/// <summary>
/// Method that implements the "Point Mutation" described in Section 2.4 of "A Field Guide to Genetic Programming"
/// In Section 5.2.2 of "A Field Guide to Genetic Programming", this is also described as node replacement mutation
/// </summary>
public virtual void MicroMutate()
{
TGPNode node = FindRandomNode();
if (node.IsTerminal)
{
TGPTerminal terminal = FindRandomTerminal();
int trials = 0;
int max_trials = 50;
while (node.Primitive == terminal)
{
terminal = FindRandomTerminal();
trials++;
if (trials > max_trials)
{
break;
}
}
if (terminal != null)
{
node.Primitive = terminal;
}
}
else
{
int parameter_count = node.Arity;
TGPOperator op = mOperatorSet.FindRandomOperator(parameter_count, (TGPOperator)node.Primitive);
if (op != null)
{
node.Primitive = op;
}
}
}
public void Swap(TGPNode point2)
{
TGPNode parent1 = this.mParent;
TGPNode parent2 = point2.mParent;
if (parent1 == null || parent2 == null)
{
TGPPrimitive content1 = this.mData;
TGPPrimitive content2 = point2.mData;
this.mData = content2;
point2.mData = content1;
List <TGPNode> children1 = this.mChildNodes.ToList();
List <TGPNode> children2 = point2.mChildNodes.ToList();
this.RemoveAllChildren();
point2.RemoveAllChildren();
for (int i = 0; i < children1.Count; ++i)
{
point2.mChildNodes.Add(children1[i]);
children1[i].Parent = point2;
}
for (int i = 0; i < children2.Count; ++i)
{
this.mChildNodes.Add(children2[i]);
children2[i].Parent = this;
}
}
else
{
int child_index1 = parent1.IndexOfChild(this);
int child_index2 = parent2.IndexOfChild(point2);
parent1.ReplaceChildAt(child_index1, point2);
parent2.ReplaceChildAt(child_index2, this);
}
}
/// <summary>
/// Population Initialization method following the "PTC1" method described in "Sean Luke. Two fast tree-creation algorithms for genetic programming. IEEE Transactions in Evolutionary Computation, 4(3), 2000b."
/// </summary>
/// <param name="parent_node">The node for which the child nodes are generated in this method</param>
/// <param name="p">expected probability</param>
/// <param name="allowableDepth">The maximum tree depth</param>
private void PTC1(TGPNode parent_node, double p, int allowableDepth)
{
int child_count = parent_node.Arity;
for (int i = 0; i != child_count; i++)
{
TGPPrimitive data = null;
if (allowableDepth == 0)
{
data = FindRandomTerminal();
}
else if (DistributionModel.GetUniform() <= p)
{
data = mOperatorSet.FindRandomOperator();
}
else
{
data = FindRandomTerminal();
}
TGPNode child = parent_node.CreateChild(data);
if (!data.IsTerminal)
{
PTC1(child, p, allowableDepth - 1);
}
}
}
public TGPNode CreateChild(TGPPrimitive data)
{
TGPNode node = new TGPNode(data, this);
mChildNodes.Add(node);
return(node);
}
/// <summary>
/// Method that creates a GP tree with a maximum tree depth
/// </summary>
/// <param name="allowableDepth">The maximum tree depth</param>
/// <param name="method">The name of the method used to create the GP tree</param>
/// <param name="tag">The additional information used to create the GP tree if any</param>
public void CreateWithDepth(int allowableDepth, string method, object tag = null)
{
// Population Initialization method following the "RandomBranch" method described in "Kumar Chellapilla. Evolving computer programs without subtree crossover. IEEE Transactions on Evolutionary Computation, 1(3):209–216, September 1997."
if (method == INITIALIZATION_METHOD_RANDOMBRANCH)
{
int s = allowableDepth; //tree size
TGPOperator non_terminal = FindRandomOperatorWithArityLessThan(s);
if (non_terminal == null)
{
mRootNode = new TGPNode(FindRandomTerminal());
}
else
{
mRootNode = new TGPNode(non_terminal);
int b_n = non_terminal.Arity;
s = (int)System.Math.Floor((double)s / b_n);
RandomBranch(mRootNode, s);
}
CalcLength();
CalcDepth();
}
// Population Initialization method following the "PTC1" method described in "Sean Luke. Two fast tree-creation algorithms for genetic programming. IEEE Transactions in Evolutionary Computation, 4(3), 2000b."
else if (method == INITIALIZATION_METHOD_PTC1)
{
int expectedTreeSize = Convert.ToInt32(tag);
int b_n_sum = 0;
for (int i = 0; i < mOperatorSet.OperatorCount; ++i)
{
b_n_sum += mOperatorSet.FindOperatorByIndex(i).Arity;
}
double p = (1 - 1.0 / expectedTreeSize) / ((double)b_n_sum / mOperatorSet.OperatorCount);
TGPPrimitive data = null;
if (DistributionModel.GetUniform() <= p)
{
data = mOperatorSet.FindRandomOperator();
}
else
{
data = FindRandomTerminal();
}
mRootNode = new TGPNode(data);
PTC1(mRootNode, p, allowableDepth - 1);
CalcLength();
CalcDepth();
}
else // handle full and grow method
{
mRootNode = new TGPNode(FindRandomPrimitive(allowableDepth, method));
CreateWithDepth(mRootNode, allowableDepth - 1, method);
CalcLength();
CalcDepth();
}
}
/// <summary>
/// Method that traverse the subtree and randomly returns a node from one of the leave or function node
/// </summary>
/// <param name="pRoot">The root node of a subtree</param>
/// <param name="node_depth">The depth at which the selected node is returned</param>
/// <returns>The randomly selected node by traversing</returns>
public TGPNode FindRandomNodeByTraversing(TGPNode pRoot, ref int node_depth)
{
int child_count = pRoot.Arity;
int current_node_depth = node_depth;
if (child_count == 0)
{
return(pRoot);
}
TGPNode pSelectedGene = null;
int selected_child_node_depth = node_depth;
for (int iChild = 0; iChild != child_count; iChild++)
{
TGPNode pChild = pRoot.FindChildByIndex(iChild);
int child_node_depth = node_depth + 1;
TGPNode pChildPickedGene = FindRandomNodeByTraversing(pChild, ref child_node_depth);
if (pChildPickedGene != null)
{
if (pSelectedGene == null)
{
selected_child_node_depth = child_node_depth;
pSelectedGene = pChildPickedGene;
}
else
{
double selection_prob = pChildPickedGene.IsTerminal ? 0.1 : 0.9;
if (DistributionModel.GetUniform() < selection_prob)
{
selected_child_node_depth = child_node_depth;
pSelectedGene = pChildPickedGene;
}
}
}
}
if (pSelectedGene == null)
{
node_depth = current_node_depth;
pSelectedGene = pRoot;
}
else
{
node_depth = selected_child_node_depth;
if (DistributionModel.GetUniform() < 0.5)
{
node_depth = current_node_depth;
pSelectedGene = pRoot;
}
}
return(pSelectedGene);
}
/// <summary>
/// Method that performs deep copy of another GP
/// </summary>
/// <param name="rhs">The GP to copy</param>
public virtual void Copy(TGPProgram rhs)
{
mDepth = rhs.mDepth;
mLength = rhs.mLength;
if (rhs.mRootNode != null)
{
mRootNode = rhs.mRootNode.Clone();
}
}
public int IndexOfChild(TGPNode child)
{
for (int i = 0; i < mChildNodes.Count; ++i)
{
if (mChildNodes[i] == child)
{
return(i);
}
}
return(-1);
}
/// <summary>
/// Method that implements the subtree mutation or "headless chicken" crossover described in Section 2.4 of "A Field Guide to Genetic Programming"
/// </summary>
/// <param name="iMaxProgramDepth">The max depth of the tree after the mutation</param>
public void Mutate(int iMaxProgramDepth, string method, object tag = null)
{
if (method == MUTATION_SUBTREE || method == MUTATION_SUBTREE_KINNEAR)
{
TGPNode node = FindRandomNode();
if (method == MUTATION_SUBTREE)
{
int node_depth = mRootNode.FindDepth2Node(node);
node.RemoveAllChildren();
node.Primitive = FindRandomPrimitive();
if (!node.Primitive.IsTerminal)
{
int max_depth = iMaxProgramDepth - node_depth;
CreateWithDepth(node, max_depth, INITIALIZATION_METHOD_GROW);
}
}
else
{
int subtree_depth = mRootNode.FindDepth2Node(node);
int current_depth = mDepth - subtree_depth;
int max_depth = (int)(mDepth * 1.15) - current_depth;
node.RemoveAllChildren();
node.Primitive = FindRandomPrimitive();
if (!node.Primitive.IsTerminal)
{
CreateWithDepth(node, max_depth, INITIALIZATION_METHOD_GROW);
}
}
}
else if (method == MUTATION_HOIST)
{
TGPNode node = FindRandomNode();
if (node != mRootNode)
{
node.Parent = null;
mRootNode = node;
}
}
else if (method == MUTATION_SHRINK)
{
TGPNode node = FindRandomNode();
node.RemoveAllChildren();
node.Primitive = FindRandomTerminal();
}
CalcDepth();
CalcLength();
}
public virtual TGPNode Clone()
{
TGPNode clone = new TGPNode(mData, mParent);
foreach (TGPNode child_node in mChildNodes)
{
TGPNode cloned_child = child_node.Clone();
cloned_child.mParent = clone;
clone.mChildNodes.Add(cloned_child);
}
return(clone);
}
/// <summary>
/// Method that creates a subtree of maximum depth
/// </summary>
/// <param name="pRoot">The root node of the subtree</param>
/// <param name="allowableDepth">The maximum depth</param>
/// <param name="method">The method used to build the subtree</param>
public void CreateWithDepth(TGPNode pRoot, int allowableDepth, string method)
{
int child_count = pRoot.Arity;
for (int i = 0; i != child_count; ++i)
{
TGPPrimitive primitive = FindRandomPrimitive(allowableDepth, method);
TGPNode child = pRoot.CreateChild(primitive);
if (!primitive.IsTerminal)
{
CreateWithDepth(child, allowableDepth - 1, method);
}
}
}
internal int FindDepth2Node(TGPNode node, int depthSoFar = 0)
{
if (this == node)
{
return(depthSoFar);
}
int maxDepthOfChild = -1;
foreach (TGPNode child_node in mChildNodes)
{
int d = child_node.FindDepth2Node(node, depthSoFar + 1);
if (d > maxDepthOfChild)
{
maxDepthOfChild = d;
}
}
return(maxDepthOfChild);
}
/// <summary>
/// Population Initialization Method described in "Kumar Chellapilla. Evolving computer programs without subtree crossover. IEEE Transactions on Evolutionary Computation, 1(3):209–216, September 1997."
/// </summary>
/// <param name="parent_node"></param>
/// <param name="s"></param>
/// <param name="?"></param>
private void RandomBranch(TGPNode parent_node, int s)
{
int child_count = parent_node.Arity;
for (int i = 0; i != child_count; i++)
{
TGPOperator non_terminal = FindRandomOperatorWithArityLessThan(s);
if (non_terminal == null)
{
TGPNode child = parent_node.CreateChild(FindRandomTerminal());
}
else
{
TGPNode child = parent_node.CreateChild(non_terminal);
int b_n = non_terminal.Arity;
int s_pi = (int)System.Math.Floor((double)s / b_n);
RandomBranch(child, s_pi);
}
}
}
public void ReplaceChildAt(int index, TGPNode child)
{
mChildNodes[index] = child;
child.mParent = this;
}
public void RemoveChild(TGPNode child)
{
mChildNodes.Remove(child);
child.Parent = null;
}
public TGPNode(TGPPrimitive data, TGPNode parent = null)
{
mData = data;
mParent = parent;
}
/// <summary>
/// Method that implements the subtree crossover described in Section 2.4 of "A Field Guide to Genetic Programming"
/// </summary>
/// <param name="rhs">Another tree to be crossover with</param>
/// <param name="iMaxDepthForCrossover">The maximum depth of the trees after the crossover</param>
public virtual void SubtreeCrossover(TGPProgram rhs, int iMaxDepthForCrossover, string method, object tag = null)
{
if (method == CROSSOVER_SUBTREE_BIAS || method == CROSSVOER_SUBTREE_NO_BIAS)
{
bool bias = (method == CROSSOVER_SUBTREE_BIAS);
int iMaxDepth1 = CalcDepth();
int iMaxDepth2 = rhs.CalcDepth();
TGPNode pCutPoint1 = null;
TGPNode pCutPoint2 = null;
bool is_crossover_performed = false;
// Suppose that at the beginning both the current GP and the other GP do not violate max depth constraint
// then try to see whether a crossover can be performed in such a way that after the crossover, both GP still have depth <= max depth
if (iMaxDepth1 <= iMaxDepthForCrossover && iMaxDepth2 <= iMaxDepthForCrossover)
{
int max_trials = 50;
int trials = 0;
do
{
pCutPoint1 = FindRandomNode(bias);
pCutPoint2 = rhs.FindRandomNode(bias);
if (pCutPoint1 != null && pCutPoint2 != null)
{
pCutPoint1.Swap(pCutPoint2);
iMaxDepth1 = CalcDepth();
iMaxDepth2 = rhs.CalcDepth();
if (iMaxDepth1 <= iMaxDepthForCrossover && iMaxDepth2 <= iMaxDepthForCrossover) //crossover is successful
{
is_crossover_performed = true;
break;
}
else
{
pCutPoint1.Swap(pCutPoint2); // swap back so as to restore to the original GP trees if the crossover is not valid due to max depth violation
}
}
trials++;
} while (trials < max_trials);
}
// force at least one crossover even if the maximum depth is violated above so that this operator won't end up like a reproduction operator
if (!is_crossover_performed)
{
pCutPoint1 = FindRandomNode(bias);
pCutPoint2 = rhs.FindRandomNode(bias);
if (pCutPoint1 != null && pCutPoint2 != null)
{
pCutPoint1.Swap(pCutPoint2);
CalcLength();
rhs.CalcLength();
}
}
}
}
