/// <inheritdoc />
public int PerformSelection(Random rnd, ISpecies species)
{
int bestIndex = rnd.Next(species.Members.Count);
IGenome best = species.Members[bestIndex];
BasicEA.CalculateScoreAdjustment(best, Trainer.ScoreAdjusters);
for (int i = 0; i < Rounds; i++)
{
int competitorIndex = rnd.Next(species.Members.Count - 1);
IGenome competitor = species.Members[competitorIndex];
// only evaluate valid genomes
if (!Double.IsInfinity(competitor.AdjustedScore) &&
!Double.IsNaN(competitor.AdjustedScore))
{
BasicEA.CalculateScoreAdjustment(competitor,
Trainer.ScoreAdjusters);
if (Trainer.SelectionComparer.IsBetterThan(
competitor, best))
{
best = competitor;
bestIndex = competitorIndex;
}
}
}
return(bestIndex);
}
private void Window_Loaded_1(object sender, RoutedEventArgs e)
{
pop = InitPopulation();
score = new PlantScore();
this.genetic = new BasicEA(pop, score);
//this.genetic.Speciation = new ArraySpeciation<DoubleArrayGenome>();
genetic.AddOperation(0.9, new Splice(PlantUniverse.GenomeSize / 3));
genetic.AddOperation(0.1, new MutatePerturb(0.1));
// Display
this.universe = new PlantUniverse();
this.universe.Reset();
DoubleArrayGenome bestGenome = (DoubleArrayGenome)genetic.BestGenome;
PlantPhysics physics = new PlantPhysics();
PlantGrowth growth = new PlantGrowth();
for (int i = 0; i < 100; i++)
{
physics.RunPhysics(universe);
growth.RunGrowth(universe, bestGenome.Data);
}
this.display = new DisplayPlant(CanvasOutput);
this.display.Universe = this.universe;
Thread t = new Thread(DoWork);
t.Start();
}
/// <inheritdoc />
public int PerformAntiSelection(Random rnd, ISpecies species)
{
int worstIndex = rnd.Next(species.Members.Count);
IGenome worst = species.Members[worstIndex];
BasicEA.CalculateScoreAdjustment(worst,
Trainer.ScoreAdjusters);
for (int i = 0; i < Rounds; i++)
{
int competitorIndex = rnd.Next(species.Members.Count - 1);
IGenome competitor = species.Members[competitorIndex];
// force an invalid genome to lose
if (Double.IsInfinity(competitor.AdjustedScore) ||
Double.IsNaN(competitor.AdjustedScore))
{
return(competitorIndex);
}
BasicEA.CalculateScoreAdjustment(competitor,
Trainer.ScoreAdjusters);
if (!Trainer.SelectionComparer.IsBetterThan(competitor,
worst))
{
worst = competitor;
worstIndex = competitorIndex;
}
}
return(worstIndex);
}
/// <summary>
/// Run the example.
/// </summary>
public void Process()
{
// read the iris data from the resources
Assembly assembly = Assembly.GetExecutingAssembly();
Stream res = assembly.GetManifestResourceStream("AIFH_Vol2.Resources.iris.csv");
// did we fail to read the resouce
if (res == null)
{
Console.WriteLine("Can't read iris data from embedded resources.");
return;
}
// load the data
var istream = new StreamReader(res);
DataSet ds = DataSet.Load(istream);
istream.Close();
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
// The following ranges are setup for the Iris data set. If you wish to normalize other files you will
// need to modify the below function calls other files.
ds.NormalizeRange(0, -1, 1);
ds.NormalizeRange(1, -1, 1);
ds.NormalizeRange(2, -1, 1);
ds.NormalizeRange(3, -1, 1);
IDictionary <string, int> species = ds.EncodeOneOfN(4);
istream.Close();
var codec = new RBFNetworkGenomeCODEC(4, RbfCount, 3);
IList <BasicData> trainingData = ds.ExtractSupervised(0,
codec.InputCount, 4, codec.OutputCount);
IPopulation pop = InitPopulation(rnd, codec);
IScoreFunction score = new ScoreRegressionData(trainingData);
var genetic = new BasicEA(pop, score)
{
CODEC = codec
};
genetic.AddOperation(0.7, new Splice(codec.Size / 3));
genetic.AddOperation(0.3, new MutatePerturb(0.1));
PerformIterations(genetic, 100000, 0.05, true);
var winner = (RBFNetwork)codec.Decode(genetic.BestGenome);
QueryOneOfN(winner, trainingData, species);
}
/// <summary>
/// Setup and solve the TSP.
/// </summary>
public void Solve()
{
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
var builder = new StringBuilder();
InitCities(rnd);
IPopulation pop = InitPopulation(rnd);
IScoreFunction score = new TSPScore(_cities);
_genetic = new BasicEA(pop, score);
_genetic.AddOperation(0.9, new SpliceNoRepeat(Cities / 3));
_genetic.AddOperation(0.1, new MutateShuffle());
int sameSolutionCount = 0;
int iteration = 1;
double lastSolution = double.MaxValue;
while (sameSolutionCount < MaxSameSolution)
{
_genetic.Iteration();
double thisSolution = _genetic.LastError;
builder.Length = 0;
builder.Append("Iteration: ");
builder.Append(iteration++);
builder.Append(", Best Path Length = ");
builder.Append(thisSolution);
Console.WriteLine(builder.ToString());
if (Math.Abs(lastSolution - thisSolution) < 1.0)
{
sameSolutionCount++;
}
else
{
sameSolutionCount = 0;
}
lastSolution = thisSolution;
}
Console.WriteLine("Good solution found:");
var best = (IntegerArrayGenome)_genetic.BestGenome;
DisplaySolution(best);
_genetic.FinishTraining();
}
/// <summary>
/// Demonstrate the crossover splice operator. Two offspring will be created by swapping three
/// segments of the parents (two cut points). Some genes may repeat.
/// </summary>
public static void Splice()
{
Console.WriteLine("Crossover Splice");
// Create a random number generator
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
// Create a new population.
IPopulation pop = new BasicPopulation();
pop.GenomeFactory = new IntegerArrayGenomeFactory(10);
// Create a trainer with a very simple score function. We do not care
// about the calculation of the score, as they will never be calculated.
IEvolutionaryAlgorithm train = new BasicEA(pop, new NullScore());
// Create a splice operator, length = 5. Use it 1.0 (100%) of the time.
var opp = new Splice(5);
train.AddOperation(1.0, opp);
// Create two parents, the genes are set to 1,2,3,4,5,7,8,9,10
// and 10,9,8,7,6,5,4,3,2,1.
var parents = new IntegerArrayGenome[2];
parents[0] = (IntegerArrayGenome)pop.GenomeFactory.Factor();
parents[1] = (IntegerArrayGenome)pop.GenomeFactory.Factor();
for (int i = 1; i <= 10; i++)
{
parents[0].Data[i - 1] = i;
parents[1].Data[i - 1] = 11 - i;
}
// Create an array to hold the offspring.
var offspring = new IntegerArrayGenome[2];
// Perform the operation
opp.PerformOperation(rnd, parents, 0, offspring, 0);
// Display the results
Console.WriteLine("Parent 1: " + string.Join(",", parents[0].Data));
Console.WriteLine("Parent 2: " + string.Join(",", parents[1].Data));
Console.WriteLine("Offspring 1: " + string.Join(",", offspring[0].Data));
Console.WriteLine("Offspring 2: " + string.Join(",",
offspring[1].Data));
}
/// <summary>
/// Demonstrate the mutate peterb operator. An offspring will be created by randomly changing each
/// gene.
/// </summary>
public static void MutatePeterb()
{
Console.WriteLine("Mutate Perturb");
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
// Create a new population.
IPopulation pop = new BasicPopulation();
pop.GenomeFactory = new DoubleArrayGenomeFactory(5);
// Create a trainer with a very simple score function. We do not care
// about the calculation of the score, as they will never be calculated.
IEvolutionaryAlgorithm train = new BasicEA(pop, new NullScore());
var opp = new MutatePerturb(0.1);
train.AddOperation(1.0, opp);
// Create a peterb operator. Use it 1.0 (100%) of the time.
var parents = new DoubleArrayGenome[1];
parents[0] = (DoubleArrayGenome)pop.GenomeFactory.Factor();
parents[0].Population = pop;
for (int i = 1; i <= 5; i++)
{
parents[0].Data[i - 1] = i;
}
// Create an array to hold the offspring.
var offspring = new DoubleArrayGenome[1];
offspring[0] = new DoubleArrayGenome(5);
// Perform the operation
opp.PerformOperation(rnd, parents, 0, offspring, 0);
// Display the results
Console.WriteLine("Parent: " + string.Join(",", parents[0].Data));
Console.WriteLine("Offspring: " + string.Join(",", offspring[0].Data));
}
/// <summary>
/// Perform the task.
/// </summary>
public void PerformTask()
{
IMLMethod phenotype = owner.CODEC.Decode(genome);
if (phenotype != null)
{
double score;
try
{
score = scoreFunction.CalculateScore(phenotype);
}
catch (EARuntimeError e)
{
score = Double.NaN;
}
genome.Score = score;
genome.AdjustedScore = score;
BasicEA.CalculateScoreAdjustment(genome, adjusters);
}
}
/// <summary>
/// The entry point for this example. If you would like to make this example
/// stand alone, then add to its own project and rename to Main.
/// </summary>
/// <param name="args">Not used.</param>
public static void ExampleMain(string[] args)
{
// Create a new population.
IPopulation pop = new BasicPopulation();
ISpecies species = pop.CreateSpecies();
// Create 1000 genomes, assign the score to be the index number.
for (int i = 0; i < 1000; i++)
{
IGenome genome = new IntegerArrayGenome(1);
genome.Score = i;
genome.AdjustedScore = i;
pop.Species[0].Add(genome);
}
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
// Create a trainer with a very simple score function. We do not care
// about the calculation of the score, as they will never be calculated.
// We only care that we are maximizing.
IEvolutionaryAlgorithm train = new BasicEA(pop, new NullScore());
// Perform the test for round counts between 1 and 10.
for (int roundCount = 1; roundCount <= 10; roundCount++)
{
var selection = new TournamentSelection(train, roundCount);
int sum = 0;
int count = 0;
for (int i = 0; i < 100000; i++)
{
int genomeID = selection.PerformSelection(rnd, species);
IGenome genome = species.Members[genomeID];
sum += (int)genome.AdjustedScore;
count++;
}
sum /= count;
Console.WriteLine("Rounds: " + roundCount + ", Avg Score: " + sum);
}
}
/// <summary>
/// Process the specified file.
/// </summary>
/// <param name="filename">The filename to process.</param>
public void Process(String filename)
{
// read the data from the resources
Assembly assembly = Assembly.GetExecutingAssembly();
Stream res = assembly.GetManifestResourceStream("AIFH_Vol2.Resources.simple-poly.csv");
// did we fail to read the resouce
if (res == null)
{
Console.WriteLine("Can't read iris data from embedded resources.");
return;
}
// load the data
var istream = new StreamReader(res);
DataSet ds = DataSet.Load(istream);
istream.Close();
// Extract supervised training.
IList <BasicData> training = ds.ExtractSupervised(0, 1, 1, 1);
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
var eval = new EvaluateExpression(rnd);
IPopulation pop = InitPopulation(rnd, eval);
IScoreFunction score = new ScoreSmallExpression(training, 30);
IEvolutionaryAlgorithm genetic = new BasicEA(pop, score);
genetic.AddOperation(0.3, new MutateTree(3));
genetic.AddOperation(0.7, new CrossoverTree());
genetic.ShouldIgnoreExceptions = false;
int sameSolutionCount = 0;
int iteration = 1;
double lastSolution = double.MaxValue;
var builder = new StringBuilder();
while (sameSolutionCount < MaxSameSolution && iteration < 1000)
{
genetic.Iteration();
double thisSolution = genetic.LastError;
builder.Length = 0;
builder.Append("Iteration: ");
builder.Append(iteration++);
builder.Append(", Current error = ");
builder.Append(thisSolution);
builder.Append(", Best Solution Length = ");
builder.Append(genetic.BestGenome.Count);
Console.WriteLine(builder.ToString());
if (Math.Abs(lastSolution - thisSolution) < 1.0)
{
sameSolutionCount++;
}
else
{
sameSolutionCount = 0;
}
lastSolution = thisSolution;
}
Console.WriteLine("Good solution found:");
var best = (TreeGenome)genetic.BestGenome;
Console.WriteLine(eval.DisplayExpressionNormal(best.Root));
genetic.FinishTraining();
}
