BIG update
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@210 331e1502-861f-0410-8da2-ba01fb791d7f
This commit is contained in:
liefooga
parent
cd07971ba2
commit
528a149107
27 changed files with 1891 additions and 344 deletions
|
|
@ -13,14 +13,14 @@
|
|||
#ifndef MOEOINDICATORBASEDFITNESSASSIGNMENT_H_
|
||||
#define MOEOINDICATORBASEDFITNESSASSIGNMENT_H_
|
||||
|
||||
#include <math.h>
|
||||
#include <eoPop.h>
|
||||
#include <moeoConvertPopToObjectiveVectors.h>
|
||||
#include <moeoFitnessAssignment.h>
|
||||
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
|
||||
#include <metric/moeoVectorVsSolutionBinaryMetric.h>
|
||||
|
||||
/**
|
||||
*
|
||||
* Default is exponential
|
||||
*/
|
||||
template < class MOEOT >
|
||||
class moeoIndicatorBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT >
|
||||
|
|
@ -30,47 +30,193 @@ public:
|
|||
/** The type of objective vector */
|
||||
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
|
||||
|
||||
|
||||
/**
|
||||
* Default ctor
|
||||
* @param ...
|
||||
*/
|
||||
moeoIndicatorBasedFitnessAssignment(moeoVectorVsSolutionBinaryMetric < ObjectiveVector, double > * _metric) : metric(_metric)
|
||||
{}
|
||||
|
||||
|
||||
/**
|
||||
* Ctor
|
||||
* @param ...
|
||||
*/
|
||||
moeoIndicatorBasedFitnessAssignment(moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > * _solutionVsSolutionMetric, const double _kappa)// : metric(moeoExponentialVectorVsSolutionBinaryMetric < ObjectiveVector > (_solutionVsSolutionMetric, _kappa))
|
||||
{
|
||||
metric = new moeoExponentialVectorVsSolutionBinaryMetric < ObjectiveVector > (_solutionVsSolutionMetric, _kappa);
|
||||
}
|
||||
*/
|
||||
moeoIndicatorBasedFitnessAssignment(moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > * _metric, const double _kappa) : metric(_metric), kappa(_kappa)
|
||||
{}
|
||||
|
||||
|
||||
/**
|
||||
*
|
||||
*
|
||||
*/
|
||||
void operator()(eoPop < MOEOT > & _pop)
|
||||
{
|
||||
eoPop < MOEOT > tmp_pop;
|
||||
moeoConvertPopToObjectiveVectors < MOEOT > convertor;
|
||||
for (unsigned i=0; i<_pop.size() ; i++)
|
||||
// 1 - setting of the bounds
|
||||
setup(_pop);
|
||||
// 2 - computing every indicator values
|
||||
computeValues(_pop);
|
||||
// 3 - setting fitnesses
|
||||
setFitnesses(_pop);
|
||||
}
|
||||
|
||||
|
||||
/////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
// A SIMPLIFIER ! => utiliser la fonction d'en dessous ;-)
|
||||
void updateByDeleting(eoPop < MOEOT > & _pop, MOEOT & _moeo)
|
||||
{
|
||||
vector < double > v;
|
||||
v.resize(_pop.size());
|
||||
for (unsigned i=0; i<_pop.size(); i++)
|
||||
{
|
||||
tmp_pop.clear();
|
||||
tmp_pop = _pop;
|
||||
tmp_pop.erase(tmp_pop.begin() + i);
|
||||
_pop[i].fitness((*metric) (convertor(tmp_pop), _pop[i].objectiveVector()));
|
||||
v[i] = (*metric)(_moeo.objectiveVector(), _pop[i].objectiveVector());
|
||||
}
|
||||
for (unsigned i=0; i<_pop.size(); i++)
|
||||
{
|
||||
_pop[i].fitness( _pop[i].fitness() + exp(-v[i]/kappa) );
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
|
||||
{
|
||||
vector < double > v;
|
||||
v.resize(_pop.size());
|
||||
for (unsigned i=0; i<_pop.size(); i++)
|
||||
{
|
||||
v[i] = (*metric)(_objVec, _pop[i].objectiveVector());
|
||||
}
|
||||
for (unsigned i=0; i<_pop.size(); i++)
|
||||
{
|
||||
_pop[i].fitness( _pop[i].fitness() + exp(-v[i]/kappa) );
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
private:
|
||||
moeoVectorVsSolutionBinaryMetric < ObjectiveVector, double > * metric;
|
||||
// IDEM !
|
||||
void updateByAdding(eoPop < MOEOT > & _pop, MOEOT & _moeo)
|
||||
{
|
||||
vector < double > v;
|
||||
// update every fitness values to take the new individual into account
|
||||
v.resize(_pop.size());
|
||||
for (unsigned i=0; i<_pop.size(); i++)
|
||||
{
|
||||
v[i] = (*metric)(_moeo.objectiveVector(), _pop[i].objectiveVector());
|
||||
}
|
||||
for (unsigned i=0; i<_pop.size(); i++)
|
||||
{
|
||||
_pop[i].fitness( _pop[i].fitness() - exp(-v[i]/kappa) );
|
||||
}
|
||||
// compute the fitness of the new individual
|
||||
v.clear();
|
||||
v.resize(_pop.size());
|
||||
for (unsigned i=0; i<_pop.size(); i++)
|
||||
{
|
||||
v[i] = (*metric)(_pop[i].objectiveVector(), _moeo.objectiveVector());
|
||||
}
|
||||
double fitness = 0;
|
||||
for (unsigned i=0; i<v.size(); i++)
|
||||
{
|
||||
fitness -= exp(-v[i]/kappa);
|
||||
}
|
||||
_moeo.fitness(fitness);
|
||||
}
|
||||
|
||||
|
||||
// update _pop et retourne la valeur de fitness de _objVec
|
||||
double updateByAdding(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
|
||||
{
|
||||
vector < double > v;
|
||||
// update every fitness values to take the new individual into account
|
||||
v.resize(_pop.size());
|
||||
for (unsigned i=0; i<_pop.size(); i++)
|
||||
{
|
||||
v[i] = (*metric)(_objVec, _pop[i].objectiveVector());
|
||||
}
|
||||
for (unsigned i=0; i<_pop.size(); i++)
|
||||
{
|
||||
_pop[i].fitness( _pop[i].fitness() - exp(-v[i]/kappa) );
|
||||
}
|
||||
// compute the fitness of the new individual
|
||||
v.clear();
|
||||
v.resize(_pop.size());
|
||||
for (unsigned i=0; i<_pop.size(); i++)
|
||||
{
|
||||
v[i] = (*metric)(_pop[i].objectiveVector(), _objVec);
|
||||
}
|
||||
double result = 0;
|
||||
for (unsigned i=0; i<v.size(); i++)
|
||||
{
|
||||
result -= exp(-v[i]/kappa);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
/////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
protected:
|
||||
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > * metric;
|
||||
double kappa;
|
||||
std::vector < std::vector<double> > values;
|
||||
|
||||
|
||||
/**
|
||||
* Sets the bounds for every objective using the min and the max value for every objective vector of _pop
|
||||
* @param _pop the population
|
||||
*/
|
||||
void setup(const eoPop < MOEOT > & _pop)
|
||||
{
|
||||
double min, max;
|
||||
for (unsigned i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
|
||||
{
|
||||
min = _pop[0].objectiveVector()[i];
|
||||
max = _pop[0].objectiveVector()[i];
|
||||
for (unsigned j=1; j<_pop.size(); j++)
|
||||
{
|
||||
min = std::min(min, _pop[j].objectiveVector()[i]);
|
||||
max = std::max(max, _pop[j].objectiveVector()[i]);
|
||||
}
|
||||
// setting of the bounds for the objective i
|
||||
(*metric).setup(min, max, i);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Compute every indicator value : values[i] = I(_v[i], _o) !!!!!!!!!!!
|
||||
* @param ...
|
||||
*/
|
||||
void computeValues(const eoPop < MOEOT > & _pop)
|
||||
{
|
||||
values.clear();
|
||||
values.resize(_pop.size());
|
||||
for (unsigned i=0; i<_pop.size(); i++)
|
||||
{
|
||||
values[i].resize(_pop.size());
|
||||
for (unsigned j=0; j<_pop.size(); j++)
|
||||
{
|
||||
if (i != j)
|
||||
{
|
||||
values[i][j] = (*metric)(_pop[i].objectiveVector(), _pop[j].objectiveVector());
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void setFitnesses(eoPop < MOEOT > & _pop)
|
||||
{
|
||||
for (unsigned i=0; i<_pop.size(); i++)
|
||||
{
|
||||
_pop[i].fitness(computeFitness(i));
|
||||
}
|
||||
}
|
||||
|
||||
double computeFitness(const unsigned _idx)
|
||||
{
|
||||
double result = 0;
|
||||
for (unsigned i=0; i<values.size(); i++)
|
||||
{
|
||||
if (i != _idx)
|
||||
{
|
||||
result -= exp(-values[i][_idx]/kappa);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
};
|
||||
|
||||
#endif /*MOEOINDICATORBASEDFITNESSASSIGNMENT_H_*/
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue