/// <summary>
/// Assuming this chromosome is the "mother" mate with the passed in
/// "father".
/// </summary>
///
/// <param name="mother">The mother.</param>
/// <param name="father">The father.</param>
/// <param name="offspring1">Returns the first offspring</param>
/// <param name="offspring2">Returns the second offspring.</param>
public void Mate(Chromosome mother, Chromosome father,
Chromosome offspring1, Chromosome offspring2)
{
int geneLength = mother.Genes.Count;
// the chromosome must be cut at two positions, determine them
var cutpoint1 = (int)(ThreadSafeRandom.NextDouble()*(geneLength - _cutLength));
int cutpoint2 = cutpoint1 + _cutLength;
// handle cut section
for (int i = 0; i < geneLength; i++)
{
if (!((i < cutpoint1) || (i > cutpoint2)))
{
offspring1.GetGene(i).Copy(father.GetGene(i));
offspring2.GetGene(i).Copy(mother.GetGene(i));
}
}
// handle outer sections
for (int i = 0; i < geneLength; i++)
{
if ((i < cutpoint1) || (i > cutpoint2))
{
offspring1.GetGene(i).Copy(mother.GetGene(i));
offspring2.GetGene(i).Copy(father.GetGene(i));
}
}
}
public void Mate(Chromosome mother, Chromosome father, Chromosome offspring1, Chromosome offspring2)
{
int num2;
int num3;
int num4;
int count = mother.Genes.Count;
goto Label_0116;
Label_0097:
num4++;
Label_009B:
if (num4 >= count)
{
int gene = 0;
if ((((uint) count) & 0) != 0)
{
if (((uint) num3) >= 0)
{
goto Label_00BB;
}
goto Label_0097;
}
while (true)
{
if (gene >= count)
{
if ((((uint) num2) + ((uint) count)) < 0)
{
break;
}
if ((((uint) num2) - ((uint) num2)) >= 0)
{
return;
}
goto Label_0116;
}
if ((gene < num2) || (gene > num3))
{
offspring1.GetGene(gene).Copy(mother.GetGene(gene));
offspring2.GetGene(gene).Copy(father.GetGene(gene));
}
gene++;
}
}
Label_00BB:
if ((num4 >= num2) && (num4 <= num3))
{
offspring1.GetGene(num4).Copy(father.GetGene(num4));
offspring2.GetGene(num4).Copy(mother.GetGene(num4));
if ((((uint) count) & 0) != 0)
{
goto Label_0116;
}
}
goto Label_0097;
Label_0116:
num2 = (int) (ThreadSafeRandom.NextDouble() * (count - this._x8bd2fc977ef263b3));
num3 = num2 + this._x8bd2fc977ef263b3;
num4 = 0;
goto Label_009B;
}
/// <summary>
/// Construct an innovation list.
/// </summary>
///
/// <param name="population_0">The population.</param>
/// <param name="links">The links.</param>
/// <param name="neurons">THe neurons.</param>
public NEATInnovationList(IPopulation population_0,
Chromosome links, Chromosome neurons)
{
nextNeuronID = 0;
population = population_0;
foreach (IGene gene in neurons.Genes)
{
var neuronGene = (NEATNeuronGene) gene;
var innovation = new NEATInnovation(neuronGene,
population_0.AssignInnovationID(), AssignNeuronID());
Add(innovation);
}
foreach (IGene gene_1 in links.Genes)
{
var linkGene = (NEATLinkGene) gene_1;
var innovation_2 = new NEATInnovation(
linkGene.FromNeuronID, linkGene.ToNeuronID,
NEATInnovationType.NewLink,
population.AssignInnovationID());
Add(innovation_2);
}
}
public void PerformMutation(Chromosome chromosome)
{
using (List<IGene>.Enumerator enumerator = chromosome.Genes.GetEnumerator())
{
IGene gene;
DoubleGene gene2;
double num;
goto Label_0031;
Label_000E:
if (0 != 0)
{
goto Label_003C;
}
if (0 != 0)
{
goto Label_009D;
}
goto Label_0031;
Label_0019:
if ((((uint) num) - ((uint) num)) < 0)
{
goto Label_000E;
}
Label_0031:
if (enumerator.MoveNext())
{
goto Label_008C;
}
goto Label_0046;
Label_003C:
if (gene is DoubleGene)
{
goto Label_0096;
}
goto Label_0019;
Label_0046:
if ((((uint) num) + ((uint) num)) >= 0)
{
return;
}
goto Label_008C;
Label_0060:
num += this._x0133fea5e9a09a63 - ((ThreadSafeRandom.NextDouble() * this._x0133fea5e9a09a63) * 2.0);
gene2.Value = num;
goto Label_000E;
Label_008C:
gene = enumerator.Current;
goto Label_003C;
Label_0096:
gene2 = (DoubleGene) gene;
Label_009D:
num = gene2.Value;
goto Label_0060;
}
}
/// <summary>
/// Perform a perturb mutation on the specified chromosome.
/// </summary>
///
/// <param name="chromosome">The chromosome to mutate.</param>
public void PerformMutation(Chromosome chromosome)
{
foreach (IGene gene in chromosome.Genes)
{
if (gene is DoubleGene)
{
var doubleGene = (DoubleGene) gene;
double v = doubleGene.Value;
v += (_perturbAmount - (ThreadSafeRandom.NextDouble()*_perturbAmount*2));
doubleGene.Value = v;
}
}
}
public NEATGenome(NEATGenome other)
{
goto Label_0182;
Label_0017:
this.inputCount = other.inputCount;
this.outputCount = other.outputCount;
Label_002F:
this.speciesID = other.speciesID;
foreach (IGene gene in other.Neurons.Genes)
{
NEATNeuronGene gene2 = (NEATNeuronGene) gene;
if (0xff != 0)
{
NEATNeuronGene gene3 = new NEATNeuronGene(gene2.NeuronType, gene2.Id, gene2.SplitY, gene2.SplitX, gene2.Recurrent, gene2.ActivationResponse);
this.Neurons.Add(gene3);
}
}
foreach (IGene gene4 in other.Links.Genes)
{
NEATLinkGene gene5 = (NEATLinkGene) gene4;
NEATLinkGene gene6 = new NEATLinkGene((long) gene5.FromNeuronID, (long) gene5.ToNeuronID, gene5.Enabled, gene5.InnovationId, gene5.Weight, gene5.Recurrent);
this.Links.Add(gene6);
}
return;
Label_0182:
this.neuronsChromosome = new Chromosome();
this.linksChromosome = new Chromosome();
base.GA = other.GA;
base.Chromosomes.Add(this.neuronsChromosome);
if (0 != 0)
{
goto Label_002F;
}
base.Chromosomes.Add(this.linksChromosome);
base.GenomeID = other.GenomeID;
this.networkDepth = other.networkDepth;
base.Population = other.Population;
base.Score = other.Score;
if (0x7fffffff == 0)
{
goto Label_0017;
}
base.AdjustedScore = other.AdjustedScore;
if (0 == 0)
{
base.AmountToSpawn = other.AmountToSpawn;
goto Label_0017;
}
goto Label_0182;
}
/// <summary>
/// Construct a neural genome.
/// </summary>
///
/// <param name="network">The network to use.</param>
public NeuralGenome(BasicNetwork network)
{
Organism = network;
_networkChromosome = new Chromosome();
// create an array of "double genes"
int size = network.Structure.CalculateSize();
for (int i = 0; i < size; i++)
{
IGene gene = new DoubleGene();
_networkChromosome.Genes.Add(gene);
}
Chromosomes.Add(_networkChromosome);
Encode();
}
public TSPGenome(GeneticAlgorithm owner, City[] cities)
{
int[] organism = new int[cities.Length];
bool[] taken = new bool[cities.Length];
for (int i = 0; i < organism.Length; i++)
{
taken[i] = false;
}
for (int i = 0; i < organism.Length - 1; i++)
{
int icandidate;
do
{
icandidate = (int)(ThreadSafeRandom.NextDouble() * organism.Length);
} while (taken[icandidate]);
organism[i] = icandidate;
taken[icandidate] = true;
if (i == organism.Length - 2)
{
icandidate = 0;
while (taken[icandidate])
{
icandidate++;
}
organism[i + 1] = icandidate;
}
}
this.pathChromosome = new Chromosome();
this.Chromosomes.Add(this.pathChromosome);
for (int i = 0; i < organism.Length; i++)
{
IntegerGene gene = new IntegerGene();
gene.Value = organism[i];
this.pathChromosome.Genes.Add(gene);
}
Organism = organism;
Encode();
}
public NEATInnovationList(IPopulation population_0, Chromosome links, Chromosome neurons)
{
this.nextNeuronID = 0L;
this.population = population_0;
foreach (IGene gene in neurons.Genes)
{
NEATNeuronGene neuronGene = (NEATNeuronGene) gene;
NEATInnovation innovation = new NEATInnovation(neuronGene, population_0.AssignInnovationID(), this.AssignNeuronID());
if (15 == 0)
{
break;
}
base.Add(innovation);
}
foreach (IGene gene3 in links.Genes)
{
NEATLinkGene gene4 = (NEATLinkGene) gene3;
NEATInnovation innovation2 = new NEATInnovation((long) gene4.FromNeuronID, (long) gene4.ToNeuronID, NEATInnovationType.NewLink, this.population.AssignInnovationID());
base.Add(innovation2);
}
}
/// <summary>
/// Perform a shuffle mutation.
/// </summary>
///
/// <param name="chromosome">The chromosome to mutate.</param>
public void PerformMutation(Chromosome chromosome)
{
int length = chromosome.Genes.Count;
var iswap1 = (int)(ThreadSafeRandom.NextDouble()*length);
var iswap2 = (int)(ThreadSafeRandom.NextDouble() * length);
// can't be equal
if (iswap1 == iswap2)
{
// move to the next, but
// don't go out of bounds
if (iswap1 > 0)
{
iswap1--;
}
else
{
iswap1++;
}
}
// make sure they are in the right order
if (iswap1 > iswap2)
{
int temp = iswap1;
iswap1 = iswap2;
iswap2 = temp;
}
IGene gene1 = chromosome.Genes[iswap1];
IGene gene2 = chromosome.Genes[iswap2];
// remove the two genes
chromosome.Genes.Remove(gene1);
chromosome.Genes.Remove(gene2);
// insert them back in, reverse order
chromosome.Genes.Insert(iswap1, gene2);
chromosome.Genes.Insert(iswap2, gene1);
}
/// <summary>
/// Assuming this chromosome is the "mother" mate with the passed in
/// "father".
/// </summary>
/// <param name="mother">The mother.</param>
/// <param name="father">The father.</param>
/// <param name="offspring1">The first offspring.</param>
/// <param name="offspring2">The second offspring.</param>
public void Mate(Chromosome mother, Chromosome father,
Chromosome offspring1, Chromosome offspring2)
{
int geneLength = father.Genes.Count;
// the chromosome must be cut at two positions, determine them
var cutpoint1 = (int) (ThreadSafeRandom.NextDouble()*(geneLength - _cutLength));
int cutpoint2 = cutpoint1 + _cutLength;
// keep track of which genes have been taken in each of the two
// offspring, defaults to false.
IList<IGene> taken1 = new List<IGene>();
IList<IGene> taken2 = new List<IGene>();
// handle cut section
for (int i = 0; i < geneLength; i++)
{
if (!((i < cutpoint1) || (i > cutpoint2)))
{
offspring1.Genes[i].Copy(father.Genes[i]);
offspring2.Genes[i].Copy(mother.Genes[i]);
taken1.Add(offspring1.Genes[i]);
taken2.Add(offspring2.Genes[i]);
}
}
// handle outer sections
for (int i = 0; i < geneLength; i++)
{
if ((i < cutpoint1) || (i > cutpoint2))
{
offspring1.Genes[i].Copy(
GetNotTaken(mother, taken1));
offspring2.Genes[i].Copy(
GetNotTaken(father, taken2));
}
}
}
public NeuralGenome(BasicNetwork network)
{
int num;
int num2;
goto Label_0058;
Label_0008:
if (num2 >= num)
{
if ((((uint) num2) & 0) == 0)
{
base.Chromosomes.Add(this._x8b1a2d051765aa13);
this.Encode();
return;
}
goto Label_0058;
}
IGene item = new DoubleGene();
this._x8b1a2d051765aa13.Genes.Add(item);
Label_0052:
num2++;
if ((((uint) num) + ((uint) num)) <= uint.MaxValue)
{
goto Label_0008;
}
return;
Label_0058:
base.Organism = network;
this._x8b1a2d051765aa13 = new Chromosome();
num = network.Structure.CalculateSize();
num2 = 0;
if (0 != 0)
{
goto Label_0052;
}
goto Label_0008;
}
public NEATGenome(long id, int inputCount_0, int outputCount_1)
{
double num3;
int num4;
int num5;
int num6;
base.GenomeID = id;
base.AdjustedScore = 0.0;
if (((uint) num5) < 0)
{
goto Label_00A7;
}
this.inputCount = inputCount_0;
if ((((uint) num4) & 0) != 0)
{
goto Label_00AC;
}
this.outputCount = outputCount_1;
base.AmountToSpawn = 0.0;
this.speciesID = 0L;
double num = 0.8 / ((double) inputCount_0);
this.neuronsChromosome = new Chromosome();
this.linksChromosome = new Chromosome();
if (2 != 0)
{
base.Chromosomes.Add(this.neuronsChromosome);
base.Chromosomes.Add(this.linksChromosome);
for (int i = 0; i < inputCount_0; i++)
{
this.neuronsChromosome.Add(new NEATNeuronGene(NEATNeuronType.Input, (long) i, 0.0, 0.1 + (i * num)));
}
this.neuronsChromosome.Add(new NEATNeuronGene(NEATNeuronType.Bias, (long) inputCount_0, 0.0, 0.9));
num3 = 1.0 / ((double) (outputCount_1 + 1));
num4 = 0;
goto Label_00C8;
}
return;
Label_0079:
while (num6 < outputCount_1)
{
this.linksChromosome.Add(new NEATLinkGene(((NEATNeuronGene) this.neuronsChromosome.Get(num5)).Id, ((NEATNeuronGene) this.Neurons.Get((inputCount_0 + num6) + 1)).Id, true, (long) (((inputCount_0 + outputCount_1) + 1) + this.NumGenes), RangeRandomizer.Randomize(-1.0, 1.0), false));
num6++;
}
num5++;
Label_0084:
if (num5 >= (inputCount_0 + 1))
{
if (((uint) outputCount_1) >= 0)
{
return;
}
goto Label_0079;
}
Label_00A7:
num6 = 0;
goto Label_0079;
Label_00AC:
num4++;
if ((((uint) id) + ((uint) num5)) < 0)
{
goto Label_00A7;
}
Label_00C8:
if (num4 < outputCount_1)
{
this.neuronsChromosome.Add(new NEATNeuronGene(NEATNeuronType.Output, (long) ((num4 + inputCount_0) + 1), 1.0, (num4 + 1) * num3));
goto Label_00AC;
}
if (((uint) inputCount_0) >= 0)
{
num5 = 0;
}
goto Label_0084;
}
public void PerformMutation(Chromosome chromosome)
{
int num2;
int num3;
int num4;
IGene gene;
IGene gene2;
int count = chromosome.Genes.Count;
if ((((uint) count) - ((uint) num3)) <= uint.MaxValue)
{
goto Label_010A;
}
Label_0027:
gene2 = chromosome.Genes[num3];
chromosome.Genes.Remove(gene);
chromosome.Genes.Remove(gene2);
chromosome.Genes.Insert(num2, gene2);
Label_005F:
chromosome.Genes.Insert(num3, gene);
if ((((uint) count) + ((uint) num4)) >= 0)
{
return;
}
goto Label_010A;
Label_008F:
if (num2 > num3)
{
num4 = num2;
num2 = num3;
num3 = num4;
if (((uint) num4) < 0)
{
goto Label_0027;
}
}
Label_0093:
gene = chromosome.Genes[num2];
goto Label_0027;
Label_00B9:
num2++;
if ((((uint) num4) + ((uint) num3)) >= 0)
{
goto Label_008F;
}
Label_010A:
num2 = (int) (ThreadSafeRandom.NextDouble() * count);
num3 = (int) (ThreadSafeRandom.NextDouble() * count);
if (num2 == num3)
{
if (num2 > 0)
{
num2--;
if ((((uint) num2) | 0x7fffffff) != 0)
{
if ((((uint) num4) & 0) != 0)
{
goto Label_005F;
}
goto Label_008F;
}
}
else
{
goto Label_00B9;
}
if (((uint) num4) > uint.MaxValue)
{
goto Label_0093;
}
if ((((uint) num3) + ((uint) num2)) <= uint.MaxValue)
{
goto Label_00B9;
}
}
goto Label_008F;
}
/// <summary>
/// Construct a genome, do not provide links and neurons.
/// </summary>
///
/// <param name="id">The genome id.</param>
/// <param name="inputCount_0">The input count.</param>
/// <param name="outputCount_1">The output count.</param>
public NEATGenome(long id, int inputCount_0, int outputCount_1)
{
GenomeID = id;
AdjustedScore = 0;
inputCount = inputCount_0;
outputCount = outputCount_1;
AmountToSpawn = 0;
speciesID = 0;
double inputRowSlice = 0.8d/(inputCount_0);
neuronsChromosome = new Chromosome();
linksChromosome = new Chromosome();
Chromosomes.Add(neuronsChromosome);
Chromosomes.Add(linksChromosome);
for (int i = 0; i < inputCount_0; i++)
{
neuronsChromosome.Add(new NEATNeuronGene(NEATNeuronType.Input,
i, 0, 0.1d + i*inputRowSlice));
}
neuronsChromosome.Add(new NEATNeuronGene(NEATNeuronType.Bias,
inputCount_0, 0, 0.9d));
double outputRowSlice = 1/(double) (outputCount_1 + 1);
for (int i_2 = 0; i_2 < outputCount_1; i_2++)
{
neuronsChromosome.Add(new NEATNeuronGene(
NEATNeuronType.Output, i_2 + inputCount_0 + 1, 1, (i_2 + 1)
*outputRowSlice));
}
for (int i_3 = 0; i_3 < inputCount_0 + 1; i_3++)
{
for (int j = 0; j < outputCount_1; j++)
{
linksChromosome.Add(new NEATLinkGene(
((NEATNeuronGene) neuronsChromosome.Get(i_3)).Id,
((NEATNeuronGene) Neurons.Get(
inputCount_0 + j + 1)).Id, true, inputCount_0
+ outputCount_1 + 1 + NumGenes,
RangeRandomizer.Randomize(-1, 1), false));
}
}
}
/// <summary>
/// Get a list of the genes that have not been taken before. This is useful
/// if you do not wish the same gene to appear more than once in a
/// chromosome.
/// </summary>
/// <param name="source">The pool of genes to select from.</param>
/// <param name="taken">An array of the taken genes.</param>
/// <returns>Those genes in source that are not taken.</returns>
private static IGene GetNotTaken(Chromosome source,
IList<IGene> taken)
{
int geneLength = source.Genes.Count;
for (int i = 0; i < geneLength; i++)
{
IGene trial = source.Genes[i];
bool found = Enumerable.Contains(taken, trial);
if (!found)
{
taken.Add(trial);
return trial;
}
}
return null;
}
/// <summary>
/// Construct a genome by copying another.
/// </summary>
///
/// <param name="other">The other genome.</param>
public NEATGenome(NEATGenome other)
{
neuronsChromosome = new Chromosome();
linksChromosome = new Chromosome();
GA = other.GA;
Chromosomes.Add(neuronsChromosome);
Chromosomes.Add(linksChromosome);
GenomeID = other.GenomeID;
networkDepth = other.networkDepth;
Population = other.Population;
Score = other.Score;
AdjustedScore = other.AdjustedScore;
AmountToSpawn = other.AmountToSpawn;
inputCount = other.inputCount;
outputCount = other.outputCount;
speciesID = other.speciesID;
// copy neurons
foreach (IGene gene in other.Neurons.Genes)
{
var oldGene = (NEATNeuronGene) gene;
var newGene = new NEATNeuronGene(
oldGene.NeuronType, oldGene.Id,
oldGene.SplitY, oldGene.SplitX,
oldGene.Recurrent, oldGene.ActivationResponse);
Neurons.Add(newGene);
}
// copy links
foreach (IGene gene_0 in other.Links.Genes)
{
var oldGene_1 = (NEATLinkGene) gene_0;
var newGene_2 = new NEATLinkGene(
oldGene_1.FromNeuronID, oldGene_1.ToNeuronID,
oldGene_1.Enabled, oldGene_1.InnovationId,
oldGene_1.Weight, oldGene_1.Recurrent);
Links.Add(newGene_2);
}
}
public NEATGenome Crossover(NEATGenome mom, NEATGenome dad)
{
x47361738465134f2 xf;
Chromosome chromosome;
Chromosome chromosome2;
List<long> list;
int num;
int num2;
NEATLinkGene gene;
NEATLinkGene gene2;
NEATLinkGene gene3;
int num3;
NEATGenome genome;
if (mom.Score != dad.Score)
{
while (base.Comparator.IsBetterThan(mom.Score, dad.Score))
{
xf = x47361738465134f2.x431cb293a9d996e8;
if (((uint) num3) >= 0)
{
if ((((uint) num3) & 0) == 0)
{
goto Label_03BA;
}
goto Label_0399;
}
if ((((uint) num) & 0) == 0)
{
goto Label_0478;
}
}
xf = x47361738465134f2.x86f3d3d96a9a0f93;
goto Label_03BA;
}
goto Label_0478;
Label_002E:
genome = new NEATGenome(base.Population.AssignGenomeID(), chromosome, chromosome2, mom.InputCount, mom.OutputCount);
genome.GA = this;
if (15 != 0)
{
if (0 != 0)
{
goto Label_031C;
}
genome.Population = base.Population;
if ((((uint) num3) & 0) == 0)
{
return genome;
}
goto Label_02C3;
}
goto Label_00B2;
Label_0071:
num3++;
Label_0077:
if (num3 < list.Count)
{
chromosome.Add(this.Innovations.CreateNeuronFromID(list[num3]));
goto Label_0071;
}
goto Label_002E;
Label_00B2:
this.AddNeuronID((long) gene3.FromNeuronID, list);
this.AddNeuronID((long) gene3.ToNeuronID, list);
Label_00D0:
if ((num < mom.NumGenes) || (num2 < dad.NumGenes))
{
if (num >= mom.NumGenes)
{
gene = null;
}
else
{
gene = (NEATLinkGene) mom.Links.Get(num);
}
goto Label_034F;
}
list.Sort();
num3 = 0;
if ((((uint) num2) + ((uint) num)) > uint.MaxValue)
{
goto Label_0071;
}
if (0 != 0)
{
goto Label_034F;
}
goto Label_0077;
Label_0160:
chromosome2.Add(gene3);
goto Label_00B2;
Label_016D:
if (chromosome2.Size() == 0)
{
goto Label_0160;
}
if ((0 != 0) || (((NEATLinkGene) chromosome2.Get(chromosome2.Size() - 1)).InnovationId != gene3.InnovationId))
{
chromosome2.Add(gene3);
}
goto Label_00B2;
Label_01C8:
num++;
if (((uint) num) <= uint.MaxValue)
{
if (((uint) num3) > uint.MaxValue)
{
goto Label_002E;
}
if ((((uint) num2) | 4) == 0)
{
goto Label_025D;
}
num2++;
goto Label_016D;
}
Label_01E0:
if ((((uint) num2) | 2) != 0)
{
if ((((uint) num3) + ((uint) num3)) <= uint.MaxValue)
{
goto Label_016D;
}
goto Label_0160;
}
if (-2147483648 != 0)
{
goto Label_0234;
}
if ((((uint) num) + ((uint) num3)) < 0)
{
goto Label_024D;
}
Label_0222:
while (ThreadSafeRandom.NextDouble() < 0.5)
{
gene3 = gene;
goto Label_01C8;
}
gene3 = gene2;
goto Label_01C8;
Label_0234:
gene3 = gene2;
Label_0238:
num2++;
goto Label_016D;
Label_024D:
if (gene.InnovationId < gene2.InnovationId)
{
if (xf == x47361738465134f2.x431cb293a9d996e8)
{
gene3 = gene;
}
num++;
goto Label_016D;
}
Label_025D:
if (gene2.InnovationId >= gene.InnovationId)
{
if (gene2.InnovationId == gene.InnovationId)
{
goto Label_0222;
}
goto Label_01E0;
}
if ((((uint) num2) + ((uint) num)) > uint.MaxValue)
{
goto Label_024D;
}
if (xf != x47361738465134f2.x86f3d3d96a9a0f93)
{
goto Label_0238;
}
goto Label_0234;
Label_02C3:
if (gene2 == null)
{
if (gene != null)
{
if (xf == x47361738465134f2.x431cb293a9d996e8)
{
gene3 = gene;
if ((((uint) num2) + ((uint) num)) > uint.MaxValue)
{
goto Label_03BA;
}
}
num++;
goto Label_016D;
}
if ((((uint) num3) | 0xfffffffe) != 0)
{
}
}
goto Label_024D;
Label_031C:
if (gene2 == null)
{
goto Label_02C3;
}
if (xf == x47361738465134f2.x86f3d3d96a9a0f93)
{
if (1 == 0)
{
goto Label_01C8;
}
gene3 = gene2;
}
num2++;
goto Label_016D;
Label_034F:
if (num2 < dad.NumGenes)
{
gene2 = (NEATLinkGene) dad.Links.Get(num2);
}
else
{
gene2 = null;
}
if (gene != null)
{
goto Label_02C3;
}
goto Label_031C;
Label_0399:
if ((((uint) num) & 0) != 0)
{
goto Label_00B2;
}
gene3 = null;
goto Label_00D0;
Label_03BA:
chromosome = new Chromosome();
chromosome2 = new Chromosome();
list = new List<long>();
num = 0;
num2 = 0;
if (0 == 0)
{
goto Label_0399;
}
Label_0478:
if (mom.NumGenes != dad.NumGenes)
{
if (mom.NumGenes < dad.NumGenes)
{
xf = x47361738465134f2.x431cb293a9d996e8;
}
else
{
xf = x47361738465134f2.x86f3d3d96a9a0f93;
}
}
else if (((((uint) num) - ((uint) num3)) >= 0) && (ThreadSafeRandom.NextDouble() <= 0.0))
{
if ((((uint) num3) + ((uint) num2)) <= uint.MaxValue)
{
xf = x47361738465134f2.x86f3d3d96a9a0f93;
}
}
else
{
xf = x47361738465134f2.x431cb293a9d996e8;
}
goto Label_03BA;
}
public void Mate(Chromosome mother, Chromosome father, Chromosome offspring1, Chromosome offspring2)
{
int num2;
int num3;
IList<IGene> list;
IList<IGene> list2;
int num4;
int num5;
int count = father.Genes.Count;
goto Label_0148;
Label_002C:
num5++;
Label_0032:
if (num5 < count)
{
if (num5 < num2)
{
goto Label_0047;
}
if ((((uint) num2) + ((uint) num5)) > uint.MaxValue)
{
goto Label_0162;
}
}
else
{
if (0 == 0)
{
return;
}
goto Label_0148;
}
Label_0042:
if (num5 <= num3)
{
goto Label_002C;
}
Label_0047:
offspring1.Genes[num5].Copy(x1a4a8d31470092e5(mother, list));
if (-1 != 0)
{
offspring2.Genes[num5].Copy(x1a4a8d31470092e5(father, list2));
goto Label_002C;
}
return;
Label_00A6:
if (num4 >= count)
{
if (((uint) num3) > uint.MaxValue)
{
goto Label_00CE;
}
num5 = 0;
if ((((uint) num3) + ((uint) count)) < 0)
{
goto Label_0042;
}
goto Label_0032;
}
if ((num4 >= num2) && (num4 <= num3))
{
offspring1.Genes[num4].Copy(father.Genes[num4]);
offspring2.Genes[num4].Copy(mother.Genes[num4]);
list.Add(offspring1.Genes[num4]);
list2.Add(offspring2.Genes[num4]);
}
Label_00CE:
num4++;
goto Label_00A6;
Label_0148:
num2 = (int) (ThreadSafeRandom.NextDouble() * (count - this._x8bd2fc977ef263b3));
num3 = num2 + this._x8bd2fc977ef263b3;
Label_0162:
list = new List<IGene>();
list2 = new List<IGene>();
num4 = 0;
goto Label_00A6;
}
private static IGene x1a4a8d31470092e5(Chromosome x337e217cb3ba0627, IList<IGene> xa41924b42e357ba2)
{
IGene gene;
int count = x337e217cb3ba0627.Genes.Count;
int num2 = 0;
Label_0030:
if (num2 < count)
{
gene = x337e217cb3ba0627.Genes[num2];
}
else if ((((uint) num2) | 1) != 0)
{
goto Label_0081;
}
bool flag = xa41924b42e357ba2.Contains<IGene>(gene);
if ((((uint) flag) + ((uint) count)) <= uint.MaxValue)
{
if (((uint) count) >= 0)
{
}
if (!flag)
{
xa41924b42e357ba2.Add(gene);
return gene;
}
num2++;
goto Label_0030;
}
Label_0081:
return null;
}
/// <summary>
/// Perform a cross over.
/// </summary>
/// <param name="mom">The mother genome.</param>
/// <param name="dad">The father genome.</param>
/// <returns></returns>
public new NEATGenome Crossover(NEATGenome mom, NEATGenome dad)
{
NEATParent best;
// first determine who is more fit, the mother or the father?
if (mom.Score == dad.Score)
{
if (mom.NumGenes == dad.NumGenes)
{
if (ThreadSafeRandom.NextDouble() > 0)
{
best = NEATParent.Mom;
}
else
{
best = NEATParent.Dad;
}
}
else
{
if (mom.NumGenes < dad.NumGenes)
{
best = NEATParent.Mom;
}
else
{
best = NEATParent.Dad;
}
}
}
else
{
if (Comparator.IsBetterThan(mom.Score, dad.Score))
{
best = NEATParent.Mom;
}
else
{
best = NEATParent.Dad;
}
}
var babyNeurons = new Chromosome();
var babyGenes = new Chromosome();
var vecNeurons = new List<long>();
int curMom = 0;
int curDad = 0;
NEATLinkGene momGene;
NEATLinkGene dadGene;
NEATLinkGene selectedGene = null;
while ((curMom < mom.NumGenes) || (curDad < dad.NumGenes))
{
if (curMom < mom.NumGenes)
{
momGene = (NEATLinkGene) mom.Links.Get(curMom);
}
else
{
momGene = null;
}
if (curDad < dad.NumGenes)
{
dadGene = (NEATLinkGene) dad.Links.Get(curDad);
}
else
{
dadGene = null;
}
if ((momGene == null) && (dadGene != null))
{
if (best == NEATParent.Dad)
{
selectedGene = dadGene;
}
curDad++;
}
else if ((dadGene == null) && (momGene != null))
{
if (best == NEATParent.Mom)
{
selectedGene = momGene;
}
curMom++;
}
else if (momGene.InnovationId < dadGene.InnovationId)
{
if (best == NEATParent.Mom)
{
selectedGene = momGene;
}
curMom++;
}
else if (dadGene.InnovationId < momGene.InnovationId)
{
if (best == NEATParent.Dad)
{
selectedGene = dadGene;
}
curDad++;
}
else if (dadGene.InnovationId == momGene.InnovationId)
{
if (ThreadSafeRandom.NextDouble() < 0.5f)
{
selectedGene = momGene;
}
else
{
selectedGene = dadGene;
}
curMom++;
curDad++;
}
if (babyGenes.Size() == 0)
{
babyGenes.Add(selectedGene);
}
else
{
if (((NEATLinkGene) babyGenes.Get(babyGenes.Size() - 1))
.InnovationId != selectedGene.InnovationId)
{
babyGenes.Add(selectedGene);
}
}
// Check if we already have the nodes referred to in SelectedGene.
// If not, they need to be added.
AddNeuronID(selectedGene.FromNeuronID, vecNeurons);
AddNeuronID(selectedGene.ToNeuronID, vecNeurons);
} // end while
// now create the required nodes. First sort them into order
vecNeurons.Sort();
for (int i = 0; i < vecNeurons.Count; i++)
{
babyNeurons.Add(Innovations.CreateNeuronFromID(
vecNeurons[i]));
}
// finally, create the genome
var babyGenome = new NEATGenome(Population
.AssignGenomeID(), babyNeurons, babyGenes, mom.InputCount,
mom.OutputCount);
babyGenome.GA = this;
babyGenome.Population = Population;
return babyGenome;
}
public NEATGenome(long genomeID, Chromosome neurons, Chromosome links, int inputCount_0, int outputCount_1)
{
base.GenomeID = genomeID;
while (true)
{
this.linksChromosome = links;
this.neuronsChromosome = neurons;
base.AmountToSpawn = 0.0;
base.AdjustedScore = 0.0;
this.inputCount = inputCount_0;
this.outputCount = outputCount_1;
base.Chromosomes.Add(this.neuronsChromosome);
if (0 == 0)
{
base.Chromosomes.Add(this.linksChromosome);
return;
}
}
}
/// <summary>
/// Create a NEAT gnome.
/// </summary>
///
/// <param name="genomeID">The genome id.</param>
/// <param name="neurons">The neurons.</param>
/// <param name="links">The links.</param>
/// <param name="inputCount_0">The input count.</param>
/// <param name="outputCount_1">The output count.</param>
public NEATGenome(long genomeID, Chromosome neurons,
Chromosome links, int inputCount_0, int outputCount_1)
{
GenomeID = genomeID;
linksChromosome = links;
neuronsChromosome = neurons;
AmountToSpawn = 0;
AdjustedScore = 0;
inputCount = inputCount_0;
outputCount = outputCount_1;
Chromosomes.Add(neuronsChromosome);
Chromosomes.Add(linksChromosome);
}
