#region License Information
/* HeuristicLab
* Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Random;
using System;
namespace HeuristicLab.Encodings.RealVectorEncoding {
[Item("CovarianceMatrixMutator", "Mutates the solution vector according to the CMA-ES scheme.")]
[StorableClass]
public sealed class CMAMutator : SingleSuccessorOperator, IStochasticOperator, IRealVectorOperator, ICMAESManipulator, IIterationBasedOperator {
private const int MaxTries = 1000;
public Type CMAType {
get { return typeof(CMAParameters); }
}
#region Parameter Properties
public ILookupParameter RandomParameter {
get { return (ILookupParameter)Parameters["Random"]; }
}
public ILookupParameter IterationsParameter {
get { return (ILookupParameter)Parameters["Iterations"]; }
}
public IValueLookupParameter MaximumIterationsParameter {
get { return (IValueLookupParameter)Parameters["MaximumIterations"]; }
}
public ILookupParameter RealVectorParameter {
get { return (ILookupParameter)Parameters["RealVector"]; }
}
public IValueLookupParameter BoundsParameter {
get { return (IValueLookupParameter)Parameters["Bounds"]; }
}
public ILookupParameter StrategyParametersParameter {
get { return (ILookupParameter)Parameters["StrategyParameters"]; }
}
#endregion
[StorableConstructor]
private CMAMutator(bool deserializing) : base(deserializing) { }
private CMAMutator(CMAMutator original, Cloner cloner) : base(original, cloner) { }
public CMAMutator()
: base() {
Parameters.Add(new LookupParameter("Random", "The random number generator to use."));
Parameters.Add(new LookupParameter("Iterations", "The current iteration that is being processed."));
Parameters.Add(new ValueLookupParameter("MaximumIterations", "The maximum number of iterations to be processed."));
Parameters.Add(new LookupParameter("RealVector", "The solution vector of real values."));
Parameters.Add(new ValueLookupParameter("Bounds", "The bounds for the dimensions."));
Parameters.Add(new LookupParameter("StrategyParameters", "The CMA-ES strategy parameters used for mutation."));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new CMAMutator(this, cloner);
}
public override IOperation Apply() {
var random = RandomParameter.ActualValue;
var arx = RealVectorParameter.ActualValue;
var sp = StrategyParametersParameter.ActualValue;
var iterations = IterationsParameter.ActualValue.Value;
var initialIterations = sp.InitialIterations.Value;
var bounds = BoundsParameter.ActualValue;
var nd = new NormalDistributedRandom(random, 0, 1);
var copy = (RealVector)arx.Clone();
int tries;
if (initialIterations > iterations) {
for (int i = 0; i < arx.Length; i++) {
tries = 0;
do {
tries++;
arx[i] = copy[i] + sp.Sigma.Value * sp.D[i] * nd.NextDouble();
} while ((bounds[i % bounds.Rows, 0] > arx[i] || arx[i] > bounds[i % bounds.Rows, 1]) && tries < MaxTries);
}
} else {
bool inRange;
var B = sp.B;
tries = 0;
do {
tries++;
inRange = true;
var artmp = new double[arx.Length];
for (int i = 0; i < arx.Length; ++i)
artmp[i] = sp.D[i] * nd.NextDouble();
for (int i = 0; i < arx.Length; i++) {
var sum = 0.0;
for (int j = 0; j < arx.Length; j++)
sum += B[i, j] * artmp[j];
arx[i] = copy[i] + sp.Sigma.Value * sum; // m + sig * Normal(0,C)
if (bounds[i % bounds.Rows, 0] > arx[i] || arx[i] > bounds[i % bounds.Rows, 1])
inRange = false;
}
} while (!inRange && tries < MaxTries);
}
return base.Apply();
}
}
}