nojhan/paradiseo
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

add achievement fitness assignment scheme

Browse source 
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@309 331e1502-861f-0410-8da2-ba01fb791d7f
This commit is contained in:
liefooga 2007-06-18 15:39:15 +00:00
commit fca8cf40ea
1 changed files with 86 additions and 86 deletions
Download patch file Download diff file Expand all files Collapse all files

168
branches/paradiseo-moeo-1.0/src/moeoAchievementFitnessAssignment.h
View file

@ -17,100 +17,100 @@
#include <moeoFitnessAssignment.h>
/**
*
*
*/
template < class MOEOT >
class moeoAchievementFitnessAssignment : public moeoScalarFitnessAssignment < MOEOT >
{
public:
/** the objective vector type of the solutions */
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Default ctor
* @param _reference reference point vector
* @param _lambdas weighted coefficients vector
* @param _spn arbitrary small positive number (0 < _spn << 1)
*/
moeoAchievementFitnessAssignment(ObjectiveVector & _reference, vector < double > & _lambdas, double _spn=0.0001) : reference(_reference), lambdas(_lambdas), spn(_spn)
{
// consistency check
if ((spn < 0.0) || (spn > 1.0))
/**
* Default ctor
* @param _reference reference point vector
* @param _lambdas weighted coefficients vector
* @param _spn arbitrary small positive number (0 < _spn << 1)
*/
moeoAchievementFitnessAssignment(ObjectiveVector & _reference, vector < double > & _lambdas, double _spn=0.0001) : reference(_reference), lambdas(_lambdas), spn(_spn)
{
// consistency check
if ((spn < 0.0) || (spn > 1.0))
{
std::cout << "Warning, the arbitrary small positive number should be > 0 and <<1, adjusted to 0.0001\n";
spn = 0.0001;
}
}
/**
* Ctor with default values for lambdas (1/nObjectives)
* @param _reference reference point vector
* @param _spn arbitrary small positive number (0 < _spn << 1)
*/
moeoAchievementFitnessAssignment(ObjectiveVector & _reference, double _spn=0.0001) : reference(_reference), spn(_spn)
{
// compute the default values for lambdas
lambdas = vector < double > (ObjectiveVector::nObjectives());
for (unsigned i=0 ; i<lambdas.size(); i++)
{
lambdas[i] = 1.0 / ObjectiveVector::nObjectives();
}
// consistency check
if ((spn < 0.0) || (spn > 1.0))
}
/**
* Ctor with default values for lambdas (1/nObjectives)
* @param _reference reference point vector
* @param _spn arbitrary small positive number (0 < _spn << 1)
*/
moeoAchievementFitnessAssignment(ObjectiveVector & _reference, double _spn=0.0001) : reference(_reference), spn(_spn)
{
// compute the default values for lambdas
lambdas = vector < double > (ObjectiveVector::nObjectives());
for (unsigned i=0 ; i<lambdas.size(); i++)
{
lambdas[i] = 1.0 / ObjectiveVector::nObjectives();
}
// consistency check
if ((spn < 0.0) || (spn > 1.0))
{
std::cout << "Warning, the arbitrary small positive number should be > 0 and <<1, adjusted to 0.0001\n";
spn = 0.0001;
}
}
/**
* Sets the fitness values for every solution contained in the population _pop
* @param _pop the population
*/
virtual void operator()(eoPop < MOEOT > & _pop)
{
for (unsigned i=0; i<_pop.size() ; i++)
{
compute(_pop[i]);
}
}
}
/**
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account (nothing to do).
* @param _pop the population
* @param _objVec the objective vector
/**
* Sets the fitness values for every solution contained in the population _pop
* @param _pop the population
*/
virtual void operator()(eoPop < MOEOT > & _pop)
{
for (unsigned i=0; i<_pop.size() ; i++)
{
compute(_pop[i]);
}
}
/**
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account (nothing to do).
* @param _pop the population
* @param _objVec the objective vector
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
// nothing to do ;-)
}
/**
* Sets the reference point
* @param _reference the new reference point
*/
void setReference(const ObjectiveVector & _reference)
{
reference = _reference;
}
{
reference = _reference;
}
private:
/** the reference point */
ObjectiveVector reference;
/** the weighted coefficients vector */
vector < double > lambdas;
/** an arbitrary small positive number (0 < _spn << 1) */
double spn;
/** the reference point */
ObjectiveVector reference;
/** the weighted coefficients vector */
vector < double > lambdas;
/** an arbitrary small positive number (0 < _spn << 1) */
double spn;
/**
* Returns a big value (regarded as infinite)
*/
@ -118,26 +118,26 @@ private:
{
return std::numeric_limits<double>::max();
}
/**
* Computes the fitness value for a solution
* @param _moeo the solution
*/
void compute(MOEOT & _moeo)
{
unsigned nobj = MOEOT::ObjectiveVector::nObjectives();
double temp;
double min = inf();
double sum = 0;
for (unsigned obj=0; obj<nobj; obj++)
{
temp = lambdas[obj] * (reference[obj] - _moeo.objectiveVector()[obj]);
min = std::min(min, temp);
sum += temp;
}
_moeo.fitness(min + spn*sum);
}
/**
* Computes the fitness value for a solution
* @param _moeo the solution
*/
void compute(MOEOT & _moeo)
{
unsigned nobj = MOEOT::ObjectiveVector::nObjectives();
double temp;
double min = inf();
double sum = 0;
for (unsigned obj=0; obj<nobj; obj++)
{
temp = lambdas[obj] * (reference[obj] - _moeo.objectiveVector()[obj]);
min = std::min(min, temp);
sum += temp;
}
_moeo.fitness(min + spn*sum);
}
};