diff --git a/src/eva2/server/go/operators/mutation/MutateESCovarianceMatrixAdaptionPlus.java b/src/eva2/server/go/operators/mutation/MutateESCovarianceMatrixAdaptionPlus.java
index 4a4c2abc..e8d126ce 100644
--- a/src/eva2/server/go/operators/mutation/MutateESCovarianceMatrixAdaptionPlus.java
+++ b/src/eva2/server/go/operators/mutation/MutateESCovarianceMatrixAdaptionPlus.java
@@ -6,190 +6,225 @@ import eva2.server.go.populations.Population;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.Jama.Matrix;
 
-public class MutateESCovarianceMatrixAdaptionPlus extends MutateESCovarianceMatrixAdaption implements InterfaceMutationGenerational 
-{
+public class MutateESCovarianceMatrixAdaptionPlus extends
+		MutateESCovarianceMatrixAdaption implements
+		InterfaceMutationGenerational {
 	protected double m_psuccess;
 	protected double m_cp;
-	protected double m_psuccesstarget=0.44;
+	protected double m_psuccesstarget = 0.44;
 	protected double m_stepd;
 	protected double m_pthresh;
-	protected int m_lambda=1;
+	protected int m_lambda = 1;
 
-	
 	public MutateESCovarianceMatrixAdaptionPlus() {
 		super();
 	}
-	
-	
-	public MutateESCovarianceMatrixAdaptionPlus(MutateESCovarianceMatrixAdaptionPlus mutator) {
+
+	public MutateESCovarianceMatrixAdaptionPlus(
+			MutateESCovarianceMatrixAdaptionPlus mutator) {
 		super(mutator);
-		m_psuccess=mutator.m_psuccess;
-		m_cp=mutator.m_cp;
-		m_psuccesstarget=mutator.m_psuccesstarget;
-		m_lambda=mutator.m_lambda;
-		m_pthresh=mutator.m_pthresh;
-		m_stepd=mutator.m_stepd;
+		m_psuccess = mutator.m_psuccess;
+		m_cp = mutator.m_cp;
+		m_psuccesstarget = mutator.m_psuccesstarget;
+		m_lambda = mutator.m_lambda;
+		m_pthresh = mutator.m_pthresh;
+		m_stepd = mutator.m_stepd;
 	}
 
-	/** This method will enable you to clone a given mutation operator
+	/**
+	 * This method will enable you to clone a given mutation operator
+	 * 
 	 * @return The clone
 	 */
-	 public Object clone() {
+	public Object clone() {
 		return new MutateESCovarianceMatrixAdaptionPlus(this);
-	 }
+	}
 
+	/**
+	 * This method allows you to init the mutation operator
+	 * 
+	 * @param individual
+	 *            The individual that will be mutated.
+	 * @param opt
+	 *            The optimization problem.
+	 */
+	public void init(AbstractEAIndividual individual,
+			InterfaceOptimizationProblem opt) {
 
-	 /** This method allows you to init the mutation operator
-	  * @param individual        The individual that will be mutated.
-	  * @param opt               The optimization problem.
-	  */
-	 public void init(AbstractEAIndividual individual, InterfaceOptimizationProblem opt) {
-		 
-		 if (!(individual instanceof InterfaceESIndividual)) return;
-		 super.init(individual,opt);
-		 m_psuccesstarget=1.0/(5+Math.sqrt(m_lambda)/2);
-		 m_psuccess=m_psuccesstarget;
-		 m_stepd=1.0+m_D/(2.0*m_lambda);
-		 m_cp=m_psuccesstarget*m_lambda/(2+m_psuccesstarget*m_lambda);
-		 m_c=2.0/(2.0+m_D);
-		 this.cov            = 2.0/(6.0+Math.pow(m_D, 2)); //ATTN: differs from the standard CMA-ES
-		 m_pthresh=0.44;
+		if (!(individual instanceof InterfaceESIndividual))
+			return;
+		super.init(individual, opt);
+		m_psuccesstarget = 1.0 / (5 + Math.sqrt(m_lambda) / 2);
+		m_psuccess = m_psuccesstarget;
+		m_stepd = 1.0 + m_D / (2.0 * m_lambda);
+		m_cp = m_psuccesstarget * m_lambda / (2 + m_psuccesstarget * m_lambda);
+		m_c = 2.0 / (2.0 + m_D);
+		this.cov = 2.0 / (6.0 + Math.pow(m_D, 2)); // ATTN: differs from the
+													// standard CMA-ES
+		m_pthresh = 0.44;
 
-	 }
+	}
 
+	protected void adaptStrategyGen(AbstractEAIndividual child,
+			AbstractEAIndividual parent) {
+		if (child.getFitness(0) <= parent.getFitness(0)) {
+			// updatecov
+			updateCovariance(child, parent);
+			// updateCovariance();
+		}
 
+	}
 
-	 protected void adaptStrategyGen(AbstractEAIndividual child,AbstractEAIndividual parent) {
-		 if(child.getFitness(0)<=parent.getFitness(0)){
-			 //updatecov
-			 updateCovariance(child, parent);
-			 //updateCovariance();
-		 }
-
-	 }
-	 
 	/**
 	 * @param parent
 	 * @param child
 	 */
-	public void updateCovariance(AbstractEAIndividual child,AbstractEAIndividual parent) {
-		double[] step=new double[m_D]; 
-		for(int i=0;i<m_D;i++){
-			 step[i]=((InterfaceESIndividual)parent).getDGenotype()[i]-((InterfaceESIndividual)child).getDGenotype()[i];			 
-		 }
-		updateCovariance(step);
-	}
-	
-	public void updateCovariance() {
-		double[] step=new double[m_D]; 
-		for(int i=0;i<m_D;i++){
-			 step[i]=Bz[i];
+	public void updateCovariance(AbstractEAIndividual child,
+			AbstractEAIndividual parent) {
+		double[] step = new double[m_D];
+		for (int i = 0; i < m_D; i++) {
+			step[i] = ((InterfaceESIndividual) parent).getDGenotype()[i]
+					- ((InterfaceESIndividual) child).getDGenotype()[i];
 		}
 		updateCovariance(step);
 	}
+
+	public void updateCovariance() {
+		double[] step = new double[m_D];
+		for (int i = 0; i < m_D; i++) {
+			step[i] = Bz[i];
+		}
+		updateCovariance(step);
+	}
+
 	/**
 	 * @param step
 	 * 
 	 */
 	public void updateCovariance(double[] step) {
-		for(int i=0;i<m_D;i++){
-			 if(m_psuccess<m_pthresh){
-				 m_PathS[i]=(1.0-m_c)*m_PathS[i]+Math.sqrt(m_c*(2.0-m_c))*(step[i])/m_SigmaGlobal;
-			 }else{
-				 m_PathS[i]=(1.0-m_c)*m_PathS[i];
-			 }
+		for (int i = 0; i < m_D; i++) {
+			if (m_psuccess < m_pthresh) {
+				m_PathS[i] = (1.0 - m_c) * m_PathS[i]
+						+ Math.sqrt(m_c * (2.0 - m_c)) * (step[i])
+						/ m_SigmaGlobal;
+			} else {
+				m_PathS[i] = (1.0 - m_c) * m_PathS[i];
+			}
 
-		 }
-		 if(m_psuccess<m_pthresh){
-			 m_C=m_C.multi((1.0-cov));
-			 m_C.plusEquals(Matrix.outer(m_PathS, m_PathS).multi(cov));
-		 }else{
-			 m_C=m_C.multi((1.0-cov)).plus(
-					 (Matrix.outer(m_PathS, m_PathS).plus(
-							 m_C.multi(m_c*(2.0-m_c))
-					 ).multi(cov))
-			 );
-		 }
+		}
+		if (m_psuccess < m_pthresh) {
+			m_C = m_C.multi((1.0 - cov));
+			m_C.plusEquals(Matrix.outer(m_PathS, m_PathS).multi(cov));
+		} else {
+			m_C = m_C.multi((1.0 - cov)).plus(
+					(Matrix.outer(m_PathS, m_PathS).plus(
+							m_C.multi(m_c * (2.0 - m_c))).multi(cov)));
+		}
 	}
-	
-	 protected void adaptStrategy() {
-	 }
 
-	 public int getM_lambda() {
-		 return m_lambda;
-	 }
+	protected void adaptStrategy() {
+	}
 
-	 public void setM_lambda(int mLambda) {
-		 m_lambda = mLambda;
-	 }
+	public int getM_lambda() {
+		return m_lambda;
+	}
 
+	public void setM_lambda(int mLambda) {
+		m_lambda = mLambda;
+	}
 
-	 @Override
-	 public void crossoverOnStrategyParameters(AbstractEAIndividual indy1,
-			 Population partners) {
-		 // ATTN: Crossover is not defined for this 
-		 
-	 }
-	 
-	 @Override
-	 public void adaptAfterSelection(Population oldPop, Population selectedPop) {
-		 // TODO Auto-generated method stub
+	@Override
+	public void crossoverOnStrategyParameters(AbstractEAIndividual indy1,
+			Population partners) {
+		// ATTN: Crossover is not defined for this
 
-	 }
-	 
-	 @Override
-	 public void adaptGenerational(Population selectedPop, Population parentPop, Population newPop, boolean updateSelected) {
-		 double rate = 0.;
-		 for (int i = 0; i < parentPop.size(); i++) {
-			 // calculate success rate
-			 //            System.out.println("new fit / old fit: " + BeanInspector.toString(newPop.getEAIndividual(i).getFitness()) + " , " + BeanInspector.toString(parentPop.getEAIndividual(i).getFitness()));
-			 if (newPop.getEAIndividual(i).getFitness(0) < parentPop.getEAIndividual(i).getFitness(0)) rate++;
-		 }
-		 rate = rate / parentPop.size(); 
+	}
 
-		 if (updateSelected) for (int i = 0; i < selectedPop.size(); i++) { // applied to the old population as well in case of plus strategy
-			 MutateESCovarianceMatrixAdaptionPlus mutator =  (MutateESCovarianceMatrixAdaptionPlus)((AbstractEAIndividual)selectedPop.get(i)).getMutationOperator();
-			 updateMutator(rate, mutator);
-			 if(selectedPop.getEAIndividual(i).getFitness(0)<=parentPop.getEAIndividual(0).getFitness(0)){
-				 mutator.adaptStrategyGen(selectedPop.getEAIndividual(i),parentPop.getEAIndividual(0));
-			 }
-			 //            System.out.println("old pop step size " + mutator.getSigma()+ " (" + mutator+ ")");
-		 }
-		 for (int i = 0; i < newPop.size(); i++) {
-			 MutateESCovarianceMatrixAdaptionPlus mutator =  (MutateESCovarianceMatrixAdaptionPlus)((AbstractEAIndividual)newPop.get(i)).getMutationOperator();
-			 updateMutator(rate, mutator);
-			 if(newPop.getEAIndividual(i).getFitness(0)<=parentPop.getEAIndividual(0).getFitness(0)){
-				 mutator.adaptStrategyGen(newPop.getEAIndividual(i),parentPop.getEAIndividual(0));
-			 }
-			 //            System.out.println("new pop step size " + mutator.getSigma()+ " (" + mutator+ ")");
-		 }
-	 }
+	// @Override
+	public void adaptAfterSelection(Population oldPop, Population selectedPop) {
+		// TODO Auto-generated method stub
 
-	 private void updateMutator(double rate, MutateESCovarianceMatrixAdaptionPlus mutator) {
-		 mutator.updateStepSize(rate);
-	 }
-	 public double getM_psuccess() {
-		 return m_psuccess;
-	 }
-	 public void updateStepSize(double psuccess) {
-		 this.m_psuccess=(1-m_cp)*m_psuccess+m_cp*psuccess;
-		 m_SigmaGlobal=m_SigmaGlobal*Math.exp(1/m_stepd*(m_psuccess-m_psuccesstarget)/(1-m_psuccesstarget));
-	 }
+	}
 
-	 public String getName() {
-		 return "CMA mutation for plus Strategies";
-	 }
-	 /** This method returns a global info string
-	  * @return description
-	  */
-	 public static String globalInfo() {
-		 return "This is the CMA mutation according to Igel,Hansen,Roth 2007";
-	 }
-	 
-	 @Override
-	 public String getStringRepresentation() {
-		 // TODO Auto-generated method stub
-		 return "CMA-plus mutation";
-	 }
+	// @Override
+	public void adaptGenerational(Population selectedPop, Population parentPop,
+			Population newPop, boolean updateSelected) {
+		double rate = 0.;
+		for (int i = 0; i < parentPop.size(); i++) {
+			// calculate success rate
+			// System.out.println("new fit / old fit: " +
+			// BeanInspector.toString(newPop.getEAIndividual(i).getFitness()) +
+			// " , " +
+			// BeanInspector.toString(parentPop.getEAIndividual(i).getFitness()));
+			if (newPop.getEAIndividual(i).getFitness(0) < parentPop
+					.getEAIndividual(i).getFitness(0))
+				rate++;
+		}
+		rate = rate / parentPop.size();
+
+		if (updateSelected)
+			for (int i = 0; i < selectedPop.size(); i++) { // applied to the old
+															// population as
+															// well in case of
+															// plus strategy
+				MutateESCovarianceMatrixAdaptionPlus mutator = (MutateESCovarianceMatrixAdaptionPlus) ((AbstractEAIndividual) selectedPop
+						.get(i)).getMutationOperator();
+				updateMutator(rate, mutator);
+				if (selectedPop.getEAIndividual(i).getFitness(0) <= parentPop
+						.getEAIndividual(0).getFitness(0)) {
+					mutator.adaptStrategyGen(selectedPop.getEAIndividual(i),
+							parentPop.getEAIndividual(0));
+				}
+				// System.out.println("old pop step size " + mutator.getSigma()+
+				// " (" + mutator+ ")");
+			}
+		for (int i = 0; i < newPop.size(); i++) {
+			MutateESCovarianceMatrixAdaptionPlus mutator = (MutateESCovarianceMatrixAdaptionPlus) ((AbstractEAIndividual) newPop
+					.get(i)).getMutationOperator();
+			updateMutator(rate, mutator);
+			if (newPop.getEAIndividual(i).getFitness(0) <= parentPop
+					.getEAIndividual(0).getFitness(0)) {
+				mutator.adaptStrategyGen(newPop.getEAIndividual(i), parentPop
+						.getEAIndividual(0));
+			}
+			// System.out.println("new pop step size " + mutator.getSigma()+
+			// " (" + mutator+ ")");
+		}
+	}
+
+	private void updateMutator(double rate,
+			MutateESCovarianceMatrixAdaptionPlus mutator) {
+		mutator.updateStepSize(rate);
+	}
+
+	public double getM_psuccess() {
+		return m_psuccess;
+	}
+
+	public void updateStepSize(double psuccess) {
+		this.m_psuccess = (1 - m_cp) * m_psuccess + m_cp * psuccess;
+		m_SigmaGlobal = m_SigmaGlobal
+				* Math.exp(1 / m_stepd * (m_psuccess - m_psuccesstarget)
+						/ (1 - m_psuccesstarget));
+	}
+
+	public String getName() {
+		return "CMA mutation for plus Strategies";
+	}
+
+	/**
+	 * This method returns a global info string
+	 * 
+	 * @return description
+	 */
+	public static String globalInfo() {
+		return "This is the CMA mutation according to Igel,Hansen,Roth 2007";
+	}
+
+	@Override
+	public String getStringRepresentation() {
+		// TODO Auto-generated method stub
+		return "CMA-plus mutation";
+	}
 
 }
diff --git a/src/eva2/server/go/strategies/MultiObjectiveCMAES.java b/src/eva2/server/go/strategies/MultiObjectiveCMAES.java
index c422ce5d..b96e95c4 100644
--- a/src/eva2/server/go/strategies/MultiObjectiveCMAES.java
+++ b/src/eva2/server/go/strategies/MultiObjectiveCMAES.java
@@ -26,44 +26,39 @@ import eva2.server.go.populations.SolutionSet;
 import eva2.server.go.problems.AbstractOptimizationProblem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
-
 import eva2.tools.math.Mathematics;
 import eva2.tools.math.RNG;
 import eva2.tools.math.Jama.EigenvalueDecomposition;
 import eva2.tools.math.Jama.Matrix;
 
-public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable
-{
+public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable {
 
-	class CounterClass{
+	class CounterClass {
 		public CounterClass(int i) {
-			value=i;
+			value = i;
 		}
+
 		public int value;
-		public boolean seen=false;
+		public boolean seen = false;
 	}
 
-
 	private String m_Identifier = "NelderMeadSimplex";
 
-
 	private Population m_Population;
 	private AbstractOptimizationProblem m_Problem;
 
 	transient private InterfacePopulationChangedEventListener m_Listener;
 
 	private int m_lambda = 1;
-	private int m_lambdamo=1;
-
-
+	private int m_lambdamo = 1;
 
 	public MultiObjectiveCMAES() {
-		m_Population=new Population(m_lambdamo);
+		m_Population = new Population(m_lambdamo);
 	}
 
 	public MultiObjectiveCMAES(MultiObjectiveCMAES a) {
-		m_Problem = (AbstractOptimizationProblem)a.m_Problem.clone();
-		setPopulation((Population)a.m_Population.clone());
+		m_Problem = (AbstractOptimizationProblem) a.m_Problem.clone();
+		setPopulation((Population) a.m_Population.clone());
 		m_lambda = a.m_lambda;
 		m_Identifier = a.m_Identifier;
 	}
@@ -72,161 +67,182 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable
 		return new MultiObjectiveCMAES(this);
 	}
 
-
-	@Override
+	// @Override
 	public void SetIdentifier(String name) {
-		m_Identifier=name;
+		m_Identifier = name;
 	}
 
-	@Override
+	// @Override
 	public void SetProblem(InterfaceOptimizationProblem problem) {
-		m_Problem = (AbstractOptimizationProblem)problem;
+		m_Problem = (AbstractOptimizationProblem) problem;
 	}
 
-	/** This method allows you to add the LectureGUI as listener to the Optimizer
+	/**
+	 * This method allows you to add the LectureGUI as listener to the Optimizer
+	 * 
 	 * @param ea
 	 */
-	public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
+	public void addPopulationChangedEventListener(
+			InterfacePopulationChangedEventListener ea) {
 		this.m_Listener = ea;
 	}
 
-
-	@Override
+	// @Override
 	public void freeWilly() {
 	}
 
-	@Override
+	// @Override
 	public InterfaceSolutionSet getAllSolutions() {
 		Population pop = getPopulation();
 		return new SolutionSet(pop, pop);
 	}
 
-	@Override
+	// @Override
 	public String getIdentifier() {
 
 		return m_Identifier;
 	}
 
-	@Override
+	// @Override
 	public String getName() {
-		return "(1+"+m_lambda+") MO-CMA-ES";
+		return "(1+" + m_lambda + ") MO-CMA-ES";
 	}
 
-	@Override
+	// @Override
 	public Population getPopulation() {
 		return m_Population;
 	}
 
-	@Override
+	// @Override
 	public InterfaceOptimizationProblem getProblem() {
 		return m_Problem;
 	}
 
-	@Override
+	// @Override
 	public String getStringRepresentation() {
 		StringBuilder strB = new StringBuilder(200);
-		strB.append("(1+"+m_lambda+") MO-CMA-ES:\nOptimization Problem: ");
+		strB.append("(1+" + m_lambda + ") MO-CMA-ES:\nOptimization Problem: ");
 		strB.append(this.m_Problem.getStringRepresentationForProblem(this));
 		strB.append("\n");
 		strB.append(this.m_Population.getStringRepresentation());
 		return strB.toString();
 	}
 
-	@Override
+	// @Override
 	public void init() {
-		//initByPopulation(m_Population, true);
+		// initByPopulation(m_Population, true);
 		this.m_Population.setTargetSize(m_lambdamo);
 		this.m_Problem.initPopulation(this.m_Population);
-		//	        children = new Population(m_Population.size());
+		// children = new Population(m_Population.size());
 		this.evaluatePopulation(this.m_Population);
 		this.firePropertyChangedEvent(Population.nextGenerationPerformed);
 
 	}
 
-
-
-	@Override
+	// @Override
 	public void initByPopulation(Population pop, boolean reset) {
 		setPopulation(pop);
 		if (reset) {
 			m_Problem.initPopulation(m_Population);
 			m_Problem.evaluate(m_Population);
-	
+
 		}
 	}
 
-	/** This method will evaluate the current population using the
-	 * given problem.
-	 * @param population The population that is to be evaluated
+	/**
+	 * This method will evaluate the current population using the given problem.
+	 * 
+	 * @param population
+	 *            The population that is to be evaluated
 	 */
 	private void evaluatePopulation(Population population) {
 		this.m_Problem.evaluate(population);
 	}
 
-	@Override
+	// @Override
 	public void optimize() {
 
-		HashMap<Long, CounterClass> SuccessCounterMap=new HashMap<Long, CounterClass>();
+		HashMap<Long, CounterClass> SuccessCounterMap = new HashMap<Long, CounterClass>();
 
-		//Eltern markieren und für die Zählung vorbereiten
-		for(int j=0;j<m_lambdamo&&j<m_Population.size();j++){
-			m_Population.getEAIndividual(j).putData("Parent",m_Population.getEAIndividual(j) );
-			SuccessCounterMap.put(m_Population.getEAIndividual(j).getIndyID(),new CounterClass(0));
+		// Eltern markieren und f�r die Z�hlung vorbereiten
+		for (int j = 0; j < m_lambdamo && j < m_Population.size(); j++) {
+			m_Population.getEAIndividual(j).putData("Parent",
+					m_Population.getEAIndividual(j));
+			SuccessCounterMap.put(m_Population.getEAIndividual(j).getIndyID(),
+					new CounterClass(0));
 		}
 
-		//Kinder erzeugen
-		Population children=new Population(m_lambdamo*m_lambda);
+		// Kinder erzeugen
+		Population children = new Population(m_lambdamo * m_lambda);
 		children.setGenerationTo(m_Population.getGeneration());
 
-		for(int j=0;j<children.getTargetSize();j++){
-			AbstractEAIndividual parent=m_Population.getEAIndividual(j%m_lambdamo); 
-			AbstractEAIndividual indy=(AbstractEAIndividual)parent.clone();
+		for (int j = 0; j < children.getTargetSize(); j++) {
+			AbstractEAIndividual parent = m_Population.getEAIndividual(j
+					% m_lambdamo);
+			AbstractEAIndividual indy = (AbstractEAIndividual) parent.clone();
 			indy.mutate();
 			indy.putData("Parent", parent);
 			children.add(indy);
 		}
 		evaluatePopulation(children);
 
-	
 		m_Population.addPopulation(children);
-		//Ranking
-		ArchivingNSGAII dummyArchive=new ArchivingNSGAIISMeasure();
-		Population []store=dummyArchive.getNonDomiatedSortedFronts(m_Population);
-		store=dummyArchive.getNonDomiatedSortedFronts(m_Population);
+		// Ranking
+		ArchivingNSGAII dummyArchive = new ArchivingNSGAIISMeasure();
+		Population[] store = dummyArchive
+				.getNonDomiatedSortedFronts(m_Population);
+		store = dummyArchive.getNonDomiatedSortedFronts(m_Population);
 		dummyArchive.calculateCrowdingDistance(store);
 
-		//Vergleichen und den Successcounter hochzählen wenn wir besser als unser Elter sind
-		for(int j=0;j<m_Population.size();j++){
-			AbstractEAIndividual parent= (AbstractEAIndividual) m_Population.getEAIndividual(j).getData("Parent");
-			if(m_Population.getEAIndividual(j)!=parent){ //Eltern nicht mit sich selber vergleichen
-				int parentParetoLevel=((Integer) parent.getData("ParetoLevel")).intValue();
-				double parentSMeasure=((Double) parent.getData("HyperCube")).doubleValue();
-				int childParetoLevel=((Integer) m_Population.getEAIndividual(j).getData("ParetoLevel")).intValue();
-				double childSMeasure=((Double) m_Population.getEAIndividual(j).getData("HyperCube")).doubleValue();
-				if( childParetoLevel<parentParetoLevel||((childParetoLevel==parentParetoLevel)&&childSMeasure>parentSMeasure )      ){
-					SuccessCounterMap.get(parent.getIndyID()).value++;		
+		// Vergleichen und den Successcounter hochz�hlen wenn wir besser als
+		// unser Elter sind
+		for (int j = 0; j < m_Population.size(); j++) {
+			AbstractEAIndividual parent = (AbstractEAIndividual) m_Population
+					.getEAIndividual(j).getData("Parent");
+			if (m_Population.getEAIndividual(j) != parent) { // Eltern nicht mit
+																// sich selber
+																// vergleichen
+				int parentParetoLevel = ((Integer) parent
+						.getData("ParetoLevel")).intValue();
+				double parentSMeasure = ((Double) parent.getData("HyperCube"))
+						.doubleValue();
+				int childParetoLevel = ((Integer) m_Population.getEAIndividual(
+						j).getData("ParetoLevel")).intValue();
+				double childSMeasure = ((Double) m_Population
+						.getEAIndividual(j).getData("HyperCube")).doubleValue();
+				if (childParetoLevel < parentParetoLevel
+						|| ((childParetoLevel == parentParetoLevel) && childSMeasure > parentSMeasure)) {
+					SuccessCounterMap.get(parent.getIndyID()).value++;
 				}
-			}else{ //Debug
+			} else { // Debug
 
-				SuccessCounterMap.get(parent.getIndyID()).seen=true;	
+				SuccessCounterMap.get(parent.getIndyID()).seen = true;
 			}
 		}
 
-
-		//Selection
+		// Selection
 		m_Population.clear();
-		for(int i=0;i<store.length;i++){
-			if(m_Population.size()+store[i].size()<=m_lambdamo){ //Die Front passt noch komplett
+		for (int i = 0; i < store.length; i++) {
+			if (m_Population.size() + store[i].size() <= m_lambdamo) { // Die
+																		// Front
+																		// passt
+																		// noch
+																		// komplett
 				m_Population.addPopulation(store[i]);
 
-			}else{ //die besten aus der aktuellen Front heraussuchen bis voll
-				while(store[i].size()>0&&m_Population.size()<m_lambdamo){
-					AbstractEAIndividual indy=store[i].getEAIndividual(0);
-					double bestMeasure=((Double) indy.getData("HyperCube")).doubleValue(); //TODO mal noch effizient machen (sortieren und die besten n herausholen)
-					for(int j=1;j<store[i].size();j++ ){
-						if(bestMeasure<((Double) store[i].getEAIndividual(j).getData("HyperCube")).doubleValue()){
-							bestMeasure=((Double) store[i].getEAIndividual(j).getData("HyperCube")).doubleValue();
-							indy=store[i].getEAIndividual(j);
+			} else { // die besten aus der aktuellen Front heraussuchen bis voll
+				while (store[i].size() > 0 && m_Population.size() < m_lambdamo) {
+					AbstractEAIndividual indy = store[i].getEAIndividual(0);
+					double bestMeasure = ((Double) indy.getData("HyperCube"))
+							.doubleValue(); // TODO mal noch effizient machen
+											// (sortieren und die besten n
+											// herausholen)
+					for (int j = 1; j < store[i].size(); j++) {
+						if (bestMeasure < ((Double) store[i].getEAIndividual(j)
+								.getData("HyperCube")).doubleValue()) {
+							bestMeasure = ((Double) store[i].getEAIndividual(j)
+									.getData("HyperCube")).doubleValue();
+							indy = store[i].getEAIndividual(j);
 						}
 					}
 					m_Population.add(indy);
@@ -236,51 +252,67 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable
 
 		}
 
-		//Strategieparemeter updaten
-		for(int j=0;j<m_Population.size();j++){
+		// Strategieparemeter updaten
+		for (int j = 0; j < m_Population.size(); j++) {
 
-			AbstractEAIndividual indy=m_Population.getEAIndividual(j);
-			if(indy.getMutationOperator() instanceof MutateESCovarianceMatrixAdaptionPlus ){ //Das geht nur wenn wir auch die richtige Mutation haben
-				AbstractEAIndividual parent=(AbstractEAIndividual)indy.getData("Parent");
-				MutateESCovarianceMatrixAdaptionPlus muta=(MutateESCovarianceMatrixAdaptionPlus) indy.getMutationOperator();
-				double rate=((double) SuccessCounterMap.get(parent.getIndyID()).value)/((double) m_lambda);
+			AbstractEAIndividual indy = m_Population.getEAIndividual(j);
+			if (indy.getMutationOperator() instanceof MutateESCovarianceMatrixAdaptionPlus) { // Das
+																								// geht
+																								// nur
+																								// wenn
+																								// wir
+																								// auch
+																								// die
+																								// richtige
+																								// Mutation
+																								// haben
+				AbstractEAIndividual parent = (AbstractEAIndividual) indy
+						.getData("Parent");
+				MutateESCovarianceMatrixAdaptionPlus muta = (MutateESCovarianceMatrixAdaptionPlus) indy
+						.getMutationOperator();
+				double rate = ((double) SuccessCounterMap.get(parent
+						.getIndyID()).value)
+						/ ((double) m_lambda);
 
-				if(indy!=parent){
+				if (indy != parent) {
 					muta.updateCovariance();
 				}
-				muta.updateStepSize(rate);			
+				muta.updateStepSize(rate);
 			}
 		}
 
-		for(int j=0;j<children.size();j++){
+		for (int j = 0; j < children.size(); j++) {
 			children.getEAIndividual(j).putData("Parent", null);
 		}
-		
+
 		m_Population.incrFunctionCallsBy(children.size());
 		m_Population.incrGeneration();
 		this.firePropertyChangedEvent(Population.nextGenerationPerformed);
 
 	}
 
-	@Override
+	// @Override
 	public boolean removePopulationChangedEventListener(
 			InterfacePopulationChangedEventListener ea) {
 		return false;
 	}
 
-	@Override
+	// @Override
 	public void setPopulation(Population pop) {
 		m_Population = pop;
 		m_Population.setNotifyEvalInterval(1);
-		m_lambdamo=m_Population.getTargetSize();
+		m_lambdamo = m_Population.getTargetSize();
 
 	}
 
-	/** Something has changed
+	/**
+	 * Something has changed
+	 * 
 	 * @param name
 	 */
-	protected void firePropertyChangedEvent (String name) {
-		if (this.m_Listener != null) this.m_Listener.registerPopulationStateChanged(this, name);
+	protected void firePropertyChangedEvent(String name) {
+		if (this.m_Listener != null)
+			this.m_Listener.registerPopulationStateChanged(this, name);
 	}
 
 	public int getLambda() {
@@ -291,13 +323,10 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable
 		m_lambda = mLambda;
 	}
 
-	/*public int getLambdaMo() {
-		return m_lambdamo;
-	}
-
-	public void setLambdaMo(int mLambda) {
-		m_lambdamo = mLambda;
-	}*/
-
+	/*
+	 * public int getLambdaMo() { return m_lambdamo; }
+	 * 
+	 * public void setLambdaMo(int mLambda) { m_lambdamo = mLambda; }
+	 */
 
 }