// Select, weighted by fitness
// Preconditions: numToSelect <= Count
// Postconditions: A new Population that is a subset of this Population is created containing <numToSelect> elements
public Population SelectIndividualsAtRandomWeighted(int numToSelect)
{
// We will use this to weight the random selection by adding index numbers multiple times to select from this list
// These index numbers will select the Individual
// This way we don't modify the Population
List <int> weightedSelectionPool = new List <int>();
// We track how fit each individual is relative to the others
double totalFitness = 0d;
int iter = 0;
// Get total fitness
foreach (IReadOnlyIndividual i in this)
{
totalFitness += i.Fitness;
// We'll need this for later; happens to be a convenient location
weightedSelectionPool.Add(iter);
++iter;
}
// Get percentage of total fitness for each Individual (minimum 0.1% to 1% area)
iter = 0;
foreach (IReadOnlyIndividual i in this)
{
double relativeFitness = i.Fitness / totalFitness;
int instances = (int)(Math.Ceiling(this.Count * relativeFitness)) - 1;
// this will weight the selections appropriately using the Individuals' indices
for (int j = 0; j < instances; ++j)
{
weightedSelectionPool.Add(iter);
}
++iter;
}
// "Roll" for selection based on aforementioned percentages and select accordingly
Population subset = new Population();
IReadOnlyConfigClass config = ConfigClass.getInstance();
for (int i = 0; i < numToSelect; ++i)
{
// roll
int randIndex = config.RNG.Next(0, weightedSelectionPool.Count);
// select
subset.AddIndividual(this.GetIndividual(weightedSelectionPool[randIndex]));
}
return(subset);
}
static void Main(string[] args)
{
calc = FitnessCalculator.getInstance();
config = ConfigClass.getInstance();
//1. Initialize
Population p = new Population(true);
//4. Repeat with new generation
while (Console.ReadKey().KeyChar != 'x')
{
p = GenerationStep(p);
foreach (IReadOnlyIndividual i in p)
{
Console.WriteLine(i);
}
Console.WriteLine("Continue to next generation? (Press x to exit)");
}
}
