT = new AlgorytmEwolucyjny(problem, populationSize, generationNum, "ROU");
algorytmT.CrossoverProb = 0.9;
algorytmT.MutationProb = 0.4;
genotype = algorytmT.runAlgorithm();
temp = genotype.fitness();
sum += temp;
results[i] = genotype.fitness();
if (temp > max)
{
max = temp;
}
if (temp < min)
{
min = temp;
}
}
watch.Stop();
avg = sum / 10;
for (int i = 0; i < 10; i++)
{
dev += (results[i] - avg) * (results[i] - avg);
}
dev = Convert.ToSingle(Math.Sqrt(dev / 10));
var elapsedTime = watch.ElapsedMilliseconds;
elapsedTime = elapsedTime / 10000;
line = "Roulette" + "; " + avg + "; " + min + "; " + max + "; " + dev + "; " + elapsedTime + ";";
file.WriteLine(line);
file.Close();
}
