#region License Information /* HeuristicLab * Copyright (C) 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 System.Linq; using HEAL.Attic; using HeuristicLab.Analysis; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Optimization; using HeuristicLab.Parameters; namespace HeuristicLab.Encodings.BinaryVectorEncoding { [StorableType("b64caac0-a23a-401a-bb7e-ffa3e22b80ea")] public abstract class BinaryVectorMultiObjectiveProblem : MultiObjectiveProblem { [Storable] protected ReferenceParameter DimensionRefParameter { get; private set; } [Storable] public IResult> BestParetoFrontResult { get; private set; } public int Dimension { get { return DimensionRefParameter.Value.Value; } protected set { DimensionRefParameter.Value.Value = value; } } protected ParetoFrontScatterPlot BestParetoFront { get => BestParetoFrontResult.Value; set => BestParetoFrontResult.Value = value; } [StorableConstructor] protected BinaryVectorMultiObjectiveProblem(StorableConstructorFlag _) : base(_) { } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { RegisterEventHandlers(); } protected BinaryVectorMultiObjectiveProblem(BinaryVectorMultiObjectiveProblem original, Cloner cloner) : base(original, cloner) { DimensionRefParameter = cloner.Clone(original.DimensionRefParameter); BestParetoFrontResult = cloner.Clone(original.BestParetoFrontResult); RegisterEventHandlers(); } protected BinaryVectorMultiObjectiveProblem() : this(new BinaryVectorEncoding() { Length = 10 }) { } protected BinaryVectorMultiObjectiveProblem(BinaryVectorEncoding encoding) : base(encoding) { EncodingParameter.ReadOnly = true; EvaluatorParameter.ReadOnly = true; Parameters.Add(DimensionRefParameter = new ReferenceParameter("Dimension", "The dimension of the binary vector problem.", Encoding.LengthParameter)); Results.Add(BestParetoFrontResult = new Result>("Best Pareto Front", "The best Pareto front found so far.")); Operators.Add(new HammingSimilarityCalculator()); Operators.Add(new PopulationSimilarityAnalyzer(Operators.OfType())); Parameterize(); RegisterEventHandlers(); } public override void Analyze(BinaryVector[] individuals, double[][] qualities, ResultCollection results, IRandom random) { base.Analyze(individuals, qualities, results, random); var fronts = DominationCalculator.CalculateAllParetoFrontsIndices(individuals, qualities, Maximization); var plot = new ParetoFrontScatterPlot(fronts, individuals, qualities, Objectives, BestKnownFront); BestParetoFront = plot; } protected override sealed void OnEvaluatorChanged() { throw new InvalidOperationException("Evaluator may not change!"); } protected override sealed void OnEncodingChanged() { throw new InvalidOperationException("Encoding may not change!"); } protected override void ParameterizeOperators() { base.ParameterizeOperators(); Parameterize(); } private void Parameterize() { foreach (var similarityCalculator in Operators.OfType()) { similarityCalculator.SolutionVariableName = Encoding.Name; similarityCalculator.QualityVariableName = Evaluator.QualitiesParameter.ActualName; } } private void RegisterEventHandlers() { IntValueParameterChangeHandler.Create(DimensionRefParameter, DimensionOnChanged); } protected virtual void DimensionOnChanged() { } } }