Why the Genetic Algorithym implementation for TSP doesn't work efficiently?

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C#4.0

algorithm

I am working on Genetic Algorithyms to solve Travelling Salesman Problem. My chromosom representation is like 32415 as the order of the cities. I have tried 3 Crossing-Over methods.These are Classic, OX1 and Greedy. But any of them couldnt help me to solve TSP efficiently. The basic problem is that the algorithym always leaves an unfixed crossed path. This means it doesnt works good.

To be clearly understanded, please see this image. The circled paths can be fixed to get a better solution as you see.But GA doesn't do that. How can I fix this problem?

I am using tournament with group of 5 to select parents, generally %3 mutation odds and 10000 fixed population size.
My code for Crossing Over, Mutation and GA Procedure:

Tour OrderingCrossingOver(Tour mother, Tour father)
{
   //Selected number of gens for CO
    int numberOfGens = rnd.Next(1, N - 2);
    //Selected position to start for CO
    int pos = rnd.Next(0, N - numberOfGens);
    City[] gens = new City[N];

    List<City> noneGens = new List<City>(father.Cities);
    List<int> nonePos = new List<int>();
    for (int i = 0; i < N; i++)
    {
        if (i < pos || i >= pos + numberOfGens)
        {
            //Get gen positions to be empty
            nonePos.Add(i);
            continue;
        }
        gens[i] = mother.Cities[i];
        noneGens.Remove(gens[i]);
    }
    for (int i = 0; i < noneGens.Count; i++)
    {
        //Fill empty gens using father
        gens[nonePos[i]] = noneGens[i];
    }
    noneGens.Clear();
    nonePos.Clear();
    return new Tour(map) { Cities = gens };
}
void Mutation(Tour tour)
{
    int i = rnd.Next(N - 1);
    int j = rnd.Next(N - 1);

    City temp = tour.Cities[i];
    tour.Cities[i] = tour.Cities[j];
    tour.Cities[j] = temp;

    mut++;
}
public void StartAsync()
{
    Task.Run(() =>
    {
        isRunning = true;
        CreatePopulation();
        int st = 0;
        while (currentProducedPopSize < maxPopNumber && !stopped)
        {
            //Get parents ascending
            int[] nums = selectParents();

            //Get best two chromosomes
            Tour parent1 = population[nums[0]];
            Tour parent2 = population[nums[1]];

            #region First Child

            Tour child = OrderingCrossingOver(parent1, parent2);
            Tour old = new Tour(map) { Cities = child.Cities };
            if (mutPosibility >= rnd.Next(1, 100))
                Mutation(child);

            population[nums[nums.Length - 1]] = child;

            if (this.Best.Value > child.Value)
                this.Best = child;

            #endregion

            #region Second Child

            child = OrderingCrossingOver(parent2, parent1);

            if (mutPosibility >= rnd.Next(1, 100))
                Mutation(child);

            population[nums[nums.Length - 2]] = child;

            if (this.Best.Value > child.Value)
                this.Best = child;

            #endregion

            currentProducedPopSize += 2;
            st++;
            this.Progress = double.Parse((currentProducedPopSize * 1.0 / maxPopNumber * 100).ToString("00.00"));

        }

        isRunning = false;
        stopped = false;
        this.Best = population[population.Length - 1];
        if (GACompleted != null)
            GACompleted(this, EventArgs.Empty);
    });
}