#region License Information /* HeuristicLab * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using System.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HEAL.Attic; using HeuristicLab.Random; namespace HeuristicLab.Problems.DataAnalysis.Symbolic { [Item("ProbabilisticFunctionalCrossover", "An operator which performs subtree swapping based on the behavioral similarity between subtrees:\n" + "- Take two parent individuals P0 and P1\n" + "- Randomly choose a node N from P0\n" + "- For each matching node M from P1, calculate the behavioral distance:\n" + "\t\tD(N,M) = 0.5 * ( abs(max(N) - max(M)) + abs(min(N) - min(M)) )\n" + "- Make a probabilistic weighted choice of node M from P1, based on the inversed and normalized behavioral distance")] [StorableType("EC1901B8-689B-4FC9-A79E-2778B3D0609E")] public sealed class SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover : SymbolicDataAnalysisExpressionCrossover where T : class, IDataAnalysisProblemData { [StorableConstructor] private SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover(StorableConstructorFlag _) : base(_) { } private SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover(SymbolicDataAnalysisExpressionCrossover original, Cloner cloner) : base(original, cloner) { } public SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover() : base() { name = "ProbabilisticFunctionalCrossover"; } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover(this, cloner); } public override ISymbolicExpressionTree Crossover(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) { ISymbolicDataAnalysisExpressionTreeInterpreter interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue; List rows = GenerateRowsToEvaluate().ToList(); T problemData = ProblemDataParameter.ActualValue; return Cross(random, parent0, parent1, interpreter, problemData, rows, MaximumSymbolicExpressionTreeDepth.Value, MaximumSymbolicExpressionTreeLength.Value); } /// /// Takes two parent individuals P0 and P1. /// Randomly choose a node i from the first parent, then for each matching node j from the second parent, calculate the behavioral distance based on the range: /// d(i,j) = 0.5 * ( abs(max(i) - max(j)) + abs(min(i) - min(j)) ). /// Next, assign probabilities for the selection of a node j based on the inversed and normalized behavioral distance, then make a random weighted choice. /// public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, T problemData, IList rows, int maxDepth, int maxLength) { var crossoverPoints0 = new List(); parent0.Root.ForEachNodePostfix((n) => { // the if clauses prevent the root or the startnode from being selected, although the startnode can be the parent of the node being swapped. if (n.Parent != null && n.Parent != parent0.Root) { crossoverPoints0.Add(new CutPoint(n.Parent, n)); } }); var crossoverPoint0 = crossoverPoints0.SampleRandom(random); int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child); int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength(); var allowedBranches = new List(); parent1.Root.ForEachNodePostfix((n) => { if (n.Parent != null && n.Parent != parent1.Root) { if (n.GetDepth() + level <= maxDepth && n.GetLength() + length <= maxLength && crossoverPoint0.IsMatchingPointType(n)) allowedBranches.Add(n); } }); if (allowedBranches.Count == 0) return parent0; var dataset = problemData.Dataset; // create symbols in order to improvize an ad-hoc tree so that the child can be evaluated var rootSymbol = new ProgramRootSymbol(); var startSymbol = new StartSymbol(); var tree0 = CreateTreeFromNode(random, crossoverPoint0.Child, rootSymbol, startSymbol); // this will change crossoverPoint0.Child.Parent double min0 = 0.0, max0 = 0.0; foreach (double v in interpreter.GetSymbolicExpressionTreeValues(tree0, dataset, rows)) { if (min0 > v) min0 = v; if (max0 < v) max0 = v; } crossoverPoint0.Child.Parent = crossoverPoint0.Parent; // restore correct parent var weights = new List(); foreach (var node in allowedBranches) { var parent = node.Parent; var tree1 = CreateTreeFromNode(random, node, rootSymbol, startSymbol); double min1 = 0.0, max1 = 0.0; foreach (double v in interpreter.GetSymbolicExpressionTreeValues(tree1, dataset, rows)) { if (min1 > v) min1 = v; if (max1 < v) max1 = v; } double behavioralDistance = (Math.Abs(min0 - min1) + Math.Abs(max0 - max1)) / 2; // this can be NaN of Infinity because some trees are crazy like exp(exp(exp(...))), we correct that below weights.Add(behavioralDistance); node.Parent = parent; // restore correct node parent } // remove branches with an infinite or NaN behavioral distance for (int i = weights.Count - 1; i >= 0; --i) { if (Double.IsNaN(weights[i]) || Double.IsInfinity(weights[i])) { weights.RemoveAt(i); allowedBranches.RemoveAt(i); } } // check if there are any allowed branches left if (allowedBranches.Count == 0) return parent0; ISymbolicExpressionTreeNode selectedBranch; double sum = weights.Sum(); if (sum.IsAlmost(0.0) || weights.Count == 1) // if there is only one allowed branch, or if all weights are zero selectedBranch = allowedBranches[0]; else { for (int i = 0; i != weights.Count; ++i) // normalize and invert values weights[i] = 1 - weights[i] / sum; sum = weights.Sum(); // take new sum // compute the probabilities (selection weights) for (int i = 0; i != weights.Count; ++i) weights[i] /= sum; #pragma warning disable 612, 618 selectedBranch = allowedBranches.SelectRandom(weights, random); #pragma warning restore 612, 618 } Swap(crossoverPoint0, selectedBranch); return parent0; } } }