source: branches/2520_PersistenceReintegration/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/HillClimber.cs @ 16453

Last change on this file since 16453 was 16453, checked in by jkarder, 10 months ago

#2520: updated year of copyrights

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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 * and the BEACON Center for the Study of Evolution in Action.
5 *
6 * This file is part of HeuristicLab.
7 *
8 * HeuristicLab is free software: you can redistribute it and/or modify
9 * it under the terms of the GNU General Public License as published by
10 * the Free Software Foundation, either version 3 of the License, or
11 * (at your option) any later version.
12 *
13 * HeuristicLab is distributed in the hope that it will be useful,
14 * but WITHOUT ANY WARRANTY; without even the implied warranty of
15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
16 * GNU General Public License for more details.
17 *
18 * You should have received a copy of the GNU General Public License
19 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
20 */
21#endregion
22
23using System;
24using System.Collections.Generic;
25using System.Linq;
26using System.Threading;
27using HeuristicLab.Common;
28using HeuristicLab.Core;
29using HeuristicLab.Data;
30using HeuristicLab.Encodings.BinaryVectorEncoding;
31using HeuristicLab.Optimization;
32using HeuristicLab.Parameters;
33using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
34using HeuristicLab.Problems.Binary;
35using HeuristicLab.Random;
36
37
38namespace HeuristicLab.Algorithms.ParameterlessPopulationPyramid {
39  // This code is based off the publication
40  // B. W. Goldman and W. F. Punch, "Parameter-less Population Pyramid," GECCO, pp. 785–792, 2014
41  // and the original source code in C++11 available from: https://github.com/brianwgoldman/Parameter-less_Population_Pyramid
42  [Item("Hill Climber (HC)", "Binary Hill Climber.")]
43  [StorableClass]
44  [Creatable(CreatableAttribute.Categories.SingleSolutionAlgorithms, Priority = 150)]
45  public class HillClimber : BasicAlgorithm {
46    [Storable]
47    private IRandom random;
48
49    private const string IterationsParameterName = "Iterations";
50    private const string BestQualityResultName = "Best quality";
51    private const string IterationsResultName = "Iterations";
52
53    public override Type ProblemType {
54      get { return typeof(BinaryProblem); }
55    }
56
57    public override bool SupportsPause { get { return false; } }
58
59    public new BinaryProblem Problem {
60      get { return (BinaryProblem)base.Problem; }
61      set { base.Problem = value; }
62    }
63
64    public IFixedValueParameter<IntValue> IterationsParameter {
65      get { return (IFixedValueParameter<IntValue>)Parameters[IterationsParameterName]; }
66    }
67
68    public int Iterations {
69      get { return IterationsParameter.Value.Value; }
70      set { IterationsParameter.Value.Value = value; }
71    }
72
73    #region ResultsProperties
74    private double ResultsBestQuality {
75      get { return ((DoubleValue)Results[BestQualityResultName].Value).Value; }
76      set { ((DoubleValue)Results[BestQualityResultName].Value).Value = value; }
77    }
78    private int ResultsIterations {
79      get { return ((IntValue)Results[IterationsResultName].Value).Value; }
80      set { ((IntValue)Results[IterationsResultName].Value).Value = value; }
81    }
82    #endregion
83
84    [StorableConstructor]
85    protected HillClimber(bool deserializing) : base(deserializing) { }
86    protected HillClimber(HillClimber original, Cloner cloner)
87      : base(original, cloner) {
88    }
89    public override IDeepCloneable Clone(Cloner cloner) {
90      return new HillClimber(this, cloner);
91    }
92
93    public HillClimber()
94      : base() {
95      random = new MersenneTwister();
96      Parameters.Add(new FixedValueParameter<IntValue>(IterationsParameterName, "", new IntValue(100)));
97    }
98
99
100    protected override void Initialize(CancellationToken cancellationToken) {
101      Results.Add(new Result(BestQualityResultName, new DoubleValue(double.NaN)));
102      Results.Add(new Result(IterationsResultName, new IntValue(0)));
103      base.Initialize(cancellationToken);
104    }
105    protected override void Run(CancellationToken cancellationToken) {
106      while (ResultsIterations < Iterations) {
107        cancellationToken.ThrowIfCancellationRequested();
108
109        var solution = new BinaryVector(Problem.Length);
110        for (int i = 0; i < solution.Length; i++) {
111          solution[i] = random.Next(2) == 1;
112        }
113
114        var fitness = Problem.Evaluate(solution, random);
115
116        fitness = ImproveToLocalOptimum(Problem, solution, fitness, random);
117        if (double.IsNaN(ResultsBestQuality) || Problem.IsBetter(fitness, ResultsBestQuality)) {
118          ResultsBestQuality = fitness;
119        }
120
121        ResultsIterations++;
122      }
123    }
124    // In the GECCO paper, Section 2.1
125    public static double ImproveToLocalOptimum(BinaryProblem problem, BinaryVector solution, double fitness, IRandom rand) {
126      var tried = new HashSet<int>();
127      do {
128        var options = Enumerable.Range(0, solution.Length).Shuffle(rand);
129        foreach (var option in options) {
130          if (tried.Contains(option)) continue;
131          solution[option] = !solution[option];
132          double newFitness = problem.Evaluate(solution, rand);
133          if (problem.IsBetter(newFitness, fitness)) {
134            fitness = newFitness;
135            tried.Clear();
136          } else {
137            solution[option] = !solution[option];
138          }
139          tried.Add(option);
140        }
141      } while (tried.Count != solution.Length);
142      return fitness;
143    }
144  }
145}
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