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 HeuristicLab.Common;
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23 | using HeuristicLab.Core;
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24 | using HeuristicLab.Optimization;
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25 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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26 |
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27 | namespace HeuristicLab.Problems.Programmable {
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28 | [Item("Multi-objective Problem Definition Script", "Script that defines the parameter vector and evaluates the solution for a programmable problem.")]
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29 | [StorableClass]
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30 | public class MultiObjectiveProblemDefinitionScript : ProblemDefinitionScript, IMultiObjectiveProblemDefinition, IStorableContent {
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31 | public string Filename { get; set; }
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32 |
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33 | protected override string CodeTemplate {
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34 | get {
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35 | return @"using System;
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36 | using System.Linq;
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37 | using System.Collections.Generic;
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38 | using HeuristicLab.Common;
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39 | using HeuristicLab.Core;
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40 | using HeuristicLab.Data;
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41 | using HeuristicLab.Encodings.PermutationEncoding;
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42 | using HeuristicLab.Optimization;
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43 | using HeuristicLab.Problems.Programmable;
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44 |
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45 | public class CustomProblemDefinition : CompiledProblemDefinition, IMultiObjectiveProblemDefinition {
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46 | public bool[] Maximization { get { return new [] { false, false }; } }
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47 |
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48 | public CustomProblemDefinition() {
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49 | // Define the solution encoding which can also consist of multiple vectors, examples below
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50 | // Encoding = new BinaryEncoding(""b"", length: 5);
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51 | // Encoding = new IntegerEncoding(""i"", lenght: 5, min: 2, max: 14, step: 4);
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52 | // Encoding = new RealEncoding(""r"", length: 5, min: -1.0, max: 1.0);
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53 | // Encoding = new PermutationEncoding(""P"", length: 5, type: PermutationTypes.Absolute);
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54 | // Encoding = new MultiEncoding()
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55 | // .AddBinaryVector(""b"", length: 5)
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56 | // .AddIntegerVector(""i"", length: 5, min: 2, max: 14, step: 4)
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57 | // .AddRealVector(""r"", length: 5, min: -1.0, max: 1.0)
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58 | // .AddPermutation(""P"", length: 5, type: PermutationTypes.Absolute)
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59 | ;
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60 | }
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61 |
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62 | public override void Initialize() {
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63 | // when the definition is created here you can initialize variables in the variable store
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64 | }
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65 |
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66 | public double[] Evaluate(IRandom random, Individual individual) {
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67 | var qualities = new [] { 0.0, 0.0 };
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68 | // use vars.yourVariable to access variables in the variable store i.e. yourVariable
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69 | // qualities = new [] { individual.RealVector(""r"").Sum(x => x * x), individual.RealVector(""r"").Sum(x => x * x * x) };
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70 | return qualities;
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71 | }
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72 |
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73 | public void Analyze(Individual[] individuals, double[][] qualities, ResultCollection results) {
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74 | // write or update results given the range of vectors and resulting qualities
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75 | // use e.g. vars.yourVariable to access variables in the variable store i.e. yourVariable
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76 | }
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77 |
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78 | public override IEnumerable<Individual> GetNeighbors(IRandom random, Individual individual) {
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79 | // Create new vectors, based on the given one that represent small changes
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80 | // This method is only called from move-based algorithms (LocalSearch, SimulatedAnnealing, etc.)
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81 | while (true) {
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82 | // this is not an infinite loop as only a finite amount of samples will be drawn
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83 | // it is possible to return a concrete amount of neighbors also
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84 | var neighbor = (Individual)individual.Clone();
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85 | //e.g. make a bit flip in a binary parameter
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86 | //var bIndex = random.Next(neighbor.BinaryVector(""b"").Length);
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87 | //neighbor.BinaryVector(""b"")[bIndex] = !neighbor.BinaryVector(""b"")[bIndex];
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88 | yield return neighbor;
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89 | }
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90 | }
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91 |
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92 | // implement further classes and methods
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93 | }";
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94 | }
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95 | }
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96 |
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97 | [StorableConstructor]
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98 | protected MultiObjectiveProblemDefinitionScript(bool deserializing) : base(deserializing) { }
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99 | protected MultiObjectiveProblemDefinitionScript(MultiObjectiveProblemDefinitionScript original, Cloner cloner)
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100 | : base(original, cloner) { }
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101 |
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102 | public MultiObjectiveProblemDefinitionScript() {
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103 | Code = CodeTemplate;
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104 | }
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105 |
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106 | public override IDeepCloneable Clone(Cloner cloner) {
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107 | return new MultiObjectiveProblemDefinitionScript(this, cloner);
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108 | }
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109 |
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110 | public new IMultiObjectiveProblemDefinition CompiledProblemDefinition {
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111 | get { return (IMultiObjectiveProblemDefinition)base.CompiledProblemDefinition; }
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112 | }
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113 |
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114 | bool[] IMultiObjectiveProblemDefinition.Maximization {
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115 | get { return CompiledProblemDefinition != null ? CompiledProblemDefinition.Maximization : new bool[0]; }
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116 | }
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117 |
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118 | double[] IMultiObjectiveProblemDefinition.Evaluate(Individual individual, IRandom random) {
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119 | return CompiledProblemDefinition.Evaluate(individual, random);
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120 | }
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121 |
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122 | void IMultiObjectiveProblemDefinition.Analyze(Individual[] individuals, double[][] qualities, ResultCollection results) {
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123 | CompiledProblemDefinition.Analyze(individuals, qualities, results);
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124 | }
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125 | }
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126 | }
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