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source: branches/Robocode/HeuristicLab.Problems.Robocode/Crossover/RobocodeCrossover.cs @ 10838

Last change on this file since 10838 was 9565, checked in by melkaref, 11 years ago

Robocode Plugin code without Mutation Operators

File size: 10.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
31
32namespace HeuristicLab.Problems.Robocode {
33  /// <summary>
34  /// Takes two parent individuals P0 and P1 each. Selects a random node N0 of P0 and a random node N1 of P1.
35  /// And replaces the branch with root0 N0 in P0 with N1 from P1 if the tree-size limits are not violated.
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
37  /// until a valid configuration is found.
38  /// </summary> 
39  [Item("RobocodeCrossover", "An operator which performs subtree swapping crossover.")]
40  [StorableClass]
41  public class RobocodeCrossover : SymbolicExpressionTreeCrossover, ISymbolicExpressionTreeSizeConstraintOperator {
42    private const string InternalCrossoverPointProbabilityParameterName = "InternalCrossoverPointProbability";
43    private const string MaximumSymbolicExpressionTreeLengthParameterName = "MaximumSymbolicExpressionTreeLength";
44    private const string MaximumSymbolicExpressionTreeDepthParameterName = "MaximumSymbolicExpressionTreeDepth";
45
46    #region Parameter Properties
47    public IValueLookupParameter<PercentValue> InternalCrossoverPointProbabilityParameter {
48      get { return (IValueLookupParameter<PercentValue>)Parameters[InternalCrossoverPointProbabilityParameterName]; }
49    }
50    public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeLengthParameter {
51      get { return (IValueLookupParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeLengthParameterName]; }
52    }
53    public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeDepthParameter {
54      get { return (IValueLookupParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeDepthParameterName]; }
55    }
56    #endregion
57    #region Properties
58    public PercentValue InternalCrossoverPointProbability {
59      get { return InternalCrossoverPointProbabilityParameter.ActualValue; }
60    }
61    public IntValue MaximumSymbolicExpressionTreeLength {
62      get { return MaximumSymbolicExpressionTreeLengthParameter.ActualValue; }
63    }
64    public IntValue MaximumSymbolicExpressionTreeDepth {
65      get { return MaximumSymbolicExpressionTreeDepthParameter.ActualValue; }
66    }
67    #endregion
68    [StorableConstructor]
69    protected RobocodeCrossover(bool deserializing) : base(deserializing) { }
70    protected RobocodeCrossover(RobocodeCrossover original, Cloner cloner) : base(original, cloner) { }
71    public RobocodeCrossover()
72      : base() {
73      Parameters.Add(new ValueLookupParameter<IntValue>(MaximumSymbolicExpressionTreeLengthParameterName, "The maximal length (number of nodes) of the symbolic expression tree."));
74      Parameters.Add(new ValueLookupParameter<IntValue>(MaximumSymbolicExpressionTreeDepthParameterName, "The maximal depth of the symbolic expression tree (a tree with one node has depth = 0)."));
75      Parameters.Add(new ValueLookupParameter<PercentValue>(InternalCrossoverPointProbabilityParameterName, "The probability to select an internal crossover point (instead of a leaf node).", new PercentValue(0.9)));
76    }
77
78    public override IDeepCloneable Clone(Cloner cloner) {
79      return new RobocodeCrossover(this, cloner);
80    }
81
82    public override ISymbolicExpressionTree Crossover(IRandom random,
83      ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
84      return Cross(random, parent0, parent1, InternalCrossoverPointProbability.Value,
85        MaximumSymbolicExpressionTreeLength.Value, MaximumSymbolicExpressionTreeDepth.Value);
86    }
87
88    public static ISymbolicExpressionTree Cross(IRandom random,
89      ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1,
90      double internalCrossoverPointProbability, int maxTreeLength, int maxTreeDepth) {
91      // select a random crossover point in the first parent
92      CutPoint crossoverPoint0;
93      SelectCrossoverPoint(random, parent0, internalCrossoverPointProbability, maxTreeLength, maxTreeDepth, out crossoverPoint0);
94
95      int childLength = crossoverPoint0.Child != null ? crossoverPoint0.Child.GetLength() : 0;
96      // calculate the max length and depth that the inserted branch can have
97      int maxInsertedBranchLength = maxTreeLength - (parent0.Length - childLength);
98      int maxInsertedBranchDepth = maxTreeDepth - parent0.Root.GetBranchLevel(crossoverPoint0.Parent);
99
100      List<ISymbolicExpressionTreeNode> allowedBranches = new List<ISymbolicExpressionTreeNode>();
101      parent1.Root.ForEachNodePostfix((n) => {
102        if (n.GetLength() <= maxInsertedBranchLength &&
103            n.GetDepth() <= maxInsertedBranchDepth && crossoverPoint0.IsMatchingPointType(n))
104          allowedBranches.Add(n);
105      });
106      // empty branch
107      if (crossoverPoint0.IsMatchingPointType(null)) allowedBranches.Add(null);
108
109      if (allowedBranches.Count == 0) {
110        return parent0;
111      } else {
112        var selectedBranch = SelectRandomBranch(random, allowedBranches, internalCrossoverPointProbability);
113
114        if (crossoverPoint0.Child != null) {
115          // manipulate the tree of parent0 in place
116          // replace the branch in tree0 with the selected branch from tree1
117          crossoverPoint0.Parent.RemoveSubtree(crossoverPoint0.ChildIndex);
118          if (selectedBranch != null) {
119            crossoverPoint0.Parent.InsertSubtree(crossoverPoint0.ChildIndex, selectedBranch);
120          }
121        } else {
122          // child is null (additional child should be added under the parent)
123          if (selectedBranch != null) {
124            crossoverPoint0.Parent.AddSubtree(selectedBranch);
125          }
126        }
127        return parent0;
128      }
129    }
130
131    private static void SelectCrossoverPoint(IRandom random, ISymbolicExpressionTree parent0, double internalNodeProbability, int maxBranchLength, int maxBranchDepth, out CutPoint crossoverPoint) {
132      if (internalNodeProbability < 0.0 || internalNodeProbability > 1.0) throw new ArgumentException("internalNodeProbability");
133      List<CutPoint> internalCrossoverPoints = new List<CutPoint>();
134      List<CutPoint> leafCrossoverPoints = new List<CutPoint>();
135      parent0.Root.ForEachNodePostfix((n) => {
136        if (n.SubtreeCount > 0 && n != parent0.Root) {
137          foreach (var child in n.Subtrees) {
138            if (child.GetLength() <= maxBranchLength &&
139                child.GetDepth() <= maxBranchDepth) {
140              if (child.SubtreeCount > 0)
141                internalCrossoverPoints.Add(new CutPoint(n, child));
142              else
143                leafCrossoverPoints.Add(new CutPoint(n, child));
144            }
145          }
146
147          // add one additional extension point if the number of sub trees for the symbol is not full
148          if (n.SubtreeCount < n.Grammar.GetMaximumSubtreeCount(n.Symbol)) {
149            // empty extension point
150            internalCrossoverPoints.Add(new CutPoint(n, n.SubtreeCount));
151          }
152        }
153      }
154    );
155
156      if (random.NextDouble() < internalNodeProbability) {
157        // select from internal node if possible
158        if (internalCrossoverPoints.Count > 0) {
159          // select internal crossover point or leaf
160          crossoverPoint = internalCrossoverPoints[random.Next(internalCrossoverPoints.Count)];
161        } else {
162          // otherwise select external node
163          crossoverPoint = leafCrossoverPoints[random.Next(leafCrossoverPoints.Count)];
164        }
165      } else if (leafCrossoverPoints.Count > 0) {
166        // select from leaf crossover point if possible
167        crossoverPoint = leafCrossoverPoints[random.Next(leafCrossoverPoints.Count)];
168      } else {
169        // otherwise select internal crossover point
170        crossoverPoint = internalCrossoverPoints[random.Next(internalCrossoverPoints.Count)];
171      }
172    }
173
174    private static ISymbolicExpressionTreeNode SelectRandomBranch(IRandom random, IEnumerable<ISymbolicExpressionTreeNode> branches, double internalNodeProbability) {
175      if (internalNodeProbability < 0.0 || internalNodeProbability > 1.0) throw new ArgumentException("internalNodeProbability");
176      List<ISymbolicExpressionTreeNode> allowedInternalBranches;
177      List<ISymbolicExpressionTreeNode> allowedLeafBranches;
178      if (random.NextDouble() < internalNodeProbability) {
179        // select internal node if possible
180        allowedInternalBranches = (from branch in branches
181                                   where branch != null && branch.SubtreeCount > 0
182                                   select branch).ToList();
183        if (allowedInternalBranches.Count > 0) {
184          return allowedInternalBranches.SelectRandom(random);
185        } else {
186          // no internal nodes allowed => select leaf nodes
187          allowedLeafBranches = (from branch in branches
188                                 where branch == null || branch.SubtreeCount == 0
189                                 select branch).ToList();
190          return allowedLeafBranches.SelectRandom(random);
191        }
192      } else {
193        // select leaf node if possible
194        allowedLeafBranches = (from branch in branches
195                               where branch == null || branch.SubtreeCount == 0
196                               select branch).ToList();
197        if (allowedLeafBranches.Count > 0) {
198          return allowedLeafBranches.SelectRandom(random);
199        } else {
200          allowedInternalBranches = (from branch in branches
201                                     where branch != null && branch.SubtreeCount > 0
202                                     select branch).ToList();
203          return allowedInternalBranches.SelectRandom(random);
204        }
205      }
206    }
207  }
208}
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