static PyObject* selNSGA2(PyObject *self, PyObject *args){
/* Args[0] : Individual list
* Args[1] : Number of individuals wanted in output
* Return : k selected individuals from input individual list
*/
PyObject *lListIndv = PyTuple_GetItem(args, 0);
#ifdef PY3K
unsigned long k = (unsigned long)PyLong_AS_LONG(PyTuple_GetItem(args, 1));
#else
unsigned int k = (unsigned int)PyInt_AS_LONG(PyTuple_GetItem(args, 1));
#endif
PyObject *lListSelect = PyList_New(0);
unsigned int lLenListIndv = (unsigned int)PyList_Size(lListIndv);
unsigned int lNbrObjectives = (unsigned int)PyTuple_Size(PyObject_GetAttrString(PyObject_GetAttrString(PyList_GetItem(lListIndv,0), "fitness"), "values"));
if(k == 0)
return lListSelect;
// First : copy fitness values into an std::vector<std::vector<double> >
// First vector index is used to identify individuals
// Second vector index represents an objective
std::vector<std::vector<double> > lPopFit(lLenListIndv, std::vector<double>(lNbrObjectives,0.));
for(unsigned int i = 0; i < lLenListIndv; i++){
for(unsigned int j = 0; j < lNbrObjectives; j++)
lPopFit[i][j] = PyFloat_AS_DOUBLE(PyTuple_GetItem(PyObject_GetAttrString(PyObject_GetAttrString(PyList_GetItem(lListIndv,i), "fitness"), "wvalues"), j));
}
unsigned int lParetoSorted = 0;
unsigned int lFrontIndex = 0;
std::vector<std::vector<unsigned int> > lParetoFront(1, std::vector<unsigned int>(0));
std::vector<unsigned int> lDominating(lLenListIndv, 0);
std::vector<std::vector<unsigned int> > lDominatedInds(lLenListIndv, std::vector<unsigned int>(0));
// Rank first pareto front
for(unsigned int i = 0; i < lLenListIndv; i++){
for(unsigned int j = i+1; j < lLenListIndv; j++){
if(isDominated(lPopFit[j], lPopFit[i])){
lDominating[j]++;
lDominatedInds[i].push_back(j);
}
else if(isDominated(lPopFit[i], lPopFit[j])){
lDominating[i]++;
lDominatedInds[j].push_back(i);
}
}
if(lDominating[i] == 0){
lParetoFront[lFrontIndex].push_back(i);
lParetoSorted++;
}
}
// Rank other pareto fronts, until we reach the *k* limit
while(lParetoSorted < k && lParetoSorted < lLenListIndv){
lFrontIndex++;
lParetoFront.push_back(std::vector<unsigned int>(0));
for(unsigned int i = 0; i < lParetoFront[lFrontIndex-1].size(); i++){
unsigned int lIndiceP = lParetoFront[lFrontIndex-1][i];
for(unsigned int j = 0; j < lDominatedInds[lIndiceP].size(); j++){
unsigned int lIndiceD = lDominatedInds[lIndiceP][j];
if(--lDominating[lIndiceD] == 0){
lParetoFront[lFrontIndex].push_back(lIndiceD);
lParetoSorted++;
}
}
}
}
// Append individuals from pareto ranking until we reach the limit
for(unsigned int i = 0; i < lParetoFront.size(); i++){
if(PyList_Size(lListSelect)+lParetoFront[i].size() <= k){
for(unsigned int j = 0; j < lParetoFront[i].size(); j++)
PyList_Append(lListSelect, PyList_GetItem(lListIndv,lParetoFront[i][j]));
}
else{
break;
}
}
// Crowding distance on the last front
if(PyList_Size(lListSelect) == k)
return lListSelect;
FitComp lCmpIndvObj;
std::vector<unsigned int> lLastParetoFront = lParetoFront.back();
std::vector<std::pair<double, unsigned int> > lDistances(0);
std::vector<std::pair<std::vector<double>, unsigned int> > lCrowdingList(0);
double lInfinity = std::numeric_limits<double>::infinity();
// Reserve sufficient memory for the subsequent push_back
lDistances.reserve(lLastParetoFront.size());
lCrowdingList.reserve(lLastParetoFront.size());
for(unsigned int i = 0; i < lLastParetoFront.size(); i++){
// Push initial distance (0.0) and individual index in lPopFit and lListIndv for each individual
lDistances.push_back(std::pair<double, unsigned int>(0., lLastParetoFront[i]));
// Push fitness and individual index in lDistances for each individual
lCrowdingList.push_back(std::pair<std::vector<double>, unsigned int>(lPopFit[lLastParetoFront[i]],i));
}
for(unsigned int i = 0; i < lNbrObjectives; i++){
// For each objective
// Set the current objective in the comparison class
lCmpIndvObj.mCompIndex = i;
// Sort (stable, in order to keep the same order for equal fitness values)
stable_sort(lCrowdingList.begin(), lCrowdingList.end(), lCmpIndvObj);
// Set an infinite distance to the extremums
lDistances[lCrowdingList[0].second].first = lInfinity;
lDistances[lCrowdingList.back().second].first = lInfinity;
for(unsigned int j = 1; j < lCrowdingList.size()-1; j++){
if(lDistances[lCrowdingList[j].second].first < lInfinity)
lDistances[lCrowdingList[j].second].first += lCrowdingList[j+1].first[i]-lCrowdingList[j-1].first[i];
}
}
// Final sorting (again, must be stable)
stable_sort(lDistances.begin(), lDistances.end(), crowdDistComp);
// Pick the last individuals (with the higher crowding distance) first
for(unsigned int i = lDistances.size()-1; i >= 0; i--){
if(PyList_Size(lListSelect) >= k)
break;
// While the size of the return list is lesser than *k*, append the next individual
PyList_Append(lListSelect, PyList_GetItem(lListIndv,lDistances[i].second));
}
return lListSelect;
}
//------------------------------------------------------------------------------
BranchInst *findOutermostBranch(BranchSet &branches, const DominatorTree *dt) {
for (auto branch : branches)
if (!isDominated(branch, branches, dt))
return branch;
return nullptr;
}
