public void RastriginTestMany()
{
double[] expected = new[] { 0.0, 0.0, 0.0, 0.0, 0.0 };
CmaEs target = new CmaEs(
(x, o) =>
{
if (x.Min() < -5.12 || x.Max() > 5.12)
{
return(100.0 + 10.0 * x.Length + x.Sum(xi => Math.Pow(xi, 2) - 10 * Math.Cos(2.0 * Math.PI * xi)));
}
else
{
return(10.0 * x.Length + x.Sum(xi => Math.Pow(xi, 2) - 10 * Math.Cos(2.0 * Math.PI * xi)));
}
},
initialParameters: Utilities.Randoms(expected.Length, null, null),
stopEvalsMultiple: 1e4);
Solution solution = target.Solve();
double[] actual = solution.BestParameters;
for (int i = 0; i < expected.Length; i++)
{
Assert.IsTrue(Math.Abs(expected[i] - actual[i]) < SMALL_VALUE);
}
}
/// <inheritdoc />
protected override void InitializeContinuousOptimizer(
Population basePopulation,
IncumbentGenomeWrapper <TResult> currentIncumbent)
{
if (basePopulation == null)
{
throw new ArgumentNullException(nameof(basePopulation));
}
if (currentIncumbent == null)
{
LoggingHelper.WriteLine(
VerbosityLevel.Warn,
"CMA-ES with focus on incumbent can only be executed if an incumbent exists, i.e. it is not possible to run it on its own.");
throw new ArgumentNullException(nameof(currentIncumbent));
}
// We do not reuse anything from potential old configurations, because old information may be
// outdated at the point a new phase is started.
var initialMean = PartialGenomeSearchPoint.CreateFromGenome(
currentIncumbent.IncumbentGenome,
this.ParameterTree,
this.StrategyConfiguration.MinimumDomainSize).Values;
var cmaEsConfiguration = new CmaEsConfiguration(
populationSize: basePopulation.CompetitiveCount,
initialDistributionMean: initialMean,
initialStepSize: this.StrategyConfiguration.InitialStepSize);
this._cmaEsRunner = this.CreateCmaEsRunner(evaluationBase: currentIncumbent.IncumbentGenome);
this._cmaEsRunner.Initialize(cmaEsConfiguration, this.CreateTerminationCriteria());
}
public void RosenbrockThirtyWithParamsTest()
{
double[] expected = new[] { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
CmaEs target = new CmaEs((x, o) =>
{
int dimension = x.Length;
double res = 0;
for (int i = 0; i < (dimension - 1); ++i)
{
res += Math.Pow(1 - x[i], 2) + 100.0 * Math.Pow(x[i + 1] - Math.Pow(x[i], 2), 2);
}
return(res);
},
initialParameters: Utilities.Randoms(expected.Length, null, null),
stopEvalsMultiple: 1e5);
double[] actual = target.Solve().BestParameters;
for (int i = 0; i < expected.Length; i++)
{
Assert.IsTrue(Math.Abs(expected[i] - actual[i]) < SMALL_VALUE);
}
}
/// <summary>
/// Initializes a new instance of the <see cref="CmaEsTest"/> class.
/// </summary>
public CmaEsTest()
{
this._runner = new CmaEs <SearchPoint>(this._sorter, this._searchPointFactory);
this._configuration = new CmaEsConfiguration(7, Vector <double> .Build.Dense(3), 0.1);
this._terminationCriteria = new List <ITerminationCriterion> {
new MaxIterations(3)
};
Randomizer.Configure(0);
}
/// <inheritdoc />
public override void UseStatusDump(IGeneticEngineering evaluationModel)
{
// The CMA-ES runner must be reinitialized for every phase in order to use the correct genome as base.
// Recreate the latest runner.
var strategyStatus = this.DeserializeStrategyStatusFile();
var originalIncumbent = strategyStatus.OriginalIncumbent;
if (originalIncumbent != null)
{
this._cmaEsRunner = this.CreateCmaEsRunner(evaluationBase: originalIncumbent);
}
base.UseStatusDump(evaluationModel);
}
/// <summary>
/// Initializes a new instance of the <see cref="LocalCovarianceMatrixAdaptationStrategy{TInstance, TResult}"/>
/// class.
/// </summary>
/// <param name="configuration">Options used for this instance.</param>
/// <param name="parameterTree">Provides the tunable parameters.</param>
/// <param name="genomeBuilder">Responsible for creation, modification and crossover of genomes.
/// Needs to be compatible with the given parameter tree and configuration.</param>
/// <param name="genomeSorter">
/// An <see cref="IActorRef" /> to a <see cref="GenomeSorter{TInstance,TResult}" />.
/// </param>
/// <param name="targetRunResultStorage">
/// An <see cref="IActorRef" /> to a <see cref="ResultStorageActor{TInstance,TResult}" />
/// which knows about all executed target algorithm runs and their results.
/// </param>
public LocalCovarianceMatrixAdaptationStrategy(
AlgorithmTunerConfiguration configuration,
ParameterTree parameterTree,
GenomeBuilder genomeBuilder,
IActorRef genomeSorter,
IActorRef targetRunResultStorage)
: base(configuration, parameterTree, genomeSorter, targetRunResultStorage)
{
this._genomeBuilder = genomeBuilder ?? throw new ArgumentNullException(nameof(genomeBuilder));
// Create a dummy search point factory to enable CMA-ES dumps.
this._searchPointFactory = vector => throw new InvalidOperationException("Called search point factory without initialization!");
this._cmaEsRunner = new CmaEs <PartialGenomeSearchPoint>(this.SearchPointSorter, this._searchPointFactory);
}
/// <summary>
/// Initializes a new instance of the
/// <see cref="GlobalCovarianceMatrixAdaptationStrategy{TInstance, TResult}"/> class.
/// </summary>
/// <param name="configuration">Options used for this instance.</param>
/// <param name="parameterTree">Provides the tunable parameters.</param>
/// <param name="genomeBuilder">Responsible for creation, modification and crossover of genomes.
/// Needs to be compatible with the given parameter tree and configuration.</param>
/// <param name="generationEvaluationActor">
/// An <see cref="IActorRef" /> to a <see cref="GenerationEvaluationActor{TTargetAlgorithm,TInstance,TResult}"/>.
/// </param>
/// <param name="targetRunResultStorage">
/// An <see cref="IActorRef" /> to a <see cref="ResultStorageActor{TInstance,TResult}" />
/// which knows about all executed target algorithm runs and their results.
/// </param>
public GlobalCovarianceMatrixAdaptationStrategy(
AlgorithmTunerConfiguration configuration,
ParameterTree parameterTree,
GenomeBuilder genomeBuilder,
IActorRef generationEvaluationActor,
IActorRef targetRunResultStorage)
: base(configuration, parameterTree, generationEvaluationActor, targetRunResultStorage)
{
if (genomeBuilder == null)
{
throw new ArgumentNullException(nameof(genomeBuilder));
}
ContinuizedGenomeSearchPoint.ObtainParameterBounds(parameterTree, out var lowerBounds, out var upperBounds);
this._searchPointFactory = vector =>
new ContinuizedGenomeSearchPoint(vector, parameterTree, genomeBuilder, lowerBounds, upperBounds);
this._cmaEsRunner = new CmaEs <ContinuizedGenomeSearchPoint>(this.SearchPointSorter, this._searchPointFactory);
}
public void SchwefelOneGenerationTest()
{
double[] expected = new[] { 420.9687, 420.9687, 420.9687, 420.9687, 420.9687, };
CmaEs newCMA = new CmaEs((x, o) =>
{
if ((x.Min() < -500.0) || (x.Max() > 500.0))
{
return(10000.0 * x.Length + x.Sum(xi => - xi * Math.Sin(Math.Sqrt(Math.Abs(xi)))));
}
else
{
return(418.9829 * x.Length + x.Sum(xi => - xi * Math.Sin(Math.Sqrt(Math.Abs(xi)))));
}
},
initialParameters: Utilities.Randoms(expected.Length, null, null));
Solution lastSolution;
int count = 0;
while (true)
{
lastSolution = newCMA.Solve(isContinueFromLastSolution: true, isOneGenerationOnly: true);
if (lastSolution.MinimizedFitness < SMALL_VALUE)
{
break;
}
count += lastSolution.LatestNumEvals;
}
Assert.IsTrue(count < 1e5);
for (int i = 0; i < expected.Length; i++)
{
Assert.IsTrue(Math.Abs(expected[i] - lastSolution.BestParameters[i]) < 1.0);
}
}
/// <inheritdoc />
protected override void InitializeContinuousOptimizer(
Population basePopulation,
IncumbentGenomeWrapper <TResult> currentIncumbent)
{
if (basePopulation == null)
{
throw new ArgumentNullException(nameof(basePopulation));
}
// We do not reuse anything from potential old configurations, because old information may be
// outdated at the point a new phase is started.
var initialMean = currentIncumbent != null
? ContinuizedGenomeSearchPoint.CreateFromGenome(currentIncumbent.IncumbentGenome, this.ParameterTree).Values
: this.ComputeMeanOfCompetitivePopulationPart(basePopulation);
var cmaEsConfiguration = new CmaEsConfiguration(
populationSize: basePopulation.CompetitiveCount,
initialDistributionMean: initialMean,
initialStepSize: this.StrategyConfiguration.InitialStepSize);
this._cmaEsRunner = new CmaEs <ContinuizedGenomeSearchPoint>(this.SearchPointSorter, this._searchPointFactory);
this._cmaEsRunner.Initialize(cmaEsConfiguration, this.CreateTerminationCriteria());
}
public void SchwefelContinueFromLastTest()
{
int numTests = 10;
double[] expected = new[] { 420.9687, 420.9687, 420.9687, 420.9687, 420.9687, };
CmaEs newCMA = new CmaEs((x, o) =>
{
if (x.Min() < -500.0)
{
return(Math.Pow(Math.Abs(x.Min()), 4));
}
else if (x.Max() > 500.0)
{
return(Math.Pow(Math.Abs(x.Max()), 4));
}
else
{
return(418.9829 * x.Length + x.Sum(xi => - xi * Math.Sin(Math.Sqrt(Math.Abs(xi)))));
}
},
stopEvalsMultiple: 1e3,
stopFitnessMinimization: 1e-5,
initialParameters: Utilities.Randoms(expected.Length, null, null));
Stopwatch clock = new Stopwatch();
int count = 0;
List <TimeSpan> times = new List <TimeSpan>();
for (int j = 0; j < numTests; j++)
{
clock.Restart();
var actual = newCMA.Solve(isContinueFromLastSolution: true);
clock.Stop();
times.Add(clock.Elapsed);
bool isAllOK = true;
for (int i = 0; i < expected.Length; i++)
{
if (Math.Abs(expected[i] - actual.BestParameters[i]) > 1.0)
{
isAllOK = false;
}
}
if (isAllOK)
{
count++;
}
}
double percRight = count * 100.0 / numTests;
double averageTime = times.Average(x => x.Seconds);
Assert.IsTrue(percRight >= 90.0);
Assert.IsTrue(averageTime < 2.0);
}
