public override int CompareTo(object a)
{
EquationGenome Gene1 = this;
EquationGenome Gene2 = (EquationGenome)a;
return(Math.Sign(Gene2.CurrentFitness - Gene1.CurrentFitness));
}
public override double CalculateFitness()
{
try
{
lock (this)
{
// CurrentFitness = CalculateClosestProductSum();
// CurrentFitness = CalculateClosestSumTo10();
// CurrentFitness = CalculateMazeDistanceFitness();
CurrentFitness = CalculateSeriesFitness();
if (CurrentFitness < 0.0f)
{
CurrentFitness = 0.01f;
}
if (CurrentFitness == 1)
{
PerfectFitness = true;
PerfectGenome = this;
}
}
}
catch (Exception ex)
{
CurrentFitness = .01f; // ignore occasional exception
}
return(CurrentFitness);
}
public override void CopyGeneInfo(Genome dest)
{
EquationGenome theGene = (EquationGenome)dest;
theGene.Length = Length;
theGene.TheMin = TheMin;
theGene.TheMax = TheMax;
}
public override Genome Crossover2Point(Genome g)
{
EquationGenome aGene1 = new EquationGenome();
EquationGenome aGene2 = new EquationGenome();
g.CopyGeneInfo(aGene1);
g.CopyGeneInfo(aGene2);
// Pick a random crossover point
int CrossoverPoint1 = TheSeed.Next(1, (int)Length);
int CrossoverPoint2 = TheSeed.Next(CrossoverPoint1, (int)Length);
// normalize
if (CrossoverPoint1 > CrossoverPoint2)
{
int temp = CrossoverPoint1;
CrossoverPoint1 = CrossoverPoint2;
CrossoverPoint2 = temp;
}
EquationGenome CrossingGene = (EquationGenome)g;
for (int j = 0; j < CrossoverPoint1; j++)
{
aGene1.TheArray.Add(TheArray[j]);
aGene2.TheArray.Add(CrossingGene.TheArray[j]);
}
for (int j = CrossoverPoint1; j < CrossoverPoint2; j++)
{
aGene1.TheArray.Add(CrossingGene.TheArray[j]);
aGene2.TheArray.Add(TheArray[j]);
}
for (int j = CrossoverPoint2; j < Length; j++)
{
aGene1.TheArray.Add(TheArray[j]);
aGene2.TheArray.Add(CrossingGene.TheArray[j]);
}
// 50/50 chance of returning gene1 or gene2
EquationGenome aGene = null;
if (TheSeed.Next(2) == 1)
{
aGene = aGene1;
}
else
{
aGene = aGene2;
}
return(aGene);
}
public Population()
{
//
// TODO: Add constructor logic here
//
for (int i = 0; i < kInitialPopulation; i++)
{
EquationGenome aGenome = new EquationGenome(kLength, kMin, kMax);
int nCrossOver = EquationGenome.TheSeed.Next(1, (int)aGenome.Length);
aGenome.SetCrossoverPoint(nCrossover);
aGenome.CalculateFitness();
Genomes.Add(aGenome);
}
}
public Population()
{
//
// TODO: Add constructor logic here
//
for (int i = 0; i < kInitialPopulation; i++)
{
EquationGenome aGenome = new EquationGenome(kLength, kMin, kMax);
int nCrossOver = EquationGenome.TheSeed.Next(1, (int)aGenome.Length);
aGenome.SetCrossoverPoint(nCrossover);
aGenome.CalculateFitness();
Genomes.Add(aGenome);
}
}
public override void CopyGeneFrom(Genome src)
{
EquationGenome theGene = (EquationGenome)src;
for (int i = 0; i < TheArray.Count; i++)
{
TheArray[i] = (Gene)theGene.TheArray[i];
}
Length = theGene.Length;
TheMin = theGene.TheMin;
TheMax = theGene.TheMax;
CurrentFitness = theGene.CurrentFitness;
}
public Genome[] GetHighestScoreGenomes(int number)
{
Genomes.Sort();
EquationGenome[] strongestGenomes = new EquationGenome[number];
if (number > Genomes.Count)
{
number = Genomes.Count;
}
for (int i = 0; i < number; i++)
{
strongestGenomes[i] = new EquationGenome(kLength, kMin, kMax);
strongestGenomes[i].SetCrossoverPoint(kCrossover);
strongestGenomes[i].CopyGeneFrom((EquationGenome)Genomes[i]);
}
return(strongestGenomes);
}
EquationGenome GetBestGeneFromAllPopulations()
{
float topFitness = -1;
EquationGenome BestGenome = null;
foreach (Population p in Populations)
{
EquationGenome g = (EquationGenome)p.GetHighestScoreGenome();
if (g.CurrentFitness > topFitness)
{
BestGenome = g;
topFitness = (float)g.CurrentFitness;
}
}
return(BestGenome);
}
public override Genome UniformCrossover(Genome g)
{
EquationGenome aGene1 = new EquationGenome();
EquationGenome aGene2 = new EquationGenome();
g.CopyGeneInfo(aGene1);
g.CopyGeneInfo(aGene2);
// swap genes randomly
EquationGenome CrossingGene = (EquationGenome)g;
for (int i = 0; i < Length; i++)
{
if (TheSeed.Next(100) % 2 == 0)
{
aGene1.TheArray.Add(CrossingGene.TheArray[i]);
aGene2.TheArray.Add(TheArray[i]);
}
else
{
aGene1.TheArray.Add(TheArray[i]);
aGene2.TheArray.Add(CrossingGene.TheArray[i]);
}
}
// 50/50 chance of returning gene1 or gene2
EquationGenome aGene = null;
if (TheSeed.Next(2) == 1)
{
aGene = aGene1;
}
else
{
aGene = aGene2;
}
return(aGene);
}
public override Genome Crossover(Genome g)
{
EquationGenome aGene1 = new EquationGenome();
EquationGenome aGene2 = new EquationGenome();
g.CopyGeneInfo(aGene1);
g.CopyGeneInfo(aGene2);
// Pick a random crossover point
CrossoverPoint = TheSeed.Next(1, (int)Length);
EquationGenome CrossingGene = (EquationGenome)g;
for (int i = 0; i < CrossoverPoint; i++)
{
aGene1.TheArray.Add(CrossingGene.TheArray[i]);
aGene2.TheArray.Add(TheArray[i]);
}
for (int j = CrossoverPoint; j < Length; j++)
{
aGene1.TheArray.Add(TheArray[j]);
aGene2.TheArray.Add(CrossingGene.TheArray[j]);
}
// 50/50 chance of returning gene1 or gene2
EquationGenome aGene = null;
if (TheSeed.Next(2) == 1)
{
aGene = aGene1;
}
else
{
aGene = aGene2;
}
return(aGene);
}
public void RunPopulation(object thePopulationObject)
{
Population thePopulation = (Population)thePopulationObject;
int NumberOfGenerations = 50000;
for (int i = 0; i < NumberOfGenerations; i++)
{
thePopulation.NextGeneration();
if (i % (NumberOfGenerations / 20) == 0)
{
thePopulation.WriteNextGenerationTop(10);
if (EquationGenome.PerfectFitness == true)
{
NumberOfGenerations = i;
break;
}
}
if (i % (NumberOfGenerations / 4) == 0)
{
EquationGenome[] EquationGenomesBest = (EquationGenome[])thePopulation.GetHighestScoreGenomes(Population.kNumberOfChoiceGenomes);
CopyHighestFitnessToOtherPopulations(EquationGenomesBest);
}
}
thePopulation.FinishedRunning = true;
thePopulation.CalculateFitnessForAll();
EquationGenome EquationGenomeToIllustrate = (EquationGenome)thePopulation.GetHighestScoreGenome();
Console.WriteLine("");
Console.WriteLine("Best Genome in {1} after {0} Generations", NumberOfGenerations, thePopulation.Name);
Console.WriteLine(EquationGenomeToIllustrate.ToString());
EquationGenomeToIllustrate = GetBestGeneFromAllPopulations();
Console.WriteLine("The next value in the series = {0}", EquationGenomeToIllustrate.CalculateNextValue());
Class1.EquationGenomeToIllustrate = EquationGenomeToIllustrate;
// Console.ReadLine();
}
public Genome[] GetHighestScoreGenomes(int number)
{
Genomes.Sort();
EquationGenome[] strongestGenomes = new EquationGenome[number];
if (number > Genomes.Count)
number = Genomes.Count;
for (int i = 0; i < number; i++)
{
strongestGenomes[i] = new EquationGenome(kLength, kMin, kMax);
strongestGenomes[i].SetCrossoverPoint(kCrossover);
strongestGenomes[i].CopyGeneFrom((EquationGenome)Genomes[i]);
}
return strongestGenomes;
}
public override double CalculateFitness()
{
try
{
lock(this)
{
// CurrentFitness = CalculateClosestProductSum();
// CurrentFitness = CalculateClosestSumTo10();
// CurrentFitness = CalculateMazeDistanceFitness();
CurrentFitness = CalculateSeriesFitness();
if (CurrentFitness < 0.0f)
CurrentFitness = 0.01f;
if (CurrentFitness == 1)
{
PerfectFitness = true;
PerfectGenome = this;
}
}
}
catch (Exception ex)
{
CurrentFitness = .01f; // ignore occasional exception
}
return CurrentFitness;
}
void CopyHighestFitnessToOtherPopulations(EquationGenome[] BestGenomes)
{
// inject the genomes into all the populations
foreach (Population p in Populations)
{
for (int i = 0; i < BestGenomes.Length; i++)
{
int randomnum = EquationGenome.TheSeed.Next(p.Count);
p.InjectGenome(BestGenomes[i], randomnum);
}
}
}
public override Genome Crossover(Genome g)
{
EquationGenome aGene1 = new EquationGenome();
EquationGenome aGene2 = new EquationGenome();
g.CopyGeneInfo(aGene1);
g.CopyGeneInfo(aGene2);
// Pick a random crossover point
CrossoverPoint = TheSeed.Next(1, (int)Length);
EquationGenome CrossingGene = (EquationGenome)g;
for (int i = 0; i < CrossoverPoint; i++)
{
aGene1.TheArray.Add(CrossingGene.TheArray[i]);
aGene2.TheArray.Add(TheArray[i]);
}
for (int j = CrossoverPoint; j < Length; j++)
{
aGene1.TheArray.Add(TheArray[j]);
aGene2.TheArray.Add(CrossingGene.TheArray[j]);
}
// 50/50 chance of returning gene1 or gene2
EquationGenome aGene = null;
if (TheSeed.Next(2) == 1)
{
aGene = aGene1;
}
else
{
aGene = aGene2;
}
return aGene;
}
public override Genome Crossover2Point(Genome g)
{
EquationGenome aGene1 = new EquationGenome();
EquationGenome aGene2 = new EquationGenome();
g.CopyGeneInfo(aGene1);
g.CopyGeneInfo(aGene2);
// Pick a random crossover point
int CrossoverPoint1 = TheSeed.Next(1, (int)Length);
int CrossoverPoint2 = TheSeed.Next(CrossoverPoint1, (int)Length);
// normalize
if (CrossoverPoint1 > CrossoverPoint2)
{
int temp = CrossoverPoint1;
CrossoverPoint1 = CrossoverPoint2;
CrossoverPoint2 = temp;
}
EquationGenome CrossingGene = (EquationGenome)g;
for (int j = 0; j < CrossoverPoint1; j++)
{
aGene1.TheArray.Add(TheArray[j]);
aGene2.TheArray.Add(CrossingGene.TheArray[j]);
}
for (int j = CrossoverPoint1; j < CrossoverPoint2; j++)
{
aGene1.TheArray.Add(CrossingGene.TheArray[j]);
aGene2.TheArray.Add(TheArray[j]);
}
for (int j = CrossoverPoint2; j < Length; j++)
{
aGene1.TheArray.Add(TheArray[j]);
aGene2.TheArray.Add(CrossingGene.TheArray[j]);
}
// 50/50 chance of returning gene1 or gene2
EquationGenome aGene = null;
if (TheSeed.Next(2) == 1)
{
aGene = aGene1;
}
else
{
aGene = aGene2;
}
return aGene;
}
public void InjectGenome(EquationGenome g, int index)
{
((EquationGenome)Genomes[index]).CopyGeneFrom(g);
}
public void InjectGenome(EquationGenome g, int index)
{
((EquationGenome)Genomes[index]).CopyGeneFrom(g);
}
public override Genome UniformCrossover(Genome g)
{
EquationGenome aGene1 = new EquationGenome();
EquationGenome aGene2 = new EquationGenome();
g.CopyGeneInfo(aGene1);
g.CopyGeneInfo(aGene2);
// swap genes randomly
EquationGenome CrossingGene = (EquationGenome)g;
for (int i = 0; i < Length; i++)
{
if (TheSeed.Next(100) % 2 == 0)
{
aGene1.TheArray.Add(CrossingGene.TheArray[i]);
aGene2.TheArray.Add(TheArray[i]);
}
else
{
aGene1.TheArray.Add(TheArray[i]);
aGene2.TheArray.Add(CrossingGene.TheArray[i]);
}
}
// 50/50 chance of returning gene1 or gene2
EquationGenome aGene = null;
if (TheSeed.Next(2) == 1)
{
aGene = aGene1;
}
else
{
aGene = aGene2;
}
return aGene;
}
public void DoCrossover(ArrayList genes)
{
ArrayList GeneMoms = new ArrayList();
ArrayList GeneDads = new ArrayList();
for (int i = 0; i < genes.Count; i++)
{
// randomly pick the moms and dad's
if (EquationGenome.TheSeed.Next(100) % 2 > 0)
{
GeneMoms.Add(genes[i]);
}
else
{
GeneDads.Add(genes[i]);
}
}
// now make them equal
if (GeneMoms.Count > GeneDads.Count)
{
while (GeneMoms.Count > GeneDads.Count)
{
GeneDads.Add(GeneMoms[GeneMoms.Count - 1]);
GeneMoms.RemoveAt(GeneMoms.Count - 1);
}
if (GeneDads.Count > GeneMoms.Count)
{
GeneDads.RemoveAt(GeneDads.Count - 1); // make sure they are equal
}
}
else
{
while (GeneDads.Count > GeneMoms.Count)
{
GeneMoms.Add(GeneDads[GeneDads.Count - 1]);
GeneDads.RemoveAt(GeneDads.Count - 1);
}
if (GeneMoms.Count > GeneDads.Count)
{
GeneMoms.RemoveAt(GeneMoms.Count - 1); // make sure they are equal
}
}
// now cross them over and add them according to fitness
for (int i = 0; i < GeneDads.Count; i++)
{
// pick best 2 from parent and children
EquationGenome babyGene1 = null;
EquationGenome babyGene2 = null;
int randomnum = EquationGenome.TheSeed.Next(100) % 3;
if (randomnum == 0)
{
babyGene1 = (EquationGenome)((EquationGenome)GeneDads[i]).Crossover((EquationGenome)GeneMoms[i]);
babyGene2 = (EquationGenome)((EquationGenome)GeneMoms[i]).Crossover((EquationGenome)GeneDads[i]);
}
else if (randomnum == 1)
{
babyGene1 = (EquationGenome)((EquationGenome)GeneDads[i]).Crossover2Point((EquationGenome)GeneMoms[i]);
babyGene2 = (EquationGenome)((EquationGenome)GeneMoms[i]).Crossover2Point((EquationGenome)GeneDads[i]);
}
else
{
babyGene1 = (EquationGenome)((EquationGenome)GeneDads[i]).UniformCrossover((EquationGenome)GeneMoms[i]);
babyGene2 = (EquationGenome)((EquationGenome)GeneMoms[i]).UniformCrossover((EquationGenome)GeneDads[i]);
}
GenomeFamily.Clear();
GenomeFamily.Add(GeneDads[i]);
GenomeFamily.Add(GeneMoms[i]);
GenomeFamily.Add(babyGene1);
GenomeFamily.Add(babyGene2);
CalculateFitnessForAll(GenomeFamily);
for (int j = 0; j < 4; j++)
{
CheckForUndefinedFitness((Genome)GenomeFamily[j]);
}
GenomeFamily.Sort();
if (Best2 == true)
{
// if Best2 is true, add top fitness genes
GenomeResults.Add(GenomeFamily[0]);
GenomeResults.Add(GenomeFamily[1]);
}
else
{
GenomeResults.Add(babyGene1);
GenomeResults.Add(babyGene2);
}
// if population shrunk, you can add rest of genes
if (Population.kPopulationLimit < Genomes.Count)
{
GenomeResults.Add(GenomeFamily[3]);
GenomeResults.Add(GenomeFamily[4]);
}
}
}
