[7523] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[16077] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[7523] | 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;
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[15490] | 23 | using System.Linq;
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[7523] | 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.IntegerVectorEncoding;
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| 28 | using HeuristicLab.Parameters;
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| 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[15490] | 30 | using HeuristicLab.Random;
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[7523] | 31 |
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| 32 | namespace HeuristicLab.Problems.GeneralizedQuadraticAssignment {
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| 33 | [Item("CordeauCrossover", "The merge procedure that is described in Cordeau, J.-F., Gaudioso, M., Laporte, G., Moccia, L. 2006. A memetic heuristic for the generalized quadratic assignment problem. INFORMS Journal on Computing, 18, pp. 433–443.")]
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| 34 | [StorableClass]
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| 35 | public class CordeauCrossover : GQAPCrossover,
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[15504] | 36 | IQualitiesAwareGQAPOperator, IProblemInstanceAwareGQAPOperator {
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[15506] | 37 |
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[7523] | 38 | public IScopeTreeLookupParameter<DoubleValue> QualityParameter {
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| 39 | get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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| 40 | }
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[15504] | 41 | public IScopeTreeLookupParameter<Evaluation> EvaluationParameter {
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| 42 | get { return (IScopeTreeLookupParameter<Evaluation>)Parameters["Evaluation"]; }
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[7523] | 43 | }
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[15490] | 44 | public ILookupParameter<IntValue> EvaluatedSolutionsParameter {
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| 45 | get { return (ILookupParameter<IntValue>)Parameters["EvaluatedSolutions"]; }
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| 46 | }
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[7523] | 47 |
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| 48 | [StorableConstructor]
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| 49 | protected CordeauCrossover(bool deserializing) : base(deserializing) { }
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| 50 | protected CordeauCrossover(CordeauCrossover original, Cloner cloner)
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| 51 | : base(original, cloner) {
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| 52 | }
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| 53 | public CordeauCrossover()
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| 54 | : base() {
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[15504] | 55 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The quality of the parents", 1));
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| 56 | Parameters.Add(new ScopeTreeLookupParameter<Evaluation>("Evaluation", GQAP.EvaluationDescription, 1));
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[15490] | 57 | Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of evaluated solutions."));
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[7523] | 58 | }
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| 59 |
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| 60 | public override IDeepCloneable Clone(Cloner cloner) {
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| 61 | return new CordeauCrossover(this, cloner);
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| 62 | }
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| 63 |
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[15506] | 64 | public static IntegerVector Apply(IRandom random,
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[7523] | 65 | IntegerVector parent1, DoubleValue quality1,
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| 66 | IntegerVector parent2, DoubleValue quality2,
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[15504] | 67 | GQAPInstance problemInstance, IntValue evaluatedSolutions) {
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| 68 | var distances = problemInstance.Distances;
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| 69 | var capacities = problemInstance.Capacities;
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| 70 | var demands = problemInstance.Demands;
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| 71 |
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[15490] | 72 | var medianDistances = Enumerable.Range(0, distances.Rows).Select(x => distances.GetRow(x).Median()).ToArray();
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| 73 |
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[7523] | 74 | int m = capacities.Length;
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| 75 | int n = demands.Length;
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[15490] | 76 |
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| 77 | bool onefound = false;
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[15506] | 78 | double fbest, fbestAttempt = double.MaxValue;
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[15490] | 79 | IntegerVector bestAttempt = null;
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| 80 | IntegerVector result = null;
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[7523] | 81 |
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| 82 | fbest = quality1.Value;
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[15506] | 83 | if (quality1.Value > quality2.Value) {
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[7523] | 84 | var temp = parent1;
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| 85 | parent1 = parent2;
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| 86 | parent2 = temp;
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| 87 | fbest = quality2.Value;
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| 88 | }
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| 89 | var cap = new double[m];
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[15490] | 90 | for (var i = 0; i < m; i++) {
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| 91 | int unassigned;
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| 92 | Array.Clear(cap, 0, m);
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| 93 | var child = Merge(parent1, parent2, distances, demands, medianDistances, m, n, i, cap, out unassigned);
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| 94 | if (unassigned > 0)
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| 95 | TryRandomAssignment(random, demands, capacities, m, n, cap, child, ref unassigned);
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| 96 | if (unassigned == 0) {
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[15504] | 97 | var childFit = problemInstance.ToSingleObjective(problemInstance.Evaluate(child));
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[15490] | 98 | evaluatedSolutions.Value++;
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[15506] | 99 | if (childFit <= fbest) {
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[15490] | 100 | fbest = childFit;
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| 101 | result = child;
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| 102 | onefound = true;
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[7523] | 103 | }
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[15506] | 104 | if (!onefound && fbestAttempt > childFit) {
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[15490] | 105 | bestAttempt = child;
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| 106 | fbestAttempt = childFit;
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[7523] | 107 | }
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| 108 | }
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| 109 | }
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[15490] | 110 |
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| 111 | if (!onefound) {
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| 112 | var i = random.Next(m);
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| 113 | int unassigned;
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| 114 | Array.Clear(cap, 0, m);
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| 115 | var child = Merge(parent1, parent2, distances, demands, medianDistances, m, n, i, cap, out unassigned);
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| 116 | RandomAssignment(random, demands, capacities, m, n, cap, child, ref unassigned);
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| 117 |
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[15504] | 118 | var childFit = problemInstance.ToSingleObjective(problemInstance.Evaluate(child));
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[15490] | 119 | evaluatedSolutions.Value++;
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| 120 | if (childFit < fbest) {
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| 121 | fbest = childFit;
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| 122 | result = child;
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| 123 | onefound = true;
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[7523] | 124 | }
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[15490] | 125 |
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[15506] | 126 | if (!onefound && fbestAttempt > childFit) {
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[15490] | 127 | bestAttempt = child;
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| 128 | fbestAttempt = childFit;
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[7523] | 129 | }
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[15490] | 130 | }
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[7523] | 131 | /*if (tabufix(&son, 0.5 * sqrt(n * m), round(n * m * log10(n)), &tabufix_it)) {
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| 132 | solution_cost(&son);
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| 133 | if (son.cost < fbest) {
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| 134 | fbest = son.cost;
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| 135 | *sptr = son;
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| 136 | onefound = TRUE;
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| 137 | merge_fixed++;
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| 138 | }*/
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[15490] | 139 | return result ?? bestAttempt;
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| 140 | }
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| 141 |
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| 142 | private static IntegerVector Merge(IntegerVector p1, IntegerVector p2,
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| 143 | DoubleMatrix distances, DoubleArray demands, double[] mediana,
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| 144 | int m, int n, int i, double[] cap, out int unassigned) {
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| 145 | unassigned = n;
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| 146 | var child = new IntegerVector(n);
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| 147 | for (var k = 0; k < n; k++) {
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| 148 | child[k] = -1;
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| 149 | var ik1 = p1[k];
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| 150 | var ik2 = p2[k];
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| 151 | if (distances[i, ik1] < mediana[i]) {
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| 152 | child[k] = ik1;
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| 153 | cap[ik1] += demands[k];
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| 154 | unassigned--;
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| 155 | } else if (distances[i, ik2] > mediana[i]) {
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| 156 | child[k] = ik2;
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| 157 | cap[ik2] += demands[k];
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| 158 | unassigned--;
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| 159 | };
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[7523] | 160 | }
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[15490] | 161 | return child;
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[7523] | 162 | }
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| 163 |
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[15490] | 164 | private static bool TryRandomAssignment(IRandom random, DoubleArray demands, DoubleArray capacities, int m, int n, double[] cap, IntegerVector child, ref int unassigned) {
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| 165 | var unbiasedOrder = Enumerable.Range(0, n).Shuffle(random).ToList();
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| 166 | for (var idx = 0; idx < n; idx++) {
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| 167 | var k = unbiasedOrder[idx];
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| 168 | if (child[k] < 0) {
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| 169 | var feasibleInserts = Enumerable.Range(0, m)
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| 170 | .Select((v, i) => new { Pos = i, Slack = capacities[i] - cap[i] })
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| 171 | .Where(x => x.Slack >= demands[k]).ToList();
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| 172 | if (feasibleInserts.Count == 0) return false;
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| 173 | var j = feasibleInserts.SampleRandom(random).Pos;
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| 174 | child[k] = j;
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| 175 | cap[j] += demands[k];
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| 176 | unassigned--;
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| 177 | }
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| 178 | }
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| 179 | return true;
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| 180 | }
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| 181 |
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| 182 | private static void RandomAssignment(IRandom random, DoubleArray demands, DoubleArray capacities, int m, int n, double[] cap, IntegerVector child, ref int unassigned) {
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| 183 | for (var k = 0; k < n; k++) {
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| 184 | if (child[k] < 0) {
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| 185 | var j = random.Next(m);
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| 186 | child[k] = j;
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| 187 | cap[j] += demands[k];
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| 188 | unassigned--;
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| 189 | }
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| 190 | }
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| 191 | }
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| 192 |
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[15504] | 193 | protected override IntegerVector Cross(IRandom random, ItemArray<IntegerVector> parents,
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| 194 | GQAPInstance problemInstance) {
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[7523] | 195 | if (parents == null) throw new ArgumentNullException("parents");
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| 196 | if (parents.Length != 2) throw new ArgumentException(Name + " works only with exactly two parents.");
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| 197 |
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| 198 | var qualities = QualityParameter.ActualValue;
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[15506] | 199 | return Apply(random,
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[7523] | 200 | parents[0], qualities[0],
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| 201 | parents[1], qualities[1],
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[15504] | 202 | problemInstance,
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| 203 | EvaluatedSolutionsParameter.ActualValue);
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[7523] | 204 | }
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| 205 | }
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| 206 | }
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