[9565] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2012 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;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Parameters;
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| 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 30 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 31 |
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| 32 | namespace HeuristicLab.Problems.Robocode {
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| 33 | /// <summary>
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| 34 | /// Takes two parent individuals P0 and P1 each. Selects a random node N0 of P0 and a random node N1 of P1.
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| 35 | /// And replaces the branch with root0 N0 in P0 with N1 from P1 if the tree-size limits are not violated.
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| 36 | /// When recombination with N0 and N1 would create a tree that is too large or invalid the operator randomly selects new N0 and N1
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| 37 | /// until a valid configuration is found.
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| 38 | /// </summary>
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| 39 | [Item("RobocodeCrossover", "An operator which performs subtree swapping crossover.")]
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| 40 | [StorableClass]
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| 41 | public class RobocodeCrossover : SymbolicExpressionTreeCrossover, ISymbolicExpressionTreeSizeConstraintOperator {
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| 42 | private const string InternalCrossoverPointProbabilityParameterName = "InternalCrossoverPointProbability";
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| 43 | private const string MaximumSymbolicExpressionTreeLengthParameterName = "MaximumSymbolicExpressionTreeLength";
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| 44 | private const string MaximumSymbolicExpressionTreeDepthParameterName = "MaximumSymbolicExpressionTreeDepth";
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| 45 |
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| 46 | #region Parameter Properties
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| 47 | public IValueLookupParameter<PercentValue> InternalCrossoverPointProbabilityParameter {
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| 48 | get { return (IValueLookupParameter<PercentValue>)Parameters[InternalCrossoverPointProbabilityParameterName]; }
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| 49 | }
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| 50 | public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeLengthParameter {
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| 51 | get { return (IValueLookupParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeLengthParameterName]; }
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| 52 | }
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| 53 | public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeDepthParameter {
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| 54 | get { return (IValueLookupParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeDepthParameterName]; }
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| 55 | }
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| 56 | #endregion
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| 57 | #region Properties
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| 58 | public PercentValue InternalCrossoverPointProbability {
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| 59 | get { return InternalCrossoverPointProbabilityParameter.ActualValue; }
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| 60 | }
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| 61 | public IntValue MaximumSymbolicExpressionTreeLength {
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| 62 | get { return MaximumSymbolicExpressionTreeLengthParameter.ActualValue; }
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| 63 | }
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| 64 | public IntValue MaximumSymbolicExpressionTreeDepth {
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| 65 | get { return MaximumSymbolicExpressionTreeDepthParameter.ActualValue; }
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| 66 | }
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| 67 | #endregion
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| 68 | [StorableConstructor]
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| 69 | protected RobocodeCrossover(bool deserializing) : base(deserializing) { }
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| 70 | protected RobocodeCrossover(RobocodeCrossover original, Cloner cloner) : base(original, cloner) { }
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| 71 | public RobocodeCrossover()
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| 72 | : base() {
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| 73 | Parameters.Add(new ValueLookupParameter<IntValue>(MaximumSymbolicExpressionTreeLengthParameterName, "The maximal length (number of nodes) of the symbolic expression tree."));
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| 74 | Parameters.Add(new ValueLookupParameter<IntValue>(MaximumSymbolicExpressionTreeDepthParameterName, "The maximal depth of the symbolic expression tree (a tree with one node has depth = 0)."));
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| 75 | Parameters.Add(new ValueLookupParameter<PercentValue>(InternalCrossoverPointProbabilityParameterName, "The probability to select an internal crossover point (instead of a leaf node).", new PercentValue(0.9)));
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| 76 | }
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| 77 |
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| 78 | public override IDeepCloneable Clone(Cloner cloner) {
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| 79 | return new RobocodeCrossover(this, cloner);
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| 80 | }
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| 81 |
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| 82 | public override ISymbolicExpressionTree Crossover(IRandom random,
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| 83 | ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
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| 84 | return Cross(random, parent0, parent1, InternalCrossoverPointProbability.Value,
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| 85 | MaximumSymbolicExpressionTreeLength.Value, MaximumSymbolicExpressionTreeDepth.Value);
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| 86 | }
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| 87 |
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| 88 | public static ISymbolicExpressionTree Cross(IRandom random,
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| 89 | ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1,
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| 90 | double internalCrossoverPointProbability, int maxTreeLength, int maxTreeDepth) {
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| 91 | // select a random crossover point in the first parent
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| 92 | CutPoint crossoverPoint0;
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| 93 | SelectCrossoverPoint(random, parent0, internalCrossoverPointProbability, maxTreeLength, maxTreeDepth, out crossoverPoint0);
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| 94 |
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| 95 | int childLength = crossoverPoint0.Child != null ? crossoverPoint0.Child.GetLength() : 0;
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| 96 | // calculate the max length and depth that the inserted branch can have
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| 97 | int maxInsertedBranchLength = maxTreeLength - (parent0.Length - childLength);
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| 98 | int maxInsertedBranchDepth = maxTreeDepth - parent0.Root.GetBranchLevel(crossoverPoint0.Parent);
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| 99 |
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| 100 | List<ISymbolicExpressionTreeNode> allowedBranches = new List<ISymbolicExpressionTreeNode>();
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| 101 | parent1.Root.ForEachNodePostfix((n) => {
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| 102 | if (n.GetLength() <= maxInsertedBranchLength &&
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| 103 | n.GetDepth() <= maxInsertedBranchDepth && crossoverPoint0.IsMatchingPointType(n))
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| 104 | allowedBranches.Add(n);
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| 105 | });
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| 106 | // empty branch
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| 107 | if (crossoverPoint0.IsMatchingPointType(null)) allowedBranches.Add(null);
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| 108 |
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| 109 | if (allowedBranches.Count == 0) {
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| 110 | return parent0;
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| 111 | } else {
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| 112 | var selectedBranch = SelectRandomBranch(random, allowedBranches, internalCrossoverPointProbability);
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| 113 |
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| 114 | if (crossoverPoint0.Child != null) {
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| 115 | // manipulate the tree of parent0 in place
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| 116 | // replace the branch in tree0 with the selected branch from tree1
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| 117 | crossoverPoint0.Parent.RemoveSubtree(crossoverPoint0.ChildIndex);
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| 118 | if (selectedBranch != null) {
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| 119 | crossoverPoint0.Parent.InsertSubtree(crossoverPoint0.ChildIndex, selectedBranch);
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| 120 | }
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| 121 | } else {
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| 122 | // child is null (additional child should be added under the parent)
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| 123 | if (selectedBranch != null) {
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| 124 | crossoverPoint0.Parent.AddSubtree(selectedBranch);
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| 125 | }
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| 126 | }
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| 127 | return parent0;
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| 128 | }
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| 129 | }
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| 130 |
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| 131 | private static void SelectCrossoverPoint(IRandom random, ISymbolicExpressionTree parent0, double internalNodeProbability, int maxBranchLength, int maxBranchDepth, out CutPoint crossoverPoint) {
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| 132 | if (internalNodeProbability < 0.0 || internalNodeProbability > 1.0) throw new ArgumentException("internalNodeProbability");
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| 133 | List<CutPoint> internalCrossoverPoints = new List<CutPoint>();
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| 134 | List<CutPoint> leafCrossoverPoints = new List<CutPoint>();
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| 135 | parent0.Root.ForEachNodePostfix((n) => {
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| 136 | if (n.SubtreeCount > 0 && n != parent0.Root) {
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| 137 | foreach (var child in n.Subtrees) {
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| 138 | if (child.GetLength() <= maxBranchLength &&
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| 139 | child.GetDepth() <= maxBranchDepth) {
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| 140 | if (child.SubtreeCount > 0)
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| 141 | internalCrossoverPoints.Add(new CutPoint(n, child));
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| 142 | else
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| 143 | leafCrossoverPoints.Add(new CutPoint(n, child));
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| 144 | }
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| 145 | }
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| 146 |
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| 147 | // add one additional extension point if the number of sub trees for the symbol is not full
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| 148 | if (n.SubtreeCount < n.Grammar.GetMaximumSubtreeCount(n.Symbol)) {
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| 149 | // empty extension point
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| 150 | internalCrossoverPoints.Add(new CutPoint(n, n.SubtreeCount));
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| 151 | }
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| 152 | }
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| 153 | }
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| 154 | );
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| 155 |
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| 156 | if (random.NextDouble() < internalNodeProbability) {
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| 157 | // select from internal node if possible
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| 158 | if (internalCrossoverPoints.Count > 0) {
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| 159 | // select internal crossover point or leaf
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| 160 | crossoverPoint = internalCrossoverPoints[random.Next(internalCrossoverPoints.Count)];
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| 161 | } else {
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| 162 | // otherwise select external node
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| 163 | crossoverPoint = leafCrossoverPoints[random.Next(leafCrossoverPoints.Count)];
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| 164 | }
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| 165 | } else if (leafCrossoverPoints.Count > 0) {
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| 166 | // select from leaf crossover point if possible
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| 167 | crossoverPoint = leafCrossoverPoints[random.Next(leafCrossoverPoints.Count)];
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| 168 | } else {
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| 169 | // otherwise select internal crossover point
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| 170 | crossoverPoint = internalCrossoverPoints[random.Next(internalCrossoverPoints.Count)];
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| 171 | }
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| 172 | }
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| 173 |
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| 174 | private static ISymbolicExpressionTreeNode SelectRandomBranch(IRandom random, IEnumerable<ISymbolicExpressionTreeNode> branches, double internalNodeProbability) {
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| 175 | if (internalNodeProbability < 0.0 || internalNodeProbability > 1.0) throw new ArgumentException("internalNodeProbability");
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| 176 | List<ISymbolicExpressionTreeNode> allowedInternalBranches;
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| 177 | List<ISymbolicExpressionTreeNode> allowedLeafBranches;
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| 178 | if (random.NextDouble() < internalNodeProbability) {
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| 179 | // select internal node if possible
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| 180 | allowedInternalBranches = (from branch in branches
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| 181 | where branch != null && branch.SubtreeCount > 0
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| 182 | select branch).ToList();
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| 183 | if (allowedInternalBranches.Count > 0) {
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| 184 | return allowedInternalBranches.SelectRandom(random);
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| 185 | } else {
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| 186 | // no internal nodes allowed => select leaf nodes
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| 187 | allowedLeafBranches = (from branch in branches
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| 188 | where branch == null || branch.SubtreeCount == 0
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| 189 | select branch).ToList();
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| 190 | return allowedLeafBranches.SelectRandom(random);
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| 191 | }
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| 192 | } else {
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| 193 | // select leaf node if possible
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| 194 | allowedLeafBranches = (from branch in branches
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| 195 | where branch == null || branch.SubtreeCount == 0
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| 196 | select branch).ToList();
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| 197 | if (allowedLeafBranches.Count > 0) {
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| 198 | return allowedLeafBranches.SelectRandom(random);
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| 199 | } else {
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| 200 | allowedInternalBranches = (from branch in branches
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| 201 | where branch != null && branch.SubtreeCount > 0
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| 202 | select branch).ToList();
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| 203 | return allowedInternalBranches.SelectRandom(random);
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| 204 | }
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| 205 | }
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| 206 | }
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| 207 | }
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| 208 | }
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