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

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Arnaud Liefooghe 2014-10-17 15:04:32 +02:00
commit c5e5af64d0
1 changed files with 137 additions and 113 deletions
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46
moeo/src/fitness/moeoExpBinaryIndicatorBasedFitnessAssignment.h
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@ -1,10 +1,15 @@
/*
* <moeoExpBinaryIndicatorBasedFitnessAssignment.h>
* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2007
* (C) OPAC Team, LIFL, 2002-2007
* 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
@ -60,13 +65,24 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
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();
}
/**
@ -101,6 +117,7 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
{
_pop[i].fitness( _pop[i].fitness() + exp(-v[i]/kappa) );
}
v.clear();
}
@ -124,8 +141,6 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
_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);
@ -135,6 +150,7 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
{
result -= exp(-v[i]/kappa);
}
v.clear();
return result;
}
@ -177,11 +193,18 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
*/
virtual void computeValues(const eoPop < MOEOT > & _pop)
{
values.clear();
// initialize the values structure (if it is used for the first time with such a population size)
if (values.size() < _pop.size())
{
values.resize(_pop.size());
for (unsigned int i=0; i<_pop.size(); i++)
for (unsigned int i=0; i<values.size(); i++)
{
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++)
{
@ -202,19 +225,20 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
{
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++)
for (unsigned int i=0; i<_pop.size(); i++)
{
if (i != _idx)
{