public void initPool()
{
int genomeI;
for( genomeI = 0; genomeI < 10; genomeI++ )
{
Genome genomeWithRating;
int bitI;
genomeWithRating = new Genome(genomeBits);
for( bitI = 0; bitI < genomeBits; bitI++ )
{
genomeWithRating.genome[bitI] = random.Next(1) == 0;
}
genomesWithRating.Add(genomeWithRating);
}
}
public void run()
{
List <Genome> newRound, crossOverGenoms;
float fitnessSum;
float currentThreshold;
int eliteI;
int genomeI;
newRound = new List <Genome>();
crossOverGenoms = new List <Genome>();
rating();
// elitism selection algorithm
// we choose the top 3 genoms and transfer them into the winner
currentThreshold = float.PositiveInfinity;
for (eliteI = 0; eliteI < eliteCount; eliteI++)
{
int highestIndex;
bool resultValid;
highestIndex = getHighestGenomWithRatingBelow(out resultValid, currentThreshold);
newRound.Add(genomesWithRating[highestIndex]);
currentThreshold = genomesWithRating[highestIndex].rating;
// update the highest rating
if (eliteI == 0)
{
highestRating = currentThreshold;
}
}
newRound.AddRange(getAllGenomsWithRatingBelow(currentThreshold));
// selection
// we use the Roulette Wheel Selection algorithm
fitnessSum = 0.0f;
foreach (Genome iterationGenome in newRound)
{
fitnessSum += iterationGenome.rating;
}
for (;;)
{
float currentSum;
int mateI;
int[] mateIndices; // indices of genoms which need to be combined
float[] roulettValues;
bool[] crossoverResult;
mateIndices = new int[2];
roulettValues = new float[2];
roulettValues = GeneticAlgorithm.getRoulettPair(random, fitnessSum, newRound.Count);
currentSum = 0.0f;
mateI = 0;
for (genomeI = 0; genomeI < newRound.Count; genomeI++)
{
currentSum += newRound[genomeI].rating;
if (currentSum > roulettValues[mateI])
{
mateIndices[mateI] = genomeI;
mateI++;
if (mateI == 2)
{
break;
}
}
}
// sanity check
if (mateI == 0)
{
System.Diagnostics.Debug.Assert(false, "Should not happen!");
}
else if (mateI == 1)
{
// if not all mates have been chosen
System.Diagnostics.Debug.Assert(false, "TODO???");
}
crossoverResult = crossover(newRound[mateIndices[0]].genome, newRound[mateIndices[1]].genome);
// create the new crossover genom
Genome crossOverGenom;
crossOverGenom = new Genome(genomeBits);
crossOverGenom.genome = crossoverResult;
crossOverGenom.rating = float.PositiveInfinity;
crossOverGenoms.Add(crossOverGenom);
break;
}
System.Diagnostics.Debug.Assert(crossOverGenoms.Count == 1);
newRound.AddRange(crossOverGenoms);
genomesWithRating = newRound;
while (genomesWithRating.Count > 10)
{
// remove the genom of the lowest rating
int lowestFittnessI;
lowestFittnessI = GeneticAlgorithm.getIndexOfLowestFittness(genomesWithRating);
genomesWithRating.RemoveAt(lowestFittnessI);
}
// mutate
// we mutate all but the elites not
for (genomeI = 2; genomeI < genomesWithRating.Count; genomeI++)
{
Genome iterationGenome;
iterationGenome = genomesWithRating[genomeI];
mutate(ref iterationGenome.genome);
}
}
public void run()
{
List<Genome> newRound, crossOverGenoms;
float fitnessSum;
float currentThreshold;
int eliteI;
int genomeI;
newRound = new List<Genome>();
crossOverGenoms = new List<Genome>();
rating();
// elitism selection algorithm
// we choose the top 3 genoms and transfer them into the winner
currentThreshold = float.PositiveInfinity;
for( eliteI = 0; eliteI < eliteCount; eliteI++ )
{
int highestIndex;
bool resultValid;
highestIndex = getHighestGenomWithRatingBelow(out resultValid, currentThreshold);
newRound.Add(genomesWithRating[highestIndex]);
currentThreshold = genomesWithRating[highestIndex].rating;
// update the highest rating
if( eliteI == 0 )
{
highestRating = currentThreshold;
}
}
newRound.AddRange(getAllGenomsWithRatingBelow(currentThreshold));
// selection
// we use the Roulette Wheel Selection algorithm
fitnessSum = 0.0f;
foreach( Genome iterationGenome in newRound )
{
fitnessSum += iterationGenome.rating;
}
for(;;)
{
float currentSum;
int mateI;
int[] mateIndices; // indices of genoms which need to be combined
float[] roulettValues;
bool[] crossoverResult;
mateIndices = new int[2];
roulettValues = new float[2];
roulettValues = GeneticAlgorithm.getRoulettPair(random, fitnessSum, newRound.Count);
currentSum = 0.0f;
mateI = 0;
for( genomeI = 0; genomeI < newRound.Count; genomeI++ )
{
currentSum += newRound[genomeI].rating;
if( currentSum > roulettValues[mateI] )
{
mateIndices[mateI] = genomeI;
mateI++;
if( mateI == 2 )
{
break;
}
}
}
// sanity check
if( mateI == 0 )
{
System.Diagnostics.Debug.Assert(false, "Should not happen!");
}
else if( mateI == 1 )
{
// if not all mates have been chosen
System.Diagnostics.Debug.Assert(false, "TODO???");
}
crossoverResult = crossover(newRound[mateIndices[0]].genome, newRound[mateIndices[1]].genome);
// create the new crossover genom
Genome crossOverGenom;
crossOverGenom = new Genome(genomeBits);
crossOverGenom.genome = crossoverResult;
crossOverGenom.rating = float.PositiveInfinity;
crossOverGenoms.Add(crossOverGenom);
break;
}
System.Diagnostics.Debug.Assert(crossOverGenoms.Count == 1);
newRound.AddRange(crossOverGenoms);
genomesWithRating = newRound;
while( genomesWithRating.Count > 10 )
{
// remove the genom of the lowest rating
int lowestFittnessI;
lowestFittnessI = GeneticAlgorithm.getIndexOfLowestFittness(genomesWithRating);
genomesWithRating.RemoveAt(lowestFittnessI);
}
// mutate
// we mutate all but the elites not
for( genomeI = 2; genomeI < genomesWithRating.Count; genomeI++ )
{
Genome iterationGenome;
iterationGenome = genomesWithRating[genomeI];
mutate(ref iterationGenome.genome);
}
}