public TrainEA RunGA(TrainEA geneticAlgorithm, int maxSameSolutionCount)
{
try
{
var sameSolutionCount = 0;
var lastSolutionError = double.MaxValue;
var iteration = 1;
while (sameSolutionCount < maxSameSolutionCount)
{
geneticAlgorithm.Iteration();
var currentSolutionError = geneticAlgorithm.Error;
if (Math.Abs(lastSolutionError - currentSolutionError) < 1.0)
{
sameSolutionCount++;
}
else
{
sameSolutionCount = 0;
}
lastSolutionError = currentSolutionError;
Console.WriteLine($"{iteration++} Iteration - Error {currentSolutionError}");
}
geneticAlgorithm.FinishTraining();
Console.WriteLine("Good Solutions Found");
return(geneticAlgorithm);
}
catch (Exception e)
{
Console.WriteLine(e);
throw;
}
}
public int[] Run()
{
StringBuilder builder = new StringBuilder();
IPopulation pop = InitPopulation();
ICalculateScore score = new FitnessFunction(cities);
genetic = new TrainEA(pop, score);
genetic.AddOperation(0.9, new SpliceNoRepeat(cityCount / 3));
genetic.AddOperation(0.1, new MutateShuffle());
int sameSolutionCount = 0;
int iteration = 1;
double lastSolution = Double.MaxValue;
while (sameSolutionCount < maxSameSolition)
{
genetic.Iteration();
double thisSolution = genetic.Error;
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:");
IntegerArrayGenome best = (IntegerArrayGenome)genetic.BestGenome;
genetic.FinishTraining();
return(best.Data);
}
/// <summary>
/// Setup and solve the TSP.
/// </summary>
public void Execute(IExampleInterface app)
{
this.app = app;
var builder = new StringBuilder();
initCities();
IPopulation pop = initPopulation();
ICalculateScore score = new TSPScore(cities);
genetic = new TrainEA(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 < MAX_SAME_SOLUTION)
{
genetic.Iteration();
double thisSolution = genetic.Error;
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:");
displaySolution();
genetic.FinishTraining();
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
IMLDataSet trainingData = GenerationUtil.GenerateSingleDataRange(
(x) => (3 * Math.Pow(x, 2) + (12 * x) + 4)
, 0, 100, 1);
EncogProgramContext context = new EncogProgramContext();
context.DefineVariable("x");
StandardExtensions.CreateNumericOperators(context);
PrgPopulation pop = new PrgPopulation(context, 1000);
MultiObjectiveFitness score = new MultiObjectiveFitness();
score.AddObjective(1.0, new TrainingSetScore(trainingData));
TrainEA genetic = new TrainEA(pop, score);
genetic.ValidationMode = true;
genetic.CODEC = new PrgCODEC();
genetic.AddOperation(0.5, new SubtreeCrossover());
genetic.AddOperation(0.25, new ConstMutation(context, 0.5, 1.0));
genetic.AddOperation(0.25, new SubtreeMutation(context, 4));
genetic.AddScoreAdjuster(new ComplexityAdjustedScore(10, 20, 10, 20.0));
genetic.Rules.AddRewriteRule(new RewriteConstants());
genetic.Rules.AddRewriteRule(new RewriteAlgebraic());
genetic.Speciation = new PrgSpeciation();
(new RampedHalfAndHalf(context, 1, 6)).Generate(new EncogRandom(), pop);
genetic.ShouldIgnoreExceptions = false;
EncogProgram best = null;
genetic.ThreadCount = 1;
try
{
for (int i = 0; i < 1000; i++)
{
genetic.Iteration();
best = (EncogProgram)genetic.BestGenome;
Console.Out.WriteLine(genetic.IterationNumber + ", Error: "
+ best.Score + ",Best Genome Size:" + best.Size
+ ",Species Count:" + pop.Species.Count + ",best: " + best.DumpAsCommonExpression());
}
//EncogUtility.evaluate(best, trainingData);
Console.Out.WriteLine("Final score:" + best.Score
+ ", effective score:" + best.AdjustedScore);
Console.Out.WriteLine(best.DumpAsCommonExpression());
//pop.dumpMembers(Integer.MAX_VALUE);
//pop.dumpMembers(10);
}
catch (Exception t)
{
Console.Out.WriteLine(t.ToString());
}
finally
{
genetic.FinishTraining();
EncogFramework.Instance.Shutdown();
}
}