public Genome(int ID, int inputs, int outputs, MutParameters mutPar)
{
network = null;
this.ID = ID;
fitness = 0;
adjustedFitness = 0;
this.inputs = inputs;
this.outputs = outputs;
amountToSpawn = 0;
species = 0;
perceptrons = new List <NodeGene>();
connections = new List <ConnectionGene>();
for (int i = 0; i < inputs; i++)
{
perceptrons.Add(new NodeGene(NodeType.Sensor, i, new Vector2(0, (float)i / inputs)));
}
perceptrons.Add(new NodeGene(NodeType.Bias, inputs, new Vector2(0, 1), false));
for (int i = 0; i < outputs; i++)
{
perceptrons.Add(new NodeGene(NodeType.Output, i + inputs + 1, new Vector2(1, (float)i / (outputs - 1))));
}
for (int i = 0; i < inputs + 1; i++)
{
for (int j = 0; j < outputs; j++)
{
connections.Add(new ConnectionGene(perceptrons[i].ID, perceptrons[inputs + j + 1].ID, true, inputs + outputs + 1 + NumGenes(), Random.Range(-1f, 1f)));
}
}
this.mutPar = mutPar;
}
public void SetGenome(int ID, List <NodeGene> perceptrons, List <ConnectionGene> connections, int inputs, int outputs, MutParameters mutPar)
{
nodeGeneIndex = 0;
connectionGeneIndex = 0;
this.ID = ID;
for (int i = 0; i < perceptrons.Count; i++)
{
NodeGene curGene = perceptrons[i];
AddNodeGene(curGene);
}
for (int i = 0; i < connections.Count; i++)
{
ConnectionGene curGene = connections[i];
AddConnectionGeneGene(curGene);
}
FinishGenome();
this.inputs = inputs;
this.outputs = outputs;
fitness = 0;
adjustedFitness = 0;
amountToSpawn = 0;
species = 0;
this.mutPar = mutPar;
}
public void SetGenome(Genome genome)
{
nodeGeneIndex = 0;
connectionGeneIndex = 0;
ID = genome.ID;
for (int i = 0; i < genome.perceptrons.Count; i++)
{
NodeGene curGene = genome.perceptrons[i];
AddNodeGene(curGene);
}
for (int i = 0; i < genome.connections.Count; i++)
{
ConnectionGene curGene = genome.connections[i];
AddConnectionGeneGene(curGene);
}
FinishGenome();
inputs = genome.inputs;
outputs = genome.outputs;
fitness = 0;
adjustedFitness = 0;
amountToSpawn = 0;
species = 0;
mutPar = genome.mutPar;
}
public Genome(Genome genome)
{
ID = genome.ID;
perceptrons = new List <NodeGene>();
for (int i = 0; i < genome.perceptrons.Count; i++)
{
NodeGene curGene = genome.perceptrons[i];
perceptrons.Add(new NodeGene(curGene.type, curGene.ID, curGene.splitValues, curGene.actResponse, curGene.recurrent));
}
connections = new List <ConnectionGene>();
for (int i = 0; i < genome.connections.Count; i++)
{
ConnectionGene curGene = genome.connections[i];
connections.Add(new ConnectionGene(curGene.from, curGene.to, curGene.enabled, curGene.innovNum, curGene.weight, curGene.recurrent));
}
inputs = genome.inputs;
outputs = genome.outputs;
fitness = genome.fitness;
adjustedFitness = genome.adjustedFitness;
amountToSpawn = genome.amountToSpawn;
species = genome.species;
mutPar = genome.mutPar;
}
public static Genome RecycledGenome(int ID, int inputs, int outputs, MutParameters mutPar)
{
Genome recycledGenome = genomePool[0];
genomePool.RemoveAt(0);
recycledGenome.SetGenome(ID, inputs, outputs, mutPar);
return(recycledGenome);
//return new Genome(ID, inputs, outputs, mutPar);
}
public Genome(int ID, List <NodeGene> perceptrons, List <ConnectionGene> connections, int inputs, int outputs, MutParameters mutPar)
{
network = null;
this.connections = connections;
this.perceptrons = perceptrons;
amountToSpawn = 0;
fitness = 0;
adjustedFitness = 0;
this.inputs = inputs;
this.outputs = outputs;
this.mutPar = mutPar;
}
public Genome()
{
network = null;
ID = 0;
fitness = 0;
adjustedFitness = 0;
inputs = 0;
outputs = 0;
amountToSpawn = 0;
perceptrons = new List <NodeGene>();
connections = new List <ConnectionGene>();
mutPar = new MutParameters();
//Random.InitState((int)System.DateTime.Now.Ticks);
}
public static Genome Crossover(ref Genome mum, ref Genome dad, GeneticAlgorithm ga)
{
BestParent bestParent = GetBestParent(ref mum, ref dad);
List <int> newPerceptrons = new List <int>();
int curMum = 0;
int curDad = 0;
ConnectionGene selectedGene = null;
Genome newGenome = RecycledGenome();
while (!(curMum == mum.connections.Count && curDad == dad.connections.Count))
{
// Dad has more genes
if (curMum == mum.connections.Count && curDad != dad.connections.Count)
{
if (bestParent == BestParent.dad)
{
selectedGene = dad.GetConnectionGenes()[curDad];
}
curDad++;
if (selectedGene == null)
{
continue;
}
}
// Mum has more genes
else if (curMum != mum.connections.Count && curDad == dad.connections.Count)
{
if (bestParent == BestParent.mum)
{
selectedGene = mum.GetConnectionGenes()[curMum];
}
curMum++;
if (selectedGene == null)
{
continue;
}
}
// Mum gene innovation number is lower
else if (mum.GetConnectionGenes()[curMum].innovNum < dad.GetConnectionGenes()[curDad].innovNum)
{
if (bestParent == BestParent.mum)
{
selectedGene = mum.GetConnectionGenes()[curMum];
}
curMum++;
if (selectedGene == null)
{
continue;
}
}
// Dad gene innovation number is lower
else if (mum.GetConnectionGenes()[curMum].innovNum > dad.GetConnectionGenes()[curDad].innovNum)
{
if (bestParent == BestParent.dad)
{
selectedGene = dad.GetConnectionGenes()[curDad];
}
curDad++;
if (selectedGene == null)
{
continue;
}
}
// Both innovation numbers are equal
else if (mum.GetConnectionGenes()[curMum].innovNum == dad.GetConnectionGenes()[curDad].innovNum)
{
if (Random.Range(0f, 1f) > 0.5f)
{
selectedGene = mum.GetConnectionGenes()[curMum];
}
else
{
selectedGene = dad.GetConnectionGenes()[curDad];
}
curDad++;
curMum++;
}
else
{
Debug.Log("wtf");
}
if (selectedGene == null)
{
Debug.Log("Weird");
continue;
}
if (newGenome.connectionGeneIndex == 0)
{
newGenome.AddConnectionGeneGene(new ConnectionGene(selectedGene));
}
else
{
if (newGenome.connections[newGenome.connectionGeneIndex - 1].innovNum != selectedGene.innovNum)
{
newGenome.AddConnectionGeneGene(new ConnectionGene(selectedGene));
}
}
newPerceptrons = AddPercepID(selectedGene.from, newPerceptrons);
newPerceptrons = AddPercepID(selectedGene.to, newPerceptrons);
}
newPerceptrons.Sort();
for (int i = 0; i < GA_Parameters.inputs + 1; i++)
{
newGenome = ga.innovations.CreateNeuronFromID(i, newGenome);
}
for (int i = 0; i < GA_Parameters.outputs; i++)
{
newGenome = ga.innovations.CreateNeuronFromID(GA_Parameters.inputs + 1 + i, newGenome);
}
for (int i = 0; i < newPerceptrons.Count; i++)
{
newGenome = ga.innovations.CreateNeuronFromID(newPerceptrons[i], newGenome);
}
MutParameters mutPar = new MutParameters(mum, dad);
newGenome.ID = ga.nextGenomeID;
newGenome.inputs = mum.inputs;
newGenome.outputs = mum.outputs;
newGenome.mutPar = mutPar;
ga.nextGenomeID++;
newGenome.FinishGenome();
return(newGenome);
}
public Genome(int ID, int inputs, int hidden, int[] hiddenNodes, int outputs, MutParameters mutPar)
{
network = null;
this.ID = ID;
fitness = 0;
adjustedFitness = 0;
this.inputs = inputs;
this.outputs = outputs;
amountToSpawn = 0;
species = 0;
//Random.InitState((int)System.DateTime.Now.Ticks);
perceptrons = new List <NodeGene>();
connections = new List <ConnectionGene>();
int count = 0;
for (int i = 0; i < inputs; i++)
{
perceptrons.Add(new NodeGene(NodeType.Sensor, count, new Vector2(0, (float)i / inputs)));
count++;
}
perceptrons.Add(new NodeGene(NodeType.Bias, count, new Vector2(0, 1), false));
count++;
inputs++;
for (int i = 0; i < hidden; i++)
{
for (int j = 0; j < hiddenNodes[i]; j++)
{
perceptrons.Add(new NodeGene(NodeType.Hidden, count, new Vector2((float)(i + 1) / (hidden + 1), (float)j / hiddenNodes[i])));
count++;
}
}
for (int i = 0; i < outputs; i++)
{
perceptrons.Add(new NodeGene(NodeType.Output, count, new Vector2(1, (float)i / (outputs - 1))));
count++;
}
int percepCount = 0;
for (int layer = 0; layer < hidden + 1; layer++)
{
int numInCurLayer = 0;
int numInNextLayer = 0;
if (layer == 0)
{
numInCurLayer = inputs;
numInNextLayer = hiddenNodes[0];
}
else if (layer - 1 == hidden)
{
numInCurLayer = hiddenNodes[hidden - 1];
numInNextLayer = outputs;
}
else
{
numInCurLayer = hiddenNodes[layer - 1];
if (hiddenNodes.Length - 1 >= layer)
{
numInNextLayer = hiddenNodes[layer];
}
else
{
numInNextLayer = outputs;
}
}
int startPercep = percepCount;
for (int j = 0; j < numInCurLayer; j++)
{
for (int z = 0; z < numInNextLayer; z++)
{
int connec = startPercep + numInCurLayer + z;
connections.Add(new ConnectionGene(perceptrons[percepCount].ID, perceptrons[connec].ID, true, count + NumGenes(), Random.Range(-1f, 1f)));
}
percepCount++;
}
}
this.mutPar = mutPar;
}
public static Genome RecycledGenome(int ID, List <NodeGene> perceptrons, List <ConnectionGene> connections, int inputs, int outputs, MutParameters mutPar)
{
Genome recycledGenome = genomePool[0];
genomePool.RemoveAt(0);
recycledGenome.SetGenome(ID, perceptrons, connections, inputs, outputs, mutPar);
return(recycledGenome);
//return new Genome(ID, perceptrons, connections, inputs, outputs, mutPar);
}
