moeoIndicatorBasedFitnessAssignment.h

// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-

//-----------------------------------------------------------------------------
// moeoIndicatorBasedFitnessAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
    This library...

    Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
 */
//-----------------------------------------------------------------------------

#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>

template < class MOEOT >
class moeoIndicatorBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT >
{
public:

    typedef typename MOEOT::ObjectiveVector ObjectiveVector;


    moeoIndicatorBasedFitnessAssignment(moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa = 0.05) : metric(_metric), kappa(_kappa)
    {}


    void operator()(eoPop < MOEOT > & _pop)
    {
        // 1 - setting of the bounds
        setup(_pop);
        // 2 - computing every indicator values
        computeValues(_pop);
        // 3 - setting fitnesses
        setFitnesses(_pop);
    }


    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) );
        }
    }


    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:

    moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & metric;
    double kappa;
    std::vector < std::vector<double> > values;


    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);
        }
    }


    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());
                }
            }
        }
    }


    void setFitnesses(eoPop < MOEOT > & _pop)
    {
        for (unsigned int i=0; i<_pop.size(); i++)
        {
            _pop[i].fitness(computeFitness(i));
        }
    }


    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;
    }

};

#endif /*MOEOINDICATORBASEDFITNESSASSIGNMENT_H_*/

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