update with new fitness assignment
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@577 331e1502-861f-0410-8da2-ba01fb791d7f
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liefooga
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3 changed files with 238 additions and 23 deletions
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@ -13,6 +13,7 @@
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#ifndef MOEOIBMOLS_H_
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#define MOEOIBMOLS_H_
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#include <math.h>
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#include <eoContinue.h>
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#include <eoEvalFunc.h>
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#include <eoPop.h>
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@ -21,7 +22,7 @@
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#include <moNextMove.h>
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#include <algo/moeoLS.h>
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#include <archive/moeoArchive.h>
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#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
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#include <fitness/moeoBinaryIndicatorBasedFitnessAssignment.h>
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#include <move/moeoMoveIncrEval.h>
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/**
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@ -51,7 +52,7 @@ public:
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moNextMove < Move > & _nextMove,
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eoEvalFunc < MOEOT > & _eval,
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moeoMoveIncrEval < Move > & _moveIncrEval,
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moeoIndicatorBasedFitnessAssignment < MOEOT > & _fitnessAssignment,
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moeoBinaryIndicatorBasedFitnessAssignment < MOEOT > & _fitnessAssignment,
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eoContinue < MOEOT > & _continuator
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) :
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moveInit(_moveInit),
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@ -86,13 +87,16 @@ public:
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moeoArchive < MOEOT > previousArchive;
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// update the archive with the initial population
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archive.update(_pop);
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unsigned int count = 0;
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do
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{
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previousArchive.update(archive);
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oneStep(_pop);
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count++;
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archive.update(_pop);
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} while ( (! archive.equals(previousArchive)) && (continuator(_arch)) );
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_arch.update(archive);
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cout << "\t" << count << " steps" << endl;
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}
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@ -107,7 +111,7 @@ private:
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/** the incremental evaluation */
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moeoMoveIncrEval < Move > & moveIncrEval;
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/** the fitness assignment strategy */
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moeoIndicatorBasedFitnessAssignment < MOEOT > & fitnessAssignment;
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moeoBinaryIndicatorBasedFitnessAssignment < MOEOT > & fitnessAssignment;
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/** the stopping criteria */
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eoContinue < MOEOT > & continuator;
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@ -116,12 +120,8 @@ private:
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* Apply one step of the local search to the population _pop
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* @param _pop the population
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*/
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void oneStep (eoPop < MOEOT > & _pop)
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void old_oneStep (eoPop < MOEOT > & _pop)
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{
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////////////////////////////////////////////
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int ext_0_idx, ext_1_idx;
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ObjectiveVector ext_0_objVec, ext_1_objVec;
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///////////////////////////////////////////
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// the move
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Move move;
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// the objective vector and the fitness of the current solution
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@ -131,7 +131,13 @@ private:
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int worst_idx;
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ObjectiveVector worst_objVec;
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double worst_fitness;
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// the index current of the current solution to be explored
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////////////////////////////////////////////
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// the indexes and the objective vectors of the extreme non-dominated points
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int ext_0_idx, ext_1_idx;
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ObjectiveVector ext_0_objVec, ext_1_objVec;
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unsigned int ind;
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////////////////////////////////////////////
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// the index of the current solution to be explored
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unsigned int i=0;
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// initilization of the move for the first individual
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moveInit(move, _pop[i]);
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@ -146,7 +152,7 @@ private:
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// qui sont les extremes ? (=> min only !!!)
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// extreme solutions (min only!)
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ext_0_idx = -1;
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ext_0_objVec = x_objVec;
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ext_1_idx = -1;
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@ -176,10 +182,10 @@ private:
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ext_1_objVec = _pop[k].objectiveVector();
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}
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}
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// worst init
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// worst init
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if (ext_0_idx == -1)
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{
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unsigned int ind = 0;
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ind = 0;
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while (ind == ext_1_idx)
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{
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ind++;
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@ -190,7 +196,7 @@ private:
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}
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else if (ext_1_idx == -1)
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{
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unsigned int ind = 0;
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ind = 0;
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while (ind == ext_0_idx)
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{
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ind++;
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@ -275,6 +281,216 @@ private:
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}
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}
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// INUTILE !!!!
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/**
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* Apply one step of the local search to the population _pop
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* @param _pop the population
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*/
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void new_oneStep (eoPop < MOEOT > & _pop)
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{
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// the move
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Move move;
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// the objective vector and the fitness of the current solution
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ObjectiveVector x_objVec;
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double x_fitness;
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// the index, the objective vector and the fitness of the worst solution in the population (-1 implies that the worst is the newly created one)
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int worst_idx;
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ObjectiveVector worst_objVec;
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double worst_fitness;
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////////////////////////////////////////////
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// the index of the extreme non-dominated points
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int ext_0_idx, ext_1_idx;
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unsigned int ind;
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////////////////////////////////////////////
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// the index current of the current solution to be explored
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unsigned int i=0;
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// initilization of the move for the first individual
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moveInit(move, _pop[i]);
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while (i<_pop.size() && continuator(_pop))
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{
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// x = one neigbour of pop[i]
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// evaluate x in the objective space
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x_objVec = moveIncrEval(move, _pop[i]);
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// update every fitness values to take x into account and compute the fitness of x
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x_fitness = fitnessAssignment.updateByAdding(_pop, x_objVec);
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// extremes solutions
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OneObjectiveComparator comp0(0);
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ext_0_idx = std::min_element(_pop.begin(), _pop.end(), comp0) - _pop.begin();
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OneObjectiveComparator comp1(1);
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ext_1_idx = std::min_element(_pop.begin(), _pop.end(), comp1) - _pop.begin();
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// new = extreme ?
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if (x_objVec[0] < _pop[ext_0_idx].objectiveVector()[0])
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{
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ext_0_idx = -1;
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}
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else if ( (x_objVec[0] == _pop[ext_0_idx].objectiveVector()[0]) && (x_objVec[1] < _pop[ext_0_idx].objectiveVector()[1]) )
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{
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ext_0_idx = -1;
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}
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else if (x_objVec[1] < _pop[ext_1_idx].objectiveVector()[1])
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{
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ext_1_idx = -1;
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}
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else if ( (x_objVec[1] == _pop[ext_1_idx].objectiveVector()[1]) && (x_objVec[0] < _pop[ext_1_idx].objectiveVector()[0]) )
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{
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ext_1_idx = -1;
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}
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// worst init
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if (ext_0_idx == -1)
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{
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ind = 0;
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while (ind == ext_1_idx)
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{
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ind++;
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}
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worst_idx = ind;
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worst_objVec = _pop[ind].objectiveVector();
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worst_fitness = _pop[ind].fitness();
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}
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else if (ext_1_idx == -1)
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{
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ind = 0;
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while (ind == ext_0_idx)
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{
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ind++;
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}
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worst_idx = ind;
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worst_objVec = _pop[ind].objectiveVector();
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worst_fitness = _pop[ind].fitness();
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}
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else
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{
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worst_idx = -1;
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worst_objVec = x_objVec;
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worst_fitness = x_fitness;
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// who is the worst ?
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for (unsigned int j=0; j<_pop.size(); j++)
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{
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if ( (j!=ext_0_idx) && (j!=ext_1_idx) )
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{
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if (_pop[j].fitness() < worst_fitness)
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{
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worst_idx = j;
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worst_objVec = _pop[j].objectiveVector();
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worst_fitness = _pop[j].fitness();
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}
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}
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}
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// if the worst solution is the new one
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if (worst_idx == -1)
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{
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// if all its neighbours have been explored,
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// let's explore the neighborhoud of the next individual
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if (! nextMove(move, _pop[i]))
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{
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i++;
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if (i<_pop.size())
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{
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// initilization of the move for the next individual
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moveInit(move, _pop[i]);
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}
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}
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}
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// if the worst solution is located before _pop[i]
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else if (worst_idx <= i)
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{
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// the new solution takes place insteed of _pop[worst_idx]
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_pop[worst_idx] = _pop[i];
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move(_pop[worst_idx]);
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_pop[worst_idx].objectiveVector(x_objVec);
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_pop[worst_idx].fitness(x_fitness);
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// let's explore the neighborhoud of the next individual
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i++;
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if (i<_pop.size())
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{
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// initilization of the move for the next individual
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moveInit(move, _pop[i]);
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}
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}
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// if the worst solution is located after _pop[i]
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else if (worst_idx > i)
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{
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// the new solution takes place insteed of _pop[i+1] and _pop[worst_idx] is deleted
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_pop[worst_idx] = _pop[i+1];
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_pop[i+1] = _pop[i];
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move(_pop[i+1]);
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_pop[i+1].objectiveVector(x_objVec);
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_pop[i+1].fitness(x_fitness);
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// let's explore the neighborhoud of the individual _pop[i+2]
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i += 2;
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if (i<_pop.size())
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{
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// initilization of the move for the next individual
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moveInit(move, _pop[i]);
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}
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}
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// update fitness values
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fitnessAssignment.updateByDeleting(_pop, worst_objVec);
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////
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class OneObjectiveComparator : public moeoComparator < MOEOT >
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{
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public:
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OneObjectiveComparator(unsigned int _obj) : obj(_obj)
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{
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if (obj > MOEOT::ObjectiveVector::nObjectives())
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{
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throw std::runtime_error("Problem with the index of objective in OneObjectiveComparator");
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}
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}
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const bool operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
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{
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if (_moeo1.objectiveVector()[obj] < _moeo2.objectiveVector()[obj])
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{
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return true;
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}
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else
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{
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return (_moeo1.objectiveVector()[obj] == _moeo2.objectiveVector()[obj]) && (_moeo1.objectiveVector()[(obj+1)%2] < _moeo2.objectiveVector()[(obj+1)%2]);
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}
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}
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private:
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unsigned int obj;
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};
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//////////////////////////////////////////////////////////////////////////////////////////
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};
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#endif /*MOEOIBMOLS_H_*/
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@ -24,7 +24,7 @@
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#include <algo/moeoIBMOLS.h>
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#include <algo/moeoLS.h>
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#include <archive/moeoArchive.h>
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#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
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#include <fitness/moeoBinaryIndicatorBasedFitnessAssignment.h>
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#include <move/moeoMoveIncrEval.h>
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@ -63,7 +63,7 @@ public:
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moNextMove < Move > & _nextMove,
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eoEvalFunc < MOEOT > & _eval,
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moeoMoveIncrEval < Move > & _moveIncrEval,
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moeoIndicatorBasedFitnessAssignment < MOEOT > & _fitnessAssignment,
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moeoBinaryIndicatorBasedFitnessAssignment < MOEOT > & _fitnessAssignment,
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eoContinue < MOEOT > & _continuator,
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eoMonOp < MOEOT > & _monOp,
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eoMonOp < MOEOT > & _randomMonOp,
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@ -134,19 +134,17 @@ private:
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// start the creation of new solutions
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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if (shuffle[i] < _arch.size())
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// the given archive contains the individual i
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if (shuffle[i] < _arch.size()) // the given archive contains the individual i
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{
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// add it to the resulting pop
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_pop[i] = _arch[shuffle[i]];
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// then, apply the operator nIterationsNoise times
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// apply noise
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for (unsigned int j=0; j<nNoiseIterations; j++)
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{
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monOp(_pop[i]);
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}
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}
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else
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// a randomly generated solution needs to be added
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else // a random solution needs to be added
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{
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// random initialization
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randomMonOp(_pop[i]);
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@ -22,7 +22,8 @@
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#include <algo/moeoIteratedIBMOLS.h>
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#include <algo/moeoLS.h>
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#include <archive/moeoArchive.h>
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#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
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#include <fitness/moeoBinaryIndicatorBasedFitnessAssignment.h>
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#include <fitness/moeoExpBinaryIndicatorBasedFitnessAssignment.h>
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#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
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#include <move/moeoMoveIncrEval.h>
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@ -66,7 +67,7 @@ moeoLS < MOEOT, eoPop<MOEOT> & > & do_make_ls_moeo (
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'r', "Evolution Engine").value();
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double kappa = _parser.getORcreateParam(0.05, "kappa", "Scaling factor kappa for IndicatorBased",
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'k', "Evolution Engine").value();
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moeoIndicatorBasedFitnessAssignment < MOEOT > * fitnessAssignment;
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moeoBinaryIndicatorBasedFitnessAssignment < MOEOT > * fitnessAssignment;
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if (fitnessParam == std::string("IndicatorBased"))
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{
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// metric
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@ -84,7 +85,7 @@ moeoLS < MOEOT, eoPop<MOEOT> & > & do_make_ls_moeo (
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std::string stmp = std::string("Invalid binary quality indicator: ") + indicatorParam;
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throw std::runtime_error(stmp.c_str());
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}
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fitnessAssignment = new moeoIndicatorBasedFitnessAssignment < MOEOT> (*metric, kappa);
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fitnessAssignment = new moeoExpBinaryIndicatorBasedFitnessAssignment < MOEOT> (*metric, kappa);
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}
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else
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{
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