public void CheckProperties()
{
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(a => a))
{
Assert.AreEqual(ExpressionEvolverGeneticAlgorithmParameters.CrossoverProbabilityValue,
parameters.CrossoverProbability);
Assert.AreEqual(ExpressionEvolverGeneticAlgorithmParameters.MutationProbabilityValue,
parameters.MutationProbability);
Assert.AreEqual(ExpressionEvolverGeneticAlgorithmParameters.RunCountValue,
parameters.NumberOfGenerationRuns);
Assert.AreEqual(ExpressionEvolverGeneticAlgorithmParameters.GenerationCountValue,
parameters.NumberOfGenerations);
Assert.AreEqual(ExpressionEvolverGeneticAlgorithmParameters.PopulationSizeValue,
parameters.PopulationSize);
Assert.AreEqual(Environment.ProcessorCount,
parameters.TaskCount);
try
{
var length = parameters.ChromosomeLength;
Assert.Fail();
}
catch (NotImplementedException)
{
}
catch
{
Assert.Fail();
}
}
}
public void Mutate()
{
var random = Substitute.For <SecureRandom>();
var nextCallCount = 0;
random.Next(Arg.Any <int>()).Returns((_) =>
{
var result = 0;
if (nextCallCount == 0)
{
result = 1;
}
else if (nextCallCount == 1)
{
result = 0;
}
else
{
throw new InvalidOperationException("Too many Next(int) calls.");
}
nextCallCount++;
return(result);
});
random.NextDouble().Returns(0d);
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
new RandomObjectGenerator().Generate <ReadOnlyCollection <ExpressionEvolverResult> >(),
random))
{
Assert.AreEqual("a => (a + a)", parameters.Mutator(a => 3 + a).ToString());
}
}
public void CreateWithFuncReturnNaNAndInfinity()
{
const double parameterResult = 3d;
var count = 0;
Func <double, double> func = a =>
{
var result = 0d;
if (count == 0)
{
result = double.NaN;
}
else if (count == 1)
{
result = double.PositiveInfinity;
}
else
{
result = parameterResult;
}
count++;
return(result);
};
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(func, 1, 1d))
{
Assert.AreEqual(3, count);
Assert.AreEqual(1, parameters.Results.Count);
Assert.AreEqual(parameterResult, parameters.Results[0].Result);
Assert.AreEqual(-9.00000000000068E-06, parameters.AcceptableAverageMeanSquareError, 0.00000000001);
Assert.AreEqual(9, parameters.ExceptionPenaltyFactor, 0.1);
}
}
public void SelectFittestChildrenOnNullArgument()
{
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
(a) => { return(a); }))
{
parameters.SelectFittestChildren(null);
}
}
public void CheckTaskCountOnDisposedObject()
{
ExpressionEvolverGeneticAlgorithmParameters parameters = null;
using (parameters = new ExpressionEvolverGeneticAlgorithmParameters(a => a)) { }
var value = parameters.TaskCount;
}
public void CrossoverWithEmptyParents()
{
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
a => a))
{
parameters.Crossover(new List <Chromosome <Expression <Func <double, double> > > >().AsReadOnly());
}
}
public void EvaluateFitnessWithNullArgument()
{
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
a => a))
{
parameters.FitnessEvaluator(null);
}
}
public void CrossoverWithNullParents()
{
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
a => a))
{
parameters.Crossover(null);
}
}
public void TerminatorOnNullArgument()
{
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
(a) => { return(a); }))
{
parameters.Terminator(null);
}
}
public void CheckNumberOfGenerationsOnDisposedObject()
{
ExpressionEvolverGeneticAlgorithmParameters parameters = null;
using (parameters = new ExpressionEvolverGeneticAlgorithmParameters(a => a)) { }
var value = parameters.NumberOfGenerations;
}
public void CheckMutationProbabilityOnDisposedObject()
{
ExpressionEvolverGeneticAlgorithmParameters parameters = null;
using (parameters = new ExpressionEvolverGeneticAlgorithmParameters(a => a)) { }
var value = parameters.MutationProbability;
}
public void CreateViaResultsAndNullRandom()
{
var generator = new RandomObjectGenerator();
var results = generator.Generate <ReadOnlyCollection <ExpressionEvolverResult> >();
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
results, null)) { }
}
public void MutateWithNullArgument()
{
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
a => a))
{
parameters.Mutator(null);
}
}
public void CheckPopulationSizeOnDisposedObject()
{
ExpressionEvolverGeneticAlgorithmParameters parameters = null;
using (parameters = new ExpressionEvolverGeneticAlgorithmParameters(a => a)) { }
var value = parameters.PopulationSize;
}
public void MutateOnDisposedObject()
{
ExpressionEvolverGeneticAlgorithmParameters parameters = null;
using (parameters = new ExpressionEvolverGeneticAlgorithmParameters(
a => a)) { }
parameters.Mutator(null);
}
public void CrossoverOnDisposedObject()
{
ExpressionEvolverGeneticAlgorithmParameters parameters = null;
using (parameters = new ExpressionEvolverGeneticAlgorithmParameters(
a => a)) { }
parameters.Crossover(new List <Chromosome <Expression <Func <double, double> > > >().AsReadOnly());
}
static void Main()
{
//a => ((Math.Pow(a, 0.5) * a * a) + a);
//a => (a + a) * ((a + a) / (a * a));
//a => a * a * a;
//a => a + a + a + Math.Pow(a, 0.5) + (a * a * a);
//a => a - (a - Math.Pow(a, 0.5) + (a * a * Math.Pow(a, 0.5)) - a);
//a => a - (Math.Pow(a, 0.5));
//a => (4 - a) + (Math.Pow(a, 0.5) * 3);
//a => (4 - a) + 3;
//a => ((a / 4) - a) + (Math.Pow((33 * a), 0.5));
//a => (1000 / Math.Pow(a, 0.5) * (a / 54));
//a => (((47 + (a * a) / (3 - a) + (Math.Pow(2, a)))));
//a => (-2 * Math.Pow(a, 3)) - (15 * Math.Pow(a, 2)) - (6 * a) + 7;
//a => (a - 1) * Math.Pow((a - 4), 2);
//a => (2 * Math.Pow(a, 4)) - (11 * Math.Pow(a, 3)) - (6 * Math.Pow(a, 2)) + (64 * a) + 32;
Console.Out.WriteLine("Baking expression...");
var baker = new Baker <Func <double, double> >(
"a => (4 - a) + (Math.Pow(a, 0.5) * 3)");
var func = baker.Bake();
Console.Out.WriteLine("Target expression: " + func.ToString());
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(func.Compile()))
{
var ga = new GeneticAlgorithm <Expression <Func <double, double> > >(parameters);
var generationCount = 1;
var generationRunCount = 1;
var generationRunCompletedHandler = new EventHandler <EventArgs <Population <Expression <Func <double, double> > > > >(
(sender, e) =>
{
Program.PrintPopulation(e.Value, generationRunCount, "Run");
generationRunCount++;
});
var generationCompletedHandler = new EventHandler <EventArgs <Population <Expression <Func <double, double> > > > >(
(sender, e) =>
{
Program.PrintPopulation(e.Value, generationCount, "Generation");
generationCount++;
});
ga.GenerationCompleted += generationCompletedHandler;
ga.GenerationRunCompleted += generationRunCompletedHandler;
ga.Run();
ga.GenerationCompleted -= generationCompletedHandler;
ga.GenerationRunCompleted -= generationRunCompletedHandler;
Console.Out.WriteLine(ga.WasOptimalSolutionFound);
var best = (from chromosome in ga.Final.Chromosomes
orderby chromosome.Fitness descending
select chromosome).Take(1).FirstOrDefault();
Console.Out.WriteLine(best.Value.ToString());
}
}
public void GeneratePopulationOnDisposedObject()
{
ExpressionEvolverGeneticAlgorithmParameters parameters = null;
using (parameters = new ExpressionEvolverGeneticAlgorithmParameters(
(a) => { return(a); })) { }
parameters.GeneratePopulation();
}
public void SelectFittestChildrenOnDisposedObject()
{
ExpressionEvolverGeneticAlgorithmParameters parameters = null;
using (parameters = new ExpressionEvolverGeneticAlgorithmParameters(
(a) => { return(a); })) { }
parameters.SelectFittestChildren(null);
}
public void CopyOnDisposedObject()
{
ExpressionEvolverGeneticAlgorithmParameters parameters = null;
using (parameters = new ExpressionEvolverGeneticAlgorithmParameters(
(a) => { return(a); })) { }
parameters.Copy(a => a);
}
public void Copy()
{
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
(a) => { return(a); }))
{
Expression <Func <double, double> > expression = a => a;
Assert.AreSame(expression, parameters.Copy(expression));
}
}
public void EvaluateFitnessOnDisposedObject()
{
ExpressionEvolverGeneticAlgorithmParameters parameters = null;
using (parameters = new ExpressionEvolverGeneticAlgorithmParameters(
a => a)) { }
parameters.FitnessEvaluator(null);
}
public void TerminatorOnDisposedObject()
{
ExpressionEvolverGeneticAlgorithmParameters parameters = null;
using (parameters = new ExpressionEvolverGeneticAlgorithmParameters(
(a) => { return(a); })) { }
parameters.Terminator(null);
}
public void CreateViaFunc()
{
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
a => { return(a); }))
{
Assert.AreEqual(ExpressionEvolverGeneticAlgorithmParameters.ResultGenerationCount,
parameters.Results.Count);
}
}
public void CreateViaResults()
{
var generator = new RandomObjectGenerator();
var results = generator.Generate <ReadOnlyCollection <ExpressionEvolverResult> >();
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(results))
{
CollectionAssert.AreEquivalent(results, parameters.Results);
}
}
public void EvaluateFitness()
{
var results = new List <ExpressionEvolverResult> {
new ExpressionEvolverResult(1d, 1d)
}.AsReadOnly();
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
results))
{
Assert.AreEqual(0d, parameters.FitnessEvaluator(a => a));
}
}
public void EvaluateFitnessWithExceptionAndResultExpectationIsException()
{
var results = new List <ExpressionEvolverResult> {
new ExpressionEvolverResult(0d, new ArithmeticException())
}.AsReadOnly();
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
results))
{
Assert.AreEqual(0d, parameters.FitnessEvaluator(a => (double)(1 / (int)a)));
}
}
public void CreateViaFuncAndRandom()
{
var random = new SecureRandom();
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
(a) => { return(a); }, random))
{
Assert.AreEqual(ExpressionEvolverGeneticAlgorithmParameters.ResultGenerationCount,
parameters.Results.Count);
Assert.AreSame(random, parameters.Random);
}
}
public void Evolve()
{
this.IsEvolveEnabled = false;
var baker = new Baker <Func <double, double> >(this.Expression);
var func = baker.Bake();
var parameters = new ExpressionEvolverGeneticAlgorithmParameters(func.Compile());
var generationCount = 1;
this.AcceptableFitnessValue = parameters.AcceptableAverageMeanSquareError.ToString("#.00");
this.BaseLine = this.GetPoints(parameters.Results);
this.worker = new BackgroundWorker();
this.worker.WorkerReportsProgress = true;
this.worker.RunWorkerCompleted += (s, e) =>
{
var args = e.Result as Population <Expression <Func <double, double> > >;
this.PrintPopulation(args, generationCount, parameters.Results);
parameters.Dispose();
this.IsEvolveEnabled = true;
};
this.worker.ProgressChanged += (ps, pe) =>
{
if (generationCount % 10 == 0)
{
var args = pe.UserState as EventArgs <Population <Expression <Func <double, double> > > >;
this.PrintPopulation(args.Value, generationCount, parameters.Results);
}
generationCount++;
};
this.worker.DoWork += (s, e) =>
{
var ga = new GeneticAlgorithm <Expression <Func <double, double> > >(parameters);
var generationCompletedHandler = new EventHandler <EventArgs <Population <Expression <Func <double, double> > > > >(
(gs, ge) =>
{
this.worker.ReportProgress(0, ge);
});
ga.GenerationCompleted += generationCompletedHandler;
ga.Run();
ga.GenerationCompleted -= generationCompletedHandler;
e.Result = ga.Final;
};
this.worker.RunWorkerAsync();
}
public void MutateWithNoMutation()
{
var random = Substitute.For <SecureRandom>();
random.NextDouble().Returns(1d);
using (var parameters = new ExpressionEvolverGeneticAlgorithmParameters(
new RandomObjectGenerator().Generate <ReadOnlyCollection <ExpressionEvolverResult> >(),
random))
{
Assert.AreEqual("a => (3 + a)", parameters.Mutator(a => 3 + a).ToString());
}
}
