/// <summary>
/// Method that returns a deep-copy clone of the current GP
/// </summary>
/// <returns>The deep-copy clone</returns>
public virtual TGPProgram Clone()
{
TGPProgram clone = new TGPProgram(mOperatorSet, mVariableSet, mConstantSet, mPrimitiveSet);
clone.Copy(this);
return(clone);
}
/// <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();
}
}
/// <summary>
/// Method that returns whether the current GP is better than another GP in quality
/// </summary>
/// <param name="rhs">The GP to compare with</param>
/// <returns>True if the current GP is better</returns>
public virtual bool IsBetterThan(TGPProgram rhs)
{
if (mDepth < rhs.Depth)
{
return(true);
}
else if (mDepth == rhs.Depth)
{
if (mLength < rhs.Length)
{
return(true);
}
else
{
return(false);
}
}
else
{
return(false);
}
}
public void SubtreeCrossover(IGPSolution _rhs, int iMaxDepthForCrossOver, string method, object tag = null)
{
TGPSolution rhs = (TGPSolution)_rhs;
int tree_count = mTrees.Count;
for (int i = 0; i < tree_count; ++i)
{
if (tree_count > 1 && DistributionModel.GetUniform() < 0.5)
{
TGPProgram temp = rhs.mTrees[i];
rhs.mTrees[i] = mTrees[i];
mTrees[i] = temp;
}
else
{
mTrees[i].SubtreeCrossover(rhs.mTrees[i], iMaxDepthForCrossOver, method, tag);
}
}
TrashFitness();
rhs.TrashFitness();
}
public void Analyze(List <S> solutions)
{
int[] primitive_count_array = new int[mPrimitives.Count];
int[] variable_count_array = new int[mVariables.Count];
double[] variable_fitness_array = new double[mVariables.Count];
for (int i = 0; i < primitive_count_array.Length; ++i)
{
primitive_count_array[i] = 0;
}
for (int i = 0; i < variable_fitness_array.Length; ++i)
{
variable_fitness_array[i] = 0;
variable_count_array[i] = 0;
}
double average_fitness = 0;
foreach (S solution in solutions)
{
average_fitness += FindSolutionFitness(solution);
}
if (solutions.Count > 0)
{
average_fitness /= solutions.Count;
}
foreach (S solution in solutions)
{
double fitness = FindSolutionFitness(solution);
if (fitness < average_fitness)
{
fitness = 0;
}
for (int i = 0; i < solution.ProgramCount; ++i)
{
TGPProgram p = solution.FindProgramAt(i) as TGPProgram;
List <TGPPrimitive> plist = p.FlattenPrimitives();
int variable_count = 0;
foreach (TGPPrimitive primitive in plist)
{
if (IsVariable(primitive))
{
variable_count++;
}
}
double primitive_fitness = fitness;
if (variable_count > 0)
{
primitive_fitness /= variable_count;
}
foreach (TGPPrimitive te in plist)
{
int id = mPrimitiveIdentifiers[te.Symbol];
primitive_count_array[id] += 1;
if (mVariableIdentifiers.ContainsKey(te.Symbol))
{
id = mVariableIdentifiers[te.Symbol];
variable_fitness_array[id] += primitive_fitness;
variable_count_array[id] += 1;
}
}
}
}
mPrimitiveCountMatrix.Add(primitive_count_array);
mVariableCountMatrix.Add(variable_count_array);
mFitnessMatrix.Add(variable_fitness_array);
}
/// <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();
}
}
}
}
