public static void ReinstateGenome(ChihuahuaComponent comp, List <decimal> Ngenome)
{
GH_Document docu = comp.OnPingDocument();
//Change value of sliders
IList <IGH_Param> sliders = comp.Params.Input[0].Sources;
List <decimal> genome = new List <decimal>();
if (sliders.Count != Ngenome.Count)
{
throw new System.ArgumentException("Numbers of genes not equal to number of sliders", "Genome");
}
for (int i = 0; i < sliders.Count; i++)
{
Grasshopper.Kernel.Special.GH_NumberSlider slider = sliders[i] as Grasshopper.Kernel.Special.GH_NumberSlider;
if (slider != null)
{
decimal N_gene_value = Ngenome[i];
decimal gene_value = N_gene_value.Remap(0, 1, slider.Slider.Minimum, slider.Slider.Maximum);
slider.SetSliderValue(gene_value);
genome.Add(gene_value);
}
}
// Get a new solution
docu.NewSolution(false);
}
public static Individual CreateIndividualFromNGenome(ChihuahuaComponent comp, List <decimal> Ngenome)
{
GH_Document docu = comp.OnPingDocument();
//Change value of sliders
IList <IGH_Param> sliders = comp.Params.Input[0].Sources;
List <decimal> genome = new List <decimal>();
if (sliders.Count != Ngenome.Count)
{
throw new System.ArgumentException("Numbers of genes not equal to number of sliders", "Genome");
}
for (int i = 0; i < sliders.Count; i++)
{
Grasshopper.Kernel.Special.GH_NumberSlider slider = sliders[i] as Grasshopper.Kernel.Special.GH_NumberSlider;
if (slider != null)
{
decimal N_gene_value = Ngenome[i];
decimal gene_value = N_gene_value.Remap(0, 1, slider.Slider.Minimum, slider.Slider.Maximum);
slider.SetSliderValue(gene_value);
genome.Add(gene_value);
}
}
// Get a new solution
docu.NewSolution(false);
//Read new fitness value
decimal fitness = 0;
IGH_Param param = comp.Params.Input[1].Sources[0];
GH_Structure <GH_Number> outcome = comp.Params.Input[1].Sources[0].VolatileData as GH_Structure <GH_Number>;
if (outcome == null)
{
throw new System.ArgumentException("Fitness input is not a number", "Fitness");
}
if (outcome != null)
{
fitness = (decimal)outcome.Branches[0][0].Value;
}
Individual ind = new Individual(fitness, genome, Ngenome);
return(ind);
}
public static Generation CreateRandomGeneration(ChihuahuaComponent comp, int numberOfIndividuals)
{
List <Individual> tempIndi = new List <Individual>();
for (int i = 0; i < numberOfIndividuals; i++)
{
Individual ind = Chihuahua.CreateRandomIndividual(comp);
tempIndi.Add(ind);
}
Generation gen = new Generation(tempIndi);
return(gen);
}
public static Generation EvolveGeneration(ChihuahuaComponent comp, Generation previousgen, int survivors, int GenerationSize, double mutationPercentage)
{
// Get onlt the fittest individuals
List <Individual> validIndividuals = previousgen.populations.GetRange(0, survivors);
List <Individual> nextGenerationList = new List <Individual>();
for (int i = 0; i < GenerationSize; i++)
{
//Randomly select two individuals
int firstIndividualIndx = random.Next(0, survivors);
int scondIndividualIndx = random.Next(0, survivors);
//Make sure the same individual is not being chosen twice
while (firstIndividualIndx == scondIndividualIndx)
{
scondIndividualIndx = random.Next(0, survivors);
}
Individual firstIndividual = validIndividuals[firstIndividualIndx];
Individual secondtIndividual = validIndividuals[scondIndividualIndx];
//Mix individuals and add mutations
Individual newIndividual = mixTwoIndividuals(comp, firstIndividual, secondtIndividual, mutationPercentage);
//Add new individual to new generation list
nextGenerationList.Add(newIndividual);
}
//Create new generation
Generation newGeneration = new Generation(nextGenerationList);
return(newGeneration);
}
private static Individual mixTwoIndividuals(ChihuahuaComponent comp, Individual ind1, Individual ind2, double mutationPercentage)
{
if (mutationPercentage > 1 || mutationPercentage < 0)
{
throw new System.ArgumentException("The mutation percentage must be betwen 0 and 1", "Mutation percentage");
}
//Get both genomes
List <decimal> gen1 = ind1.NormalizeGenome;
List <decimal> gen2 = ind2.NormalizeGenome;
//Creat a new genome by randomly chosing of the genes
List <decimal> newNGen = new List <decimal>();
for (int i = 0; i < gen1.Count; i++)
{
if (random.NextDouble() < 0.5)
{
newNGen.Add(gen1[i]);
}
else
{
newNGen.Add(gen2[i]);
}
}
//Iterate over genes and mutate then
//First mutation type will set the gene to a competely new random value
for (int i = 0; i < newNGen.Count; i++)
{
if (random.NextDouble() <= mutationPercentage)
{
newNGen[i] = (decimal)random.NextDouble();
}
}
//Second type of mutation will increase/decrese the gene value by a small percentage
for (int i = 0; i < newNGen.Count; i++)
{
if (random.NextDouble() <= 0.15)
{
decimal factor = ((decimal)random.NextDouble()).Remap(0, 1, 0.85m, 1.15m);
newNGen[i] *= factor;
//Make sure the modification does not take the value out of range
if (newNGen[i] < 0)
{
newNGen[i] = 0;
}
if (newNGen[i] > 1)
{
newNGen[i] = 1;
}
}
}
//Create a new individual with the given genome
Individual newInd = CreateIndividualFromNGenome(comp, newNGen);
return(newInd);
}
public static Individual CreateRandomIndividual(ChihuahuaComponent comp)
{
GH_Document docu = comp.OnPingDocument();
//Change value of sliders
List <decimal> genome = new List <decimal>();
List <decimal> Ngenome = new List <decimal>();
IList <IGH_Param> sliders = comp.Params.Input[0].Sources;
foreach (IGH_Param param in sliders)
{
Grasshopper.Kernel.Special.GH_NumberSlider slider = param as Grasshopper.Kernel.Special.GH_NumberSlider;
if (slider != null)
{
decimal N_gene_value = (decimal)random.NextDouble();
decimal gene_value = N_gene_value.Remap(0, 1, slider.Slider.Minimum, slider.Slider.Maximum);
slider.SetSliderValue(gene_value);
Ngenome.Add(N_gene_value);
genome.Add(gene_value);
}
else
{
Rhino.RhinoApp.WriteLine("At least one of the input parameters is not a number slider" + Environment.NewLine);
throw new System.ArgumentException("At least one of the input parameters is not a number slider", "Genome");
}
}
// Get a new solution
docu.NewSolution(false);
//Read new fitness value
decimal fitness = 0;
if (comp.Params.Input[1].SourceCount != 1)
{
Rhino.RhinoApp.Write("Fitness input must be a unique value" + Environment.NewLine);
throw new System.ArgumentException("Fitness input must be a unique value", "Fitness");
}
else
{
IGH_Param param = comp.Params.Input[1].Sources[0];
GH_Structure <GH_Number> outcome = comp.Params.Input[1].Sources[0].VolatileData as GH_Structure <GH_Number>;
if (outcome == null)
{
Rhino.RhinoApp.WriteLine("Fitness input is not a number" + Environment.NewLine);
throw new System.ArgumentException("Fitness input is not a number", "Fitness");
}
if (outcome != null)
{
fitness = (decimal)outcome.Branches[0][0].Value;
}
}
Individual ind = new Individual(fitness, genome, Ngenome);
return(ind);
}
