int zerosSum; // how many inactive genes are in chromozome?
#endregion Fields
#region Constructors
/// <summary>
/// Creates new random population and sets parameters of evolution
/// </summary>
/// <param name="populationSize">Population size</param>
/// <param name="probCrossOver">Probability of crossover</param>
/// <param name="probMutability">Probability of mutation</param>
/// <param name="elitism">How many best ones will survive?</param>
/// <param name="shareSigma">Distance, two chromozome with distance below that parameter are similiar</param>
/// <param name="shareAlfa">Sharing constant</param>
/// <param name="scaleSigma">Scaling constant, best fitnuss would be scaleSigma times greater than average population fitness</param>
/// <param name="newBorn">True if extincted ones should be replace by random ones</param>
/// <param name="zeros">Which genes arent active in this evolution? (ex. building fort is banned in a scenario)</param>
public Genetic(int populationSize, double probCrossOver, double probMutability, int elitism, double shareSigma, double shareAlfa, double scaleSigma, bool newBorn, bool [] zeros)
{
rnd = new Random();
this.shareSigma = shareSigma;
this.shareAlfa = shareAlfa;
this.scaleSigma = scaleSigma;
this.elitism = elitism;
this.probCrossOver = probCrossOver;
this.probMutability = probMutability;
this.populationSize = populationSize;
this.extinction = 0.0;
this.newBorn = newBorn;
this.zeros = zeros;
generationNumber = 0;
zerosSum = 0;
for (int loop1 = 0; loop1 < zeros.Length; loop1++)
if (zeros[loop1])
zerosSum++;
population = new Chromozone[populationSize];
// generate random start population
for (int loop1 = 0; loop1 < populationSize; loop1++)
population[loop1] = new Chromozone(rnd, zeros);
theBestOne = population[0];
fitnessBestOne = 0.0;
activeChromozomeID = 0;
lastFitness = 0.0;
// start time of evolving
stopwatch = new Stopwatch();
stopwatch.Start();
}
private void NewPopulation()
{
// create empty list of new population
List<Chromozone> newPopulation = new List<Chromozone>();
// sort chromozomes according their fitness
Array.Sort(population);
Log();
if (generationNumber % 3 == 0) // in each third generation, print all chromozomes in text file
PrintAllChromozones();
// replace best one in all populations
if (population[0].GetFitness() > fitnessBestOne)
{
fitnessBestOne = population[0].GetFitness();
theBestOne = population[0];
}
// At least the worst one wont produce itself
// Adds something to exctinction
for (int loop1 = population.Length - 1; loop1 >= population.Length / 3; loop1--)
{
if (population[loop1].GetFitness() > extinction &&
population[loop1].GetFitness() != population[population.Length / 3 - 1].GetFitness())
{
extinction = population[loop1].GetFitness();
break;
}
}
// apply elitism
for (int loop1 = 0; loop1 < elitism; loop1++)
{
newPopulation.Add(new Chromozone(population[loop1].GetGenes(), zeros));
}
// kill all chromozome with fitness less than extinction value
KillTheWorsts(extinction);
// replace extincted ones by new random ones
if (newBorn || population.Length < 2)
AddFreshOnes(newPopulation);
// linear scaling of fitness
ScaleFactor();
// change fitnesses according share value
ShareFactor();
// prepare roulete wheel for selectioin
double sumFitness = 0.0;
double sumProb = 0.0;
foreach (Chromozone ch in population)
{
sumFitness += ch.GetFitness();
}
foreach (Chromozone ch in population)
{
double prob = sumProb + ch.GetFitness() / sumFitness;
ch.SetProbability(prob);
sumProb += ch.GetFitness() / sumFitness;
}
// end of preparing roulete wheel
int[][][] sons;
while (newPopulation.Count < populationSize)
{
// clone selected mum and dad
int[][] dad = Chromozone.CloneArray2D(Selection());
int[][] mum = Chromozone.CloneArray2D(Selection());
sons = CrossOver(dad, mum);
for (int loop1 = 0; loop1 < sons.Length; loop1++)
{
sons[loop1] = Mutation(sons[loop1]);
newPopulation.Add(new Chromozone(sons[loop1], zeros));
}
}
population = newPopulation.ToArray();
// when there are exactly same chromozome in new population, mutate the copyiest until thera arent two same chromozomes
DifferPopulation();
}
/// <summary>
/// For the last step of evolution new generation.
/// If there are 2 exactly same chromozomes, mutate one of them.
/// </summary>
private void DifferPopulation()
{
double tempMutability = probMutability;
probMutability = 0.1;
for (int loop1 = 0; loop1 < population.Length; loop1++)
{
for (int loop2 = 0; loop2 < population.Length; loop2++)
{
if (loop1 != loop2 && population[loop1].DistanceTo(population[loop2]) == 0)
{
population[loop1] = new Chromozone(Mutation(population[loop1].GetGenes()), zeros);
loop2 = 0;
}
}
}
probMutability = tempMutability;
}
private void KillTheWorsts(double trashhold)
{
int alive = 0;
// how many will survive?
for (alive = 0; alive < population.Length; alive++)
{
if (population[alive].GetFitness() < trashhold)
{
break;
}
}
// copy survived chromozome to new array and replace with it the old one
if (alive != population.Length)
{
Chromozone[] oldPopulation = new Chromozone[alive];
for (int loop1 = 0; loop1 < oldPopulation.Length; loop1++)
{
oldPopulation[loop1] = population[loop1];
}
population = oldPopulation;
}
}
/// <summary>
/// Returns weighted manhatton distance between two chromozomes
/// </summary>
/// <param name="b">Second chromozome</param>
/// <returns>Distance</returns>
internal double DistanceTo(Chromozone b)
{
double chromDistance = 0.0;
for (int loop1 = 0; loop1 < genes.Length; loop1++)
{
for (int loop2 = 0; loop2 < genes[loop1].Length; loop2++)
{
int genDistance = Math.Abs(genes[loop1][loop2] - b.genes[loop1][loop2]);
if (loop2 == 0)
{
chromDistance += genDistance;
}
else
{
chromDistance += 5 * genDistance;
}
}
}
return chromDistance;
}