diff --git a/branches/paradiseo-moeo-1.0/src/moeoAchievementFitnessAssignment.h b/branches/paradiseo-moeo-1.0/src/moeoAchievementFitnessAssignment.h
index 281127f54..b609f6c50 100644
--- a/branches/paradiseo-moeo-1.0/src/moeoAchievementFitnessAssignment.h
+++ b/branches/paradiseo-moeo-1.0/src/moeoAchievementFitnessAssignment.h
@@ -17,100 +17,100 @@
 #include <moeoFitnessAssignment.h>
 
 /**
- * 
+ *
  */
 template < class MOEOT >
 class moeoAchievementFitnessAssignment : public moeoScalarFitnessAssignment < MOEOT >
 {
 public:
-	
-	/** the objective vector type of the solutions */
+
+    /** the objective vector type of the solutions */
     typedef typename MOEOT::ObjectiveVector ObjectiveVector;
 
 
-	/**
-     * Default ctor
-     * @param _reference reference point vector
-     * @param _lambdas weighted coefficients vector
-     * @param _spn arbitrary small positive number (0 < _spn << 1)
-     */
-	moeoAchievementFitnessAssignment(ObjectiveVector & _reference, vector < double > & _lambdas, double _spn=0.0001) : reference(_reference), lambdas(_lambdas), spn(_spn)
-	{
-		// consistency check
-		if ((spn < 0.0) || (spn > 1.0))
+    /**
+        * Default ctor
+        * @param _reference reference point vector
+        * @param _lambdas weighted coefficients vector
+        * @param _spn arbitrary small positive number (0 < _spn << 1)
+        */
+    moeoAchievementFitnessAssignment(ObjectiveVector & _reference, vector < double > & _lambdas, double _spn=0.0001) : reference(_reference), lambdas(_lambdas), spn(_spn)
+    {
+        // consistency check
+        if ((spn < 0.0) || (spn > 1.0))
         {
             std::cout << "Warning, the arbitrary small positive number should be > 0 and <<1, adjusted to 0.0001\n";
             spn = 0.0001;
         }
-	}
-	
-	
-	/**
-     * Ctor with default values for lambdas (1/nObjectives)
-     * @param _reference reference point vector
-     * @param _spn arbitrary small positive number (0 < _spn << 1)
-     */
-	moeoAchievementFitnessAssignment(ObjectiveVector & _reference, double _spn=0.0001) : reference(_reference), spn(_spn)
-	{
-		// compute the default values for lambdas
-		lambdas = vector < double > (ObjectiveVector::nObjectives());
-		for (unsigned i=0 ; i<lambdas.size(); i++)
-		{
-			lambdas[i] = 1.0 / ObjectiveVector::nObjectives();
-		}
-		// consistency check
-		if ((spn < 0.0) || (spn > 1.0))
+    }
+
+
+    /**
+        * Ctor with default values for lambdas (1/nObjectives)
+        * @param _reference reference point vector
+        * @param _spn arbitrary small positive number (0 < _spn << 1)
+        */
+    moeoAchievementFitnessAssignment(ObjectiveVector & _reference, double _spn=0.0001) : reference(_reference), spn(_spn)
+    {
+        // compute the default values for lambdas
+        lambdas = vector < double > (ObjectiveVector::nObjectives());
+        for (unsigned i=0 ; i<lambdas.size(); i++)
+        {
+            lambdas[i] = 1.0 / ObjectiveVector::nObjectives();
+        }
+        // consistency check
+        if ((spn < 0.0) || (spn > 1.0))
         {
             std::cout << "Warning, the arbitrary small positive number should be > 0 and <<1, adjusted to 0.0001\n";
             spn = 0.0001;
         }
-	}
-	
-	
-	/**
-	 * Sets the fitness values for every solution contained in the population _pop
-     * @param _pop the population
-	 */
-	virtual void operator()(eoPop < MOEOT > & _pop)
-	{
-		for (unsigned i=0; i<_pop.size() ; i++)
-		{
-			compute(_pop[i]);
-		}
-	}
+    }
 
 
-	/**
-     * Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account (nothing to do).
-     * @param _pop the population
-     * @param _objVec the objective vector
+    /**
+     * Sets the fitness values for every solution contained in the population _pop
+        * @param _pop the population
      */
+    virtual void operator()(eoPop < MOEOT > & _pop)
+    {
+        for (unsigned i=0; i<_pop.size() ; i++)
+        {
+            compute(_pop[i]);
+        }
+    }
+
+
+    /**
+        * Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account (nothing to do).
+        * @param _pop the population
+        * @param _objVec the objective vector
+        */
     void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
     {
         // nothing to do ;-)
     }
-    
-    
+
+
     /**
      * Sets the reference point
      * @param _reference the new reference point
      */
     void setReference(const ObjectiveVector & _reference)
-	{
-		reference = _reference;
-	}
-	
-	
+    {
+        reference = _reference;
+    }
+
+
 private:
 
-	/** the reference point */
-	ObjectiveVector reference;
-	/** the weighted coefficients vector */
-	vector < double > lambdas;
-	/** an arbitrary small positive number (0 < _spn << 1) */
-	double spn;
-	
-	
+    /** the reference point */
+    ObjectiveVector reference;
+    /** the weighted coefficients vector */
+    vector < double > lambdas;
+    /** an arbitrary small positive number (0 < _spn << 1) */
+    double spn;
+
+
     /**
      * Returns a big value (regarded as infinite)
      */
@@ -118,26 +118,26 @@ private:
     {
         return std::numeric_limits<double>::max();
     }
-	
-	
-	/**
-	 * Computes the fitness value for a solution
-	 * @param _moeo the solution
-	 */
-	void compute(MOEOT & _moeo)
-	{
-		unsigned nobj = MOEOT::ObjectiveVector::nObjectives();
-		double temp;
-		double min = inf();
-		double sum = 0; 
-		for (unsigned obj=0; obj<nobj; obj++)
-		{
-			temp = lambdas[obj] * (reference[obj] - _moeo.objectiveVector()[obj]);
-			min = std::min(min, temp);
-			sum += temp;
-		}
-		_moeo.fitness(min + spn*sum);
-	}
+
+
+    /**
+     * Computes the fitness value for a solution
+     * @param _moeo the solution
+     */
+    void compute(MOEOT & _moeo)
+    {
+        unsigned nobj = MOEOT::ObjectiveVector::nObjectives();
+        double temp;
+        double min = inf();
+        double sum = 0;
+        for (unsigned obj=0; obj<nobj; obj++)
+        {
+            temp = lambdas[obj] * (reference[obj] - _moeo.objectiveVector()[obj]);
+            min = std::min(min, temp);
+            sum += temp;
+        }
+        _moeo.fitness(min + spn*sum);
+    }
 
 };