source: branches/gp-crossover/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionContextAwareCrossover.cs @ 7109

#region License Information
/* HeuristicLab
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 *
 * This file is part of HeuristicLab.
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 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
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 * GNU General Public License for more details.
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#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Data;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic {

  [Item("ContextAwareCrossover", "A crossover operator which deterministically choses the best insertion point for a randomly selected node.")]
  public sealed class SymbolicDataAnalysisExpressionContextAwareCrossover<T> : SymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
    [StorableConstructor]
    private SymbolicDataAnalysisExpressionContextAwareCrossover(bool deserializing) : base(deserializing) { }
    private SymbolicDataAnalysisExpressionContextAwareCrossover(SymbolicDataAnalysisExpressionContextAwareCrossover<T> original, Cloner cloner)
      : base(original, cloner) {
    }
    public SymbolicDataAnalysisExpressionContextAwareCrossover()
      : base() {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new SymbolicDataAnalysisExpressionContextAwareCrossover<T>(this, cloner);
    }
    protected override ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
      if (this.ExecutionContext == null)
        throw new InvalidOperationException("ExecutionContext not set.");
      IEnumerable<int> rows = GenerateRowsToEvaluate();
      T problemData = ProblemDataParameter.ActualValue;
      ISymbolicDataAnalysisSingleObjectiveEvaluator<T> evaluator = EvaluatorParameter.ActualValue;

      return Cross(random, parent0, parent1, this.ExecutionContext, evaluator, problemData, rows, MaximumSymbolicExpressionTreeDepth.Value, MaximumSymbolicExpressionTreeLength.Value);
    }
    /// <summary>
    /// Takes two parent individuals P0 and P1. 
    /// Randomly choose a node i from the second parent, then test all possible crossover points from the first parent to determine the best location for i to be inserted.
    /// </summary>
    public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1, IExecutionContext context,
                                                ISymbolicDataAnalysisSingleObjectiveEvaluator<T> evaluator, T problemData, IEnumerable<int> rows, int maxDepth, int maxLength) {
      // randomly choose a node from the second parent
      var possibleChildren = new List<ISymbolicExpressionTreeNode>();
      parent1.Root.ForEachNodePostfix((n) => {
        if (n.Subtrees.Any() && n != parent0.Root)
          foreach (var child in n.Subtrees)
            possibleChildren.Add(child);
      });
      var selectedChild = possibleChildren[random.Next(possibleChildren.Count)];
      var crossoverPoints = new List<CutPoint>();
      var qualities = new List<Tuple<CutPoint, double>>();

      parent0.Root.ForEachNodePostfix((n) => {
        if (n.Subtrees.Any() && n != parent0.Root)
          foreach (var child in n.Subtrees) {
            var crossoverPoint = new CutPoint(n, child);
            int totalDepth = parent0.Root.GetBranchLevel(child) + selectedChild.GetDepth();
            int totalLength = parent0.Root.GetLength() - child.GetLength() + selectedChild.GetLength();
            if (crossoverPoint.IsMatchingPointType(selectedChild) && totalDepth <= maxDepth && totalLength <= maxLength) {
              crossoverPoints.Add(crossoverPoint);
            }
          }
      });

      if (crossoverPoints.Any()) {
        // this loop will perform two swap operations per each crossover point
        foreach (var crossoverPoint in crossoverPoints) {
          // save the old parent so we can restore it later
          var parent = selectedChild.Parent;
          // perform a swap and check the quality of the solution
          swap(crossoverPoint, selectedChild);
          double quality = evaluator.Evaluate(context, parent0, problemData, rows);
          qualities.Add(new Tuple<CutPoint, double>(crossoverPoint, quality));
          // restore the correct parent
          selectedChild.Parent = parent;
          // swap the replaced subtree back into the tree so that the structure is preserved
          swap(crossoverPoint, crossoverPoint.Child);
        }

        qualities.Sort((a, b) => a.Item2.CompareTo(b.Item2)); // assuming this sorts the list in ascending order
        var crossoverPoint0 = evaluator.Maximization ? qualities.Last().Item1 : qualities.First().Item1;
        // swap the node that would create the best offspring
        // this last swap makes a total of (2 * crossoverPoints.Count() + 1) swap operations.
        swap(crossoverPoint0, selectedChild);
      }

      return parent0;
    }

    private static void swap(CutPoint crossoverPoint, ISymbolicExpressionTreeNode selectedBranch) {
      if (crossoverPoint.Child != null) {
        // manipulate the tree of parent0 in place
        // replace the branch in tree0 with the selected branch from tree1
        crossoverPoint.Parent.RemoveSubtree(crossoverPoint.ChildIndex);
        if (selectedBranch != null) {
          crossoverPoint.Parent.InsertSubtree(crossoverPoint.ChildIndex, selectedBranch);
        }
      } else {
        // child is null (additional child should be added under the parent)
        if (selectedBranch != null) {
          crossoverPoint.Parent.AddSubtree(selectedBranch);
        }
      }
    }
  }
}