1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System.Collections.Generic;
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23 | using System.Linq;
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24 | using HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.Encodings.BinaryVectorEncoding;
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28 | using HeuristicLab.Optimization;
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29 | using HeuristicLab.Parameters;
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30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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31 | using HeuristicLab.Random;
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32 |
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33 |
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34 | namespace HeuristicLab.Algorithms.ParameterlessPopulationPyramid {
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35 | [Item("Hill Climber", "Test algorithm.")]
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36 | [StorableClass]
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37 | [Creatable("Parameterless Population Pyramid")]
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38 | // In the GECCO paper, Section 2.1
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39 | public class HillClimber : AlgorithmBase {
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40 | [Storable]
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41 | private IRandom random;
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42 |
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43 | private const string IterationsParameterName = "Iterations";
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44 |
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45 | public IFixedValueParameter<IntValue> IterationsParameter {
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46 | get { return (IFixedValueParameter<IntValue>)Parameters[IterationsParameterName]; }
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47 | }
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48 |
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49 | public int Iterations {
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50 | get { return IterationsParameter.Value.Value; }
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51 | set { IterationsParameter.Value.Value = value; }
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52 | }
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53 |
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54 | [StorableConstructor]
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55 | protected HillClimber(bool deserializing) : base(deserializing) { }
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56 | protected HillClimber(HillClimber original, Cloner cloner)
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57 | : base(original, cloner) {
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58 | }
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59 | public override IDeepCloneable Clone(Cloner cloner) {
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60 | return new HillClimber(this, cloner);
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61 | }
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62 |
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63 | public HillClimber()
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64 | : base() {
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65 | random = new MersenneTwister();
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66 | Parameters.Add(new FixedValueParameter<IntValue>(IterationsParameterName, "", new IntValue(100)));
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67 | }
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68 | protected override void Run() {
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69 | var BestQuality = new DoubleValue(double.NaN);
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70 | Results.Add(new Result("Best quality", BestQuality));
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71 | for (int iteration = 0; iteration < Iterations; iteration++) {
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72 | bool[] solution = new bool[Problem.Length];
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73 | for (int i = 0; i < solution.Length; i++) {
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74 | solution[i] = random.Next(2) == 1;
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75 | }
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76 |
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77 | var fitness = Problem.Evaluate(solution);
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78 |
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79 | fitness = ImproveToLocalOptimum(Problem, solution, fitness, random);
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80 | if (double.IsNaN(BestQuality.Value) || Problem.IsBetter(fitness, BestQuality.Value)) {
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81 | BestQuality.Value = fitness;
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82 | }
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83 | }
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84 | }
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85 |
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86 | public static double ImproveToLocalOptimum(IBinaryVectorProblem problem, bool[] solution, double fitness, IRandom rand) {
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87 | var tried = new HashSet<int>();
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88 | do {
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89 | var options = Enumerable.Range(0, solution.Length).Shuffle(rand);
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90 | foreach (var option in options) {
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91 | if (tried.Contains(option)) continue;
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92 | solution[option] = !solution[option];
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93 | double newFitness = problem.Evaluate(solution);
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94 | if (problem.IsBetter(newFitness, fitness)) {
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95 | fitness = newFitness;
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96 | tried.Clear();
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97 | } else {
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98 | solution[option] = !solution[option];
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99 | }
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100 | tried.Add(option);
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101 | }
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102 | } while (tried.Count != solution.Length);
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103 | return fitness;
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104 | }
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105 | }
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106 | }
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