public static double NumericDistance(CombinatorialSemantics desired, Semantics candidate, IEnumerable <Semantics> allCandidates)
{
double distance = 0.0;
for (int i = 0; i < Math.Min(desired.Length, candidate.Length); i++)
{
var desiredSemantics = desired[i];
foreach (var value in desiredSemantics)
{
if (candidate[i] == null)
{
// if candidate has no semantic value, take the worst distance of the other candidates
// if no other candidate has a semantic value, ignore i'th value
/*bool distanceFound;
* double worstDistance = GetWorstDistance((int) value, allCandidates, i, out distanceFound);
* if (distanceFound) {
* distance += worstDistance;
* }*/
}
else
{
var dist = Math.Abs((int)value - (int)candidate[i]);
distance += dist;
}
}
}
return(distance);
}
public bool Operate(Semantics resultSemantics, Individual individual, ISemanticSubTreePool subTreePool,
int maxTreeDepth, out bool triedBackPropagation)
{
SyntaxTree tree = (SyntaxTree)individual.SyntaxTree.DeepClone();
TreeNode exchangeNode = tree.GetRandomNode();
Type nodeType = exchangeNode.Type;
TreeNode root;
if (exchangeNode.IsBackPropagable(out root))
{
triedBackPropagation = true;
var desiredSemantics = DoSemanticBackPropagation(resultSemantics, exchangeNode, root);
TreeNode newNode = DoLibrarySearch(individual, subTreePool, nodeType, desiredSemantics);
if (newNode != null)
{
bool replaced = tree.ReplaceTreeNode(exchangeNode, newNode);
if (replaced && tree.Height <= maxTreeDepth)
{
individual.SyntaxTree = tree;
return(true);
}
return(false);
}
}
triedBackPropagation = false;
return(false);
}
private Semantics GetRandom(int length)
{
var semantics = new Semantics(length);
for (int i = 0; i < length; i++)
{
semantics[i] = RandomValueGenerator.Instance.GetBool();
}
return(semantics);
}
public static double NumericDistance(Semantics semantics1, Semantics semantics2)
{
double distance = 0.0;
for (int i = 0; i < Math.Min(semantics1.Length, semantics2.Length); i++)
{
double s1 = (double)semantics1[i];
double s2 = (double)semantics2[i];
distance += Math.Abs(s1 - s2);
}
return(distance);
}
// Algorithm details: Pawlak et al. - Competent Geometric Semantic GP page 187
public Semantics GetMidpoint(Semantics semantics1, Semantics semantics2)
{
int length = semantics1.Length;
Semantics midpoint = new Semantics(length);
for (int i = 0; i < length; i++)
{
int s1 = semantics1[i] != null ? (int)semantics1[i] : 0; // use 0 if no semantics evaluated
int s2 = semantics2[i] != null ? (int)semantics2[i] : 0;
midpoint[i] = (s1 + s2) / 2;
}
return(midpoint);
}
// Algorithm details: Pawlak et al. - Competent Geometric Semantic GP page 187
public Semantics GetMidpoint(Semantics semantics1, Semantics semantics2)
{
int length = semantics1.Length;
var midpoint = new Semantics(length);
var random = GetRandom(length);
for (int i = 0; i < length; i++)
{
bool s1 = semantics1[i] != null ? (bool)semantics1[i] : false; // use false if no semantics evaluated
bool s2 = semantics2[i] != null ? (bool)semantics2[i] : false;
bool sx = (bool)random[i];
midpoint[i] = (s1 && sx) || (!sx && s2);
}
return(midpoint);
}
public static double HammingDistance(CombinatorialSemantics desired, Semantics candidate, IEnumerable <Semantics> allCandidates)
{
double distance = 0.0;
for (int i = 0; i < Math.Min(desired.Length, candidate.Length); i++)
{
var semantics = desired[i];
foreach (var value in semantics)
{
if (candidate == null || !value.Equals(candidate[i]))
{
distance++;
}
}
}
return(distance);
}
// Pawlak et al. Algorithm 1 in paper Semantic Backpropagation for Designing Search Operators in GP:
// Naming: n ... node, p ... root, t ... target
// semantic types may be mixed up in expression: e.g. bool ret = (a + b) == (c - a)
// => have to use object type instead of one generic type
public CombinatorialSemantics Propagate(TreeNode root, TreeNode node, Semantics target)
{
CombinatorialSemantics semantics = new CombinatorialSemantics(target.Length);
var path = root.GetPathTo(node);
for (int i = 0; i < target.Length; i++) // for all ti element of t do:
{
ISet <object> currentValueSet = new HashSet <object>(); // Di
currentValueSet.Add(target[i]); // Di <- { ti }
var currentNode = root; // a <- p
bool ambiguityFound = false; // * not element of Di
int pathIndex = 0; // index of path element
while (currentNode != node && currentValueSet.Count > 0 && !ambiguityFound)
{
int k = currentNode.Children.IndexOf(path[pathIndex]);
ISet <object> valueSet = new HashSet <object>(); // D' <- {}
var invertibleExpr = currentNode as IInvertible; // if not invertible, loop will end
if (invertibleExpr != null && invertibleExpr.IsInvertible)
{
foreach (var desiredValue in currentValueSet)
{
bool ambiguous;
var complementValue = invertibleExpr.GetComplementValue(k, i);
if (complementValue != null) // complement value can be null if semantics not evaluated
{
valueSet.UnionWith(invertibleExpr.Invert(desiredValue, k, complementValue, out ambiguous));
if (ambiguous)
{
ambiguityFound = true;
}
}
}
}
// valueSet is empty in case of ambiguity (* element of D')
currentValueSet = valueSet; // Di <- D'
currentNode = path[pathIndex++]; // a <- Child(a, n)
}
semantics[i] = currentValueSet;
}
return(semantics);
}
private void AssignSemanticsToTreeNodes(Individual individual, MDLFitnessResult fitnessResult)
{
var dataset = fitnessResult.Dataset;
int testCount = dataset.Count;
foreach (var id in dataset.Features)
{
var node = individual.SyntaxTree.FindNodeById(id);
var semanticsNode = node as ISemanticsHolder;
if (semanticsNode != null)
{
var semantics = new Semantics(testCount);
for (int i = 0; i < testCount; i++)
{
semantics[i] = dataset[id][i];
}
semanticsNode.Semantics = semantics;
}
}
}
public static double HammingDistance(Semantics semantics1, Semantics semantics2)
{
double distance = 0.0;
for (int i = 0; i < Math.Min(semantics1.Length, semantics2.Length); i++)
{
if (semantics1[i] == null || semantics2[i] == null)
{
if (semantics1[i] == null && semantics2[i] != null ||
semantics1[i] != null && semantics2[i] == null)
{
distance++;
}
}
else if (!semantics1[i].Equals(semantics2[i]))
{
distance++;
}
}
return(distance);
}
private Individual GenerateChildren(Individual individual1, Individual individual2,
Semantics midpoint, out bool triedBackPropagation)
{
var child1 = new Individual(individual1);
child1.FitnessEvaluated = false;
var mutated = ResultSemanticsOperator.Operate(midpoint,
child1, SubTreePool, MaxTreeDepth, out triedBackPropagation);
double fitnessChange = 0;
if (mutated && triedBackPropagation && individual1.FitnessEvaluated && FitnessEvaluator != null)
{
double fitness = FitnessEvaluator.Evaluate(child1, Problem).Fitness;
fitnessChange = fitness - individual1.Fitness;
}
Statistics.Instance.AddBackpropagationAttemptCrossover(triedBackPropagation, fitnessChange);
return(mutated ? child1 : individual1);
}
protected virtual CombinatorialSemantics DoSemanticBackPropagation(Semantics resultSemantics,
TreeNode exchangeNode, TreeNode root)
{
return(SemanticBackPropagator.Propagate(root, exchangeNode, resultSemantics));
}