#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 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 HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.Binary {
[StorableClass]
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(bool deserializing) : base(deserializing) { }
[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) { }
}
}