#region License Information
/* HeuristicLab
* Copyright (C) 2002-2008 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.Collections.Generic;
using System.Linq;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Random;
using System;
using System.Diagnostics;
namespace HeuristicLab.GP {
public class CutOutNodeManipulation : OperatorBase {
public override string Description {
get {
return @"Takes a tree, selects a random node of the tree and then tries to replace a random sub-tree
of that node with one of the childs of the selected child.
O O
/ \ / \
O X O 2
/ \ 2 is selected => / \
1 2 4 5
/ / \
3 4 5
";
}
}
public CutOutNodeManipulation()
: base() {
AddVariableInfo(new VariableInfo("Random", "Uniform random number generator", typeof(MersenneTwister), VariableKind.In));
AddVariableInfo(new VariableInfo("OperatorLibrary", "The operator library containing all available operators", typeof(GPOperatorLibrary), VariableKind.In));
AddVariableInfo(new VariableInfo("MaxTreeHeight", "The maximal allowed height of the tree", typeof(IntData), VariableKind.In));
AddVariableInfo(new VariableInfo("MaxTreeSize", "The maximal allowed size (number of nodes) of the tree", typeof(IntData), VariableKind.In));
AddVariableInfo(new VariableInfo("FunctionTree", "The tree to mutate", typeof(IFunctionTree), VariableKind.In | VariableKind.Out));
AddVariableInfo(new VariableInfo("TreeSize", "The size (number of nodes) of the tree", typeof(IntData), VariableKind.In | VariableKind.Out));
AddVariableInfo(new VariableInfo("TreeHeight", "The height of the tree", typeof(IntData), VariableKind.In | VariableKind.Out));
}
public override IOperation Apply(IScope scope) {
IFunctionTree root = GetVariableValue("FunctionTree", scope, true);
MersenneTwister random = GetVariableValue("Random", scope, true);
GPOperatorLibrary library = GetVariableValue("OperatorLibrary", scope, true);
int maxTreeHeight = GetVariableValue("MaxTreeHeight", scope, true).Data;
int maxTreeSize = GetVariableValue("MaxTreeSize", scope, true).Data;
TreeGardener gardener = new TreeGardener(random, library);
IFunctionTree parent = gardener.GetRandomParentNode(root);
// parent == null means we should cut out the root node
// => return a random sub-tree of the root
if (parent == null) {
// when there are sub-trees then replace the old tree with a random sub-tree
if (root.SubTrees.Count > 0) {
root = root.SubTrees[random.Next(root.SubTrees.Count)];
GetVariableValue("TreeSize", scope, true).Data = root.Size;
GetVariableValue("TreeHeight", scope, true).Data = root.Height;
// update the variable
scope.GetVariable(scope.TranslateName("FunctionTree")).Value = root;
Debug.Assert(gardener.IsValidTree(root));
// we reused a sub-tree so we don't have to schedule initialization operations
return null;
} else {
// we want to cut the root node and there are no sub-trees => create a new random terminal
IFunctionTree newTree;
newTree = gardener.CreateRandomTree(gardener.Terminals, 1, 1);
GetVariableValue("TreeSize", scope, true).Data = newTree.Size;
GetVariableValue("TreeHeight", scope, true).Data = newTree.Height;
// update the variable
scope.GetVariable(scope.TranslateName("FunctionTree")).Value = newTree;
Debug.Assert(gardener.IsValidTree(newTree));
// schedule an operation to initialize the whole tree
return gardener.CreateInitializationOperation(gardener.GetAllSubTrees(newTree), scope);
}
}
// select a child to cut away
int childIndex = random.Next(parent.SubTrees.Count);
IFunctionTree child = parent.SubTrees[childIndex];
// match the sub-trees of the child with the allowed sub-trees of the parent
ICollection allowedFunctions = gardener.GetAllowedSubFunctions(parent.Function, childIndex);
IFunctionTree[] possibleChilds = child.SubTrees.Where(t => allowedFunctions.Contains(t.Function)).ToArray();
if (possibleChilds.Length > 0) {
// replace child with a random child of that child
IFunctionTree selectedChild = possibleChilds[random.Next(possibleChilds.Length)];
parent.RemoveSubTree(childIndex);
parent.InsertSubTree(childIndex, selectedChild);
Debug.Assert(gardener.IsValidTree(root));
// update the size and height of our tree
GetVariableValue("TreeSize", scope, true).Data = root.Size;
GetVariableValue("TreeHeight", scope, true).Data = root.Height;
// don't need to schedule initialization operations
return null;
} else {
// can't reuse an existing branch => create a new tree
// determine the level of the parent
int parentLevel = gardener.GetBranchLevel(root, parent);
// first remove the old child (first step essential!)
parent.RemoveSubTree(childIndex);
// then determine the number of nodes left over after the child has been removed!
int remainingNodes = root.Size;
allowedFunctions = gardener.GetAllowedSubFunctions(parent.Function, childIndex);
IFunctionTree newFunctionTree = gardener.CreateRandomTree(allowedFunctions, maxTreeSize - remainingNodes, maxTreeHeight - parentLevel);
parent.InsertSubTree(childIndex, newFunctionTree);
GetVariableValue("TreeSize", scope, true).Data = root.Size;
GetVariableValue("TreeHeight", scope, true).Data = root.Height;
Debug.Assert(gardener.IsValidTree(root));
// schedule an initialization operation for the new function-tree
return gardener.CreateInitializationOperation(gardener.GetAllSubTrees(newFunctionTree), scope);
}
}
}
}