#region License Information /* HeuristicLab * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * and the BEACON Center for the Study of Evolution in Action. * * 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.BinaryVectorEncoding; using HeuristicLab.Optimization; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Algorithms.ParameterlessPopulationPyramid { // This code is based off the publication // B. W. Goldman and W. F. Punch, "Parameter-less Population Pyramid," GECCO, pp. 785–792, 2014 // and the original source code in C++11 available from: https://github.com/brianwgoldman/Parameter-less_Population_Pyramid internal sealed class EvaluationTracker : SingleObjectiveProblem { private readonly ISingleObjectiveProblem problem; private int maxEvaluations; #region Properties public double BestQuality { get; private set; } public int Evaluations { get; private set; } public int BestFoundOnEvaluation { get; private set; } public BinaryVector BestSolution { get; private set; } public new BinaryVectorEncoding Encoding { get { return problem.Encoding; } } #endregion [StorableConstructor] private EvaluationTracker(bool deserializing) : base(deserializing) { } private EvaluationTracker(EvaluationTracker original, Cloner cloner) : base(original, cloner) { problem = cloner.Clone(original.problem); maxEvaluations = original.maxEvaluations; BestQuality = original.BestQuality; Evaluations = original.Evaluations; BestFoundOnEvaluation = original.BestFoundOnEvaluation; BestSolution = cloner.Clone(BestSolution); } public override IDeepCloneable Clone(Cloner cloner) { return new EvaluationTracker(this, cloner); } public EvaluationTracker(ISingleObjectiveProblem problem, int maxEvaluations) { this.problem = problem; this.maxEvaluations = maxEvaluations; BestSolution = new BinaryVector(problem.Encoding.Length); BestQuality = double.NaN; Evaluations = 0; BestFoundOnEvaluation = 0; if (Parameters.ContainsKey("Maximization")) Parameters.Remove("Maximization"); Parameters.Add(new FixedValueParameter("Maximization", "Set to false if the problem should be minimized.", (BoolValue)new BoolValue(Maximization).AsReadOnly()) { Hidden = true }); } public override double Evaluate(BinaryVector vector, IRandom random) { if (Evaluations >= maxEvaluations) throw new OperationCanceledException("Maximum Evaluation Limit Reached"); Evaluations++; double fitness = problem.Evaluate(vector, random); if (double.IsNaN(BestQuality) || problem.IsBetter(fitness, BestQuality)) { BestQuality = fitness; BestSolution = (BinaryVector)vector.Clone(); BestFoundOnEvaluation = Evaluations; } return fitness; } public override bool Maximization { get { if (problem == null) return false; return problem.Maximization; } } public override bool IsBetter(double quality, double bestQuality) { return problem.IsBetter(quality, bestQuality); } } }