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

some cleanup of memory consumption when using IBEA with a large population

Browse source
This commit is contained in:
Arnaud Liefooghe 2014-10-17 15:04:32 +02:00
commit c5e5af64d0
1 changed files with 137 additions and 113 deletions
Download patch file Download diff file Expand all files Collapse all files

250
moeo/src/fitness/moeoExpBinaryIndicatorBasedFitnessAssignment.h
View file

@ -1,38 +1,43 @@
/*
* <moeoExpBinaryIndicatorBasedFitnessAssignment.h>
* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2007
* (C) OPAC Team, LIFL, 2002-2007
*
* Arnaud Liefooghe
*
* This software is governed by the CeCILL license under French law and
* abiding by the rules of distribution of free software. You can use,
* modify and/ or redistribute the software under the terms of the CeCILL
* license as circulated by CEA, CNRS and INRIA at the following URL
* "http://www.cecill.info".
*
* As a counterpart to the access to the source code and rights to copy,
* modify and redistribute granted by the license, users are provided only
* with a limited warranty and the software's author, the holder of the
* economic rights, and the successive licensors have only limited liability.
*
* In this respect, the user's attention is drawn to the risks associated
* with loading, using, modifying and/or developing or reproducing the
* software by the user in light of its specific status of free software,
* that may mean that it is complicated to manipulate, and that also
* therefore means that it is reserved for developers and experienced
* professionals having in-depth computer knowledge. Users are therefore
* encouraged to load and test the software's suitability as regards their
* requirements in conditions enabling the security of their systems and/or
* data to be ensured and, more generally, to use and operate it in the
* same conditions as regards security.
* The fact that you are presently reading this means that you have had
* knowledge of the CeCILL license and that you accept its terms.
*
* ParadisEO WebSite : http://paradiseo.gforge.inria.fr
* Contact: paradiseo-help@lists.gforge.inria.fr
*
*/
* <moeoExpBinaryIndicatorBasedFitnessAssignment.h>
* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2014
* (C) OPAC Team, LIFL, 2002-2014
*
* Arnaud Liefooghe
*
* Oct 17, 2014 - Arnaud Liefooghe
* Modifications on the handling of the internal data structure (values)
* in order to avoid (too much/bad) memory consumptions, in particular
* when a very large population size is used
*
* This software is governed by the CeCILL license under French law and
* abiding by the rules of distribution of free software. You can use,
* modify and/ or redistribute the software under the terms of the CeCILL
* license as circulated by CEA, CNRS and INRIA at the following URL
* "http://www.cecill.info".
*
* As a counterpart to the access to the source code and rights to copy,
* modify and redistribute granted by the license, users are provided only
* with a limited warranty and the software's author, the holder of the
* economic rights, and the successive licensors have only limited liability.
*
* In this respect, the user's attention is drawn to the risks associated
* with loading, using, modifying and/or developing or reproducing the
* software by the user in light of its specific status of free software,
* that may mean that it is complicated to manipulate, and that also
* therefore means that it is reserved for developers and experienced
* professionals having in-depth computer knowledge. Users are therefore
* encouraged to load and test the software's suitability as regards their
* requirements in conditions enabling the security of their systems and/or
* data to be ensured and, more generally, to use and operate it in the
* same conditions as regards security.
* The fact that you are presently reading this means that you have had
* knowledge of the CeCILL license and that you accept its terms.
*
* ParadisEO WebSite : http://paradiseo.gforge.inria.fr
* Contact: paradiseo-help@lists.gforge.inria.fr
*
*/
//-----------------------------------------------------------------------------
#ifndef MOEOEXPBINARYINDICATORBASEDFITNESSASSIGNMENT_H_
@ -52,38 +57,49 @@
*/
template < class MOEOT >
class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorBasedFitnessAssignment < MOEOT >
{
public:
{
public:
/** The type of objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
typedef typename ObjectiveVector::Type Type;
typedef typename MOEOT::Fitness Fitness;
/**
* Ctor.
* @param _metric the quality indicator
* @param _kappa the scaling factor
*/
moeoExpBinaryIndicatorBasedFitnessAssignment(moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa = 0.05) : metric(_metric), kappa(_kappa)
{}
moeoExpBinaryIndicatorBasedFitnessAssignment(moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa = 0.05) : metric(_metric), kappa(_kappa), values(0) {}
/**
* Dtor.
*/
~moeoExpBinaryIndicatorBasedFitnessAssignment()
{
// clear "values"
for (unsigned int i=0; i<values.size(); i++) values[i].clear();
values.clear();
}
/**
* Sets the fitness values for every solution contained in the population _pop
* @param _pop the population
*/
virtual void operator()(eoPop < MOEOT > & _pop)
{
// 1 - setting of the bounds
setup(_pop);
// 2 - computing every indicator values
computeValues(_pop);
// 3 - setting fitnesses
setFitnesses(_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
@ -91,19 +107,20 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
std::vector < double > v;
v.resize(_pop.size());
for (unsigned int i=0; i<_pop.size(); i++)
std::vector < double > v;
v.resize(_pop.size());
for (unsigned int i=0; i<_pop.size(); i++)
{
v[i] = metric(_objVec, _pop[i].objectiveVector());
v[i] = metric(_objVec, _pop[i].objectiveVector());
}
for (unsigned int i=0; i<_pop.size(); i++)
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].fitness( _pop[i].fitness() + exp(-v[i]/kappa) );
_pop[i].fitness( _pop[i].fitness() + exp(-v[i]/kappa) );
}
v.clear();
}
/**
* 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.
@ -112,118 +129,125 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
*/
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++)
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());
v[i] = metric(_objVec, _pop[i].objectiveVector());
}
for (unsigned int i=0; i<_pop.size(); i++)
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].fitness( _pop[i].fitness() - exp(-v[i]/kappa) );
_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++)
// compute the fitness of the new individual
for (unsigned int i=0; i<_pop.size(); i++)
{
v[i] = metric(_pop[i].objectiveVector(), _objVec);
v[i] = metric(_pop[i].objectiveVector(), _objVec);
}
double result = 0;
for (unsigned int i=0; i<v.size(); i++)
double result = 0;
for (unsigned int i=0; i<v.size(); i++)
{
result -= exp(-v[i]/kappa);
result -= exp(-v[i]/kappa);
}
return result;
v.clear();
return result;
}
protected:
protected:
/** the quality indicator */
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & metric;
/** the scaling factor */
double kappa;
/** the computed indicator values */
std::vector < std::vector<Type> > 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)
{
typename MOEOT::ObjectiveVector::Type min, max;
for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
typename MOEOT::ObjectiveVector::Type 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 = _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]);
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);
// 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
*/
virtual void computeValues(const eoPop < MOEOT > & _pop)
{
values.clear();
values.resize(_pop.size());
for (unsigned int i=0; i<_pop.size(); i++)
// initialize the values structure (if it is used for the first time with such a population size)
if (values.size() < _pop.size())
{
values[i].resize(_pop.size());
// the metric may not be symetric, thus neither is the matrix
for (unsigned int j=0; j<_pop.size(); j++)
values.resize(_pop.size());
for (unsigned int i=0; i<values.size(); i++)
{
if (i != j)
values[i].resize(_pop.size());
}
}
// go
for (unsigned int i=0; i<_pop.size(); i++)
{
// the metric may not be symetric, thus neither is the matrix
for (unsigned int j=0; j<_pop.size(); j++)
{
if (i != j)
{
values[i][j] = Type( metric(_pop[i].objectiveVector(), _pop[j].objectiveVector()) );
values[i][j] = Type( metric(_pop[i].objectiveVector(), _pop[j].objectiveVector()) );
}
}
}
}
/**
* Sets the fitness value of the whole population
* @param _pop the population
*/
virtual void setFitnesses(eoPop < MOEOT > & _pop)
{
for (unsigned int i=0; i<_pop.size(); i++)
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].fitness(computeFitness(i));
_pop[i].fitness(computeFitness(_pop, i));
}
}
/**
* Returns the fitness value of the _idx th individual of the population
* @param _pop the population (only useful for its size here)
* @param _idx the index
*/
virtual Fitness computeFitness(const unsigned int _idx)
virtual Fitness computeFitness(const eoPop < MOEOT > & _pop, const unsigned int _idx)
{
Fitness result(0.0);
for (unsigned int i=0; i<values.size(); i++)
Fitness result(0.0);
for (unsigned int i=0; i<_pop.size(); i++)
{
if (i != _idx)
if (i != _idx)
{
result -= exp(-values[i][_idx]/kappa);
result -= exp(-values[i][_idx]/kappa);
}
}
return result;
return result;
}
};
};
#endif /*MOEOEXPBINARYINDICATORBASEDFITNESSASSIGNMENT_H_*/