#region License Information /* HeuristicLab * Copyright (C) 2002-2019 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 HeuristicLab.Analysis; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.BinaryVectorEncoding; using HeuristicLab.Optimization; using HeuristicLab.Optimization.Operators; using HeuristicLab.Parameters; using HEAL.Attic; namespace HeuristicLab.Problems.Binary { [StorableType("2F6FEB34-BD19-47AF-9484-7F48565C0C43")] public abstract class BinaryProblem : SingleObjectiveBasicProblem { public virtual int Length { get { return Encoding.Length; } set { Encoding.Length = value; } } private IFixedValueParameter LengthParameter { get { return (IFixedValueParameter)Parameters["Length"]; } } [StorableConstructor] protected BinaryProblem(StorableConstructorFlag _) : base(_) { } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { RegisterEventHandlers(); } protected BinaryProblem(BinaryProblem original, Cloner cloner) : base(original, cloner) { RegisterEventHandlers(); } protected BinaryProblem() : base() { var lengthParameter = new FixedValueParameter("Length", "The length of the BinaryVector.", new IntValue(10)); Parameters.Add(lengthParameter); Encoding.LengthParameter = lengthParameter; Operators.Add(new HammingSimilarityCalculator()); Operators.Add(new QualitySimilarityCalculator()); Operators.Add(new PopulationSimilarityAnalyzer(Operators.OfType())); Parameterize(); RegisterEventHandlers(); } public virtual bool IsBetter(double quality, double bestQuality) { return (Maximization && quality > bestQuality || !Maximization && quality < bestQuality); } public abstract double Evaluate(BinaryVector vector, IRandom random); public sealed override double Evaluate(Individual individual, IRandom random) { return Evaluate(individual.BinaryVector(), random); } public override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) { base.Analyze(individuals, qualities, results, random); var orderedIndividuals = individuals.Zip(qualities, (i, q) => new { Individual = i, Quality = q }).OrderBy(z => z.Quality); var best = Maximization ? orderedIndividuals.Last().Individual : orderedIndividuals.First().Individual; if (!results.ContainsKey("Best Solution")) { results.Add(new Result("Best Solution", typeof(BinaryVector))); } results["Best Solution"].Value = (IItem)best.BinaryVector().Clone(); } protected override void OnEncodingChanged() { base.OnEncodingChanged(); Encoding.LengthParameter = LengthParameter; Parameterize(); } private void Parameterize() { foreach (var similarityCalculator in Operators.OfType()) { similarityCalculator.SolutionVariableName = Encoding.SolutionCreator.BinaryVectorParameter.ActualName; similarityCalculator.QualityVariableName = Evaluator.QualityParameter.ActualName; } } private void RegisterEventHandlers() { LengthParameter.Value.ValueChanged += LengthParameter_ValueChanged; } protected virtual void LengthParameter_ValueChanged(object sender, EventArgs e) { } } }