/// <summary>
/// Randomly select a group of tours from the population.
/// The top 2 are chosen as the parent tours.
/// Crossover is performed on these 2 tours.
/// The childred tours from this process replace the worst 2 tours in the group.
/// </summary>
/// <param name="groupSize">Number of tours in this group.</param>
/// <param name="mutation">Odds that a child will be mutated.</param>
bool makeChildren(int groupSize, int mutation)
{
int[] tourGroup = new int[groupSize];
int tourCount, i, topTour, childPosition, tempTour;
// pick random tours to be in the neighborhood city group
// we allow for the same tour to be included twice
for (tourCount = 0; tourCount < groupSize; tourCount++)
{
tourGroup[tourCount] = rand.Next(population.Count);
}
// bubble sort on the neighborhood city group
for (tourCount = 0; tourCount < groupSize - 1; tourCount++)
{
topTour = tourCount;
for (i = topTour + 1; i < groupSize; i++)
{
if (population[tourGroup[i]].Fitness < population[tourGroup[topTour]].Fitness)
{
topTour = i;
}
}
if (topTour != tourCount)
{
tempTour = tourGroup[tourCount];
tourGroup[tourCount] = tourGroup[topTour];
tourGroup[topTour] = tempTour;
}
}
bool foundNewBestTour = false;
// take the best 2 tours, do crossover, and replace the worst tour with it
childPosition = tourGroup[groupSize - 1];
population[childPosition] = Tour.Crossover(population[tourGroup[0]], population[tourGroup[1]], cityList, rand);
if (rand.Next(100) < mutation)
{
population[childPosition].Mutate(rand);
}
population[childPosition].DetermineFitness(cityList);
// now see if the first new tour has the best fitness
if (population[childPosition].Fitness < population.BestTour.Fitness)
{
population.BestTour = population[childPosition];
foundNewBestTour = true;
}
// take the best 2 tours (opposite order), do crossover, and replace the 2nd worst tour with it
childPosition = tourGroup[groupSize - 2];
population[childPosition] = Tour.Crossover(population[tourGroup[1]], population[tourGroup[0]], cityList, rand);
if (rand.Next(100) < mutation)
{
population[childPosition].Mutate(rand);
}
population[childPosition].DetermineFitness(cityList);
// now see if the second new tour has the best fitness
if (population[childPosition].Fitness < population.BestTour.Fitness)
{
population.BestTour = population[childPosition];
foundNewBestTour = true;
}
return(foundNewBestTour);
}
/// <summary>
/// Random select a group
/// </summary>
bool makeChildren(int groupSize, int mutation)
{
int[] tourGroup = new int[groupSize];
int tourCount, i, topTour, childPosition, tempTour;
for (tourCount = 0; tourCount < groupSize; tourCount++)
{
tourGroup[tourCount] = rand.Next(population.Count);
}
for (tourCount = 0; tourCount < groupSize - 1; tourCount++)
{
topTour = tourCount;
for (i = topTour + 1; i < groupSize; i++)
{
if (population[tourGroup[i]].Fitness < population[tourGroup[topTour]].Fitness)
{
topTour = i;
}
}
if (topTour != tourCount)
{
tempTour = tourGroup[tourCount];
tourGroup[tourCount] = tourGroup[topTour];
tourGroup[topTour] = tempTour;
}
}
bool foundNewBestTour = false;
childPosition = tourGroup[groupSize - 1];
population[childPosition] = Tour.Crossover(population[tourGroup[0]], population[tourGroup[1]], cityList, rand);
if (rand.Next(100) < mutation)
{
population[childPosition].Mutate(rand);
}
population[childPosition].DetermineFitness(cityList);
if (population[childPosition].Fitness < population.BestTour.Fitness)
{
population.BestTour = population[childPosition];
foundNewBestTour = true;
}
childPosition = tourGroup[groupSize - 2];
population[childPosition] = Tour.Crossover(population[tourGroup[1]], population[tourGroup[0]], cityList, rand);
if (rand.Next(100) < mutation)
{
population[childPosition].Mutate(rand);
}
population[childPosition].DetermineFitness(cityList);
if (population[childPosition].Fitness < population.BestTour.Fitness)
{
population.BestTour = population[childPosition];
foundNewBestTour = true;
}
return(foundNewBestTour);
}
