Add the DUAL fitness assignment class

Change a bit the machinery of moeoExpBinaryIndicatorBasedFitnessAssignment to allow subclassing.

moeo/src/fitness/moeoExpBinaryIndicatorBasedDualFitnessAssignment.h

@@ -0,0 +1,67 @@
+
+#include <fitness/moeoExpBinaryIndicatorBasedFitnessAssignment.h>
+
+template<class MOEOT>
+class moeoExpBinaryIndicatorBasedDualFitnessAssignment : public moeoExpBinaryIndicatorBasedFitnessAssignment<MOEOT>
+{
+protected:
+    eoPop<MOEOT> _feasible_pop;
+    eoPop<MOEOT> _unfeasible_pop;
+
+public:
+    typedef typename MOEOT::ObjectiveVector ObjectiveVector;
+
+    moeoExpBinaryIndicatorBasedDualFitnessAssignment(
+            moeoNormalizedSolutionVsSolutionBinaryMetric<ObjectiveVector,double> & metric,
+            const double kappa = 0.05
+        ) : moeoExpBinaryIndicatorBasedFitnessAssignment<MOEOT>( metric, kappa ) {}
+
+    //! Split up the population in two: in one pop the feasible individual, in the other the feasible ones
+    virtual void split( eoPop<MOEOT> & pop )
+    {
+          _feasible_pop.reserve(pop.size());
+        _unfeasible_pop.reserve(pop.size());
+
+        for( typename eoPop<MOEOT>::iterator it=pop.begin(), end=pop.end(); it != end; ++it ) {
+            // The ObjectiveVector should implement "is_feasible"
+            if( it->objectiveVector().is_feasible() ) {
+                  _feasible_pop.push_back( *it );
+            } else {
+                _unfeasible_pop.push_back( *it );
+            }
+        }
+    }
+
+    /*! If the population is homogeneous (only composed of feasible individuals or unfeasible ones),
+     * then apply the operators on the whole population.
+     * But, if there is at least one feasible individual, then apply them only on the feasible individuals.
+     */
+    virtual void operator()(eoPop < MOEOT > & pop)
+    {
+        // separate the pop in the members
+        split( pop );
+
+        eoPop<MOEOT>* ppop;
+        // if there is at least one feasible individual, it will supersede all the unfeasible ones
+        if( _feasible_pop.size() == 0 ) {
+            ppop = & _unfeasible_pop;
+        } else {
+            ppop = & _feasible_pop;
+        }
+
+        this->setup(*ppop);
+        this->computeValues(*ppop);
+        this->setFitnesses(*ppop);
+    }
+
+    virtual void setFitnesses(eoPop < MOEOT > & pop)
+    {
+        for (unsigned int i=0; i<pop.size(); i++) {
+            // We should maintain the feasibility of the fitness across computations
+            pop[i].fitness( this->computeFitness(i), pop[i].is_feasible() );
+        }
+    }
+
+
+};
+

moeo/src/fitness/moeoExpBinaryIndicatorBasedFitnessAssignment.h

@@ -58,6 +58,7 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
     /** The type of objective vector */
     typedef typename MOEOT::ObjectiveVector ObjectiveVector;
 
+    typedef typename ObjectiveVector::Type Type;
 
     /**
      * Ctor.
@@ -72,7 +73,7 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
      * Sets the fitness values for every solution contained in the population _pop
      * @param _pop the population
      */
-    void operator()(eoPop < MOEOT > & _pop)
+    virtual void operator()(eoPop < MOEOT > & _pop)
     {
       // 1 - setting of the bounds
       setup(_pop);
@@ -145,7 +146,7 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
     /** the scaling factor */
     double kappa;
     /** the computed indicator values */
-    std::vector < std::vector<double> > values;
+    std::vector < std::vector<Type> > values;
 
 
     /**
@@ -181,6 +182,7 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
       for (unsigned int i=0; i<_pop.size(); i++)
         {
           values[i].resize(_pop.size());
+          // the metric may not be symetric, thus neither is the matrix
           for (unsigned int j=0; j<_pop.size(); j++)
             {
               if (i != j)
@@ -193,10 +195,10 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
 
 
     /**
-     * Sets the fitness value of the whple population
+     * Sets the fitness value of the whole population
      * @param _pop the population
      */
-    void setFitnesses(eoPop < MOEOT > & _pop)
+    virtual void setFitnesses(eoPop < MOEOT > & _pop)
     {
       for (unsigned int i=0; i<_pop.size(); i++)
         {
@@ -209,9 +211,9 @@ class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorB
      * Returns the fitness value of the _idx th individual of the population
      * @param _idx the index
      */
-    double computeFitness(const unsigned int _idx)
+    Type computeFitness(const unsigned int _idx)
     {
-      double result = 0;
+      Type result(0.0);
       for (unsigned int i=0; i<values.size(); i++)
         {
           if (i != _idx)

moeo/src/moeo

@@ -127,6 +127,7 @@
 #include <fitness/moeoDominanceRankFitnessAssignment.h>
 #include <fitness/moeoDummyFitnessAssignment.h>
 #include <fitness/moeoExpBinaryIndicatorBasedFitnessAssignment.h>
+#include <fitness/moeoExpBinaryIndicatorBasedDualFitnessAssignment.h>
 #include <fitness/moeoFitnessAssignment.h>
 #include <fitness/moeoIndicatorBasedFitnessAssignment.h>
 #include <fitness/moeoReferencePointIndicatorBasedFitnessAssignment.h>