#region License Information
/* HeuristicLab
* Copyright (C) 2002-2018 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 System;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.TestFunctions {
///
/// The Ackley function as described in Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg
/// is highly multimodal. It has a single global minimum at the origin with value 0.
///
/// Returns false as the Ackley function is a minimization problem.
///
public override bool Maximization {
get { return false; }
}
///
/// Gets the optimum function value (0).
///
public override double BestKnownQuality {
get { return 0; }
}
///
/// Gets the lower and upper bound of the function.
///
public override DoubleMatrix Bounds {
get { return new DoubleMatrix(new double[,] { { -32.768, 32.768 } }); }
}
///
/// Gets the minimum problem size (1).
///
public override int MinimumProblemSize {
get { return 1; }
}
///
/// Gets the (theoretical) maximum problem size (2^31 - 1).
///
public override int MaximumProblemSize {
get { return int.MaxValue; }
}
[StorableConstructor]
protected AckleyEvaluator(bool deserializing) : base(deserializing) { }
protected AckleyEvaluator(AckleyEvaluator original, Cloner cloner) : base(original, cloner) { }
public AckleyEvaluator() : base() { }
public override IDeepCloneable Clone(Cloner cloner) {
return new AckleyEvaluator(this, cloner);
}
public override RealVector GetBestKnownSolution(int dimension) {
return new RealVector(dimension);
}
///
/// Evaluates the Ackley function for a specific .
///
/// N-dimensional point for which the test function should be evaluated.
/// The result value of the Ackley function at the given point.
public static double Apply(RealVector point) {
double result;
double val;
val = 0;
for (int i = 0; i < point.Length; i++)
val += point[i] * point[i];
val /= point.Length;
val = -0.2 * Math.Sqrt(val);
result = 20 - 20 * Math.Exp(val);
val = 0;
for (int i = 0; i < point.Length; i++)
val += Math.Cos(2 * Math.PI * point[i]);
val /= point.Length;
result += Math.E - Math.Exp(val);
return (result);
}
///
/// Evaluates the test function for a specific .
///
/// Calls .
/// N-dimensional point for which the test function should be evaluated.
/// The result value of the Ackley function at the given point.
public override double Evaluate(RealVector point) {
return Apply(point);
}
}
}