public void TestToggleConnectionEnabilityMutation()
{
Genome gen5 = gen2.Copy();
Assert.IsTrue(gen5.ConnectionGenes[7].IsEnabled == true);
gen5.ToggleEnabledMutation();
Assert.IsTrue(gen5.ConnectionGenes[7].IsEnabled == false);
}
public Genome GenerateGenesisGenome()
{
var genome = _grandFather.Copy();
foreach (var Connection in genome.Connections.Values)
{
Connection.Weight = _random.NextDouble();
}
return(_grandFather.Copy());
}
public void Load(int inputCount, int outputCount, IBitInput logic)
{
levels.Clear();
if (genome == null)
{
brainStructure = new BrainStructure()
{
InputCount = inputCount,
LevelSizes = new int[] { outputCount }
};
genome = new Genome(brainStructure);
genome.Load(logic);
}
// add memory input and output
inputCount += brainStructure.MemoryBitCount;
int genomeOffset = 0;
for (int levelIndex = 0; levelIndex < brainStructure.LevelSizes.Length; levelIndex++)
{
DigitalLevel level = new DigitalLevel();
outputCount = brainStructure.LevelSizes[levelIndex];
int logicLength = BrainStructure.GetLevelLogicLength(inputCount, outputCount);
logic = genome.Copy(genomeOffset, logicLength);
genomeOffset += logicLength;
level.Load(inputCount, outputCount, logic);
levels.Add(level);
inputCount = outputCount;
}
// Load initial memory status
if (brainStructure.MemoryBitCount > 0)
{
Memory = new BitStorage();
Memory.Load(genome.Copy(genomeOffset, brainStructure.MemoryBitCount));
}
InputCount = brainStructure.InputCount;
OutputCount = brainStructure.OutputCount;
if (autoCompile)
{
new Thread(() =>
{
Compile();
}).Start();
}
}
public List <Genome> CrossoverMutation(List <Genome> selected)
{
Crossover crossover = new Crossover();
List <Genome> newGenomes = new List <Genome>();
for (int i = 0; i < agents.Count - survivors; i++)//crossover and mutation
{
Genome parent1 = selected[Random.Range(0, selected.Count)];
//Genome child = parent1.Copy();//crossover.CrossoverGenes(parent1, parent2, gt);
Genome child = null;
if (Random.Range(0.0f, 1.0f) <= crossoverProbability)
{
Genome parent2 = selected[Random.Range(0, selected.Count)];
child = crossover.CrossoverGenes(parent1, parent2, gt);
}
else
{
child = parent1.Copy();
}
//Debug.Log(child);
child.Mutate();
newGenomes.Add(child);
//Debug.Log(newGenomes[i]);
//Debug.Log(child);
}
return(newGenomes);
}
public override void CalcFitnessDefault(Genome genome)
{
genome.Fitness = 0;
Genome tempGenome = genome.Copy();
tempGenome.Add(1);
int[,] matrix = new int[,]
{
// { 0, 5, 8, 11, 4, 7 },
// { 5, 0, 10, 4, 9, 12 },
// { 8, 10, 0, 6, 17, 8 },
// {11, 4, 6, 0, 6, 5 },
// { 4, 9, 17, 6, 0, 11 },
// { 7, 12, 8, 5, 11, 0 }
{ 0, 5, 8, 11, 4, 7, 3, 16, 4, 7 },
{ 5, 0, 10, 4, 9, 12, 9, 7, 6, 12 },
{ 8, 10, 0, 6, 17, 8, 4, 8, 9, 6 },
{11, 4, 6, 0, 6, 5, 10, 5, 7, 4 },
{ 4, 9, 17, 6, 0, 11, 5, 3, 9, 13 },
{ 7, 12, 8, 5, 11, 0, 6, 4, 3, 5 },
{ 3, 9, 4, 10, 5, 6, 0, 10, 11, 5 },
{16, 7, 8, 5, 3, 4, 10, 0, 12, 8 },
{ 4, 6, 9, 7, 9, 3, 11, 12, 0, 9 },
{ 7, 12, 6, 4, 13, 5, 5, 8, 9, 0 }
};
foreach (double gene in genome) {
genome.Fitness += matrix[(int)gene - 1,(int)tempGenome[tempGenome.IndexOf(gene)+1]-1];
}
}
public Genome GenerateGenesisGenome()
{
var genome = _GenomeGrandfather.Copy();
foreach (var connection in genome.Connections)
{
connection.Value.Weight = _random.NextDouble();
}
return(genome);
}
public void TestGenomeConstruction_ByCopying()
{
Genome genCopied = gen1.Copy();
Assert.IsTrue(genCopied.Nodes.Count == gen1.Nodes.Count);
Assert.IsTrue(genCopied.ConnectionGenes.Count == gen1.ConnectionGenes.Count);
for (int i = 0; i < genCopied.Nodes.Count; ++i)
{
Assert.IsTrue(genCopied.Nodes[i].Equals(gen1.Nodes[i]));
Assert.AreNotSame(genCopied.Nodes[i], gen1.Nodes[i]);
}
for (int i = 0; i < genCopied.ConnectionGenes.Count; ++i)
{
Assert.IsTrue(genCopied.ConnectionGenes[i].Equals(gen1.ConnectionGenes[i]));
Assert.AreNotSame(genCopied.ConnectionGenes[i], gen1.ConnectionGenes[i]);
}
}
private void DeterminePopulationFitness()
{
object syncObject = new object();
Parallel.ForEach(Population, () => new Genome(Structure), (genome, loopState, localState) =>
{
DetermineGenomeFitness(ref genome);
return(genome.Fitness < localState.Fitness ? genome : localState);
},
localState =>
{
lock (syncObject)
{
if (localState.Fitness < BestGenome.Fitness)
{
BestGenome.Copy(localState);
}
}
});
}
public void CrossoverMutationSpecies(List <Genome> selected)
{
for (int i = 0; i < selected.Count; i++)//group selected into species
{
int id = selected[i].GetSpeciesId();
//Genome genome = selected[Random.Range(0, selected.Count)];
//Debug.Log(selected[i].GetSpeciesId());
//Debug.Log("225");
for (int j = 0; j < species.Count; j++)
{
if (id == species[j].GetId())
{
//Debug.Log("id: "+id+" species: " + species[j].GetId()+"size: "+species[j].GetGenomes().Count);
species[j].GetSelectedGenomes().Add(selected[i]);
// Debug.Log("225");
break;
}
}
}
Crossover crossover = new Crossover();
int sum2 = 0;
for (int i = 0; i < species.Count; i++)
{
if (!species[i].CheckExtinct())
{
for (int j = 0; j < species[i].GetSelectedGenomes().Count; j++)
{
Genome parent1 = species[i].GetSelectedGenomes()[Random.Range(0, species[i].GetSelectedGenomes().Count)];
Genome child = null;
if (Random.Range(0.0f, 1.0f) <= crossoverProbability)
{
Genome parent2 = species[i].GetSelectedGenomes()[Random.Range(0, species[i].GetSelectedGenomes().Count)];
child = crossover.CrossoverGenes(parent1, parent2, gt);
}
else
{
child = parent1.Copy();
}
//Debug.Log(child);
child.Mutate();
child.NextGen();
InsertGenomeIntoSpeciesChild(child, species);
//newGenomes.Add(child);
sum2++;
}
}
}
// Debug.Log("new" + sum2);
// Debug.Log("count s" + species.Count);
// Debug.Log("count s" + species[species.Count-1].GetChildren().Count);
for (int i = 0; i < species.Count; i++)
{
//species[i].RemoveGen(generation-1);
species[i].SetChildrenToSpecies();
species[i].GetSelectedGenomes().Clear();
}
//Debug.Log("count s2" + species.Count);
// Debug.Log("count s" + species[species.Count-1].GetGenomes().Count);
for (int i = 0; i < species.Count; i++)
{
// Debug.Log("ss count" + species[i].GetGenomes().Count);
//species[i].RemoveGen(generation-1);
if (species[i].CheckExtinct())
{
species.RemoveAt(i);
i--;
}
}
// Debug.Log(newGenomes.Count);
//species = newSpecies;
//Debug.Log("s count"+species.Count);
// Debug.Log("s1 count" + species[0].GetGenomes().Count);
// Debug.Log(newGenomes.Count);
//return newGenomes;
}
public Generation MakeFirstGeneration(InnovationGenerator generator, InnovationGenerator genomeGenerator,
int initialPopulationSize, int selectionAlgorithm, int reproductionsPerGenome, int nBest)
{
// check if there is a Lua implementation of this
LuaFunction func = LuaState["MakeFirstGeneration"] as LuaFunction;
if (func != null)
{
try
{
return((Generation)func.Call(generator, initialPopulationSize)?.First());
}
catch (Exception e)
{
logger.Error(e, "Error running lua code for first generation.");
}
}
Generation generation = new Generation(0, selectionAlgorithm, reproductionsPerGenome, nBest);
Genome genome = new Genome(0);
int inputs = Data.Constants.NetworkInputs;
int outputs = Data.Constants.NetworkOutputs;
List <int> inputIds = new List <int>(inputs);
// input nodes
for (int i = 0; i < inputs; i++)
{
int currentId = generator.Innovate();
inputIds.Add(currentId);
genome.AddNode(new Node(currentId, NodeType.Input, int.MinValue));
}
// output nodes
for (int i = 0; i < outputs; i++)
{
int currentId = generator.Innovate();
genome.AddNode(new Node(currentId, NodeType.Output, int.MaxValue));
foreach (int hiddenId in inputIds)
{
genome.AddConnection(new Connection(hiddenId, currentId, Double() * 4f - 2f, true, generator.Innovate()));
}
}
Species original = new Species(genome);
generation.Species.Add(original);
for (int i = 0; i < initialPopulationSize; i++)
{
Genome g = genome.Copy();
g.Mutate(generator);
g.Id = genomeGenerator.Innovate();
original.AddGenome(g);
}
return(generation);
}