public void GPNodeWriteAndRead()
{
var state = new SimpleEvolutionState();
var initializer = new GPInitializer();
state.Initializer = initializer; // Ensure that our initializer is accessible
var treeConstraints = new GPTreeConstraints();
initializer.TreeConstraints = new List <GPTreeConstraints>();
initializer.TreeConstraints.Add(treeConstraints); // Ensure that our constraints are accessible
var nodeConstraints = new GPNodeConstraints();
var rootType = new GPAtomicType("MyRootType");
treeConstraints.TreeType = rootType;
var root = new MyRootNode();
var tree = new GPTree {
Child = root
};
root.Parent = tree;
root.ArgPosition = 0; // This is the root, so there is only one position (0)
}
/// <summary>
/// MakeTree, edits the tree that its given by adding a root (and all subtrees attached)
/// </summary>
/// <returns>The number of chromosomes used, or an BIG_TREE_ERROR sentinel value.</returns>
public int MakeTree(IEvolutionState state, int[] genome, GPTree tree, int position, int treeNum, int threadnum, IDictionary <int, GPNode> ercMapsForFancyPrint)
{
// hack, use an array to pass an extra value
int[] countNumberOfChromosomesUsed = { position }; // hack, use an array to pass an extra value
var gpfs = tree.Constraints((GPInitializer)state.Initializer).FunctionSet;
GPNode root;
try // get the tree, or return an error.
{
root = MakeSubtree(countNumberOfChromosomesUsed, genome, state, gpfs, Grammar[treeNum], treeNum, threadnum, ercMapsForFancyPrint, tree, (byte)0);
}
catch (BigTreeException)
{
return(BIG_TREE_ERROR);
}
if (root == null)
{
state.Output.Fatal("Invalid tree: tree #" + treeNum);
}
root.Parent = tree;
tree.Child = root;
return(countNumberOfChromosomesUsed[0]);
}
/**
* @param t Tree to be "executed"
* @return expressed output
*/
ArrayList GetSemanticOutput(GPTree t)
{
ArrayList p = new ArrayList();
ArrayList nodes = new ArrayList();
// Is there a better way to get all the nodes in a depth-first
// traversal? Note that the paper specifies inorder traversal,
// but since we're only getting the terminals, preorder,
// inorder, and postorder are equivalent.
int nterminals = t.Child.NumNodes(GPNode.NODESEARCH_TERMINALS);
for (int i = 0; i < nterminals; i++)
{
GPNodeGatherer g = new GPNodeGatherer();
t.Child.NodeInPosition(i, g, GPNode.NODESEARCH_TERMINALS);
nodes.Add(g.Node);
}
if (_problemName.Equals(P_ORDER))
{
// Order: first occurence counts
for (int i = 0; i < nodes.Count; i++)
{
SemanticNode node = (SemanticNode)nodes[i];
if (!NodeSameIndexExists(p, node.Index))
{
p.Add(node);
}
}
}
else
{
// Majority: most common counts
for (int n = 0; n < _problemSize; n++)
{
int xCount = 0;
int nCount = 0;
int lastXNode = -1;
for (int i = 0; i < nodes.Count; i++)
{
SemanticNode node = (SemanticNode)nodes[i];
if (node.Value == 'X' && node.Index == n)
{
xCount += 1;
lastXNode = i;
}
else if (node.Value == 'N' && node.Index == n)
{
nCount += 1;
}
}
if (xCount >= nCount && xCount > 0)
{
p.Add((SemanticNode)nodes[lastXNode]);
}
}
}
return(p);
}
public static string PrintCodeFromTree(GPTree tree)
{
code = "";
var startNode = tree.Child;
EvalNode(startNode);
return(code);
}
public void GPIndividualWriteAndRead()
{
// First we'll set up a Fitness for the Individual
var rand = new MersenneTwisterFast(0);
// Initialize some required context
var state = new SimpleEvolutionState();
var initializer = new GPInitializer();
state.Initializer = initializer; // Required for GPTree.WriteTree(...)
var constraints = new GPTreeConstraints {
Name = "TestTreeConstraints"
};
//initializer.TreeConstraints
var tree = new GPTree();
var f = new KozaFitness();
f.SetStandardizedFitness(null, float.MaxValue);
const int n = 10;
f.Trials = new List <double>(n);
for (var i = 0; i < n; i++)
{
f.Trials.Add(rand.NextDouble());
}
// Now we can create and initialize the Individual
var ind = new GPIndividual();
var ind2 = new GPIndividual(); // We'll read back into this instance
ind.Trees = new GPTree[1]; // This is the set of genes
ind.Trees[0] = new GPTree();
ind.Fitness = f;
ind.Evaluated = true;
using (var ms = new MemoryStream())
{
var writer = new BinaryWriter(ms);
ind.WriteIndividual(null, writer);
ms.Position = 0;
var reader = new BinaryReader(ms);
ind2.Fitness = new SimpleFitness();
ind2.ReadIndividual(null, reader);
Assert.IsTrue(ind.Fitness.EquivalentTo(ind2.Fitness));
Assert.IsTrue(ind2.Fitness.IsIdeal);
Assert.IsTrue(ind.Equals(ind2)); // Genetically equivalent
Assert.AreEqual(ind.Trees.Length, ind2.Trees.Length);
}
}
public void TestMethod1()
{
var tree = new GPTree();
var root = new ADF {
FunctionName = "ADF0", Children = new GPNode[2]
};
tree.Child = root;
var c1 = new ADM {
FunctionName = "ADM1", Parent = root, Children = new GPNode[2]
};
var c11 = new ADF {
FunctionName = "ADF11", Parent = c1
};
var c12 = new DummyERC {
FunctionName = "ERC12", Parent = c1
};
c1.Children[0] = c11;
c1.Children[1] = c12;
var c2 = new ADM {
FunctionName = "ADM2", Parent = root, Children = new GPNode[1]
};
var c21 = new ADF {
FunctionName = "ADF21", Parent = c2
};
c2.Children[0] = c21;
root.Children = new GPNode[3];
root.Children[0] = c1;
root.Children[1] = c2;
root.Children[2] = new DummyERC {
Parent = root
};
// nodesearch (0=All, 1=Terminals, 2=Nonterminals)
foreach (GPNode node in tree.Descendants(nodesearch: GPNode.NODESEARCH_ALL))
{
var nodeName = node.Name;
var t1 = node.Parent != null;
var t2 = ((GPNode)node.Parent)?.Parent != null;
var tabs = t2 ? 2 : t1 ? 1 : 0;
var s = (tabs == 2 ? "\t\t" : tabs == 1 ? "\t" : "") + $"{nodeName}";
Console.WriteLine(s);
}
}
private static void TestCode()
{
ECActivator.AddSourceAssemblies(new[] { Assembly.GetAssembly(typeof(IEvolutionState)), Assembly.GetAssembly(typeof(ClassificationProblem)) });
var parameters = Evolve.LoadParameterDatabase(new[] { "-file", @"Params\App\Iris\koza.params" });
IEvolutionState state = Evolve.Initialize(parameters, 0);
state.Setup(state, new Parameter(new string[] { "a" }));
//state.Run(EvolutionState.C_STARTED_FRESH);
// var individual = (GPIndividual)((SimpleStatistics)state.Statistics).BestOfRun[0];
var tree = new GPTree();
var reader = new System.IO.StreamReader("single_cl_ecj_graph.txt");
tree.ReadTree(state, reader);
var writer = new System.IO.StreamWriter("testcode.txt");
var code = TreeReader.PrintCodeFromTree(tree);
writer.Write(code);
writer.Close();
}
private static void CheckTreeFromFile()
{
ECActivator.AddSourceAssemblies(new[] { Assembly.GetAssembly(typeof(IEvolutionState)), Assembly.GetAssembly(typeof(ClassificationProblem)) });
var parameters = Evolve.LoadParameterDatabase(new[] { "-file", @"Params\App\Iris\koza.params" });
IEvolutionState state = Evolve.Initialize(parameters, 0);
state.Setup(state, new Parameter(new string[] { "a" }));
//state.Run(EvolutionState.C_STARTED_FRESH);
var individual = (GPIndividual)((SimpleStatistics)state.Statistics).BestOfRun[0];
var tree = new GPTree();
var reader = new System.IO.StreamReader("single_cl_ecj_graph.txt");
tree.ReadTree(state, reader);
// individual.Setup(state, new Parameter(new string[] { "numtrees = 1", "1" }));
individual.Trees[0] = tree;
// CheckClassifierOnTestData(new[] { individual }, state, (int)ClassifierType.SINGLE_CLASSIFIER, directoryName);
}
/**
* This method finds a node using the logic given in the langdon paper.
* @param nodeToSubtrees For Tree of Parent2 all precomputed stats about depth,subtrees etc
* @param sizeToNodes Quick lookup for LinkedList of size to Nodes
* @param parent1SelectedNode Node selected in parent1
* @param Tree2 Tree of parent2
* @param state Evolution State passed for getting access to Random Object of MersenneTwiser
* @param thread thread number
*/
protected GPNode FindFairSizeNode(ArrayList nodeToSubtrees,
Hashtable sizeToNodes,
GPNode parent1SelectedNode,
GPTree tree2,
IEvolutionState state,
int thread)
{
GPNode selectedNode = null;
// get the size of subtrees of parent1
int parent1SubTrees = parent1SelectedNode.NumNodes(GPNode.NODESEARCH_NONTERMINALS);
// the maximum length in mate we are looking for
int maxmatesublen = parent1SubTrees == 0 ? 0 : 2 * parent1SubTrees + 1;
// lets see if for all lengths we have trees corresponding
bool[] mateSizeAvailable = new bool[maxmatesublen + 1];
// initialize the array to false
for (int i = 0; i < maxmatesublen; i++)
{
mateSizeAvailable[i] = false;
}
// check for ones we have
for (int i = 0; i < nodeToSubtrees.Count; i++)
{
NodeInfo nodeInfo = (NodeInfo)nodeToSubtrees[i];
// get the length of trees
int subtree = nodeInfo.NumberOfSubTreesBeneath;
if (subtree <= maxmatesublen)
{
mateSizeAvailable[subtree] = true;
}
}
// choose matesublen so mean size change=0 if possible
int countOfPositives = 0;
int countOfNegatives = 0;
int sumOfPositives = 0;
int sumOfNegatives = 0;
int l;
for (l = 1; l < parent1SubTrees; l++)
{
if (mateSizeAvailable[l])
{
countOfNegatives++;
sumOfNegatives += parent1SubTrees - l;
}
}
for (l = parent1SubTrees + 1; l <= maxmatesublen; l++)
{
if (mateSizeAvailable[l])
{
countOfPositives++;
sumOfPositives += l - parent1SubTrees;
}
}
// if they are missing use the same
int mateSublengthSelected = 0;
if (sumOfPositives == 0 || sumOfNegatives == 0)
{
//if so then check if mate has the length and use that
if (mateSizeAvailable[parent1SubTrees])
{
mateSublengthSelected = parent1SubTrees;
}
//else we go with zero
}
else
{
// probability of same is dependent on do we find same sub trees
// else 0.0
double pzero = (mateSizeAvailable[parent1SubTrees]) ? 1.0 / parent1SubTrees : 0.0;
// positive probability
double ppositive = (1.0 - pzero) /
(countOfPositives +
((double)(countOfNegatives * sumOfPositives) / (sumOfNegatives)));
// negative probability
double pnegative = (1.0 - pzero) /
(countOfNegatives +
((double)(countOfPositives * sumOfNegatives) / (sumOfPositives)));
// total probability, just for making sure math is right ;-)
double total = countOfNegatives * pnegative + pzero + countOfPositives * ppositive;
// putting an assert for floating point calculations, similar to what langdon does
// assert(total<1.01&&total>.99);
// now create a Roulette Wheel
RouletteWheelSelector wheel = new RouletteWheelSelector(maxmatesublen);
// add probabilities to the wheel
// all below the length of parent node get pnegative
// all above get ppositive and one on node gets pzero
for (l = 1; l < parent1SubTrees; l++)
{
if (mateSizeAvailable[l])
{
wheel.Add(pnegative, l);
}
}
if (mateSizeAvailable[parent1SubTrees])
{
wheel.Add(pzero, parent1SubTrees);
}
for (l = parent1SubTrees + 1; l <= maxmatesublen; l++)
{
if (mateSizeAvailable[l])
{
wheel.Add(ppositive, l);
}
}
// spin the wheel
mateSublengthSelected = wheel.Roulette(state, thread);
}
// now we have length chosen, but there can be many nodes with that
//
LinkedList <NodeInfo> listOfNodes = (LinkedList <NodeInfo>)sizeToNodes[mateSublengthSelected];
if (listOfNodes == null)
{
state.Output.Fatal("In SizeFairCrossoverPipeline, nodes for tree length " + mateSublengthSelected + " is null, indicates some serious error");
}
// in size fair we choose the elements at random for given length
int chosenNode = 0;
// if using fair size get random from the list
if (!Homologous)
{
chosenNode = state.Random[thread].NextInt(listOfNodes.Count);
}
// if Homologous
else
{
if (listOfNodes.Count > 1)
{
GPInitializer initializer = ((GPInitializer)state.Initializer);
int currentMinDistance = int.MaxValue;
for (int i = 0; i < listOfNodes.Count; i++)
{
// get the GP node
GPNode selectedMateNode = listOfNodes.ElementAt(i).Node;
// now lets traverse selected and parent 1 to see divergence
GPNode currentMateNode = selectedMateNode;
GPNode currentParent1Node = parent1SelectedNode;
// found a match?
bool foundAMatchInAncestor = false;
int distance = 0;
while (currentMateNode.Parent != null &&
currentMateNode.Parent is GPNode &&
currentParent1Node.Parent != null &&
currentParent1Node.Parent is GPNode &&
!foundAMatchInAncestor)
{
GPNode parent1 = (GPNode)currentParent1Node.Parent;
GPNode parent2 = (GPNode)currentMateNode.Parent;
// if there is match between compatibility of Parents break
if (parent1.SwapCompatibleWith(initializer, parent2))
{
foundAMatchInAncestor = true;
break;
}
else
{
// need to go one level above of both
currentMateNode = parent2;
currentParent1Node = parent1;
//increment the distance
distance = distance + 1;
}
}
// find the one with least distance
if (distance < currentMinDistance)
{
currentMinDistance = distance;
chosenNode = i;
}
}
}
// else take the first node, no choice
}
NodeInfo nodeInfoSelected = listOfNodes.ElementAt(chosenNode);
selectedNode = nodeInfoSelected.Node;
return(selectedNode);
}
public override int Produce(
int min,
int max,
int subpop,
IList <Individual> inds,
IEvolutionState state,
int thread,
IDictionary <string, object> misc)
{
int start = inds.Count;
// how many individuals should we make?
int n = TypicalIndsProduced;
if (n < min)
{
n = min;
}
if (n > max)
{
n = max;
}
// should we bother?
if (!state.Random[thread].NextBoolean(Likelihood))
{
// just load from source 0 and clone 'em
Sources[0].Produce(n, n, subpop, inds, state, thread, misc);
return(n);
}
IntBag[] parentparents = null;
IntBag[] preserveParents = null;
if (misc != null && misc.ContainsKey(KEY_PARENTS))
{
preserveParents = (IntBag[])misc[KEY_PARENTS];
parentparents = new IntBag[2];
misc[KEY_PARENTS] = parentparents;
}
GPInitializer initializer = (GPInitializer)state.Initializer;
for (int q = start; q < n + start; /* no increment */) // keep on going until we're filled up
{
Parents.Clear();
// grab two individuals from our sources
if (Sources[0] == Sources[1]) // grab from the same source
{
Sources[0].Produce(2, 2, subpop, Parents, state, thread, misc);
}
else // grab from different sources
{
Sources[0].Produce(1, 1, subpop, Parents, state, thread, misc);
Sources[1].Produce(1, 1, subpop, Parents, state, thread, misc);
}
// at this point, Parents[] contains our two selected individuals
// are our tree values valid?
if (Tree1 != TREE_UNFIXED && (Tree1 < 0 || Tree1 >= ((GPIndividual)Parents[0]).Trees.Length))
{
// uh oh
state.Output.Fatal(
"GP Crossover Pipeline attempted to fix tree.0 to a value which was out of bounds of the array of the individual's trees. Check the pipeline's fixed tree values -- they may be negative or greater than the number of trees in an individual");
}
if (Tree2 != TREE_UNFIXED && (Tree2 < 0 || Tree2 >= ((GPIndividual)Parents[1]).Trees.Length))
{
// uh oh
state.Output.Fatal(
"GP Crossover Pipeline attempted to fix tree.1 to a value which was out of bounds of the array of the individual's trees. Check the pipeline's fixed tree values -- they may be negative or greater than the number of trees in an individual");
}
int t1;
int t2;
if (Tree1 == TREE_UNFIXED || Tree2 == TREE_UNFIXED)
{
do
// pick random trees -- their GPTreeConstraints must be the same
{
if (Tree1 == TREE_UNFIXED)
{
if (((GPIndividual)Parents[0]).Trees.Length > 1)
{
t1 = state.Random[thread].NextInt(((GPIndividual)Parents[0]).Trees.Length);
}
else
{
t1 = 0;
}
}
else
{
t1 = Tree1;
}
if (Tree2 == TREE_UNFIXED)
{
if (((GPIndividual)Parents[1]).Trees.Length > 1)
{
t2 = state.Random[thread].NextInt(((GPIndividual)Parents[1]).Trees.Length);
}
else
{
t2 = 0;
}
}
else
{
t2 = Tree2;
}
} while (((GPIndividual)Parents[0]).Trees[t1].Constraints(initializer) !=
((GPIndividual)Parents[1]).Trees[t2].Constraints(initializer));
}
else
{
t1 = Tree1;
t2 = Tree2;
// make sure the constraints are okay
if (((GPIndividual)Parents[0]).Trees[t1].Constraints(initializer) !=
((GPIndividual)Parents[1]).Trees[t2].Constraints(initializer)) // uh oh
{
state.Output.Fatal(
"GP Crossover Pipeline's two tree choices are both specified by the user -- but their GPTreeConstraints are not the same");
}
}
bool res1 = false;
bool res2 = false;
// BRS: This is kind of stupid to name it this way!
GPTree currTree = ((GPIndividual)Parents[1]).Trees[t2];
// pick some nodes
GPNode p1 = null;
GPNode p2 = null;
// lets walk on parent2 all nodes to get subtrees for each node, doing it once for O(N) and not O(N^2)
// because depth etc are computed and not stored
ArrayList nodeToSubtrees = new ArrayList();
// also Hashtable for size to List() of nodes in that size for O(1) lookup
Hashtable sizeToNodes = new Hashtable();
TraverseTreeForDepth(currTree.Child, nodeToSubtrees, sizeToNodes);
// sort the ArrayList with comparator that sorts by subtrees
nodeToSubtrees.Sort(new NodeComparator());
for (int x = 0; x < NumTries; x++)
{
// pick a node in individual 1
p1 = NodeSelect1.PickNode(state, subpop, thread, (GPIndividual)Parents[0], ((GPIndividual)Parents[0]).Trees[t1]);
// now lets find "similar" in parent 2
p2 = FindFairSizeNode(nodeToSubtrees, sizeToNodes, p1, currTree, state, thread);
// check for depth and swap-compatibility limits
res1 = VerifyPoints(initializer, p2, p1); // p2 can fill p1's spot -- order is important!
if (n - (q - start) < 2 || TossSecondParent)
{
res2 = true;
}
else
{
res2 = VerifyPoints(initializer, p1, p2); // p1 can fill p2's spot -- order is important!
}
// did we get something that had both nodes verified?
// we reject if EITHER of them is invalid. This is what lil-gp
// does.
// Koza only has numTries set to 1, so it's compatible as well.
if (res1 && res2)
{
break;
}
}
// at this point, res1 AND res2 are valid, OR
// either res1 OR res2 is valid and we ran out of tries, OR
// neither res1 nor res2 is valid and we rand out of tries.
// So now we will transfer to a tree which has res1 or res2
// valid, otherwise it'll just get replicated. This is
// compatible with both Koza and lil-gp.
// at this point I could check to see if my sources were breeding
// pipelines -- but I'm too lazy to write that code (it's a little
// complicated) to just swap one individual over or both over,
// -- it might still entail some copying. Perhaps in the future.
// It would make things faster perhaps, not requiring all that
// cloning.
// Create some new individuals based on the old ones -- since
// GPTree doesn't deep-clone, this should be just fine. Perhaps we
// should change this to proto off of the main species prototype,
// but
// we have to then copy so much stuff over; it's not worth it.
GPIndividual j1 = ((GPIndividual)Parents[0]).LightClone();
GPIndividual j2 = null;
if (n - (q - start) >= 2 && !TossSecondParent)
{
j2 = ((GPIndividual)Parents[1]).LightClone();
}
// Fill in various tree information that didn't get filled in there
j1.Trees = new GPTree[((GPIndividual)Parents[0]).Trees.Length];
if (n - (q - start) >= 2 && !TossSecondParent)
{
j2.Trees = new GPTree[((GPIndividual)Parents[1]).Trees.Length];
}
// at this point, p1 or p2, or both, may be null.
// If not, swap one in. Else just copy the parent.
for (int x = 0; x < j1.Trees.Length; x++)
{
if (x == t1 && res1) // we've got a tree with a kicking cross
// position!
{
j1.Trees[x] = ((GPIndividual)Parents[0]).Trees[x].LightClone();
j1.Trees[x].Owner = j1;
j1.Trees[x].Child = ((GPIndividual)Parents[0]).Trees[x].Child.CloneReplacing(p2, p1);
j1.Trees[x].Child.Parent = j1.Trees[x];
j1.Trees[x].Child.ArgPosition = 0;
j1.Evaluated = false;
} // it's changed
else
{
j1.Trees[x] = ((GPIndividual)Parents[0]).Trees[x].LightClone();
j1.Trees[x].Owner = j1;
j1.Trees[x].Child = (GPNode)((GPIndividual)Parents[0]).Trees[x].Child.Clone();
j1.Trees[x].Child.Parent = j1.Trees[x];
j1.Trees[x].Child.ArgPosition = 0;
}
}
if (n - (q - start) >= 2 && !TossSecondParent)
{
for (int x = 0; x < j2.Trees.Length; x++)
{
if (x == t2 && res2) // we've got a tree with a kicking
// cross position!
{
j2.Trees[x] = ((GPIndividual)Parents[1]).Trees[x].LightClone();
j2.Trees[x].Owner = j2;
j2.Trees[x].Child = ((GPIndividual)Parents[1]).Trees[x].Child.CloneReplacing(p1, p2);
j2.Trees[x].Child.Parent = j2.Trees[x];
j2.Trees[x].Child.ArgPosition = 0;
j2.Evaluated = false;
} // it's changed
else
{
j2.Trees[x] = ((GPIndividual)Parents[1]).Trees[x].LightClone();
j2.Trees[x].Owner = j2;
j2.Trees[x].Child = (GPNode)((GPIndividual)Parents[1]).Trees[x].Child.Clone();
j2.Trees[x].Child.Parent = j2.Trees[x];
j2.Trees[x].Child.ArgPosition = 0;
}
}
}
// add the individuals to the population
// by Ermo. I think this should be add
// inds.set(q,j1);
// Yes -- Sean
inds.Add(j1);
if (preserveParents != null)
{
parentparents[0].AddAll(parentparents[1]);
preserveParents[q] = parentparents[0];
}
q++;
if (q < n + start && !TossSecondParent)
{
// by Ermo. Same reason, should changed to add
//inds[q] = j2;
inds.Add(j2);
if (preserveParents != null)
{
preserveParents[q] = parentparents[0];
}
q++;
}
}
return(n);
}
/** Flattens an S-expression */
public IList FlattenSexp(IEvolutionState state, int threadnum, GPTree tree)
{
IList nodeList = GatherNodeString(state, threadnum, tree.Child, 0);
return(nodeList);
}
public virtual GPNode PickNode(IEvolutionState s, int subpop, int thread, GPIndividual ind, GPTree tree)
{
var rnd = s.Random[thread].NextDouble();
if (rnd > NonterminalProbability + TerminalProbability + RootProbability)
// pick anyone
{
if (Nodes == -1)
{
Nodes = tree.Child.NumNodes(GPNode.NODESEARCH_ALL);
}
{
return(tree.Child.NodeInPosition(s.Random[thread].NextInt(Nodes), GPNode.NODESEARCH_ALL));
}
}
if (rnd > NonterminalProbability + TerminalProbability)
// pick the root
{
return(tree.Child);
}
if (rnd > NonterminalProbability)
// pick terminals
{
if (Terminals == -1)
{
Terminals = tree.Child.NumNodes(GPNode.NODESEARCH_TERMINALS);
}
return(tree.Child.NodeInPosition(s.Random[thread].NextInt(Terminals), GPNode.NODESEARCH_TERMINALS));
}
// pick nonterminals if you can
if (Nonterminals == -1)
{
Nonterminals = tree.Child.NumNodes(GPNode.NODESEARCH_NONTERMINALS);
}
if (Nonterminals > 0)
// there are some nonterminals
{
return(tree.Child.NodeInPosition(s.Random[thread].NextInt(Nonterminals), GPNode.NODESEARCH_NONTERMINALS));
}
// there ARE no nonterminals! It must be the root node
return(tree.Child);
}
