diff --git a/branches/paradiseo-moeo-1.0/src/moeoCrowdingDistanceDiversityAssignment.h b/branches/paradiseo-moeo-1.0/src/moeoCrowdingDistanceDiversityAssignment.h
index 0d60bfa04..493c4fe6e 100755
--- a/branches/paradiseo-moeo-1.0/src/moeoCrowdingDistanceDiversityAssignment.h
+++ b/branches/paradiseo-moeo-1.0/src/moeoCrowdingDistanceDiversityAssignment.h
@@ -20,7 +20,6 @@
 /**
  * Diversity assignment sheme based on crowding distance proposed in:
  * K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, "A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, vol. 6, no. 2 (2002).
- * This strategy is, for instance, used in NSGA-II.
  */
 template < class MOEOT >
 class moeoCrowdingDistanceDiversityAssignment : public moeoDiversityAssignment < MOEOT >
@@ -82,8 +81,78 @@ public:
     }
 
 
+protected:
+
+    /**
+     * Sets the distance values
+     * @param _pop the population
+     */
+    virtual void setDistances (eoPop < MOEOT > & _pop)
+    {
+        double min, max, distance;
+        unsigned nObjectives = MOEOT::ObjectiveVector::nObjectives();
+        // set diversity to 0
+        for (unsigned i=0; i<_pop.size(); i++)
+        {
+            _pop[i].diversity(0);
+        }
+        // for each objective
+        for (unsigned obj=0; obj<nObjectives; obj++)
+        {
+            // comparator
+            moeoOneObjectiveComparator < MOEOT > comp(obj);
+            // sort
+            std::sort(_pop.begin(), _pop.end(), comp);
+            // min & max
+            min = _pop[0].objectiveVector()[obj];
+            max = _pop[_pop.size()-1].objectiveVector()[obj];
+            // set the diversity value to infiny for min and max
+            _pop[0].diversity(inf());
+            _pop[_pop.size()-1].diversity(inf());
+            for (unsigned i=1; i<_pop.size()-1; i++)
+            {
+                distance = (_pop[i+1].objectiveVector()[obj] - _pop[i-1].objectiveVector()[obj]) / (max-min);
+                _pop[i].diversity(_pop[i].diversity() + distance);
+            }
+        }
+    }
+
+};
+
+
+/**
+ * Diversity assignment sheme based on crowding distance proposed in:
+ * K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, "A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, vol. 6, no. 2 (2002).
+ * Tis strategy assigns diversity values FRONT BY FRONT. It is, for instance, used in NSGA-II.
+ */
+template < class MOEOT >
+class moeoFrontByFrontCrowdingDistanceDiversityAssignment : public moeoCrowdingDistanceDiversityAssignment < MOEOT >
+{
+public:
+
+    /** the objective vector type of the solutions */
+    typedef typename MOEOT::ObjectiveVector ObjectiveVector;
+
+
+    /**
+     * @warning NOT IMPLEMENTED, DO NOTHING !
+     * Updates the diversity 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
+     * @warning NOT IMPLEMENTED, DO NOTHING !
+     */
+    void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
+    {
+        cout << "WARNING : updateByDeleting not implemented in moeoFrontByFrontCrowdingDistanceDiversityAssignment" << endl;
+    }
+
+
 private:
 
+    using moeoCrowdingDistanceDiversityAssignment < MOEOT >::inf;
+    using moeoCrowdingDistanceDiversityAssignment < MOEOT >::tiny;
+
+
     /**
      * Sets the distance values
      * @param _pop the population
@@ -106,7 +175,7 @@ private:
         while (a < _pop.size())
         {
             b = lastIndex(_pop,a);	// the front ends at b
-            // if there is 2 or less individuals in the front...
+            // if there is less than 2 individuals in the front...
             if ((b-a) < 2)
             {
                 for (unsigned i=a; i<=b; i++)
@@ -164,7 +233,6 @@ private:
         return i;
     }
 
-
 };
 
 #endif /*MOEOCROWDINGDISTANCEDIVERSITYASSIGNMENT_H_*/