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add some abstract classes

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git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@492 331e1502-861f-0410-8da2-ba01fb791d7f
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liefooga 2007-07-02 13:54:16 +00:00
commit c44b212ee6
4 changed files with 252 additions and 180 deletions
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trunk/paradiseo-moeo/src/fitness/moeoIndicatorBasedFitnessAssignment.h
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@ -13,190 +13,12 @@
#ifndef MOEOINDICATORBASEDFITNESSASSIGNMENT_H_
#define MOEOINDICATORBASEDFITNESSASSIGNMENT_H_
#include <math.h>
#include <vector>
#include <eoPop.h>
#include <fitness/moeoFitnessAssignment.h>
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
#include <utils/moeoConvertPopToObjectiveVectors.h>
/**
* Fitness assignment sheme based an Indicator proposed in:
* E. Zitzler, S. Künzli, "Indicator-Based Selection in Multiobjective Search", Proc. 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII), pp. 832-842, Birmingham, UK (2004).
* This strategy is, for instance, used in IBEA.
* moeoIndicatorBasedFitnessAssignment is a moeoFitnessAssignment for Indicator-based strategies.
*/
template < class MOEOT >
class moeoIndicatorBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT >
{
public:
/** The type of objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Ctor.
* @param _metric the quality indicator
* @param _kappa the scaling factor
*/
moeoIndicatorBasedFitnessAssignment(moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa = 0.05) : metric(_metric), kappa(_kappa)
{}
/**
* Sets the fitness values for every solution contained in the population _pop
* @param _pop the population
*/
void operator()(eoPop < MOEOT > & _pop)
{
// 1 - setting of the bounds
setup(_pop);
// 2 - computing every indicator values
computeValues(_pop);
// 3 - setting fitnesses
setFitnesses(_pop);
}
/**
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
* @param _pop the population
* @param _objVec the objective vector
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
std::vector < double > v;
v.resize(_pop.size());
for (unsigned int i=0; i<_pop.size(); i++)
{
v[i] = metric(_objVec, _pop[i].objectiveVector());
}
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].fitness( _pop[i].fitness() + exp(-v[i]/kappa) );
}
}
/**
* Updates the fitness values of the whole population _pop by taking the adding of the objective vector _objVec into account
* and returns the fitness value of _objVec.
* @param _pop the population
* @param _objVec the objective vector
*/
double updateByAdding(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
std::vector < double > v;
// update every fitness values to take the new individual into account
v.resize(_pop.size());
for (unsigned int i=0; i<_pop.size(); i++)
{
v[i] = metric(_objVec, _pop[i].objectiveVector());
}
for (unsigned int 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 int i=0; i<_pop.size(); i++)
{
v[i] = metric(_pop[i].objectiveVector(), _objVec);
}
double result = 0;
for (unsigned int i=0; i<v.size(); i++)
{
result -= exp(-v[i]/kappa);
}
return result;
}
protected:
/** the quality indicator */
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & metric;
/** the scaling factor */
double kappa;
/** the computed indicator values */
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 int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
{
min = _pop[0].objectiveVector()[i];
max = _pop[0].objectiveVector()[i];
for (unsigned int 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 in values (values[i] = I(_v[i], _o))
* @param _pop the population
*/
void computeValues(const eoPop < MOEOT > & _pop)
{
values.clear();
values.resize(_pop.size());
for (unsigned int i=0; i<_pop.size(); i++)
{
values[i].resize(_pop.size());
for (unsigned int j=0; j<_pop.size(); j++)
{
if (i != j)
{
values[i][j] = metric(_pop[i].objectiveVector(), _pop[j].objectiveVector());
}
}
}
}
/**
* Sets the fitness value of the whple population
* @param _pop the population
*/
void setFitnesses(eoPop < MOEOT > & _pop)
{
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].fitness(computeFitness(i));
}
}
/**
* Returns the fitness value of the _idx th individual of the population
* @param _idx the index
*/
double computeFitness(const unsigned int _idx)
{
double result = 0;
for (unsigned int i=0; i<values.size(); i++)
{
if (i != _idx)
{
result -= exp(-values[i][_idx]/kappa);
}
}
return result;
}
};
class moeoIndicatorBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT > {};
#endif /*MOEOINDICATORBASEDFITNESSASSIGNMENT_H_*/