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moeo/src/fitness/moeoExpBinaryIndicatorBasedFitnessAssignment.h

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+/*
+* <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
+*
+*/
+//-----------------------------------------------------------------------------
+
+#ifndef MOEOEXPBINARYINDICATORBASEDFITNESSASSIGNMENT_H_
+#define MOEOEXPBINARYINDICATORBASEDFITNESSASSIGNMENT_H_
+
+#include <math.h>
+#include <vector>
+#include <eoPop.h>
+#include <fitness/moeoBinaryIndicatorBasedFitnessAssignment.h>
+#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
+#include <utils/moeoConvertPopToObjectiveVectors.h>
+
+/**
+ * Fitness assignment sheme based on 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.
+ */
+template < class MOEOT >
+class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorBasedFitnessAssignment < MOEOT >
+  {
+  public:
+
+    /** The type of objective vector */
+    typedef typename MOEOT::ObjectiveVector ObjectiveVector;
+
+
+    /**
+     * 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)
+    {}
+
+
+    /**
+     * 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;
+    }
+
+  };
+
+#endif /*MOEOEXPBINARYINDICATORBASEDFITNESSASSIGNMENT_H_*/