diff --git a/src/eva2/OptimizerFactory.java b/src/eva2/OptimizerFactory.java
index 6d4cc4d1..7db0bd17 100644
--- a/src/eva2/OptimizerFactory.java
+++ b/src/eva2/OptimizerFactory.java
@@ -101,6 +101,8 @@ public class OptimizerFactory {
 	public final static int CL_HILLCL = 10;
 	
 	public final static int CMA_ES_IPOP = 11;
+	
+	public final static int CBN_GA = 12;
 
 	public final static int defaultFitCalls = 10000;
 
@@ -142,6 +144,8 @@ public class OptimizerFactory {
 		de.addPopulationChangedEventListener(listener);
 		de.init();
 
+		listener.registerPopulationStateChanged(de.getPopulation(), "");
+
 		return de;
 	}
 
@@ -208,13 +212,14 @@ public class OptimizerFactory {
 		AbstractEAIndividual tmpIndi = problem.getIndividualTemplate();
 		AbstractEAIndividual.setOperators(tmpIndi, mutationoperator, pm, crossoveroperator, pc);
 
-		EvolutionStrategies es = new EvolutionStrategies();
-		es.addPopulationChangedEventListener(listener);
-		//es.setParentSelection(selection);
-		//es.setPartnerSelection(selection);
-		es.setEnvironmentSelection(selection);
-		es.SetProblem(problem);
-		es.init();
+		theES.addPopulationChangedEventListener(listener);
+//		theES.setParentSelection(selection);
+//		theES.setPartnerSelection(selection);
+		theES.setEnvironmentSelection(selection);
+		theES.SetProblem(problem);
+		theES.init();
+
+		if (listener != null) listener.registerPopulationStateChanged(theES.getPopulation(), "");
 
 		return theES;
 	}
@@ -254,6 +259,8 @@ public class OptimizerFactory {
 		ga.addPopulationChangedEventListener(listener);
 		ga.init();
 
+		listener.registerPopulationStateChanged(ga.getPopulation(), "");
+
 		return ga;
 	}
 
@@ -365,6 +372,8 @@ public class OptimizerFactory {
 		hc.SetProblem(problem);
 		hc.init();
 
+		listener.registerPopulationStateChanged(hc.getPopulation(), "");
+
 		return hc;
 	}
 
@@ -394,10 +403,11 @@ public class OptimizerFactory {
 		mc.SetProblem(problem);
 		mc.init();
 
+		listener.registerPopulationStateChanged(mc.getPopulation(), "");
+
 		return mc;
 	}
 
-
 	/**
 	 * This method performs a particle swarm optimization. Standard topologies are
 	 * linear (0), grid (1) and star (2).
@@ -438,6 +448,8 @@ public class OptimizerFactory {
 		pso.addPopulationChangedEventListener(listener);
 		pso.init();
 
+		listener.registerPopulationStateChanged(pso.getPopulation(), "");
+
 		return pso;
 	}
 
@@ -477,6 +489,8 @@ public class OptimizerFactory {
 		sa.addPopulationChangedEventListener(listener);
 		sa.init();
 
+		listener.registerPopulationStateChanged(sa.getPopulation(), "");
+
 		return sa;
 	}
 
@@ -526,9 +540,11 @@ public class OptimizerFactory {
 		case CBN_ES:
 			return cbnES(problem);
 		case CL_HILLCL:
-			return clusteringHillClimbing(problem);
+			return stdClusteringHillClimbing(problem);
 		case CMA_ES_IPOP: 
 			return cmaESIPOP(problem);
+		case CBN_GA:
+			return cbnGA(problem);
 		default:
 			System.err.println("Error: optimizer type " + optType
 					+ " is unknown!");
@@ -602,7 +618,6 @@ public class OptimizerFactory {
 			AbstractOptimizationProblem problem, String outputFilePrefix) {
 		return getOptRunnable(optType, problem, getTerminator(), outputFilePrefix);
 	}
-
 	/**
 	 * Return the current user-defined or, if none was set, the default terminator.
 	 * 
@@ -649,6 +664,18 @@ public class OptimizerFactory {
 		return makeParams(opt, popSize, problem, randSeed, makeDefaultTerminator());
 	}
 
+	/**
+	 * Set the population size, initialize the population and return a parameter structure containing all 
+	 * given parts.
+	 * 
+	 * @see #makeParams(InterfaceOptimizer, Population, AbstractOptimizationProblem, long, InterfaceTerminator)
+	 * @param opt
+	 * @param popSize
+	 * @param problem
+	 * @param seed
+	 * @param term
+	 * @return
+	 */
 	public static GOParameters makeParams(InterfaceOptimizer opt,
 			int popSize, AbstractOptimizationProblem problem, long seed,
 			InterfaceTerminator term) {
@@ -1035,9 +1062,20 @@ public class OptimizerFactory {
 	 */
 	public static final GOParameters hillClimbing(
 			AbstractOptimizationProblem problem) {
-		return makeParams(new HillClimbing(), 50, problem, randSeed, makeDefaultTerminator());
+		return hillClimbing(problem, 50);
 	}
-
+	
+	/**
+	 * Create a standard multi-start hill-climber parameter set with the given number of
+	 * individuals.
+	 * 
+	 * @return	a standard multi-start hill-climber
+	 */
+	public static final GOParameters hillClimbing(
+			AbstractOptimizationProblem problem, int popSize) {
+		return makeParams(new HillClimbing(), popSize, problem, randSeed, makeDefaultTerminator());
+	}
+	
 	public static final GOParameters monteCarlo(
 			AbstractOptimizationProblem problem) {
 		return makeParams(new MonteCarloSearch(), 50, problem, randSeed, makeDefaultTerminator());
@@ -1058,21 +1096,84 @@ public class OptimizerFactory {
 		return makeParams(cbn, 100, problem, randSeed, makeDefaultTerminator());
 	}
 
+	public static final GOParameters cbnGA(AbstractOptimizationProblem problem) {
+		ClusterBasedNichingEA cbn = new ClusterBasedNichingEA();
+		GeneticAlgorithm ga = new GeneticAlgorithm();
+		cbn.setOptimizer(ga);
+		ClusteringDensityBased clustering = new ClusteringDensityBased(0.1);
+		cbn.setConvergenceCA((ClusteringDensityBased) clustering.clone());
+		cbn.setDifferentationCA(clustering);
+		cbn.setShowCycle(0); // don't do graphical output
+
+		return makeParams(cbn, 100, problem, randSeed, makeDefaultTerminator());
+	}
+	
+	/**
+	 * Create a standard clustering hill climbing employing simple ES mutation with adaptive
+	 * step size, starting in parallel 100 local searches and clustering intermediate populations
+	 * to avoid optima being found several times by the same population (density based clustering with
+	 * sigma = 0.05).
+	 * The population is reinitialized if the average progress of one cycle does not exceed 1e-6.
+	 * 
+	 * @param problem
+	 * @return
+	 */
+	public static final GOParameters stdClusteringHillClimbing(
+			AbstractOptimizationProblem problem) {		
+		return clusteringHillClimbing(problem, 1000, 100, 0.000001, 
+				PostProcessMethod.hillClimber, 0.05, 0.000001, 0.05);
+	}
+
+	/**
+	 * Create a clustering hillclimber using nelder mead and additional given parameters.
+	 * 
+	 * @param problem
+	 * @param evalCycle
+	 * @param popSize
+	 * @param minImprovement
+	 * @param method
+	 * @param sigmaClust
+	 * @return
+	 */
+	public static final GOParameters clusteringHillClimbingNM(AbstractOptimizationProblem problem,
+			int evalCycle, int popSize, double minImprovement, double sigmaClust) {
+		return clusteringHillClimbing(problem, evalCycle, popSize, minImprovement, 
+				PostProcessMethod.nelderMead, 0.01, 0.00000001, sigmaClust);
+	}
+	
+	/**
+	 * Create a custom clustering hillclimber using ES mutation (simple or CMA) or nelder mead.
+	 * The parameters hcInitialStep and hcStepThresh are 
+	 * only relevant for the simple mutation based hc method.
+	 * 
+	 * @param problem
+	 * @param evalCycle
+	 * @param popSize
+	 * @param minImprovement
+	 * @param method
+	 * @param hcInitialStep
+	 * @param hcStepThresh
+	 * @param sigmaClust
+	 * @return
+	 */
 	public static final GOParameters clusteringHillClimbing(
-			AbstractOptimizationProblem problem) {
+			AbstractOptimizationProblem problem, int evalCycle, int popSize, double minImprovement, 
+			PostProcessMethod method, double hcInitialStep, double hcStepThresh, double sigmaClust) {
 		ClusteringHillClimbing chc = new ClusteringHillClimbing();
 		chc.SetProblem(problem);
 
-		chc.setHcEvalCycle(1000);
-		chc.setInitialPopSize(100);
-		chc.setStepSizeInitial(0.05);
-		chc.setMinImprovement(0.000001);
+		chc.setEvalCycle(evalCycle);
+		chc.setInitialPopSize(popSize);
+		chc.setStepSizeInitial(hcInitialStep);
+		chc.setLocalSearchMethod(method);
+		chc.setMinImprovement(minImprovement);
 		chc.setNotifyGuiEvery(0);
-		chc.setStepSizeThreshold(0.000001);
-		chc.setSigmaClust(0.05);
-		return makeParams(chc, 100, problem, randSeed, makeDefaultTerminator());
+		chc.setStepSizeThreshold(hcStepThresh);
+		chc.setSigmaClust(sigmaClust);
+		return makeParams(chc, popSize, problem, randSeed, makeDefaultTerminator());
 	}
 
+	
 	public static final GOParameters cmaES(AbstractOptimizationProblem problem) {
 		EvolutionStrategies es = new EvolutionStrategies();
 		es.setMu(15);
diff --git a/src/eva2/gui/PropertyPanel.java b/src/eva2/gui/PropertyPanel.java
index ce6e6563..377acebe 100644
--- a/src/eva2/gui/PropertyPanel.java
+++ b/src/eva2/gui/PropertyPanel.java
@@ -25,67 +25,71 @@ import javax.swing.JPanel;
 
 import eva2.tools.EVAHELP;
 /*==========================================================================*
-* CLASS DECLARATION
-*==========================================================================*/
+ * CLASS DECLARATION
+ *==========================================================================*/
 /**
  *
  */
 public class PropertyPanel extends JPanel {
-  private PropertyEditor m_PropertyEditor;
-  private PropertyDialog m_PropertyDialog;
-  /**
-   *
-   */
-  public PropertyPanel(PropertyEditor Editor) {
-    setBorder(BorderFactory.createEtchedBorder());
-    setToolTipText("Click to edit properties for this object");
-    setOpaque(true);
-    m_PropertyEditor = Editor;
-    addMouseListener(new MouseAdapter() {
-      public void mouseClicked(MouseEvent evt) {
-	if (m_PropertyEditor.getValue() != null) {
-	  if (m_PropertyDialog == null) {
-	    int x = getLocationOnScreen().x;
-	    int y = getLocationOnScreen().y;
-	    m_PropertyDialog = new PropertyDialog(m_PropertyEditor, EVAHELP.cutClassName(m_PropertyEditor.getClass().getName()) , x, y);
-	  }
-	  else {
-		  m_PropertyDialog.updateFrameTitle(m_PropertyEditor);
-	    m_PropertyDialog.setVisible(true);
-	  }
+	private PropertyEditor m_PropertyEditor;
+	private PropertyDialog m_PropertyDialog;
+	/**
+	 *
+	 */
+	public PropertyPanel(PropertyEditor Editor) {
+		setBorder(BorderFactory.createEtchedBorder());
+		setToolTipText("Click to edit properties for this object");
+		setOpaque(true);
+		m_PropertyEditor = Editor;
+		addMouseListener(new MouseAdapter() {
+			public void mouseClicked(MouseEvent evt) {
+				if (m_PropertyEditor.getValue() != null) {
+					if (m_PropertyDialog == null) {
+						int x = getLocationOnScreen().x;
+						int y = getLocationOnScreen().y;
+						m_PropertyDialog = new PropertyDialog(m_PropertyEditor, EVAHELP.cutClassName(m_PropertyEditor.getClass().getName()) , x, y);
+					}
+					else {
+						m_PropertyDialog.updateFrameTitle(m_PropertyEditor);
+						m_PropertyDialog.setVisible(true);
+					}
+				}
+			}
+		});
+		Dimension newPref = getPreferredSize();
+		newPref.height = getFontMetrics(getFont()).getHeight() * 6 / 4;  //6 / 4;
+		newPref.width = newPref.height * 6; //5
+		setPreferredSize(newPref);
 	}
-      }
-    });
-    Dimension newPref = getPreferredSize();
-    newPref.height = getFontMetrics(getFont()).getHeight() * 6 / 4;  //6 / 4;
-    newPref.width = newPref.height * 6; //5
-    setPreferredSize(newPref);
-  }
-  /**
-   *
-   */
-  public void removeNotify() {
-    if (m_PropertyDialog != null) {
-      //System.out.println("  m_PropertyDialog.dispose();");
-      m_PropertyDialog.dispose();
-      m_PropertyDialog = null;
-    }
-  }
-  /**
-   *
-   */
-  public void paintComponent(Graphics g) {
-    Insets i = getInsets();
-    Rectangle box = new Rectangle(i.left, i.top,
-      getSize().width - i.left - i.right ,
-      getSize().height - i.top - i.bottom);
-    g.clearRect(i.left, i.top,
-      getSize().width - i.right - i.left,
-      getSize().height - i.bottom - i.top);
-    m_PropertyEditor.paintValue(g, box);
+	/**
+	 *
+	 */
+	public void removeNotify() {
+		if (m_PropertyDialog != null) {
+			//System.out.println("  m_PropertyDialog.dispose();");
+			m_PropertyDialog.dispose();
+			m_PropertyDialog = null;
+		}
+	}
+	/**
+	 *
+	 */
+	public void paintComponent(Graphics g) {
+		Insets i = getInsets();
+		Rectangle box = new Rectangle(i.left, i.top,
+				getSize().width - i.left - i.right ,
+				getSize().height - i.top - i.bottom);
+		g.clearRect(i.left, i.top,
+				getSize().width - i.right - i.left,
+				getSize().height - i.bottom - i.top);
+		m_PropertyEditor.paintValue(g, box);
 
-//    Rectangle box = new Rectangle(i.left,i.top,
-//					  this.getWidth() - i.right,
-//					  this.getHeight() - i.bottom );
-  }
+//		Rectangle box = new Rectangle(i.left,i.top,
+//		this.getWidth() - i.right,
+//		this.getHeight() - i.bottom );
+	}
+	
+	public PropertyEditor getEditor() {
+		return m_PropertyEditor;
+	}
 }
diff --git a/src/eva2/gui/PropertySheetPanel.java b/src/eva2/gui/PropertySheetPanel.java
index cb72d4b0..1836d3c8 100644
--- a/src/eva2/gui/PropertySheetPanel.java
+++ b/src/eva2/gui/PropertySheetPanel.java
@@ -51,7 +51,13 @@ import eva2.tools.StringTools;
 * CLASS DECLARATION
 *==========================================================================*/
 /**
- *
+ * TODO: document those tricks somewhere
+ * Trick methods: 
+ * 	String[] getGOEPropertyUpdateLinks()
+ * 	void hideHideable()
+ * 	void globalInfo()
+ * Trick statics:
+ *  boolean hideFromGOE 
  */
 public class PropertySheetPanel extends JPanel implements PropertyChangeListener {
     public final static boolean     TRACE       = false;
@@ -232,7 +238,7 @@ public class PropertySheetPanel extends JPanel implements PropertyChangeListener
             }
         } // end for (int i = 0; i < m_Methods.length; i++) {
 
-        // Now lets search for the individual properties their
+        // Now lets search for the individual properties, their
         // values, views and editors...
         m_Editors   = new PropertyEditor[m_Properties.length];
         m_Values    = new Object[m_Properties.length];
@@ -319,77 +325,7 @@ public class PropertySheetPanel extends JPanel implements PropertyChangeListener
             } // end try
 
             // Add some specific display for some greeks here
-            if (name.equalsIgnoreCase("alpha"))
-                name = "\u03B1";
-            if (name.equalsIgnoreCase("beta"))
-                name = "\u03B2";
-            if (name.equalsIgnoreCase("gamma"))
-                name = "\u03B3";
-            if (name.equalsIgnoreCase("gammab"))
-                name = "\u0393";
-            if (name.equalsIgnoreCase("delta"))
-                name = "\u03B4";
-            if (name.equalsIgnoreCase("deltab"))
-                name = "\u0394";
-            if ((name.equalsIgnoreCase("epsi")) || (name.equalsIgnoreCase("epsilon")))
-                name = "\u03B5";
-            if (name.equalsIgnoreCase("zeta"))
-                name = "\u03B6";
-            if (name.equalsIgnoreCase("theta"))
-                name = "\u03D1";
-            if (name.equalsIgnoreCase("thetab"))
-                name = "\u0398";
-            if (name.equalsIgnoreCase("iota"))
-                name = "\u03B9";
-            if (name.equalsIgnoreCase("kappa"))
-                name = "\u03BA";
-            if (name.equalsIgnoreCase("lambda"))
-                name = "\u03BB";
-            if (name.equalsIgnoreCase("lambdab"))
-                name = "\u039B";
-            if (name.equalsIgnoreCase("rho"))
-                name = "\u03C1";
-            if (name.equalsIgnoreCase("sigma"))
-                name = "\u03C3";
-            if (name.equalsIgnoreCase("sigmab"))
-                name = "\u03A3";
-            if (name.equalsIgnoreCase("tau"))
-                name = "\u03C4";
-            if (name.equalsIgnoreCase("upsilon"))
-                name = "\u03C5";
-            if (name.equalsIgnoreCase("upsilonb"))
-                name = "\u03D2";
-            if (name.equalsIgnoreCase("omega"))
-                name = "\u03C9";
-            if (name.equalsIgnoreCase("omegab"))
-                name = "\u03A9";
-
-            // these are too small
-            if (name.equalsIgnoreCase("eta"))
-                name = "\u03B7";
-            if (name.equalsIgnoreCase("psi"))
-                name = "\u03C8";
-            if (name.equalsIgnoreCase("psib"))
-                name = "\u03A8";
-            if (name.equalsIgnoreCase("phi"))
-                name = "\u03D5";
-            if (name.equalsIgnoreCase("phib"))
-                name = "\u03A6";
-            if (name.equalsIgnoreCase("chi"))
-                name = "\u03C7";
-            if ((name.equalsIgnoreCase("mu")) || (name.equalsIgnoreCase("my")) || (name.equalsIgnoreCase("myu")))
-                name = "\u03BC";
-            if (name.equalsIgnoreCase("nu"))
-                name = "\u03BD";
-            if (name.equalsIgnoreCase("xi"))
-                name = "\u03BE";
-            if (name.equalsIgnoreCase("xib"))
-                name = "\u039E";
-            if (name.equalsIgnoreCase("pi"))
-                name = "\u03C0";
-            if (name.equalsIgnoreCase("pib"))
-                name = "\u03A0";
-
+            name = translateGreek(name);
 
             m_Labels[i]         = new JLabel(name, SwingConstants.RIGHT);
             m_Labels[i].setBorder(BorderFactory.createEmptyBorder(10,10,0,5));
@@ -449,7 +385,83 @@ public class PropertySheetPanel extends JPanel implements PropertyChangeListener
         setVisible(true);
     }
    
-    /**
+    private String translateGreek(String name) {
+        // Add some specific display for some greeks here
+        if (name.equalsIgnoreCase("alpha"))
+            return "\u03B1";
+        if (name.equalsIgnoreCase("beta"))
+            return "\u03B2";
+        if (name.equalsIgnoreCase("gamma"))
+            return "\u03B3";
+        if (name.equalsIgnoreCase("gammab"))
+            return "\u0393";
+        if (name.equalsIgnoreCase("delta"))
+            return "\u03B4";
+        if (name.equalsIgnoreCase("deltab"))
+            return "\u0394";
+        if ((name.equalsIgnoreCase("epsi")) || (name.equalsIgnoreCase("epsilon")))
+            return "\u03B5";
+        if (name.equalsIgnoreCase("zeta"))
+            return "\u03B6";
+        if (name.equalsIgnoreCase("theta"))
+            return "\u03D1";
+        if (name.equalsIgnoreCase("thetab"))
+            return "\u0398";
+        if (name.equalsIgnoreCase("iota"))
+            return "\u03B9";
+        if (name.equalsIgnoreCase("kappa"))
+            return "\u03BA";
+        if (name.equalsIgnoreCase("lambda"))
+            return "\u03BB";
+        if (name.equalsIgnoreCase("lambdab"))
+            return "\u039B";
+        if (name.equalsIgnoreCase("rho"))
+            return "\u03C1";
+        if (name.equalsIgnoreCase("sigma"))
+            return "\u03C3";
+        if (name.equalsIgnoreCase("sigmab"))
+            return "\u03A3";
+        if (name.equalsIgnoreCase("tau"))
+            return "\u03C4";
+        if (name.equalsIgnoreCase("upsilon"))
+            return "\u03C5";
+        if (name.equalsIgnoreCase("upsilonb"))
+            return "\u03D2";
+        if (name.equalsIgnoreCase("omega"))
+            return "\u03C9";
+        if (name.equalsIgnoreCase("omegab"))
+            return "\u03A9";
+
+        // these are too small
+        if (name.equalsIgnoreCase("eta"))
+            return "\u03B7";
+        if (name.equalsIgnoreCase("psi"))
+            return "\u03C8";
+        if (name.equalsIgnoreCase("psib"))
+            return "\u03A8";
+        if (name.equalsIgnoreCase("phi"))
+            return "\u03D5";
+        if (name.equalsIgnoreCase("phib"))
+            return "\u03A6";
+        if (name.equalsIgnoreCase("chi"))
+            return "\u03C7";
+        if ((name.equalsIgnoreCase("mu")) || (name.equalsIgnoreCase("my")) || (name.equalsIgnoreCase("myu")))
+            return "\u03BC";
+        if (name.equalsIgnoreCase("nu"))
+            return "\u03BD";
+        if (name.equalsIgnoreCase("xi"))
+            return "\u03BE";
+        if (name.equalsIgnoreCase("xib"))
+            return "\u039E";
+        if (name.equalsIgnoreCase("pi"))
+            return "\u03C0";
+        if (name.equalsIgnoreCase("pib"))
+            return "\u03A0";
+        
+        return name;
+	}
+
+	/**
      * Get the html help file name.
      * 
      * @return
@@ -496,6 +508,109 @@ public class PropertySheetPanel extends JPanel implements PropertyChangeListener
     public int editableProperties() {
         return m_NumEditable;
     }
+    
+    /**
+     * Return true if the modification was successful.
+     * 
+     * @param i
+     * @param newValue
+     * @return
+     */
+    synchronized boolean updateValue(int i, Object newValue) {
+    	PropertyDescriptor property = m_Properties[i];
+        Method getter   = m_Properties[i].getReadMethod();
+        m_Values[i]     = newValue;
+        Method setter   = property.getWriteMethod();
+        // @todo: Streiche so something was changed, i could check if i have to change the editor
+
+        if (TRACE) System.out.println("Updating prop index " + i + " with " + newValue);
+        PropertyEditor  tmpEdit     = null;
+        // the findEditor method using properties may retrieve a primitive editor, the other one, for obscure reasons, cant.
+        // so Ill use the mightier first.
+        tmpEdit = PropertyEditorProvider.findEditor(m_Properties[i], newValue);
+        if (tmpEdit == null)    tmpEdit = PropertyEditorProvider.findEditor(m_Properties[i].getPropertyType());
+        if (tmpEdit.getClass() != m_Editors[i].getClass()) {
+        	m_Values[i]     = newValue;
+        	m_Editors[i]    = tmpEdit;
+        	if (tmpEdit instanceof GenericObjectEditor) ((GenericObjectEditor) tmpEdit).setClassType(m_Properties[i].getPropertyType());
+        	m_Editors[i].setValue(newValue);
+        	JComponent NewView = null;
+        	if (tmpEdit instanceof sun.beans.editors.BoolEditor) {
+        		NewView = new PropertyBoolSelector(tmpEdit);
+        	} else {
+        		if (tmpEdit instanceof sun.beans.editors.DoubleEditor) {
+        			NewView = new PropertyText(tmpEdit);
+        		} else {
+        			if (tmpEdit.isPaintable() && tmpEdit.supportsCustomEditor()) {
+        				NewView = new PropertyPanel(tmpEdit);
+        			} else {
+        				if (tmpEdit.getTags() != null ) {
+        					NewView = new PropertyValueSelector(tmpEdit);
+        				} else {
+        					if (tmpEdit.getAsText() != null) {
+        						NewView = new PropertyText(tmpEdit);
+        					} else {
+        						System.out.println("Warning: Property \"" + m_Properties[i].getDisplayName()
+        								+ "\" has non-displayabale editor.  Skipping.");
+        						return false;
+        					}
+        				}
+        			}
+        		}
+        	}
+        	m_Editors[i].addPropertyChangeListener(this);
+        	m_Views[i] = NewView;
+        	if (m_TipTexts[i] != null) m_Views[i].setToolTipText(m_TipTexts[i]);
+        	m_ViewWrapper[i].removeAll();
+        	m_ViewWrapper[i].setLayout(new BorderLayout());
+        	m_ViewWrapper[i].add(m_Views[i], BorderLayout.CENTER);
+        	m_ViewWrapper[i].repaint();
+        }
+        
+//        System.out.println("Value: "+value +" / m_Values[i]: " + m_Values[i]);
+        // Now try to update the target with the new value of the property
+        // and allow the target to do some changes to the value, therefore
+        // reread the new value from the target
+        try {
+            Object  args[]  = { newValue };
+            args[0]         = newValue;
+            Object  args2[] = { };
+            // setting the current value to the target object
+            setter.invoke(m_Target, args);
+            // i could also get the new value
+            //value = getter.invoke(m_Target, args2);
+            // Now i'm reading the set value from the target to my local values
+            m_Values[i] = getter.invoke(m_Target, args2);
+
+            if (newValue instanceof Integer) {
+                // This could check whether i have to set the value back to
+                // the editor, this would allow to check myu and lambda
+                // why shouldn't i do this for every property!?
+//                System.out.println("value: "+((Integer)value).intValue());
+//                System.out.println(" m_Values[i]: "+ ((Integer) m_Values[i]).intValue());
+                if (((Integer)newValue).intValue() != ((Integer) m_Values[i]).intValue()) {
+                	m_Editors[i].setValue(m_Values[i]);
+                }
+            }
+        } catch (InvocationTargetException ex) {
+            if (ex.getTargetException() instanceof PropertyVetoException) {
+                System.out.println("PropertySheetPanel.wasModified(): WARNING: Vetoed; reason is: " + ex.getTargetException().getMessage());
+            } else {
+                System.out.println("PropertySheetPanel.wasModified(): InvocationTargetException while updating " + property.getName());
+                System.out.println("PropertySheetPanel.wasModified(): "+ex.getMessage());
+                ex.printStackTrace();
+            }
+        } catch (Exception ex) {
+            System.out.println("PropertySheetPanel.wasModified(): Unexpected exception while updating " + property.getName());
+        }
+        //revalidate();
+        if (m_Views[i] != null && m_Views[i] instanceof PropertyPanel) {
+            //System.err.println("Trying to repaint the property canvas");
+            m_Views[i].repaint();
+            revalidate();
+        }
+        return true;
+    }
 
     /** Updates the propertysheet when a value has been changed (from outside
      * the propertysheet?).
@@ -503,171 +618,48 @@ public class PropertySheetPanel extends JPanel implements PropertyChangeListener
      */
     synchronized void wasModified(PropertyChangeEvent evt) {
         if (TRACE) {
-            System.out.println("PropertySheetPanel.wasModified(): My Target is "+this.m_Target.getClass());
+            System.out.println("*********** PropertySheetPanel.wasModified(): My Target is "+this.m_Target.getClass());
             System.out.println("PropertySheetPanel.wasModified(): "+evt.toString()+" - "+evt.getNewValue());
         }
+        int propIndex=-1;
         if (evt.getSource() instanceof PropertyEditor) {
             PropertyEditor editor = (PropertyEditor) evt.getSource();
             for (int i = 0 ; i < m_Editors.length; i++) {
 	            if (m_Editors[i] == editor) {
-	                PropertyDescriptor property = m_Properties[i];
-	                Method getter   = m_Properties[i].getReadMethod();
-	                Object value    = editor.getValue();
-	                m_Values[i]     = value;
-	                Method setter   = property.getWriteMethod();
-                    // @todo: Streiche so something was changed, i could check if i have to change the editor
-
-	                PropertyEditor  tmpEdit     = null;
-	                Object          newValue    = evt.getNewValue();
-	                if (newValue == null) newValue = editor.getValue();
-	                // the findEditor method using properties may retrieve a primitive editor, the other one, for obscure reasons, cant.
-	                // so Ill use the mightier first.
-	                tmpEdit = PropertyEditorProvider.findEditor(m_Properties[i], newValue);
-	                if (tmpEdit == null)    tmpEdit = PropertyEditorProvider.findEditor(m_Properties[i].getPropertyType());
-	                if (tmpEdit.getClass() != m_Editors[i].getClass()) {
-	                	value           = newValue;
-	                	m_Values[i]     = newValue;
-	                	m_Editors[i]    = tmpEdit;
-	                	if (tmpEdit instanceof GenericObjectEditor) ((GenericObjectEditor) tmpEdit).setClassType(m_Properties[i].getPropertyType());
-	                	m_Editors[i].setValue(newValue);
-	                	JComponent NewView = null;
-	                	if (tmpEdit instanceof sun.beans.editors.BoolEditor) {
-	                		NewView = new PropertyBoolSelector(tmpEdit);
-	                	} else {
-	                		if (tmpEdit instanceof sun.beans.editors.DoubleEditor) {
-	                			NewView = new PropertyText(tmpEdit);
-	                		} else {
-	                			if (tmpEdit.isPaintable() && tmpEdit.supportsCustomEditor()) {
-	                				NewView = new PropertyPanel(tmpEdit);
-	                			} else {
-	                				if (tmpEdit.getTags() != null ) {
-	                					NewView = new PropertyValueSelector(tmpEdit);
-	                				} else {
-	                					if (tmpEdit.getAsText() != null) {
-	                						NewView = new PropertyText(tmpEdit);
-	                					} else {
-	                						System.out.println("Warning: Property \"" + m_Properties[i].getDisplayName()
-	                								+ "\" has non-displayabale editor.  Skipping.");
-	                						continue;
-	                					}
-	                				}
-	                			}
-	                		}
-	                	}
-	                	m_Editors[i].addPropertyChangeListener(this);
-	                	m_Views[i] = NewView;
-	                	if (m_TipTexts[i] != null) m_Views[i].setToolTipText(m_TipTexts[i]);
-	                	m_ViewWrapper[i].removeAll();
-	                	m_ViewWrapper[i].setLayout(new BorderLayout());
-	                	m_ViewWrapper[i].add(m_Views[i], BorderLayout.CENTER);
-	                	m_ViewWrapper[i].repaint();
-	                }
-                    
-//                    System.out.println("Value: "+value +" / m_Values[i]: " + m_Values[i]);
-                    // Now try to update the target with the new value of the property
-                    // and allow the target to do some changes to the value, therefore
-                    // reread the new value from the target
-	                try {
-	                    Object  args[]  = { value };
-	                    args[0]         = value;
-                        Object  args2[] = { };
-                        // setting the current value to the target object
-	                    setter.invoke(m_Target, args);
-                        // i could also get the new value
-                        //value = getter.invoke(m_Target, args2);
-                        // Now i'm reading the set value from the target to my local values
-                        m_Values[i] = getter.invoke(m_Target, args2);
-
-                        if (value instanceof Integer) {
-                            // This could check whether i have to set the value back to
-                            // the editor, this would allow to check myu and lambda
-                            // why shouldn't i do this for every property!?
-//                            System.out.println("value: "+((Integer)value).intValue());
-//                            System.out.println(" m_Values[i]: "+ ((Integer) m_Values[i]).intValue());
-                            if (((Integer)value).intValue() != ((Integer) m_Values[i]).intValue()) {
-                                editor.setValue(m_Values[i]);
-                            }
-                        }
-	                } catch (InvocationTargetException ex) {
-	                    if (ex.getTargetException() instanceof PropertyVetoException) {
-	                        System.out.println("PropertySheetPanel.wasModified(): WARNING: Vetoed; reason is: " + ex.getTargetException().getMessage());
-	                    } else {
-	                        System.out.println("PropertySheetPanel.wasModified(): InvocationTargetException while updating " + property.getName());
-                            System.out.println("PropertySheetPanel.wasModified(): "+ex.getMessage());
-                            ex.printStackTrace();
-	                    }
-	                } catch (Exception ex) {
-	                    System.out.println("PropertySheetPanel.wasModified(): Unexpected exception while updating " + property.getName());
-	                }
-                    //revalidate();
-	                if (m_Views[i] != null && m_Views[i] instanceof PropertyPanel) {
-	                    //System.err.println("Trying to repaint the property canvas");
-	                    m_Views[i].repaint();
-	                    revalidate();
-	                }
-	                break;
-	            }	// end if (m_Editors[i] == editor)
-            } //  end for (int i = 0 ; i < m_Editors.length; i++) {
-            boolean doRepaint = false;
-            for (int i = 0 ; i < m_Editors.length; i++) { // check the views for out-of-date information. this is different than checking the editors
-            	if (m_Editors[i] != editor) {
-            		// looking at another field (not changed explicitly, maybe implicitely
-	            	boolean valChanged = false;
-	            	Object  args[]  = { };
-	            	Method getter   = m_Properties[i].getReadMethod();
-	            	if (m_Properties[i].isHidden() || m_Properties[i].isExpert()) {
-	            		if ((m_Labels[i] != null) && (m_Labels[i].isVisible())) {
-		            		// something is set to hidden but was visible up to now
-	            			m_ViewWrapper[i].setVisible(false);
-	            			m_Labels[i].setVisible(false);
-	            			doRepaint = true;
-	            		}
-	            		continue;
-	            	} else {
-	            		if ((m_Labels[i] != null) && !(m_Labels[i].isVisible())) {
-	            			 // something is invisible but set to not hidden in the mean time
-	            			m_ViewWrapper[i].setVisible(true);
-	            			m_Labels[i].setVisible(true);
-	            			doRepaint = true;
-	            		}
-	            	}
-	            	try {	// check if view i is up to date and in sync with the value of the getter
-	            		if (m_Views[i] != null) {
-	            			Object val = getter.invoke(m_Target, args);
-	            			if (m_Views[i] instanceof PropertyBoolSelector) {
-	            				valChanged = (((PropertyBoolSelector)m_Views[i]).isSelected() != ((Boolean)val));
-	            				if (valChanged) ((PropertyBoolSelector)m_Views[i]).setSelected(((Boolean)val));
-	            			} else if (m_Views[i] instanceof PropertyText) {
-	            				valChanged = !(((PropertyText)m_Views[i]).getText()).equals(val.toString());
-	            				if (valChanged) ((PropertyText)m_Views[i]).setText(val.toString());
-	            			} else if (m_Views[i] instanceof PropertyPanel) {
-	            				valChanged = false;//!((PropertyPanel)m_Views[i]).equals(value);
-	            				// disregard whole panels and hope for the best
-	            				if (TRACE) System.out.println("not checking for internal change of PropertyPanel");
-	            			} else if (m_Views[i] instanceof PropertyValueSelector) {
-	            				//changed = !((SelectedTag)val).isSelectedString((String)((PropertyValueSelector)m_Views[i]).getSelectedItem());
-	            				// interestingly there seems to be an implicit update of the ValueSelector, possible changes
-	            				// are already applied, all we need to see it is a repaint
-	            				m_Views[i].repaint();
-	            			} else {
-	            				System.out.println("Warning: Property \"" + i
-	            						+ "\" not recognized.  Skipping.");
-	            			}
-		            	}
-	            	} catch(Exception exc) {
-		            	System.err.println("Exception in PropertySheetPanel");
-	            	}
-	            }// end if (m_Editors[i] == editor) {
-            } // end for (int i = 0 ; i < m_Editors.length; i++) {	
-            if (doRepaint) {	// some components have been hidden or reappeared
-            	// MK this finally seems to work right
-            	Container p=this;
-				while (p != null) {
-					p.setSize(p.getPreferredSize());
-					p = p.getParent();
-				}
+	            	propIndex = i;
+	            	if (wasModified(i, editor.getValue(), true)) break;
+	            }
             }
-        } // end if (evt.getSource() instanceof PropertyEditor) {
+            if (propIndex == -1) System.err.println("error: could not identify event editor! (PropertySheetPanel)");
+        } else System.err.println("unknown event source! (PropertySheetPanel)");
+    }
+    
+    /** Updates the propertysheet when a value has been changed (from outside
+     * the propertysheet?).
+     * @param evt a value of type 'PropertyChangeEvent'
+     */
+    synchronized boolean wasModified(int propIndex, Object value, boolean followDependencies) {
+        if (TRACE) {
+            System.out.println("****PropertySheetPanel.wasModified(): My Target is "+ m_Properties[propIndex].getName() + ", new val: " + BeanInspector.toString(value));
+        }	            	
+        
+        if (!updateValue(propIndex, value)) return false;
+        
+        boolean doRepaint = false;
+
+        for (int i = 0 ; i < m_Editors.length; i++) { // check the views for out-of-date information. this is different than checking the editors
+        	if (i != propIndex) {
+        		if (updateFieldView(i)) doRepaint = true;
+        	}// end if (m_Editors[i] == editor) {
+        } // end for (int i = 0 ; i < m_Editors.length; i++) {	
+        if (doRepaint) {	// some components have been hidden or reappeared
+        	// MK this finally seems to work right
+        	Container p=this;
+        	while (p != null) {
+        		p.setSize(p.getPreferredSize());
+        		p = p.getParent();
+        	}
+        }
 
         // Now re-read all the properties and update the editors
         // for any other properties that have changed.
@@ -682,7 +674,8 @@ public class PropertySheetPanel extends JPanel implements PropertyChangeListener
             } catch (Exception ex) {
 	            o = null;
             }
-            if (o == m_Values[i]) {
+            if (TRACE) System.out.println("# cmp " + BeanInspector.toString(o) + "\n# vs. " + BeanInspector.toString(m_Values[i]));
+            if (o == m_Values[i] && (BeanInspector.isJavaPrimitive(o.getClass()))) {
 	            // The property is equal to its old value.
 	            continue;
             }
@@ -705,14 +698,133 @@ public class PropertySheetPanel extends JPanel implements PropertyChangeListener
             }
         }
 
+        if (followDependencies) {
+        	// Handle the special method getGOEPropertyUpdateLinks which returns a list of pairs
+        	// of strings indicating that on an update if the i-th property, the i+1-th property
+        	// should be updated. This is useful for changes within sub-classes of the target
+        	// which are not directly displayed in this panel but in sub-panels (and there have an own view etc.)
+        	Object o = BeanInspector.callIfAvailable(m_Target, "getGOEPropertyUpdateLinks", null);
+        	if ((o != null) && (o instanceof String[])) {
+        		maybeTriggerUpdates(propIndex, (String[])o);
+        	}
+        }
+        
         // Make sure the target bean gets repainted.
         if (Beans.isInstanceOf(m_Target, Component.class)) {
             //System.out.println("Beans.getInstanceOf repaint ");
             ((Component)(Beans.getInstanceOf(m_Target, Component.class))).repaint();
         }
+        return true;
     }
 
-    /** This method simply looks for an appropriate tiptext
+    /**
+     * Check a property for consistency with the object data and update the
+     * view if necessary. Return true if a repaint is necessary.
+     * @param i
+     * @return
+     */
+    private boolean updateFieldView(int i) {
+    	// looking at another field (not changed explicitly, maybe implicitely
+    	boolean valChanged = false;
+    	boolean doRepaint = false;
+    	Object  args[]  = { };
+    	Method getter   = m_Properties[i].getReadMethod();
+    	if (m_Properties[i].isHidden() || m_Properties[i].isExpert()) {
+    		if ((m_Labels[i] != null) && (m_Labels[i].isVisible())) {
+        		// something is set to hidden but was visible up to now
+    			m_ViewWrapper[i].setVisible(false);
+    			m_Labels[i].setVisible(false);
+    			doRepaint = true;
+    		}
+    		return doRepaint;
+    	} else {
+    		if ((m_Labels[i] != null) && !(m_Labels[i].isVisible())) {
+    			 // something is invisible but set to not hidden in the mean time
+    			m_ViewWrapper[i].setVisible(true);
+    			m_Labels[i].setVisible(true);
+    			doRepaint = true;
+    		}
+    	}
+    	try {	// check if view i is up to date and in sync with the value of the getter
+    		if (m_Views[i] != null) {
+    			Object val = getter.invoke(m_Target, args);
+    			if (m_Views[i] instanceof PropertyBoolSelector) {
+    				valChanged = (((PropertyBoolSelector)m_Views[i]).isSelected() != ((Boolean)val));
+    				if (valChanged) ((PropertyBoolSelector)m_Views[i]).setSelected(((Boolean)val));
+    			} else if (m_Views[i] instanceof PropertyText) {
+    				valChanged = !(((PropertyText)m_Views[i]).getText()).equals(val.toString());
+    				if (valChanged) ((PropertyText)m_Views[i]).setText(val.toString());
+    			} else if (m_Views[i] instanceof PropertyPanel) {
+    				valChanged = false;//!((PropertyPanel)m_Views[i]).equals(value);
+    				// disregard whole panels and hope for the best
+    				if (TRACE) {
+    					System.out.println("not checking for internal change of PropertyPanel " + !((PropertyPanel)m_Views[i]).equals(val));
+    					if (!((PropertyPanel)m_Views[i]).equals(val)) {
+    						System.out.println("# " + BeanInspector.toString(m_Views[i]));
+    						System.out.println("# " + BeanInspector.toString(val));
+    						System.out.println("Ed.: " + BeanInspector.toString(((PropertyPanel)m_Views[i]).getEditor()));
+    					}
+    				}    				
+    			} else if (m_Views[i] instanceof PropertyValueSelector) {
+    				//changed = !((SelectedTag)val).isSelectedString((String)((PropertyValueSelector)m_Views[i]).getSelectedItem());
+    				// interestingly there seems to be an implicit update of the ValueSelector, possible changes
+    				// are already applied, all we need to see it is a repaint
+    				m_Views[i].repaint();
+    			} else {
+    				System.out.println("Warning: Property \"" + i
+    						+ "\" not recognized.  Skipping.");
+    			}
+        	}
+    	} catch(Exception exc) {
+        	System.err.println("Exception in PropertySheetPanel");
+    	}
+    	return doRepaint;
+	}
+
+    /**
+     * Check the given link list and trigger updates of indicated properties.
+     * 
+     * @param propIndex
+     * @param links
+     */
+	private void maybeTriggerUpdates(int propIndex, String[] links) {
+    	int max = links.length;
+    	if (max % 2 == 1) {
+    		System.err.println("Error in PropertySheetPanel:maybeTriggerUpdates: odd number of strings provided!");
+    		max -= 1;
+    	}
+    	if (TRACE) System.out.println("maybeTriggerUpdates: " + BeanInspector.toString(links));
+		for (int i = 0; i<max; i+=2) {
+			if (links[i].equals(m_Properties[propIndex].getName())) {
+				if (TRACE) System.out.println("updating linked property " + links[i+1]);
+				updateLinkedProperty(links[i+1]);
+			}
+		}
+	}
+
+	private void updateLinkedProperty(String propName) {
+		for (int i=0; i<m_Properties.length; i++) {
+			if (m_Properties[i].getName().equals(propName)) {
+				if (TRACE) System.out.println("Found linked property " + propName);
+		        Method getter   = m_Properties[i].getReadMethod();
+	            Object val = null;
+				try {
+					val = getter.invoke(m_Target, (Object[])null);
+				} catch (Exception e) {
+					// TODO Auto-generated catch block
+					val = null;
+					e.printStackTrace();
+				}
+				if (val != null) {
+					m_Editors[i].setValue(val);
+//					wasModified(i, val, false);
+				} else System.err.println("Error in PropertySheetPanel:updateLinkedProperty");
+				return;
+			}
+		}
+	}
+
+	/** This method simply looks for an appropriate tiptext
      * @param name      The name of the property
      * @param methods   A list of methods to search.
      * @param target    The target object
diff --git a/src/eva2/gui/PropertyText.java b/src/eva2/gui/PropertyText.java
index 7065a45a..632b6d58 100644
--- a/src/eva2/gui/PropertyText.java
+++ b/src/eva2/gui/PropertyText.java
@@ -62,7 +62,7 @@ public class PropertyText extends JTextField {
   protected void updateEditor() {
     try {
       String x = getText();
-      m_Editor.setAsText(x);
+      if (!m_Editor.getAsText().equals(x)) m_Editor.setAsText(x);
     } catch (IllegalArgumentException ex) {}
   }
 }
diff --git a/src/eva2/server/go/individuals/AbstractEAIndividual.java b/src/eva2/server/go/individuals/AbstractEAIndividual.java
index 9be63226..d8a8af3e 100644
--- a/src/eva2/server/go/individuals/AbstractEAIndividual.java
+++ b/src/eva2/server/go/individuals/AbstractEAIndividual.java
@@ -920,7 +920,9 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
 	 * @return double valued position of an individual or null
 	 */
 	public static double[] getDoublePosition(AbstractEAIndividual indy) {
-		if (indy instanceof InterfaceDataTypeDouble) {
+		if (indy instanceof InterfaceESIndividual) {
+			return ((InterfaceESIndividual)indy).getDGenotype();
+		} else if (indy instanceof InterfaceDataTypeDouble) {
 			return ((InterfaceDataTypeDouble)indy).getDoubleData();
 		} else if (indy instanceof InterfaceDataTypeInteger) {
 			int[] intData =  ((InterfaceDataTypeInteger)indy).getIntegerData();
diff --git a/src/eva2/server/go/individuals/GAIndividualBinaryData.java b/src/eva2/server/go/individuals/GAIndividualBinaryData.java
index 170bdfd7..096e3c84 100644
--- a/src/eva2/server/go/individuals/GAIndividualBinaryData.java
+++ b/src/eva2/server/go/individuals/GAIndividualBinaryData.java
@@ -166,8 +166,8 @@ public class GAIndividualBinaryData extends AbstractEAIndividual implements Inte
     }
 
     /** This method allows the user to read the length of the genotype.
-     * This may be necessary since BitSet.lenght only returns the index
-     * of the last significat bit.
+     * This may be necessary since BitSet.length only returns the index
+     * of the last significant bit.
      * @return The length of the genotype.
      */
     public int getGenotypeLength() {
diff --git a/src/eva2/server/go/operators/distancemetric/PhenotypeMetric.java b/src/eva2/server/go/operators/distancemetric/PhenotypeMetric.java
index fd3d29d4..59258246 100644
--- a/src/eva2/server/go/operators/distancemetric/PhenotypeMetric.java
+++ b/src/eva2/server/go/operators/distancemetric/PhenotypeMetric.java
@@ -110,13 +110,14 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
         if ((indy1 instanceof InterfaceDataTypeDouble) && (indy2 instanceof InterfaceDataTypeDouble)) {
             double[]    d1, d2;
             double[][]  r1, r2;
-            double      tmpResult = 0;
+            double      tmpResult = 0, tmp=0;
             d1 = ((InterfaceDataTypeDouble) indy1).getDoubleData();
             r1 = ((InterfaceDataTypeDouble) indy1).getDoubleRange();
             d2 = ((InterfaceDataTypeDouble) indy2).getDoubleData();
             r2 = ((InterfaceDataTypeDouble) indy2).getDoubleRange();
             for (int i = 0; (i < d1.length) && (i < d2.length); i++) {
-                tmpResult += Math.pow(((d1[i] - r1[i][0])/(r1[i][1] - r1[i][0])) - ((d2[i] - r2[i][0])/(r2[i][1] - r2[i][0])), 2);
+            	tmp=((d1[i] - r1[i][0])/(r1[i][1] - r1[i][0])) - ((d2[i] - r2[i][0])/(r2[i][1] - r2[i][0]));
+                tmpResult += (tmp*tmp);
             }
             result += Math.sqrt(tmpResult);
         }
@@ -158,13 +159,33 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
     }
     
     public static double euclidianDistance(double[] v1, double[] v2) {
-        double      result = 0;
+        double      result = 0, tmp=0;
         for (int i = 0; (i < v1.length) && (i < v2.length); i++) {
-        	result += Math.pow(v1[i] - v2[i], 2);
+        	tmp = v1[i] - v2[i];
+        	result += (tmp*tmp);
         }
         return Math.sqrt(result);
     }
     
+    public static double squaredEuclidianDistance(double[] v1, double[] v2) {
+        double      tmp, result = 0;
+        for (int i = 0; (i < v1.length) && (i < v2.length); i++) {
+        	tmp=v1[i] - v2[i];
+        	result += (tmp*tmp);
+        }
+        return result;
+    }
+    
+    public static double normedDistance(double[] pos1, double[][] range1, double[] pos2, double[][] range2) {
+    	double      tmpResult = 0, tmp=0;
+
+    	for (int i = 0; (i < pos1.length) && (i < pos2.length); i++) {
+    		tmp=((pos1[i] - range1[i][0])/(range1[i][1] - range1[i][0])) - ((pos2[i] - range2[i][0])/(range2[i][1] - range2[i][0]));
+    		tmpResult += (tmp*tmp);
+    	}
+    	return Math.sqrt(tmpResult);
+    }
+
     public static double norm(AbstractEAIndividual indy) {
         double      result = 0;
         if (indy instanceof InterfaceDataTypeBinary) {
diff --git a/src/eva2/server/go/operators/mutation/MutateESRankMuCMA.java b/src/eva2/server/go/operators/mutation/MutateESRankMuCMA.java
index 4cb8f95b..ac8d9dcd 100644
--- a/src/eva2/server/go/operators/mutation/MutateESRankMuCMA.java
+++ b/src/eva2/server/go/operators/mutation/MutateESRankMuCMA.java
@@ -99,7 +99,7 @@ class CMAParamSet implements InterfacePopulationChangedEventListener, Serializab
 //		c_u_sig = Math.sqrt(c_sig * (2.-c_sig));
 		params.d_sig = params.c_sig+1+2*Math.max(0, Math.sqrt((muEff-1)/(dim+1)) - 1);
 		
-		if (initialSigma<0) initialSigma = getAvgRange(params.range);
+		if (initialSigma<0) initialSigma = Mathematics.getAvgRange(params.range);
 		if (initialSigma <= 0) {
 			EVAERROR.errorMsgOnce("warning: initial sigma <= zero! Working with converged population?");
 			initialSigma = 10e-10;
@@ -160,18 +160,6 @@ class CMAParamSet implements InterfacePopulationChangedEventListener, Serializab
 		// default parameter value ( HK03, sec. 2)
 		return getMuEff(weights, mu);
 	}
-	
-	/**
-	 * Calculate the average length of the range intervals over all dimensions.
-	 * 
-	 * @param range
-	 * @return
-	 */
-    public static double getAvgRange(double[][] range) {
-		double sum = 0.;
-		for (int i=0; i<range.length; i++) sum+=(range[i][1]-range[i][0]);
-		return sum/range.length;
-	}
     
 	/**
 	 * Make sure that the parameter sets of each population are updated (reinitialized)
@@ -255,7 +243,7 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 			// scaled by average range as the measures are normed
 			//return initGen.getPopulationMeasures(null)[0]*getAvgRange();
 			// use euclidian measures without normation and scaling
-			return initGen.getPopulationMeasures(null)[0];
+			return initGen.getPopulationMeasures()[0];
 		//case halfRange: return getAvgRange(range)/2.;
 		case userDefined: return userDefInitSig ;
 		default: return -1.;
diff --git a/src/eva2/server/go/operators/postprocess/PostProcess.java b/src/eva2/server/go/operators/postprocess/PostProcess.java
index 624fadf8..f49cc159 100644
--- a/src/eva2/server/go/operators/postprocess/PostProcess.java
+++ b/src/eva2/server/go/operators/postprocess/PostProcess.java
@@ -1,6 +1,7 @@
 package eva2.server.go.operators.postprocess;
 
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.Collection;
 
 import eva2.OptimizerFactory;
@@ -8,6 +9,7 @@ import eva2.OptimizerRunnable;
 import eva2.gui.BeanInspector;
 import eva2.gui.Plot;
 import eva2.gui.TopoPlot;
+import eva2.server.go.IndividualInterface;
 import eva2.server.go.InterfaceTerminator;
 import eva2.server.go.enums.ESMutationInitialSigma;
 import eva2.server.go.enums.PostProcessMethod;
@@ -66,6 +68,7 @@ public class PostProcess {
 	public static final int KEEP_LONERS = 11;
 	public static final int DISCARD_LONERS = 12;
 	public static final int LONERS_AS_CLUSTERS = 13;
+	
     /** 
      * This method returns a set of individuals corresponding to an optimum in a given list.
      * The individuals returned are to be nearer than epsilon to a given optimum. For each optimum, there is
@@ -137,7 +140,7 @@ public class PostProcess {
     /**
      * Calls clusterBest with a ClusteringDensitiyBased clustering object with the given sigma and a 
      * minimum group size of 2.
-     * @see clusterBest(Population pop, InterfaceClustering clustering, double returnQuota, int lonerMode, int takeOverMode)
+     * @see #clusterBest(Population pop, InterfaceClustering clustering, double returnQuota, int lonerMode, int takeOverMode)
      * @param pop
      * @param sigmaCluster
      * @param returnQuota
@@ -398,19 +401,24 @@ public class PostProcess {
 	 * Search for a local minimum using nelder mead and return the solution found and the number of steps
 	 * (evaluations) actually performed. This uses the whole population as starting population for nelder mead
 	 * meaning that typically only one best is returned.
+	 * Returns the number of function calls really performed by the method; sets the number of function calls
+	 * in the population back to the original count.
+	 * If the baseEvals parameter (which should be >= 0) is > 0, then the number of evaluations is set as
+	 * number of evaluations before the optimization using the given terminator.
 	 * 
 	 * @param pop
 	 * @param problem
 	 * @param term
+	 * @param baseEvals
 	 * @return
 	 */
-	public static int processWithNMS(Population pop, AbstractOptimizationProblem problem, InterfaceTerminator term) {
+	public static int processWithNMS(Population pop, AbstractOptimizationProblem problem, InterfaceTerminator term, int baseEvals) {
 		NelderMeadSimplex nms = new NelderMeadSimplex();
 		nms.setProblemAndPopSize(problem);
 		nms.setGenerationCycle(5);
 		nms.initByPopulation(pop, false);
 		int funCallsBefore = pop.getFunctionCalls();
-		pop.SetFunctionCalls(0);
+		pop.SetFunctionCalls(baseEvals);
 		
 		ppRunnable = new OptimizerRunnable(OptimizerFactory.makeParams(nms, pop, problem, 0, term), true);
 		// as nms creates a new population and has already evaluated them, send a signal to stats
@@ -418,8 +426,8 @@ public class PostProcess {
 		
 		runPP();
 
-		int funCallsDone = pop.getFunctionCalls();
-		pop.SetFunctionCalls(funCallsBefore+funCallsDone);
+		int funCallsDone = pop.getFunctionCalls()-baseEvals;
+		pop.SetFunctionCalls(funCallsBefore);
 		
 		return funCallsDone;
 	}
@@ -428,13 +436,18 @@ public class PostProcess {
 	 * Search for a local minimum using nelder mead and return the solution found and the number of steps
 	 * (evaluations) actually performed. This uses the whole population as starting population for nelder mead
 	 * meaning that typically only one best is returned.
+	 * Returns the number of function calls really performed by the method; sets the number of function calls
+	 * in the population back to the original count. If the baseEvals parameter (which should be >= 0) is > 0, 
+	 * then the number of evaluations is set as
+	 * number of evaluations before the optimization using the given terminator.
 	 * 
 	 * @param pop
 	 * @param problem
 	 * @param term
+	 * @param baseEvals
 	 * @return
 	 */
-	public static int processWithCMA(Population pop, AbstractOptimizationProblem problem, InterfaceTerminator term) {
+	public static int processWithCMA(Population pop, AbstractOptimizationProblem problem, InterfaceTerminator term, int baseEvals) {
 //		GOParameters cmaParams = OptimizerFactory.cmaESIPOP(problem);
 		MutateESRankMuCMA mutator = new MutateESRankMuCMA();
 		mutator.setInitializeSigma(ESMutationInitialSigma.avgInitialDistance);
@@ -449,7 +462,7 @@ public class PostProcess {
 		GOParameters cmaParams = OptimizerFactory.makeParams(es, pop, problem, 0, term);
 		
 		int funCallsBefore = pop.getFunctionCalls();
-		pop.SetFunctionCalls(0);
+		pop.SetFunctionCalls(baseEvals);
 		
 		ppRunnable = new OptimizerRunnable(cmaParams, true);
 		ppRunnable.getStats().createNextGenerationPerformed(cmaParams.getOptimizer().getPopulation(), null);
@@ -458,8 +471,8 @@ public class PostProcess {
 		pop.clear();
 		pop.addPopulation(es.getPopulation());
 		
-		int funCallsDone = es.getPopulation().getFunctionCalls();
-		pop.SetFunctionCalls(funCallsBefore+funCallsDone);
+		int funCallsDone = es.getPopulation().getFunctionCalls()-baseEvals;
+		pop.SetFunctionCalls(funCallsBefore);
 		
 		return funCallsDone;
 	}
@@ -488,42 +501,26 @@ public class PostProcess {
 		
 		Population pop = new Population(1);
 		pop.add(cand);
-		int evalsDone = processSingleCandidates(PostProcessMethod.nelderMead, pop, hcSteps, initPerturbation, prob);
+		int evalsDone = processSingleCandidates(PostProcessMethod.nelderMead, pop, hcSteps, initPerturbation, prob, null);
 		
-		return new Pair<AbstractEAIndividual, Integer>(pop.getBestEAIndividual(), evalsDone);	
-		
-//		Population candidates = NelderMeadSimplex.createNMSPopulation(cand, initPerturbation , range, false);
-//		prob.evaluate(candidates);
-////		refSet.incrFunctionCallsby(candidates.size());
-//		int evalsDone = candidates.size();
-//		int hcStepsRest = hcSteps - candidates.size();
-//
-//		candidates.add(cand);
-//		candidates.setPopulationSize(candidates.size());
-//		if (hcStepsRest < 1) {
-//			System.err.println("Error: Less steps than dimensions! " + hcSteps + " vs " + candidates.size());
-//			return new Pair<AbstractEAIndividual, Integer>(candidates.getBestEAIndividual(), evalsDone);
-//		} else {
-////			candidates.setEvaluated(); // they have all been evaluated at this point and no mod. is due
-////			if (!candidates.isEvaluated()) {
-////				System.err.println("this is bad!");
-////			}
-//			InterfaceTerminator term = new EvaluationTerminator(hcStepsRest);
-//	
-//			evalsDone += PostProcess.processWithNMS(candidates, prob, term);
-//			if (Math.abs(evalsDone-hcSteps)>1) {
-//				System.err.println("Error in localSolverNMS " + evalsDone + " / " + hcSteps);
-//			}
-//		}
-//		return new Pair<AbstractEAIndividual, Integer>(candidates.getBestEAIndividual(), evalsDone);		
+		return new Pair<AbstractEAIndividual, Integer>(pop.getBestEAIndividual(), evalsDone);		
 	}
 
 	/**
-	 * Create a subpopulation around an indicated individual from the candidate set.
+	 * Create a sub-population around an indicated individual from the candidate set.
 	 * Depending on the post processing method, this is done slightly differently. For hill-climbing,
 	 * an error message is produced.
+	 * 
+	 * @see #createPopInSubRange(double, AbstractOptimizationProblem, AbstractEAIndividual)
+	 * @see #NelderMeadSimplex.createNMSPopulation(AbstractEAIndividual, double, double[][], boolean)
+	 * @param method
+	 * @param problem
+	 * @param candidates
+	 * @param index index of the individual for which to produce the sub population
+	 * @param maxPerturbation
+	 * @param includeCand
 	 */
-	private static Population createLSSupPopulation(PostProcessMethod method, AbstractOptimizationProblem problem, Population candidates, int index, double maxPerturbation, boolean includeCand) {
+	public static Population createLSSupPopulation(PostProcessMethod method, AbstractOptimizationProblem problem, Population candidates, int index, double maxPerturbation, boolean includeCand) {
 		Population subPop = null;
 		switch (method) {
 		case cmaES:
@@ -534,27 +531,78 @@ public class PostProcess {
 			break;
 		case nelderMead:
 			double[][] range = ((InterfaceDataTypeDouble)candidates.getEAIndividual(index)).getDoubleRange();
-			double perturb = findNMSPerturn(candidates, index, maxPerturbation);
+			double perturb = findNMSPerturb(candidates, index, maxPerturbation);
 			if (TRACE) System.out.println("perturb " + index + " is " + perturb);
 			subPop = NelderMeadSimplex.createNMSPopulation(candidates.getEAIndividual(index), perturb, range, false); 
 		}
+//		subPop.setSameParams(candidates);
 		return subPop;
 	}
 	
 	/**
-	 * For each candidate individual, create an own nm-population and optimize it separately.
-	 * The allowed steps must be large enough to perform a Nelder-Mead-Simplex step for all individuals, namely
-	 * for problem dimension n it should be (n+k)*candPopSize for a positive integer k. 
-	 * At the moment, the function calls are distributed evenly between all candidate solutions. This could be
-	 * improved by checking the convergence state in the future.
+	 * Employ hill-climbing directly or NM/CMA on the candidates. The candidate population
+	 * must not be empty and candidates must implement InterfaceDataTypeDouble.
+	 * The given number of steps gives the number of evaluations which may not be hit exactly.
+	 * The population will not be altered for Nelder-Mead or CMA-ES, and the evaluation calls will not be added to
+	 * the candidate population!
 	 * 
+	 * @see #processWithHC(Population, AbstractOptimizationProblem, InterfaceTerminator, InterfaceMutation)
+	 * @see #processSingleCandidatesNMCMA(PostProcessMethod, Population, InterfaceTerminator, double, AbstractOptimizationProblem)
+	 * @param method
 	 * @param candidates
-	 * @param steps
+	 * @param steps number of evaluations to be performed (summed up)
 	 * @param maxPerturbation
 	 * @param prob
-	 * @return
+	 * @param mute the mutator to use (in case of HC)
+	 * @return the evaluations performed
 	 */
-	public static int processSingleCandidates(PostProcessMethod method, Population candidates, int steps, double maxPerturbation, AbstractOptimizationProblem prob) {
+	public static int processSingleCandidates(PostProcessMethod method, Population candidates, int steps, double maxPerturbation, AbstractOptimizationProblem prob, InterfaceMutation mute) {
+		int dim=((InterfaceDataTypeDouble)candidates.getEAIndividual(0)).getDoubleRange().length;
+		int candCnt = candidates.size();
+		
+		if (method == PostProcessMethod.hillClimber) {
+			int evalsOld = candidates.getFunctionCalls();
+			processWithHC(candidates, prob, new EvaluationTerminator(evalsOld+steps), mute);
+			int evalsDone = candidates.getFunctionCalls()-evalsOld;
+			candidates.SetFunctionCalls(evalsOld);
+			return evalsDone;
+		} else {
+			int stepsPerCand = (steps-(candCnt*(dim-1)))/candCnt;
+			if (TRACE) System.out.println("employing " + stepsPerCand + " steps per cand.");
+			if (stepsPerCand < dim) {
+				System.err.println("Too few steps allowed in processSingleCandidates!");
+				System.err.println("Method: " + method + ", cands: " + candidates.size() + ", steps: " + steps);
+				return 0;
+			} else {
+				EvaluationTerminator term = new EvaluationTerminator(stepsPerCand);
+				return processSingleCandidatesNMCMA(method, candidates, term, maxPerturbation, prob);
+			}
+		}
+	}
+	
+	/**
+	 * For each candidate individual, create an own sub-population and optimize it separately. Candidates must have
+	 * been evaluated earlier. The candidates population is not altered.
+	 * The performed evaluation calls for problem dimension n will be (n+k)*candPopSize for a positive integer k. 
+	 * At the moment, the function calls are distributed evenly between all candidate solutions. This could be
+	 * improved by checking the convergence state in the future. The given terminator will be applied to each
+	 * candidate sub-population anew. If the terminator is null, 10*n steps will be performed on each candidate.
+	 * 
+	 * A double value is added to each solution individual that replaces its ancestor candidate, using the key "PostProcessingMovedBy".
+	 * It indicates the phenotype distance the found solution has moved relatively to the original candidate.
+	 * 
+	 * @see #findNMSPerturb(Population, int, double)
+	 * @see #createLSSupPopulation(PostProcessMethod, AbstractOptimizationProblem, Population, int, double, boolean)
+	 * @see #processWithCMA(Population, AbstractOptimizationProblem, InterfaceTerminator, int)
+	 * @see #processWithNMS(Population, AbstractOptimizationProblem, InterfaceTerminator, int)
+	 * @param method	NM or CMA is allowed here
+	 * @param candidates
+	 * @param term
+	 * @param maxPerturbation perturbation for the sub population
+	 * @param prob
+	 * @return the number of evaluations performed
+	 */
+	public static int processSingleCandidatesNMCMA(PostProcessMethod method, Population candidates, InterfaceTerminator term, double maxPerturbation, AbstractOptimizationProblem prob) {
 		ArrayList<Population> nmPops = new ArrayList<Population>();
 		int stepsPerf = 0;
 		Population subPop;
@@ -568,31 +616,39 @@ public class PostProcess {
 			nmPops.add(subPop);
 		}
 		
-		int stepsPerCand = (steps-stepsPerf)/candidates.size();
-		if (TRACE) System.out.println("rest is " + (steps-stepsPerf) + ", thats " + stepsPerCand + " per candidate.");
-		if (stepsPerCand < 1) System.err.println("Too few steps allowed!");
-		else {
-			for (int i=0; i<candidates.size(); i++) { // improve each single sub pop
-				subPop = nmPops.get(i);
-				EvaluationTerminator term = new EvaluationTerminator(stepsPerCand);
-				if (TRACE) System.out.println("*** before " + subPop.getBestEAIndividual().getStringRepresentation());
-				
-				switch (method) {
-				case nelderMead: stepsPerf += PostProcess.processWithNMS(subPop, prob, term); 
-					break;
-				case cmaES: stepsPerf += PostProcess.processWithCMA(subPop, prob, term); 
-					break;
-				}
-				if (checkRange(subPop.getBestEAIndividual())) {
-					// and replace corresponding individual (should usually be better)
-					if (subPop.getBestEAIndividual().isDominant(candidates.getEAIndividual(i))) candidates.set(i, subPop.getBestEAIndividual());
-				} else {
-					// TODO esp. in nelder mead
-					//System.err.println("Warning, individual left the problem range during PP!");				
-				}
+		if (term==null) {
+			int stepsPerCand = 10*(nmPops.get(0).size()-1); // == 10*dim for NM
+			if (TRACE) System.out.println("employing " + stepsPerCand + " per candidate.");
+			if (stepsPerCand < 1) {
+				System.err.println("Too few steps allowed!");
+				return 0;
+			}
+			term = new EvaluationTerminator(stepsPerCand);
+		}
+		for (int i=0; i<candidates.size(); i++) { // improve each single sub pop
+			subPop = nmPops.get(i);
+			term.init(prob);
+//			if (TRACE) System.out.println("*** before " + subPop.getBestEAIndividual().getStringRepresentation());
 
-				if (TRACE) System.out.println("refined to " + subPop.getBestEAIndividual().getStringRepresentation());
-			}				
+			switch (method) {
+			case nelderMead: stepsPerf += PostProcess.processWithNMS(subPop, prob, term, subPop.size()-1); 
+			break;
+			case cmaES: stepsPerf += PostProcess.processWithCMA(subPop, prob, term, subPop.size()-1); 
+			break;
+			}
+			if (checkRange(subPop.getBestEAIndividual())) {
+				// and replace corresponding individual (should usually be better)
+				if (subPop.getBestEAIndividual().isDominant(candidates.getEAIndividual(i))) {
+//					System.out.println("moved by "+ PhenotypeMetric.dist(candidates.getEAIndividual(i), subPop.getBestEAIndividual()));
+					subPop.getBestEAIndividual().putData("PostProcessingMovedBy", new Double(PhenotypeMetric.dist(candidates.getEAIndividual(i), subPop.getBestEAIndividual())));
+					candidates.set(i, subPop.getBestEAIndividual());
+				}
+			} else {
+				// TODO esp. in nelder mead
+				System.err.println("Warning, individual left the problem range during PP!");				
+			}
+
+//			if (TRACE) System.out.println("refined to " + subPop.getBestEAIndividual().getStringRepresentation());
 		}
 
 		return stepsPerf;
@@ -642,7 +698,7 @@ public class PostProcess {
 			double[][] range = getDoubleRange(indy);
 			double[] data = getDoubleData(indy);
 			int lambda= (int) (4.0 + 3.0 * Math.log(range.length));
-			double[][] newRange = new double[2][range.length];
+			double[][] newRange = new double[range.length][2];
 			for (int dim=0; dim<range.length; dim++) {
 				// create a small range array around the expected local optimum 
 				newRange[dim][0] = Math.max(range[dim][0], data[dim]-(searchBoxLen/2.));
@@ -732,7 +788,7 @@ public class PostProcess {
 	public static void main(String[] args) {
 		AbstractOptimizationProblem problem = new FM0Problem();
 		InterfaceMultimodalProblemKnown mmp = (InterfaceMultimodalProblemKnown)problem;
-		OptimizerRunnable runnable = OptimizerFactory.getOptRunnable(OptimizerFactory.STD_GA, problem, 500, null);
+		OptimizerRunnable runnable = OptimizerFactory.getOptRunnable(OptimizerFactory.STD_GA, problem, 100, null);
 		runnable.run();
 		Population pop = runnable.getGOParams().getOptimizer().getPopulation();
 //		System.out.println("no optima found: " + mmp.getNumberOfFoundOptima(pop));
@@ -742,12 +798,13 @@ public class PostProcess {
 		Pair<Population, Double> popD = new Pair<Population, Double>(pop, 1.);
 		int i=0;
 		int evalCnt = 0;
-		while (popD.tail() > 0.01) {
+		while (popD.tail() > 0.001) {
 			i++;
 //			public static PopDoublePair clusterHC(pop, problem, sigmaCluster, funCalls, keepClusterRatio, mute) {
 
-			popD = clusterHC(popD.head(), problem, 0.01, 1000 - (evalCnt % 1000), 0.5, new MutateESFixedStepSize(0.02));
+			popD = clusterLocalSearch(PostProcessMethod.hillClimber, popD.head(), problem, 0.01, 1500, 0.1, new MutateESFixedStepSize(0.02));
 			evalCnt += popD.head().getFunctionCalls();
+			System.out.println("popsize is " + popD.head().size());
 		}
 		found = getFoundOptima(popD.head(), mmp.getRealOptima(), 0.05, true);
 		System.out.println("found at " + i + " (" + found.size() + "): " + BeanInspector.toString(found));
@@ -760,11 +817,12 @@ public class PostProcess {
 	}
 	
 	/**
-	 * Cluster a population and reduce it by a certain ratio, then optimize the remaining individuals for a given number of function calls with a HC.
-	 * Return a pair of the optimized population and the improvement in the mean fitness (not normed) that was achieved by the HC run. The returned
+	 * Cluster a population and reduce it by a certain ratio, then optimize the remaining individuals for a given number of function calls with a LS.
+	 * Return a pair of the optimized population and the improvement in the mean fitness (not normed) that was achieved by the LS run. The returned
 	 * individuals are deep clones, so the given population is not altered. Of a cluster of individuals, the given
 	 * ratio of individuals is kept, more precisely, the best one is kept while the remaining are selected randomly. All loners are kept. 
 	 * 
+	 * @param method	the local search method to be used
 	 * @param pop	the population to work on
 	 * @param problem	the target problem instance 
 	 * @param sigmaCluster	minimum clustering distance 
@@ -773,71 +831,33 @@ public class PostProcess {
 	 * @param mute	the mutation operator to be used by the hill climber
 	 * @return a pair of the optimized population and the improvement in the mean fitness (not normed) achieved by the HC run
 	 */
-	public static Pair<Population, Double> clusterHC(Population pop, AbstractOptimizationProblem problem, double sigmaCluster, int funCalls, double keepClusterRatio, InterfaceMutation mute) {
+	public static Pair<Population, Double> clusterLocalSearch(PostProcessMethod method, Population pop, AbstractOptimizationProblem problem, double sigmaCluster, int funCalls, double keepClusterRatio, InterfaceMutation mute) {
+		int evalsBefore = pop.getFunctionCalls();
+
 		Population clust = (Population)clusterBest(pop, new ClusteringDensityBased(sigmaCluster, 2), keepClusterRatio, KEEP_LONERS, BEST_RAND).clone();
-//		System.out.println("keeping " + clust.size() + " for hc....");
+
+		//clust.addPopulationChangedEventListener()
 		double[] meanFit = clust.getMeanFitness();
-		processWithHC(clust, problem, new EvaluationTerminator(pop.getFunctionCalls()+funCalls), mute);
+		
+		if (TRACE) System.out.println("BEF: funcalls done: " + pop.getFunctionCalls() + ", now allowed: " + funCalls);
+		
+		int evalsDone = processSingleCandidates(method, clust, funCalls, sigmaCluster/2., problem, mute);
+		
+		clust.SetFunctionCalls(evalsBefore + evalsDone);
+		
 		double improvement = PhenotypeMetric.euclidianDistance(meanFit, clust.getMeanFitness());
-		if (TRACE) System.out.println("improvement by " + improvement);
+		if (TRACE) System.out.println("improvement by " + improvement + " funcalls done: " + evalsDone);
 		return new Pair<Population, Double>(clust, improvement);
 	}
 	
-//	/**
-//	 * Do some post processing on a multimodal problem. If the real optima are known, only the number of
-//	 * found optima is printed. Otherwise, the population is clustered and for the population of cluster-representatives, 
-//	 * some diversity measures and a fitness histogram are printed.
-//	 * 
-//	 * @see Population.getPopulationMeasures()
-//	 * @see createFitNormHistogram
-//	 * @param mmProb	the target problem
-//	 * @param pop	the solution set
-//	 * @param sigmaCluster	the min clustering distance 
-//	 * @param out	a PrintStream for data output
-//	 * @param minBnd	lower fitness bound
-//	 * @param maxBnd	upper fitness bound
-//	 * @param bins	number of bins for the fitness histogram
-//	 * @return 
-//	 */
-//	public static Population outputResult(AbstractOptimizationProblem mmProb, Population pop, double sigmaCluster, PrintStream out, double minBnd, double maxBnd, int bins, int hcSteps) {
-//		ClusteringDensityBased clust = new ClusteringDensityBased(sigmaCluster);
-//		clust.setMinimumGroupSize(2);
-//		Population clusteredBest = clusterBest(pop, clust, 0, KEEP_LONERS, BEST_ONLY);
-//		if (hcSteps > 0) { // HC post process
-//			Population tmpPop = (Population)clusteredBest.clone();
-//			processWithHC(tmpPop, (AbstractOptimizationProblem)mmProb, hcSteps, 0.001);
-//			clusteredBest = clusterBest(tmpPop, clust, 0, KEEP_LONERS, BEST_ONLY);
-//		}
-//		double[] meas = clusteredBest.getPopulationMeasures();
-//		int[] sols = createFitNormHistogram(clusteredBest, minBnd, maxBnd, bins);
-//		out.println("measures: " + BeanInspector.toString(meas));
-//		out.println("solution hist.: " + BeanInspector.toString(sols));
-//
-//		Object[] bestArr = clusteredBest.toArray();
-//		for (Object locOpt : bestArr) {
-////			out.print((AbstractEAIndividual.getDefaultDataString((IndividualInterface)locOpt)));
-//			out.println(AbstractEAIndividual.getDefaultStringRepresentation(((AbstractEAIndividual)locOpt)));
-//		}
-//		return clusteredBest;
-//	}
-
-//	public static Population outputResultKnown(InterfaceMultimodalProblemKnown mmProb, Population pop, double sigmaCluster, PrintStream out, double minBnd, double maxBnd, int bins) {
-//		Population found = getFoundOptima(pop, ((InterfaceMultimodalProblemKnown)mmProb).getRealOptima(), ((InterfaceMultimodalProblemKnown)mmProb).getEpsilon(), true);
-//		for (double epsilon=0.1; epsilon > 0.00000001; epsilon/=10.) {
-//			//out.println("no optima found: " + ((InterfaceMultimodalProblemKnown)mmProb).getNumberOfFoundOptima(pop));
-//			out.println("found " + getFoundOptima(pop, ((InterfaceMultimodalProblemKnown)mmProb).getRealOptima(), epsilon, true).size() + " for epsilon = " + epsilon);
-//		}
-//		out.println("max peak ratio is " + mmProb.getMaximumPeakRatio(found));
-//		return found;
-//	}
-	
 	/**
-	 * Do some data output for multimodal problems with known optima. The listener may be null, but then the method is
-	 * not really doing much at this state.
+	 * Do some data output for multimodal problems to the listener.
+	 * This may be expensive computationally.
 	 */
-	public static void procMultiModalKnown(Population solutions, InterfaceMultimodalProblemKnown mmkProb, InterfaceTextListener listener) {
-//		Population found = getFoundOptima(solutions, mmkProb.getRealOptima(), mmkProb.getEpsilon(), true);
-		if (listener != null) {
+	public static void evaluateMultiModal(Population solutions, AbstractOptimizationProblem prob, InterfaceTextListener listener) {
+		if (listener == null) return;
+		if (prob instanceof InterfaceMultimodalProblemKnown) {
+			InterfaceMultimodalProblemKnown mmkProb = (InterfaceMultimodalProblemKnown)prob;
 			listener.println("number of known optima is " + mmkProb.getRealOptima().size());
 			listener.println("default epsilon is " + mmkProb.getEpsilon());
 			listener.println("optima found with default epsilon: " + getFoundOptima(solutions, mmkProb.getRealOptima(), mmkProb.getEpsilon(), true).size());
@@ -846,6 +866,21 @@ public class PostProcess {
 				//	out.println("no optima found: " + ((InterfaceMultimodalProblemKnown)mmProb).getNumberOfFoundOptima(pop));
 				listener.println("found " + getFoundOptima(solutions, mmkProb.getRealOptima(), epsilon, true).size() + " for epsilon = " + epsilon + ", maxPeakRatio: " + AbstractMultiModalProblemKnown.getMaximumPeakRatio(mmkProb,solutions, epsilon));
 			}
+		} else {
+			// TODO in this form it may cost a lot of time and cant be stopped, which is bad
+//			double epsilonPhenoSpace = 0.01, epsilonFitConv = 1e-10, clusterSigma = 0.;
+//			Population extrOpts;
+//			for (int k=0; k<3; k++) {
+//				extrOpts = prob.extractPotentialOptima(solutions, epsilonPhenoSpace, epsilonFitConv, clusterSigma, -1);
+//				listener.println("estimated number of found optima: " + extrOpts.size() + " with crit. " + epsilonPhenoSpace);
+//				if (extrOpts.size() > 0) {
+//					listener.println("fit measures: ");
+//					int critCnt = extrOpts.getEAIndividual(0).getFitness().length;
+//					for (int i=0; i<critCnt; i++) listener.print(BeanInspector.toString(extrOpts.getFitnessMeasures(i)) + " ");
+//					listener.println("");
+//				}
+//				epsilonPhenoSpace /= 10.;
+//			}
 		}
 	}
 	
@@ -880,11 +915,14 @@ public class PostProcess {
 				double stepSize = selectMaxSearchRange(params.getPPMethod(), params.getPostProcessClusterSigma());
 				stateBeforeLS = (Population)clusteredPop.clone();
 				// Actual local search comes here
+				InterfaceMutation mutator;
 				if (params.getPPMethod() == PostProcessMethod.hillClimber){
-					stepsDone = processWithHC(clusteredPop, problem, params.getPostProcessSteps(), stepSize, minMutationStepSize);
+					mutator = new MutateESMutativeStepSizeControl(stepSize, minMutationStepSize, stepSize);
+//					stepsDone = processWithHC(clusteredPop, problem, params.getPostProcessSteps(), stepSize, minMutationStepSize);
 				} else {
-					stepsDone = processSingleCandidates(params.getPPMethod(), clusteredPop, params.getPostProcessSteps(), stepSize, problem);
+					mutator = null;
 				}
+				stepsDone = processSingleCandidates(params.getPPMethod(), clusteredPop, params.getPostProcessSteps(), stepSize, problem, mutator);
 
 				if (listener != null) listener.println("Post processing: " + stepsDone + " steps done.");
 				if (params.isWithPlot()) {
@@ -913,15 +951,16 @@ public class PostProcess {
 				if (listener != null) listener.println("solution histogram in [" + lowBnd + "," + upBnd + "]: " + BeanInspector.toString(sols));
 			}
 			
-			//////////// multimodal data output?
-			if (problem instanceof InterfaceMultimodalProblemKnown) procMultiModalKnown(outputPop, (InterfaceMultimodalProblemKnown)problem, listener);
-			
+			//////////// multimodal data output
+			evaluateMultiModal(outputPop, problem, listener);
+
 			Population nBestPop = outputPop.getBestNIndividuals(params.getPrintNBest()); // n individuals are returned and sorted, all of them if n<=0
 			if (listener != null) listener.println("Best after post process:" + ((outputPop.size()>nBestPop.size()) ? ( "(first " + nBestPop.size() + " of " + outputPop.size() + ")") : ""));
 			//////////// output some individual data
 			if (listener != null) for (int i=0; i<nBestPop.size(); i++) {
 				listener.println(AbstractEAIndividual.getDefaultStringRepresentation(nBestPop.getEAIndividual(i)));
 			}
+//			System.out.println(nBestPop);
 			return nBestPop;
 		} else return inputPop;
 	}
@@ -962,13 +1001,15 @@ public class PostProcess {
 	 * @param maxPerturb optional upper bound of the returned perturbation
 	 * @return
 	 */
-	private static double findNMSPerturn(Population candidates, int i, double maxPerturb) {
+	public static double findNMSPerturb(Population candidates, int i, double maxPerturb) {
 		double minDistNeighbour = Double.MAX_VALUE;
 		AbstractEAIndividual indy = candidates.getEAIndividual(i);
 		for (int k=0; k<candidates.size(); k++) {
 			if (k!=i) {
 				double dist = PhenotypeMetric.euclidianDistance(AbstractEAIndividual.getDoublePosition(indy), AbstractEAIndividual.getDoublePosition(candidates.getEAIndividual(k)));
-				if (dist < minDistNeighbour) {
+				if (dist == 0.) {
+					System.err.println("error, equal candidates in findNMSPerturb!");
+				} else if (dist < minDistNeighbour) {
 					minDistNeighbour = dist;
 				}
 			}
@@ -976,5 +1017,101 @@ public class PostProcess {
 		if (maxPerturb>0) return Math.min(maxPerturb, minDistNeighbour/3.);
 		else return minDistNeighbour/3.;
 	}
+	
+	/**
+	 * Sample a given problem randomly for the number of times specified and calculate the average
+	 * fitness returned after evaluation. This may give a general measure of how good an optimum is
+	 * on an unknown function. Of course its expensive for problems with considerable computational
+	 * cost. 
+	 * 
+	 * @param steps
+	 * @param prob
+	 * @return an averaged fitness vector
+	 */
+	public static double[] calcAvgRandomFunctionValue(int steps, AbstractOptimizationProblem prob) {
+		int cnt = 0;
+		int portion = 100;
+		int curPopSize = Math.min(portion, steps);
+		double[] portionFitSum = null;
+		double[] avgFit = null;
+		Population pop = new Population(portion);
+		IndividualInterface indy;
+		prob.initProblem();
+		while (cnt < steps) {
+			pop.clear();
+			for (int i=0; i<curPopSize; i++) {
+				// fill pop randomly
+				indy = prob.getIndividualTemplate().getClone();
+				indy.defaultInit();
+				pop.add(indy);
+			}
+			pop.synchSize();
+			// evaluate pop
+			prob.evaluate(pop);
+			if (portionFitSum==null) {
+				portionFitSum = new double[pop.getEAIndividual(0).getFitness().length];
+				avgFit = new double[portionFitSum.length];
+				Arrays.fill(avgFit, 0.);
+			}
+			Arrays.fill(portionFitSum, 0.);
+			for (int i=0; i<curPopSize; i++) {
+				// calc fit-sum
+				Mathematics.vvAdd(portionFitSum, pop.getEAIndividual(i).getFitness(), portionFitSum);
+			}
+			// add to average
+			Mathematics.svvAddScaled(1./(steps), portionFitSum, avgFit, avgFit);
+			// select next population size
+			cnt += curPopSize;
+			curPopSize = Math.min(portion, steps-cnt); // the new population size.
+		}
+		return avgFit;
+	}
+	
+	/**
+	 * Calculate the average Euclidian distance between the individual with given index
+	 * within the population to the other members of the population.
+	 * If the population has only one member, zero is returned.
+	 * 
+	 * @param index
+	 * @param pop
+	 * @return
+	 */
+	public static double getAvgDistToNeighbor(int index, Population pop) {
+		double distSum = 0;
+		int cnt = pop.size()-1;
+		if (cnt == 0) return 0.;
+		else {
+			double[] indyPos = AbstractEAIndividual.getDoublePosition(pop.getEAIndividual(index));
+			for (int i=0; i<pop.size(); i++) {
+				if (i!=index) distSum += PhenotypeMetric.euclidianDistance(AbstractEAIndividual.getDoublePosition(pop.getEAIndividual(i)), indyPos); 
+			}
+			return distSum/((double)cnt);
+		}
+	}
+	
+	/**
+	 * Calculate a score for a given population of solutions to a problem. The score depends on the 
+	 * quality and the diversity of the solutions, growing with both. It is expected that the 
+	 * population is clustered beforehand. This method is rather experimental.
+	 * 
+	 * @param avgFitSteps
+	 * @param solutions
+	 * @param criterion
+	 * @param problem
+	 * @return
+	 */
+	public static double calcQualityMeasure(int avgFitSteps, Population solutions, int criterion, AbstractOptimizationProblem problem) {
+		int solCnt = solutions.size();
+		double indyQual, indyAvgDist, indyScore = 0.;
+		double scoreSum = 0.;
+		double[] avgFit = calcAvgRandomFunctionValue(avgFitSteps, problem);
+		for (int i=0; i<solCnt; i++) {
+			indyQual = avgFit[criterion] - solutions.getEAIndividual(i).getFitness(criterion);
+			indyAvgDist = getAvgDistToNeighbor(i, solutions);
+			indyScore =  Math.pow(indyQual, 2) + Math.pow(1+indyAvgDist, 2);
+			scoreSum += indyScore;
+		}
+		return Math.sqrt(scoreSum);
+	}
 }
 
diff --git a/src/eva2/server/go/operators/postprocess/PostProcessParams.java b/src/eva2/server/go/operators/postprocess/PostProcessParams.java
index 03201162..18d43c39 100644
--- a/src/eva2/server/go/operators/postprocess/PostProcessParams.java
+++ b/src/eva2/server/go/operators/postprocess/PostProcessParams.java
@@ -40,11 +40,21 @@ public class PostProcessParams implements InterfacePostProcessParams, Serializab
 	}
 	
 	public PostProcessParams(PostProcessMethod meth, int steps, double clusterSigma, int nBest) {
+		reset(meth, steps, clusterSigma, nBest, false);
+	}
+	
+	public PostProcessParams(PostProcessMethod meth, int steps, double clusterSigma, int nBest, boolean doPlot) {
+		reset(meth, steps, clusterSigma, nBest, doPlot);
+	}
+	
+	public PostProcessParams reset(PostProcessMethod meth, int steps, double clusterSigma, int nBest, boolean doPlot) {
 		method = meth;
 		postProcessSteps = steps;
 		postProcess = true;
 		postProcessClusterSigma = clusterSigma;
 		printNBest = nBest;
+		withPlot = doPlot;
+		return this;
 	}
 	
 	public void hideHideable() {
diff --git a/src/eva2/server/go/populations/Population.java b/src/eva2/server/go/populations/Population.java
index 9ecb6957..18e45d36 100644
--- a/src/eva2/server/go/populations/Population.java
+++ b/src/eva2/server/go/populations/Population.java
@@ -2,7 +2,6 @@ package eva2.server.go.populations;
 
 import java.util.ArrayList;
 import java.util.Arrays;
-import java.util.Collection;
 import java.util.HashMap;
 import java.util.Iterator;
 import java.util.List;
@@ -40,7 +39,6 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     protected int           m_Size          = 50;
     protected Population    m_Archive       = null;
 
-    transient private ArrayList<AbstractEAIndividual> sortedArr = null;
     transient private ArrayList<InterfacePopulationChangedEventListener> listeners = null;
 //    transient protected InterfacePopulationChangedEventListener	m_Listener = null;
 
@@ -57,6 +55,8 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 
     // remember when the last sorted queue was prepared
     private int lastQModCount = -1;
+    // a sorted queue (for efficiency)
+    transient private ArrayList<AbstractEAIndividual> sortedArr = null;
     // remember when the last evaluation was performed
 	private Pair<Integer,Integer> evaluationTimeHashes = null;
     // remember when the last evaluation was performed
@@ -513,7 +513,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * 
      */
     public Population getSortedNIndividuals(int n, boolean bBestOrWorst) {
-    	Population result = new Population(n);
+    	Population result = new Population((n > 0) ? n : this.size());
     	getSortedNIndividuals(n, bBestOrWorst, result);
     	return result;
     }
@@ -557,7 +557,8 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     	if (sortedArr == null || (super.modCount != lastQModCount)) {
     		PriorityQueue<AbstractEAIndividual> sQueue = new PriorityQueue<AbstractEAIndividual>(super.size(), new AbstractEAIndividualComparator());
     		for (int i = 0; i < super.size(); i++) {
-    			sQueue.add(getEAIndividual(i));
+    			AbstractEAIndividual indy = getEAIndividual(i);
+    			if (indy != null) sQueue.add(indy);
     		}
     		lastQModCount = super.modCount;
     		if (sortedArr==null) sortedArr = new ArrayList<AbstractEAIndividual>(this.size());
@@ -794,24 +795,28 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         return "A population stores the individuals of a generation.";
     }
 
-    /** This method allows you to set the population size
+    /** 
+     * This method allows you to set the population size. Be aware that this will not 
+     * directly alter the number of individuals stored. The actual size will be
+     * adapted on a reinitialization, for example.
+     * 
      * @param size
      */
     public void setPopulationSize(int size) {
         this.m_Size = size;
-//        int rand;
-//        if (this.size() != 0) {
-//            while (this.size() < size) {
-//                rand = RNG.randomInt(0, this.size()-1);
-//                this.add(((AbstractEAIndividual)this.get(rand)).clone());
-//            }
-//            while (this.size() > size) {
-//                rand = RNG.randomInt(0, this.size()-1);
-//                this.remove(rand);
-//            }
-//        }
-//        System.out.println("This.size() = "+this.size() +" this.getSize() = " + this.m_Size);
     }
+    
+    /**
+     * Convenience method.
+     * 
+     * @param size
+     * @return
+     */
+    public Population setPopSize(int size) {
+        this.m_Size = size;
+        return this;
+    }
+    
     public int getPopulationSize() {
         return this.m_Size;
     }
@@ -949,9 +954,51 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
                 if (d > maxDist) maxDist = d;
             }
         }
-        res[0] = meanDist / (this.size() * (this.size()-1) / 2);
         res[1] = minDist;
         res[2] = maxDist;
+        if (this.size() > 1) res[0] = meanDist / (this.size() * (this.size()-1) / 2);
+        else { // only one indy?
+        	res[1]=0;
+        	res[2]=0;
+        }
+        return res;
+    }
+    
+    /**
+     * Returns the average, minimal and maximal individual fitness and std dev. for the population in the given criterion.
+     *
+     * @param fitCrit fitness dimension to be used
+     * @return the average, minimal, maximal and std dev. of fitness of individuals in an array
+     */
+    public double[] getFitnessMeasures(int fitCrit) {
+    	double d;
+    	double[] res = new double[4];
+    	
+    	res[0] = 0.;
+    	res[1] = Double.MAX_VALUE;
+    	res[2] = Double.MIN_VALUE;
+    	res[3] = 0;
+    	
+        for (int i = 0; i < this.size(); i++) {
+        		d = this.getEAIndividual(i).getFitness(fitCrit);
+        		res[0] += d;
+                if (d < res[1]) res[1] = d;
+                if (d > res[2]) res[2] = d;
+        }
+        
+        if (size()==0) {
+        	res[0]=res[1]=res[2]=res[3]=Double.NaN;
+        } else {
+        	// calc standard deviation
+        	res[0] = res[0] / this.size();
+        	for (int i=0; i< this.size(); i++) {
+        		d = res[0]-this.getEAIndividual(i).getFitness(fitCrit);
+        		res[3]+=d*d;
+        	}
+        	res[3] /= this.size();
+        	res[3] = Math.sqrt(res[3]);
+        }
+        
         
         return res;
     }
@@ -1012,6 +1059,80 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 		return mean;
 	}
 	
+	/**
+	 * Search for the closest individual which is not equal to the given individual. Return
+	 * its index or -1 if none could be found.
+	 * 
+	 * @param indy
+	 * @return closest neighbor (euclidian measure) of the given individual in the given population 
+	 */
+	public int getNeighborIndex(AbstractEAIndividual indy) {
+		// get the neighbor...
+		int index = -1;
+		double mindist = Double.POSITIVE_INFINITY;
+
+		for (int i = 0; i < size(); ++i){ 
+			AbstractEAIndividual currentindy = getEAIndividual(i);
+			if (!indy.equals(currentindy)){ // dont compare particle to itself or a copy of itself
+				double dist = PhenotypeMetric.euclidianDistance(AbstractEAIndividual.getDoublePosition(indy),
+						AbstractEAIndividual.getDoublePosition(currentindy));
+				if (dist  < mindist){ 
+					mindist = dist;
+					index = i;
+				}
+			}
+		}
+		if (index == -1){
+			System.err.println("Pop too small or all individuals in population are equal !?");
+			return -1;
+		}
+		return index;
+	}
+	
+	/**
+	 * Calculate the average of the distance of each individual to its closest neighbor in the population.
+	 * The boolean parameter switches between range-normalized and simple euclidian distance. If calcVariance
+	 * is true, the variance is calculated and returned as second entry
+	 * 
+	 * @param normalizedPhenoMetric
+	 * @return a double array containing the average (or average and variance) of the distance of each individual to its closest neighbor
+	 */
+	public double[] getAvgDistToClosestNeighbor(boolean normalizedPhenoMetric, boolean calcVariance){
+		PhenotypeMetric metric = new PhenotypeMetric();
+		ArrayList<Double> distances = null;
+		if (calcVariance) distances = new ArrayList<Double>(size());
+		double sum = 0;
+		double d=0;
+		for (int i = 0; i < size(); ++i){
+			AbstractEAIndividual neighbor, indy = getEAIndividual(i);
+			int neighborIndex = getNeighborIndex(indy);
+			if (neighborIndex >= 0) neighbor = getEAIndividual(neighborIndex);
+			else return null;
+			if (normalizedPhenoMetric){
+				d = metric.distance(indy, neighbor);
+			} else { 
+				d = PhenotypeMetric.euclidianDistance(AbstractEAIndividual.getDoublePosition(indy),
+						AbstractEAIndividual.getDoublePosition(neighbor));
+			}
+			if (calcVariance) distances.add(d);
+			sum += d;
+		}
+		double avg = sum/(double)size();
+		double[] res;
+		if (calcVariance) {
+			res = new double[2];
+			double var = 0;
+			for (int i=0; i<distances.size(); i++) {
+				var += Math.pow(distances.get(i)-avg, 2);
+			}
+			res[1]=var;
+		}
+		else res = new double[1];
+		res[0] = avg;
+
+		return res;
+	}
+	
 	/**
 	 * Fire an event every n function calls, the event sends the public String funCallIntervalReached.
 	 * Be aware that if this interval is smaller than the population size, it may happen that a notification
diff --git a/src/eva2/server/go/problems/AbstractMultiModalProblemKnown.java b/src/eva2/server/go/problems/AbstractMultiModalProblemKnown.java
index 2be8aedf..cc18713f 100644
--- a/src/eva2/server/go/problems/AbstractMultiModalProblemKnown.java
+++ b/src/eva2/server/go/problems/AbstractMultiModalProblemKnown.java
@@ -17,7 +17,7 @@ import eva2.tools.EVAERROR;
 public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDouble implements Interface2DBorderProblem, InterfaceMultimodalProblemKnown {
 	protected static InterfaceDistanceMetric   m_Metric = new PhenotypeMetric();
 	private double                    m_GlobalOpt = 0;
-	private Population                  m_Optima;
+	protected Population                  m_Optima;
 	protected double                    m_Epsilon = 0.05;
 //	protected double[][]                m_Range;
 //	protected double[]                  m_Extrema;
@@ -87,9 +87,11 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 		// population init must be last
 		// it set's fitcalls and generation to zero
 		population.init();
-		this.m_GlobalOpt = Double.NEGATIVE_INFINITY;
-		m_Optima = new Population();
-		this.initListOfOptima();
+		if (m_Optima == null) {
+			this.m_GlobalOpt = Double.NEGATIVE_INFINITY;
+			m_Optima = new Population();
+			this.initListOfOptima();
+		}
 	}
 	
 	public void initProblem() {
@@ -122,7 +124,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 	 * @return String
 	 */
 	public String getAdditionalFileStringHeader(PopulationInterface pop) {
-		return "Solution \t Number of Optima found \t Maximum Peak Ratio";
+		return "#Optima found \t Maximum Peak Ratio";
 	}
 
 	/** 
@@ -132,7 +134,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 	 */
 	public String getAdditionalFileStringValue(PopulationInterface pop) {
 		String result = "";
-		result += AbstractEAIndividual.getDefaultDataString(pop.getBestIndividual()) +"\t";
+//		result += AbstractEAIndividual.getDefaultDataString(pop.getBestIndividual()) +"\t";
 		result += this.getNumberOfFoundOptima((Population)pop)+"\t";
 		result += this.getMaximumPeakRatio((Population)pop);
 		return result;
@@ -242,7 +244,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 	public static double getMaximumPeakRatio(InterfaceMultimodalProblemKnown mmProb, Population pop, double epsilon) {
 		double          foundInvertedSum = 0, sumRealMaxima = 0;
 		Population 		realOpts = mmProb.getRealOptima();
-		double 			maxOpt = realOpts.getEAIndividual(0).getFitness(0);
+		double 			tmp, maxOpt = realOpts.getEAIndividual(0).getFitness(0);
 		sumRealMaxima = maxOpt;
 		for (int i=1; i<realOpts.size(); i++) {
 			// search for the maximum fitness (for the maximization problem)
@@ -255,7 +257,10 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 		for (int i=0; i<realOpts.size(); i++) {
 			// sum up the found optimal fitness values
 			if (optsFound[i] != null) {
-				foundInvertedSum += (maxOpt - optsFound[i].getFitness(0));
+				tmp = (maxOpt - optsFound[i].getFitness(0));
+				if (tmp < 0) EVAERROR.errorMsgOnce("warning: for the MPR calculation, negative fitness values may disturb the allover result (AbstractMultiModalProblemKnown)");
+				foundInvertedSum += Math.max(0., tmp);
+//				System.out.println("foundInvertedSum = " + foundInvertedSum);
 			}
 		}
 //		System.out.println("foundSum: " + foundInvertedSum + " realsum: " + sumRealMaxima + " ratio: " + foundInvertedSum/sumRealMaxima);
diff --git a/src/eva2/server/go/problems/AbstractOptimizationProblem.java b/src/eva2/server/go/problems/AbstractOptimizationProblem.java
index 461b3360..f72f7324 100644
--- a/src/eva2/server/go/problems/AbstractOptimizationProblem.java
+++ b/src/eva2/server/go/problems/AbstractOptimizationProblem.java
@@ -7,16 +7,23 @@ import javax.swing.JPanel;
 import javax.swing.JScrollPane;
 import javax.swing.JTextArea;
 
+import eva2.server.go.InterfaceTerminator;
 import eva2.server.go.PopulationInterface;
+import eva2.server.go.enums.PostProcessMethod;
 import eva2.server.go.individuals.AbstractEAIndividual;
+import eva2.server.go.individuals.InterfaceDataTypeDouble;
 import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
 import eva2.server.go.operators.distancemetric.PhenotypeMetric;
 import eva2.server.go.operators.moso.MOSONoConvert;
 import eva2.server.go.operators.mutation.InterfaceMutation;
 import eva2.server.go.operators.mutation.MutateESFixedStepSize;
 import eva2.server.go.operators.postprocess.PostProcess;
+import eva2.server.go.operators.terminators.CombinedTerminator;
+import eva2.server.go.operators.terminators.EvaluationTerminator;
+import eva2.server.go.operators.terminators.PhenotypeConvergenceTerminator;
 import eva2.server.go.populations.Population;
 import eva2.server.go.strategies.InterfaceOptimizer;
+import eva2.tools.Mathematics;
 
 /**
  * Created by IntelliJ IDEA.
@@ -111,6 +118,7 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
      */
     public String getAdditionalFileStringHeader(PopulationInterface pop) {
         return "Solution";
+//    	return "";
     }
 
     /** This method returns the additional data that is to be written into a file
@@ -118,6 +126,7 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
      * @return String
      */
     public String getAdditionalFileStringValue(PopulationInterface pop) {
+//    	return "";
         return AbstractEAIndividual.getDefaultDataString(pop.getBestIndividual());
     }
 
@@ -196,22 +205,27 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
     
     /**
      * This method extracts the individuals from a given population that are assumed to correspond to local or global optima.
-     * Similar inidviduals are clustered together with a density based clustering method
+     * Similar individuals are clustered together with a density based clustering method
      * @param pop
-     * @param epsilon maximal allowed improvement of an individual before considered premature (given as distance in the search space) 
+     * @param epsilonPhenoSpace maximal allowed improvement of an individual before considered premature (given as distance in the search space)
+     * @param epsilonFitConv if positive: additional absolute convergence criterion (fitness space) as termination criterion of the local search  
      * @param clusterSigma minimum cluster distance
+     * @param numOfFailures 
+     * @see #isPotentialOptimumNMS(AbstractEAIndividual, double, double, int)
      * @return 
      */
-    public Population extractPotentialOptima(Population pop, double epsilon, double clusterSigma) {
+    public Population extractPotentialOptima(Population pop, double epsilonPhenoSpace, double epsilonFitConv, double clusterSigma, int numOfFailures) {
     	Population potOptima = new Population();
     	for (int i = 0; i < pop.size(); ++i){
     		AbstractEAIndividual indy = pop.getEAIndividual(i);
-    		if (isPotentialOptimum(indy, epsilon,-1,-1)){ 
+//    		System.out.println("bef: " + indy.toString());
+    		if (isPotentialOptimumNMS(indy, epsilonPhenoSpace, epsilonFitConv, numOfFailures)){ 
+//       	if (isPotentialOptimum(indy, epsilon,-1,-1)){ 
     			potOptima.addIndividual(indy);
     		}
     	}
-    	Population clusteredPop = (Population)PostProcess.clusterBest(potOptima, clusterSigma, 0, PostProcess.KEEP_LONERS, PostProcess.BEST_ONLY).clone();
-    	return clusteredPop;
+    	if (clusterSigma > 0) return (Population)PostProcess.clusterBest(potOptima, clusterSigma, 0, PostProcess.KEEP_LONERS, PostProcess.BEST_ONLY).clone();
+    	else return potOptima;
     }
    
   /**
@@ -273,6 +287,50 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
         else return false; 
     }
     
+    /**
+     * Refine a given individual using Nelder-Mead-Simplex local search. Return true, if the refined result is within a given
+     * distance from the original individual in phenotype space. The numOfFailures parameter gives the maximum evaluations
+     * for the local search Using the epsilonFitConv parameter may define a convergence criterion as PhenotypeConvergenceTerminator 
+     * which is combined (using OR) with the evaluation counter.
+     * If numOfFailures is smaller than zero, 100*dim is used. Be aware that this may cost quite some runtime depending on the target
+     * function.
+     * 
+     * @param orig
+     * @param epsilonPhenoSpace
+     * @param epsilonFitConv
+     * @param numOfFailures
+     * @return
+     */
+    public boolean isPotentialOptimumNMS(AbstractEAIndividual orig, double epsilonPhenoSpace, double epsilonFitConv, int numOfFailures){
+    	
+    	AbstractEAIndividual indy = (AbstractEAIndividual)orig.clone();
+    	this.evaluate(indy); // indy may be evaluated in a normalised way...
+    	
+    	InterfaceDistanceMetric metric = new PhenotypeMetric();
+    	double overallDist = 0;
+    	double initPerturb = -1;
+    	int dim = -1;
+    	if (orig instanceof InterfaceDataTypeDouble) {
+    		initPerturb = epsilonPhenoSpace/(2*(Mathematics.getAvgRange(((InterfaceDataTypeDouble)orig).getDoubleRange())));
+    		dim=((InterfaceDataTypeDouble)orig).getDoubleRange().length;
+        	if (numOfFailures<0) numOfFailures = 100*AbstractEAIndividual.getDoublePosition(this.m_Template).length; // scales the effort with the number of problem dimensions
+    	} else {
+    		System.err.println("Cannot initialize NMS on non-double valued individuals!");
+    		return false;
+    	}
+    	
+    	Population pop = new Population(1);
+    	pop.add(orig);
+    	InterfaceTerminator term = new EvaluationTerminator(numOfFailures); 
+    	if (epsilonFitConv > 0) term = new CombinedTerminator(new PhenotypeConvergenceTerminator(epsilonFitConv, 10*dim, true, true), term, false);
+    	int evalsPerf = PostProcess.processSingleCandidatesNMCMA(PostProcessMethod.nelderMead, pop, term, initPerturb, this);
+    	overallDist = metric.distance(indy, pop.getBestEAIndividual());
+//    	System.out.println("aft: " + pop.getBestEAIndividual().toString() + ", evals performed: " + evalsPerf + ", opt moved by " + overallDist);
+//    	System.out.println("terminated because: " + term.lastTerminationMessage());
+        if (overallDist < epsilonPhenoSpace) return true;
+        else return false; 
+    }
+    
 /**********************************************************************************************************************
  * These are for GUI
  */
diff --git a/src/eva2/server/go/problems/F13Problem.java b/src/eva2/server/go/problems/F13Problem.java
index e725ce1e..1091eb1a 100644
--- a/src/eva2/server/go/problems/F13Problem.java
+++ b/src/eva2/server/go/problems/F13Problem.java
@@ -17,6 +17,10 @@ public class F13Problem extends F1Problem implements InterfaceMultimodalProblem
     public F13Problem(F13Problem b) {
     	super(b);
     }
+    public F13Problem(int dim) {
+    	super();
+    	setProblemDimension(dim);
+    }
 
     /** This method returns a deep clone of the problem.
      * @return  the clone
diff --git a/src/eva2/server/go/problems/F1Problem.java b/src/eva2/server/go/problems/F1Problem.java
index 163f8485..8e2e4ca0 100644
--- a/src/eva2/server/go/problems/F1Problem.java
+++ b/src/eva2/server/go/problems/F1Problem.java
@@ -38,7 +38,17 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde
     	this.m_YOffSet          = b.m_YOffSet;
     	this.m_UseTestConstraint = b.m_UseTestConstraint;
     }
-
+    
+    public F1Problem(int dim) {
+    	this();
+    	setProblemDimension(dim);
+    }
+    
+    public F1Problem(int dim, double defRange) {
+    	this(dim);
+    	setDefaultRange(defRange);
+    }
+    
     /** This method returns a deep clone of the problem.
      * @return  the clone
      */
diff --git a/src/eva2/server/go/problems/PSymbolicRegression.java b/src/eva2/server/go/problems/PSymbolicRegression.java
index 10ab5b50..b526528e 100644
--- a/src/eva2/server/go/problems/PSymbolicRegression.java
+++ b/src/eva2/server/go/problems/PSymbolicRegression.java
@@ -190,7 +190,8 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
         evaluatePopulationEnd(population);
     }
 
-    /** This method evaluate a single individual and sets the fitness values
+    /** 
+     * This method evaluates a single individual and sets the fitness values
      * @param individual    The individual that is to be evalutated
      */
     public void evaluate(AbstractEAIndividual individual) {
@@ -203,13 +204,14 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
         if ((tmpIndy instanceof GAPIndividualProgramData) && (this.m_UseInnerConst))
             this.m_C = ((GAPIndividualProgramData)tmpIndy).getDoubleData();
         fitness     = 0;
-        for (double i = 0; i < this.m_NumberOfCheckPoints; i++) {
-            for (int j = 0; j < this.m_X.length; j++)
-                this.m_X[j] = this.m_LowerBound +(i*(this.m_UpperBound-this.m_LowerBound)/this.m_NumberOfCheckPoints);
-            tmpValue = ((Double)program.evaluate(this)).doubleValue();
-            fitness += Math.pow((this.m_TargetFunction.evaulateFunction(this.m_X) - ((Double)program.evaluate(this)).doubleValue()), 2);
-        }
 
+        for (int j = 0; j < this.m_NumberOfCheckPoints; j++)
+        	for (int i = 0; i < this.m_X.length; i++) {
+                this.m_X[i] = this.m_LowerBound +(j*(this.m_UpperBound-this.m_LowerBound)/this.m_NumberOfCheckPoints);
+            tmpValue = ((Double)program.evaluate(this)).doubleValue();
+            fitness += Math.pow((this.m_TargetFunction.evaluateFunction(this.m_X) - ((Double)program.evaluate(this)).doubleValue()), 2);
+        }
+        
         fitness = fitness / (double)this.m_NumberOfCheckPoints;
         // add noise to the fitness
         fitness += RNG.gaussianDouble(this.m_Noise);
@@ -225,7 +227,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
                     for (int j = 0; j < this.m_X.length; j++) this.m_X[j] = i ;
                     tmpValue = ((Double)program.evaluate(this)).doubleValue();
                     this.m_Plot.setConnectedPoint(this.m_X[0], tmpValue, 0);
-                    tmpValue = this.m_TargetFunction.evaulateFunction(this.m_X);
+                    tmpValue = this.m_TargetFunction.evaluateFunction(this.m_X);
                     this.m_Plot.setConnectedPoint(this.m_X[0], tmpValue, 1);
                     this.m_Plot.setInfoString(1, program.getStringRepresentation(), 1.0f);
                 }
@@ -233,7 +235,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
         }
     }
 
-    /** This method returns a string describing the optimization problem.
+	/** This method returns a string describing the optimization problem.
      * @param opt       The Optimizer that is used or had been used.
      * @return The description.
      */
diff --git a/src/eva2/server/go/problems/regression/InterfaceRegressionFunction.java b/src/eva2/server/go/problems/regression/InterfaceRegressionFunction.java
index c774798e..871394f8 100644
--- a/src/eva2/server/go/problems/regression/InterfaceRegressionFunction.java
+++ b/src/eva2/server/go/problems/regression/InterfaceRegressionFunction.java
@@ -18,5 +18,5 @@ public interface InterfaceRegressionFunction {
      * @param x     Input vector.
      * @return y the function result.
      */
-    public double evaulateFunction(double[] x);
+    public double evaluateFunction(double[] x);
 }
diff --git a/src/eva2/server/go/problems/regression/RFKoza_GPI_10_1.java b/src/eva2/server/go/problems/regression/RFKoza_GPI_10_1.java
index e74021ca..21322021 100644
--- a/src/eva2/server/go/problems/regression/RFKoza_GPI_10_1.java
+++ b/src/eva2/server/go/problems/regression/RFKoza_GPI_10_1.java
@@ -25,7 +25,7 @@ public class RFKoza_GPI_10_1 implements InterfaceRegressionFunction, java.io.Ser
      * @param x     Input vector.
      * @return y the function result.
      */
-    public double evaulateFunction(double[] x) {
+    public double evaluateFunction(double[] x) {
         double result = 0;
         for (int i = 0; i < x.length; i++) result += Math.cos(2*x[i]);
         return result;
diff --git a/src/eva2/server/go/problems/regression/RFKoza_GPI_10_2.java b/src/eva2/server/go/problems/regression/RFKoza_GPI_10_2.java
index ff92b885..57d66476 100644
--- a/src/eva2/server/go/problems/regression/RFKoza_GPI_10_2.java
+++ b/src/eva2/server/go/problems/regression/RFKoza_GPI_10_2.java
@@ -25,7 +25,7 @@ public class RFKoza_GPI_10_2 implements InterfaceRegressionFunction, java.io.Ser
      * @param x     Input vector.
      * @return y the function result.
      */
-    public double evaulateFunction(double[] x) {
+    public double evaluateFunction(double[] x) {
         double result = 0;
         for (int i = 0; i < x.length; i++) result += 3.1416*x[i] + 2.718 * Math.pow(x[i], 2);
         return result;
diff --git a/src/eva2/server/go/problems/regression/RFKoza_GPI_7_3.java b/src/eva2/server/go/problems/regression/RFKoza_GPI_7_3.java
index 43a27c12..8a3ae298 100644
--- a/src/eva2/server/go/problems/regression/RFKoza_GPI_7_3.java
+++ b/src/eva2/server/go/problems/regression/RFKoza_GPI_7_3.java
@@ -25,7 +25,7 @@ public class RFKoza_GPI_7_3 implements InterfaceRegressionFunction, java.io.Seri
      * @param x     Input vector.
      * @return y the function result.
      */
-    public double evaulateFunction(double[] x) {
+    public double evaluateFunction(double[] x) {
         double result = 0;
         for (int i = 0; i < x.length; i++) result += Math.pow(x[i], 4) + Math.pow(x[i], 3) + Math.pow(x[i], 2) + Math.pow(x[i], 1);
         return result;
diff --git a/src/eva2/server/go/problems/regression/RFKoza_GPI_7_3_extended.java b/src/eva2/server/go/problems/regression/RFKoza_GPI_7_3_extended.java
index 52f23a85..90873e26 100644
--- a/src/eva2/server/go/problems/regression/RFKoza_GPI_7_3_extended.java
+++ b/src/eva2/server/go/problems/regression/RFKoza_GPI_7_3_extended.java
@@ -25,7 +25,7 @@ public class RFKoza_GPI_7_3_extended implements InterfaceRegressionFunction, jav
      * @param x     Input vector.
      * @return y the function result.
      */
-    public double evaulateFunction(double[] x) {
+    public double evaluateFunction(double[] x) {
         double result = 0;
         for (int i = 0; i < x.length; i++) result += 0.12345*Math.pow(x[i], 4) + (Math.PI/4)*Math.pow(x[i], 3) + (Math.E/2)*Math.pow(x[i], 2) + 1.23456*Math.pow(x[i], 1);
         return result;
diff --git a/src/eva2/server/go/problems/regression/RFRaidl_F1.java b/src/eva2/server/go/problems/regression/RFRaidl_F1.java
index 6dc0d6a0..ac070ecd 100644
--- a/src/eva2/server/go/problems/regression/RFRaidl_F1.java
+++ b/src/eva2/server/go/problems/regression/RFRaidl_F1.java
@@ -25,7 +25,7 @@ public class RFRaidl_F1 implements InterfaceRegressionFunction, java.io.Serializ
      * @param x     Input vector.
      * @return y the function result.
      */
-    public double evaulateFunction(double[] x) {
+    public double evaluateFunction(double[] x) {
         double result = 0;
         for (int i = 0; i < x.length; i++) result += Math.sin(x[i]);
         return result;
diff --git a/src/eva2/server/go/problems/regression/RFRaidl_F2.java b/src/eva2/server/go/problems/regression/RFRaidl_F2.java
index 4ecb0fde..63051663 100644
--- a/src/eva2/server/go/problems/regression/RFRaidl_F2.java
+++ b/src/eva2/server/go/problems/regression/RFRaidl_F2.java
@@ -25,7 +25,7 @@ public class RFRaidl_F2 implements InterfaceRegressionFunction, java.io.Serializ
      * @param x     Input vector.
      * @return y the function result.
      */
-    public double evaulateFunction(double[] x) {
+    public double evaluateFunction(double[] x) {
         double result = 0;
         for (int i = 0; i < x.length; i++) result += Math.exp(x[i]/3)*Math.cos(3*x[i])/2;
         return result;
diff --git a/src/eva2/server/go/problems/regression/RFRaidl_F3.java b/src/eva2/server/go/problems/regression/RFRaidl_F3.java
index 96fda405..79ee643d 100644
--- a/src/eva2/server/go/problems/regression/RFRaidl_F3.java
+++ b/src/eva2/server/go/problems/regression/RFRaidl_F3.java
@@ -25,7 +25,7 @@ public class RFRaidl_F3 implements InterfaceRegressionFunction, java.io.Serializ
      * @param x     Input vector.
      * @return y the function result.
      */
-    public double evaulateFunction(double[] x) {
+    public double evaluateFunction(double[] x) {
         double result = 0;
         for (int i = 0; i < x.length; i++) result += Math.log(4+2*Math.sin(x[i]*Math.sin(8*x[i])))*Math.exp(Math.cos(3*x[i]));
         return result;
diff --git a/src/eva2/server/go/strategies/ClusteringHillClimbing.java b/src/eva2/server/go/strategies/ClusteringHillClimbing.java
index 0bfca02d..782e9be4 100644
--- a/src/eva2/server/go/strategies/ClusteringHillClimbing.java
+++ b/src/eva2/server/go/strategies/ClusteringHillClimbing.java
@@ -4,6 +4,7 @@ import java.io.Serializable;
 
 import eva2.gui.GenericObjectEditor;
 import eva2.server.go.InterfacePopulationChangedEventListener;
+import eva2.server.go.enums.PostProcessMethod;
 import eva2.server.go.operators.mutation.MutateESFixedStepSize;
 import eva2.server.go.operators.postprocess.PostProcess;
 import eva2.server.go.populations.InterfaceSolutionSet;
@@ -50,6 +51,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
    	// reduce the step size when there is hardy improvement. 
    	private double							reduceFactor		= 0.2;
    	private MutateESFixedStepSize				mutator = new MutateESFixedStepSize(0.1);
+	private PostProcessMethod localSearchMethod = PostProcessMethod.nelderMead;
 
 	public ClusteringHillClimbing() {
 		hideHideable();
@@ -143,18 +145,25 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 		loopCnt++;
 		m_Population.addPopulationChangedEventListener(this);
 		m_Population.setNotifyEvalInterval(notifyGuiEvery);
-		Pair<Population, Double> popD = PostProcess.clusterHC(m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5, mutator);
+		Pair<Population, Double> popD;
+		if (TRACE) System.out.println("evalCycle: " + hcEvalCycle + ", evals now: " + (2*hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle)));
+		popD = PostProcess.clusterLocalSearch(localSearchMethod, m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, 2*hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5, mutator);
+//		(m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5);
 		improvement = popD.tail();
 		m_Population = popD.head();
+		if (TRACE) System.out.println("num inds after clusterLS: " + m_Population.size());
 
 		popD.head().setGenerationTo(m_Population.getGeneration()+1);
 		
 		if (improvement < minImprovement) {
 			if (TRACE) System.out.println("improvement below " + minImprovement);
-			if (mutator.getSigma() < stepSizeThreshold) { // reinit!
+			if ((localSearchMethod != PostProcessMethod.hillClimber) || (mutator.getSigma() < stepSizeThreshold)) { // reinit!
+				// is performed for nm and cma, and if hc has too low sigma
 				if (TRACE) System.out.println("REINIT!!");
-		        // store results
-				mutator.setSigma(initialStepSize);
+		        
+				if (localSearchMethod == PostProcessMethod.hillClimber) mutator.setSigma(initialStepSize);
+				
+				// store results
 				archive.SetFunctionCalls(m_Population.getFunctionCalls());
 				archive.addPopulation(m_Population);
 				
@@ -171,7 +180,8 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 				m_Population.addPopulation(tmpPop);
 				m_Population.incrFunctionCallsBy(tmpPop.size());
 		
-			} else  {  // decrease step size
+			} else  {  // decrease step size for hc
+				if (localSearchMethod != PostProcessMethod.hillClimber) System.err.println("Invalid case in ClusteringHillClimbing!");
 				mutator.setSigma(mutator.getSigma()*reduceFactor);
 				if (TRACE) System.out.println("mutation stepsize reduced to " + mutator.getSigma());
 			}
@@ -249,18 +259,18 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 	/**
 	 * @return the hcEvalCycle
 	 */
-	public int getHcEvalCycle() {
+	public int getEvalCycle() {
 		return hcEvalCycle;
 	}
 
 	/**
 	 * @param hcEvalCycle the hcEvalCycle to set
 	 */
-	public void setHcEvalCycle(int hcEvalCycle) {
+	public void setEvalCycle(int hcEvalCycle) {
 		this.hcEvalCycle = hcEvalCycle;
 	}
 	
-	public String hcEvalCycleTipText() {
+	public String evalCycleTipText() {
 		return "The number of evaluations between two clustering/adaption steps.";
 	}
 
@@ -369,7 +379,19 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 	}
 	
 	public String stepSizeInitialTipText() {
-		return "Initial mutation step size, relative to the problem range.";
+		return "Initial mutation step size for hill climbing, relative to the problem range.";
+	}
+
+	public PostProcessMethod getLocalSearchMethod() {
+		return localSearchMethod;
+	}
+
+	public void setLocalSearchMethod(PostProcessMethod localSearchMethod) {
+		this.localSearchMethod = localSearchMethod;
+	}
+	
+	public String localSearchMethodTipText() {
+		return "Set the method to be used for local search";
 	}
 
 //	/**
diff --git a/src/eva2/server/go/strategies/EvolutionStrategyIPOP.java b/src/eva2/server/go/strategies/EvolutionStrategyIPOP.java
index d4072f9b..447e221c 100644
--- a/src/eva2/server/go/strategies/EvolutionStrategyIPOP.java
+++ b/src/eva2/server/go/strategies/EvolutionStrategyIPOP.java
@@ -114,8 +114,8 @@ public class EvolutionStrategyIPOP extends EvolutionStrategies implements Interf
 	 *  Reinitialize population with increased mu,lambda
 	 **/
 	private void boostPopSize() {
-		// increase by at least one
-		int newLambda = Math.max((int)(getLambda()*incPopSizeFact), getLambda() + 1);
+		// potentially increase pop size, increase by at least one if the factor is > 1.
+		int newLambda = Math.max((int)(getLambda()*incPopSizeFact), getLambda() + ((incPopSizeFact > 1.) ? 1 : 0));
 		setLambda(newLambda);
 		checkPopulationConstraints();
 		// update the stagnation time in the terminator
diff --git a/src/eva2/server/go/strategies/NelderMeadSimplex.java b/src/eva2/server/go/strategies/NelderMeadSimplex.java
index 5632b751..ce05816b 100644
--- a/src/eva2/server/go/strategies/NelderMeadSimplex.java
+++ b/src/eva2/server/go/strategies/NelderMeadSimplex.java
@@ -3,6 +3,8 @@ package eva2.server.go.strategies;
 import java.io.Serializable;
 import java.util.Vector;
 
+import com.sun.org.apache.bcel.internal.generic.CHECKCAST;
+
 import eva2.OptimizerFactory;
 import eva2.OptimizerRunnable;
 import eva2.gui.BeanInspector;
@@ -17,10 +19,12 @@ import eva2.server.go.problems.AbstractOptimizationProblem;
 import eva2.server.go.problems.AbstractProblemDouble;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.server.stat.StatsParameter;
+import eva2.tools.Mathematics;
 
 /**
  * Nelder-Mead-Simplex does not guarantee an equal number of evaluations within each optimize call
  * because of the different step types.
+ * Range check is now available by projection at the bounds.
  *  
  * @author mkron
  *
@@ -35,6 +39,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 	private AbstractOptimizationProblem m_Problem;
 	private transient Vector<InterfacePopulationChangedEventListener> m_Listener;
 	private String m_Identifier = "NelderMeadSimplex";
+	private boolean checkConstraints = true;
 
 	public NelderMeadSimplex() {
 		setPopulation(new Population(populationSize));
@@ -52,10 +57,8 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 		return new NelderMeadSimplex(this);
 	}
 
-
 	public void SetIdentifier(String name) {
 		m_Identifier = name;
-
 	}
 
 	public void SetProblem(InterfaceOptimizationProblem problem) {
@@ -85,31 +88,44 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 
 	public void freeWilly() {}
 
+	protected double[] calcChallengeVect(double[] centroid, double[] refX) {
+		double[] r = new double[centroid.length];
+		for (int i=0; i<r.length; i++) r[i] = 2*centroid[i] - refX[i];
+//		double alpha = 1.3;
+//		for (int i=0; i<r.length; i++) r[i] = centroid[i] + alpha*(centroid[i] - refX[i]);
+		return r;
+	}
+	
 	public AbstractEAIndividual simplexStep(Population subpop) {
 		// parameter
 
-
 		// hole die n-1 besten individuen
 		Population bestpop = subpop.getBestNIndividuals(subpop.size()-1);
 		// und das schlechteste
 		AbstractEAIndividual worst = subpop.getWorstEAIndividual();
 		AbstractEAIndividual best=subpop.getBestEAIndividual();
-		double[] u_q = ((InterfaceDataTypeDouble) worst).getDoubleData();
-		int dim = u_q.length;
+		
+		double[][] range = ((InterfaceDataTypeDouble) worst).getDoubleRange();
+		double[] x_worst = ((InterfaceDataTypeDouble) worst).getDoubleData();
+		int dim = x_worst.length;
 
 		// Centroid berechnen
-		double[] g = new double[dim];
+		double[] centroid = new double[dim];
 		for (int i=0; i<bestpop.size(); i++) {
 			for (int j=0; j<dim; j++) {
 				AbstractEAIndividual bestIndi= (AbstractEAIndividual) bestpop.getIndividual(i);
-				g[j] +=((InterfaceDataTypeDouble)bestIndi).getDoubleData()[j]/bestpop.size(); // bug?
+				centroid[j] +=((InterfaceDataTypeDouble)bestIndi).getDoubleData()[j]/bestpop.size(); // bug?
 			}
 		}
 
 		// Reflection
-		double[] r = new double[dim];
-		for (int i=0; i<dim; i++) 
-			r[i] = 2*g[i] - u_q[i];
+		double[] r = calcChallengeVect(centroid, x_worst);
+		
+		if (checkConstraints) {
+			if (!Mathematics.isInRange(r, range)) {
+				Mathematics.reflectBounds(r, range);
+			}
+		}
 		AbstractEAIndividual r_ind = (AbstractEAIndividual)((AbstractEAIndividual)bestpop.getIndividual(1)).clone(); 
 		((InterfaceDataTypeDouble)r_ind).SetDoubleGenotype(r);
 
@@ -122,7 +138,9 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 		} else if (best.getFitness(0)>r_ind.getFitness(0)){ //neues besser als bisher bestes => Expansion
 
 			double[] e = new double[dim];
-			for (int i=0; i<dim; i++)  e[i] = 3*g[i] - 2*u_q[i];
+			for (int i=0; i<dim; i++)  e[i] = 3*centroid[i] - 2*x_worst[i];
+			if (checkConstraints && !Mathematics.isInRange(e, range)) Mathematics.projectToRange(e, range);
+			
 			AbstractEAIndividual e_ind = (AbstractEAIndividual)((AbstractEAIndividual)bestpop.getIndividual(1)).clone(); 
 			((InterfaceDataTypeDouble)e_ind).SetDoubleGenotype(e);
 			m_Problem.evaluate(e_ind);
@@ -135,7 +153,9 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 
 		} else if(r_ind.getFitness(0) >= bestpop.getWorstEAIndividual().getFitness(0)){//kontrahiere da neues indi keine verbesserung brachte
 			double[] c = new double[dim];
-			for (int i=0; i<dim; i++)  c[i] = 0.5*g[i] + 0.5*u_q[i];
+			for (int i=0; i<dim; i++)  c[i] = 0.5*centroid[i] + 0.5*x_worst[i];
+			if (checkConstraints && !Mathematics.isInRange(c, range)) Mathematics.projectToRange(c, range);
+			
 			AbstractEAIndividual c_ind = (AbstractEAIndividual)((AbstractEAIndividual)bestpop.getIndividual(1)).clone(); 
 			((InterfaceDataTypeDouble)c_ind).SetDoubleGenotype(c);
 			m_Problem.evaluate(c_ind);
@@ -152,11 +172,11 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 	}
 
 	public String getName() {
-		return "NelderMeadSimplex";
+		return m_Identifier;
 	}
 
 	public String globalInfo() {
-		return m_Identifier;
+		return "The Nelder-Mead simplex search algorithm for local search. Reflection is used as constra";
 	}
 
 	public Population getPopulation() {
@@ -182,6 +202,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 
 	public void initByPopulation(Population pop, boolean reset) {
 		setPopulation(pop);
+		pop.addPopulationChangedEventListener(this);
 		if (reset) {
 			m_Problem.initPopulation(m_Population);
 			m_Problem.evaluate(m_Population);
@@ -205,6 +226,15 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 		do {
 			AbstractEAIndividual ind = simplexStep(m_Population);
 			if(ind!=null){ //Verbesserung gefunden
+				double[] x=((InterfaceDataTypeDouble)ind).getDoubleData();
+				double[][] range = ((InterfaceDataTypeDouble)ind).getDoubleRange();
+				if (!Mathematics.isInRange(x, range)) {
+					System.err.println("WARNING: nelder mead step produced indy out of range!");
+//					Mathematics.projectToRange(x, range);
+//					((InterfaceDataTypeDouble)ind).SetDoubleGenotype(x);
+//					m_Problem.evaluate(ind);
+//					this.m_Population.incrFunctionCalls();
+				}
 				m_Population.set(m_Population.getIndexOfWorstIndividual(), ind);
 			}else{//keine Verbesserung gefunden shrink!!
 				
@@ -342,6 +372,9 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 	public static Population createNMSPopulation(AbstractEAIndividual candidate, double perturbRatio, double[][] range, boolean includeCand) {
 		Population initPop = new Population();
 		if (includeCand) initPop.add(candidate);
+		if (perturbRatio >= 1. || (perturbRatio <= 0.)) {
+			System.err.println("Warning: perturbation ratio should lie between 0 and 1! (NelderMeadSimplex:createNMSPopulation)");
+		}
 		addPerturbedPopulation(perturbRatio, initPop, range, candidate);
 		return initPop;
 	}
@@ -362,45 +395,6 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 		}
 		initialPop.setPopulationSize(initialPop.size());
 	}
-	
-//	/**
-//	 * Search for a local optimizer using nelder mead and return the solution found and the number of steps
-//	 * (evaluations) actually performed.
-//	 * 
-//	 * @param candidate
-//	 * @param problem
-//	 * @param term
-//	 * @param perturbationRatio
-//	 * @return
-//	 */
-//	public static int processWithNMS(Population candidates, AbstractOptimizationProblem problem, InterfaceTerminator term, double perturbationRatio) {
-//		NelderMeadSimplex nms = new NelderMeadSimplex();
-//		nms.setProblemAndPopSize(problem);
-//		nms.setGenerationCycle(5);
-//		nms.initByPopulation(candidates, false);
-//		int funCallsBefore = candidates.getFunctionCalls();
-//		candidates.SetFunctionCalls(0);
-//		
-//		OptimizerRunnable hcRunnable = new OptimizerRunnable(OptimizerFactory.makeParams(nms, candidates, problem, 0, term), true);
-//		// as nms creates a new population and has already evaluated them, send a signal to stats
-//		hcRunnable.getStats().createNextGenerationPerformed(nms.getPopulation(), null);
-//		hcRunnable.getGOParams().setDoPostProcessing(false);
-//		hcRunnable.setVerbosityLevel(StatsParameter.VERBOSITY_NONE);
-//		hcRunnable.run();
-//		hcRunnable.getGOParams().setDoPostProcessing(true);
-//		hcRunnable = null;
-//		int funCallsDone = candidates.getFunctionCalls();
-//		candidates.SetFunctionCalls(funCallsBefore+funCallsDone);
-//		
-//		return funCallsDone;
-//	}
-
-//	/**
-//	 * @return the generationCycle
-//	 */
-//	public int getGenerationCycle() {
-//		return generationCycle;
-//	}
 
 	/**
 	 * @param generationCycle the generationCycle to set
@@ -408,13 +402,17 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 	public void setGenerationCycle(int generationCycle) {
 		this.generationCycle = generationCycle;
 	}
+
+	public boolean isCheckRange() {
+		return checkConstraints;
+	}
+
+	public void setCheckRange(boolean checkRange) {
+		this.checkConstraints = checkRange;
+	}
 	
-//	
-//	public static final GOParameters standardNMS(AbstractOptimizationProblem problem) {
-//		NelderMeadSimplex nms = NelderMeadSimplex.createNelderMeadSimplex(problem, null);
-//		Population pop = new Population();
-//		pop.setPopulationSize(nms.getPopulationSize());
-//
-//		return makeParams(nms, pop, problem, randSeed, defaultTerminator());
-//	}
+	public String checkRangeTipText() {
+		return "Mark to check range constraints by reflection/projection";
+	}
+
 }
diff --git a/src/eva2/server/go/strategies/ParticleSwarmOptimization.java b/src/eva2/server/go/strategies/ParticleSwarmOptimization.java
index a6f0502d..cc3d9479 100644
--- a/src/eva2/server/go/strategies/ParticleSwarmOptimization.java
+++ b/src/eva2/server/go/strategies/ParticleSwarmOptimization.java
@@ -124,7 +124,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		if (a.algType != null)
 			this.algType = (SelectedTag)a.algType.clone();
 		this.m_Population                   = (Population)a.m_Population.clone();
-		this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
+		this.m_Problem                      = a.m_Problem;
 		this.m_Identifier                   = a.m_Identifier;
 		this.m_InitialVelocity              = a.m_InitialVelocity;
 		this.m_SpeedLimit                   = a.m_SpeedLimit;
@@ -169,8 +169,9 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	 * Set the initial random velocity vector.
 	 *
 	 * @param indy 	the individual to work on
+	 * @param initialV initial velocity relative to the range
 	 */
-	protected void initIndividualDefaults(AbstractEAIndividual indy) {
+	public static void initIndividualDefaults(AbstractEAIndividual indy, double initialV) {
 		double[] writeData;
 		// init velocity
 		writeData   = new double[((InterfaceDataTypeDouble)indy).getDoubleData().length];
@@ -181,7 +182,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		//sum = Math.sqrt(sum);
 		double relSpeed = getRelativeSpeed(writeData, ((InterfaceDataTypeDouble)indy).getDoubleRange());
 		for (int j = 0; j < writeData.length; j++) {
-			writeData[j]    = (writeData[j]/relSpeed)*this.m_InitialVelocity;
+			writeData[j]    = (writeData[j]/relSpeed)*initialV;
 		}
 		indy.putData(partTypeKey, defaultType);
 		indy.putData(partVelKey, writeData);
@@ -192,7 +193,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	 *
 	 * @param indy 	the individual to work on
 	 */
-	protected void initIndividualMemory(AbstractEAIndividual indy) {
+	protected static void initIndividualMemory(AbstractEAIndividual indy) {
 		// init best fitness
 		double[] tmpD        = indy.getFitness();
 		double[] writeData   = new double[tmpD.length];
@@ -338,6 +339,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	public double[] getPopulationVelocity(Population pop) {
 		return getPopulationVelSpeed(pop, 1);
 	}
+	
 	/**
 	 * Calculates the norm of the velocity vector relative to the problem range.
 	 *
@@ -345,7 +347,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	 * @param range		the problem range in each dimension
 	 * @return			measure of the speed relative to the problem range
 	 */
-	public double getRelativeSpeed(double[] curSpeed, double[][] range) {
+	public static double getRelativeSpeed(double[] curSpeed, double[][] range) {
 		double sumV        = 0;
 		double sumR        = 0;
 		for (int i = 0; i < range.length; i++) {
@@ -403,7 +405,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		for (int i = 0; i < pop.size(); i++) {
 			indy = (AbstractEAIndividual) pop.get(i);
 			if (indy instanceof InterfaceDataTypeDouble) {
-				initIndividualDefaults(indy);
+				initIndividualDefaults(indy, m_InitialVelocity);
 			}
 			indy.putData(indexKey, i);
 			indy.SetIndividualIndex(i);
@@ -470,13 +472,17 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	}
 
 	protected void resetIndividual(AbstractEAIndividual indy) {
+		resetIndividual(indy, m_InitialVelocity);
+		plotIndy(((InterfaceDataTypeDouble)indy).getDoubleData(), null, (Integer)indy.getData(indexKey));
+	}
+	
+	public static void resetIndividual(AbstractEAIndividual indy, double initialV) {
 		if (indy instanceof InterfaceDataTypeDouble) {
 			indy.setParents(null);
 			indy.defaultInit();
 			indy.putData(partTypeKey, defaultType); // turn into default type
-			initIndividualDefaults(indy);
+			initIndividualDefaults(indy, initialV);
 			initIndividualMemory(indy);
-			plotIndy(((InterfaceDataTypeDouble)indy).getDoubleData(), null, (Integer)indy.getData(indexKey));
 		} else System.err.println("error, double valued individuals required for PSO");
 	}
 	
@@ -1432,7 +1438,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 			if (indy==null) {
 				System.err.println("Error in PSO.setPopulation!");
 			} else if (!indy.hasData(partTypeKey)) {
-				initIndividualDefaults(indy);
+				initIndividualDefaults(indy, m_InitialVelocity);
 				initIndividualMemory(indy);
 				indy.putData(indexKey, i);
 				indy.SetIndividualIndex(i);
@@ -1596,7 +1602,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	public void setGOEShowProperties(Class<?> cls) {
 		GenericObjectEditor.setShowProperty(cls, "topologyRange", (m_Topology.getSelectedTag().getID() < 2) || (m_Topology.getSelectedTag().getID() == 6));
 		GenericObjectEditor.setShowProperty(cls, "subSwarmRadius", (m_Topology.getSelectedTag().getID() == 3));
-		GenericObjectEditor.setShowProperty(cls, "subSwarmSize", (m_Topology.getSelectedTag().getID() == 3));
+		GenericObjectEditor.setShowProperty(cls, "maxSubSwarmSize", (m_Topology.getSelectedTag().getID() == 3));
 		GenericObjectEditor.setShowProperty(cls, "treeStruct", (m_Topology.getSelectedTag().getID() == 4));
 		GenericObjectEditor.setShowProperty(cls, "treeBranchDegree", (m_Topology.getSelectedTag().getID() == 4) || (m_Topology.getSelectedTag().getID() == 5));
 		GenericObjectEditor.setShowProperty(cls, "wrapTopology", (m_Topology.getSelectedTag().getID() == 0));
@@ -1718,15 +1724,15 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		return "Define the maximum distance to a swarm leader in the multi-swarm variant";
 	}
 
-	public int getSubSwarmSize() {
+	public int getMaxSubSwarmSize() {
 		return maxSubSwarmSize;
 	}
 
-	public void setSubSwarmSize(int subSize) {
+	public void setMaxSubSwarmSize(int subSize) {
 		maxSubSwarmSize = subSize;
 	}
 
-	public String subSwarmSizeTipText() {
+	public String maxSubSwarmSizeTipText() {
 		return "Maximum size of a sub swarm. Violating particles will be reinitialized. 0 means no limit to the sub swarm size.";
 	}
 
diff --git a/src/eva2/server/modules/PSOParameters.java b/src/eva2/server/modules/PSOParameters.java
index 00bdd9dc..05e3259c 100644
--- a/src/eva2/server/modules/PSOParameters.java
+++ b/src/eva2/server/modules/PSOParameters.java
@@ -220,14 +220,14 @@ public class PSOParameters extends AbstractGOParameters implements InterfaceGOPa
 		return ((ParticleSwarmOptimization)this.m_Optimizer).subSwarmRadiusTipText();
 	}
 	
-	public int getSubSwarmSize() {
-		return ((ParticleSwarmOptimization)this.m_Optimizer).getSubSwarmSize();
+	public int getMaxSubSwarmSize() {
+		return ((ParticleSwarmOptimization)this.m_Optimizer).getMaxSubSwarmSize();
 	}
-	public void setSubSwarmSize(int subSize) {
-		((ParticleSwarmOptimization)this.m_Optimizer).setSubSwarmSize(subSize);
+	public void setMaxSubSwarmSize(int subSize) {
+		((ParticleSwarmOptimization)this.m_Optimizer).setMaxSubSwarmSize(subSize);
 	}
-	public String subSwarmSizeTipText() {
-		return ((ParticleSwarmOptimization)this.m_Optimizer).subSwarmSizeTipText();
+	public String maxSubSwarmSizeTipText() {
+		return ((ParticleSwarmOptimization)this.m_Optimizer).maxSubSwarmSizeTipText();
 	}
     
 	/**
diff --git a/src/eva2/server/modules/Processor.java b/src/eva2/server/modules/Processor.java
index eca52bc3..def3d933 100644
--- a/src/eva2/server/modules/Processor.java
+++ b/src/eva2/server/modules/Processor.java
@@ -1,5 +1,9 @@
 package eva2.server.modules;
 
+import java.util.Vector;
+
+import wsi.ra.jproxy.RemoteStateListener;
+import wsi.ra.math.RNG;
 import eva2.gui.BeanInspector;
 import eva2.server.go.InterfaceGOParameters;
 import eva2.server.go.InterfacePopulationChangedEventListener;
@@ -11,13 +15,12 @@ import eva2.server.go.operators.terminators.EvaluationTerminator;
 import eva2.server.go.operators.terminators.GenerationTerminator;
 import eva2.server.go.populations.Population;
 import eva2.server.go.problems.AbstractOptimizationProblem;
-import wsi.ra.math.RNG;
+import eva2.server.go.problems.InterfaceAdditionalPopulationInformer;
 import eva2.server.stat.InterfaceStatistics;
 import eva2.server.stat.InterfaceTextListener;
 import eva2.server.stat.StatisticsWithGUI;
 import eva2.tools.EVAERROR;
 import eva2.tools.EVAHELP;
-import wsi.ra.jproxy.RemoteStateListener;
 
 /**
  * The Processor may run as a thread permanently (GenericModuleAdapter) and is then stopped and started
@@ -64,7 +67,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
         m_Statistics = Stat;
     }
     
-    protected boolean isOptRunning() {
+    public boolean isOptRunning() {
     	return m_optRunning;
     }
     
@@ -213,6 +216,9 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
         	//////////////// PP or set results without further PP
         	if (isOptRunning()) {
         		resultPop = performPostProcessing();
+        		if (resultPop==null) { // post processing disabled, so use opt. solutions
+        			resultPop = goParams.getOptimizer().getAllSolutions().getSolutions();
+        		}
         	} else resultPop = goParams.getOptimizer().getAllSolutions().getSolutions();
 
         }
@@ -252,9 +258,13 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
      */
     public void registerPopulationStateChanged(Object source, String name) {
     	if (name.equals(Population.nextGenerationPerformed)) {
+//    		System.out.println(getGOParams().getOptimizer().getPopulation().getFunctionCalls() + " " + getGOParams().getOptimizer().getPopulation().getBestFitness()[0]);
+    		Vector informerList = new Vector<InterfaceAdditionalPopulationInformer>(2);
+    		informerList.add(this.goParams.getProblem());
+    		if (this.goParams.getOptimizer() instanceof InterfaceAdditionalPopulationInformer) informerList.add(this.goParams.getOptimizer());
     		m_Statistics.createNextGenerationPerformed(
     				(PopulationInterface)this.goParams.getOptimizer().getPopulation(), 
-    				this.goParams.getProblem());
+    				informerList);
     		if (m_ListenerModule != null) {
     			m_ListenerModule.updateProgress(
     					getStatusPercent(
diff --git a/src/eva2/server/stat/AbstractStatistics.java b/src/eva2/server/stat/AbstractStatistics.java
index ff71c2ed..931ef907 100644
--- a/src/eva2/server/stat/AbstractStatistics.java
+++ b/src/eva2/server/stat/AbstractStatistics.java
@@ -236,14 +236,20 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 		return (resultOut != null) || (textListeners.size()>0);
 	}
 	
-	protected String getOutputHeader(InterfaceAdditionalPopulationInformer informer, PopulationInterface pop) {
+	protected String getOutputHeader(List<InterfaceAdditionalPopulationInformer> informerList, PopulationInterface pop) {
+		
 		String headline = "Fit.-calls \t Best \t Mean \t Worst ";
-		if ((informer == null) || !m_StatsParams.isOutputAdditionalInfo()) {
+		if ((informerList == null) || !m_StatsParams.isOutputAdditionalInfo()) {
 			return headline;
-		} else return headline + "\t " + informer.getAdditionalFileStringHeader(pop);
+		} else {
+			for (InterfaceAdditionalPopulationInformer informer : informerList) {
+				headline = headline + "\t " + informer.getAdditionalFileStringHeader(pop);
+			}
+			return headline;
+		}
 	}
 	
-	protected String getOutputLine(InterfaceAdditionalPopulationInformer informer, PopulationInterface pop) {
+	protected String getOutputLine(List<InterfaceAdditionalPopulationInformer> informerList, PopulationInterface pop) {
 		StringBuffer sbuf = new StringBuffer(Integer.toString(functionCalls));
 		sbuf.append(" \t ");
 		sbuf.append(BeanInspector.toString(currentBestFit));
@@ -255,9 +261,11 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 			sbuf.append(" \t ");
 			sbuf.append(BeanInspector.toString(currentWorstFit));
 		} else sbuf.append(" # \t");
-		if (informer != null && m_StatsParams.isOutputAdditionalInfo()) {
-			sbuf.append(" \t ");
-			sbuf.append(informer.getAdditionalFileStringValue(pop));
+		if (informerList != null && m_StatsParams.isOutputAdditionalInfo()) {
+			for (InterfaceAdditionalPopulationInformer informer : informerList) {
+				sbuf.append(" \t ");
+				sbuf.append(informer.getAdditionalFileStringValue(pop));
+			}
 		}		
 		return sbuf.toString();
 	}
@@ -289,7 +297,7 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 	 * @param pop
 	 * @param informer
 	 */
-	public abstract void plotSpecificData(PopulationInterface pop, InterfaceAdditionalPopulationInformer informer);
+	public abstract void plotSpecificData(PopulationInterface pop, List<InterfaceAdditionalPopulationInformer> informerList);
 	
 	protected abstract void plotCurrentResults();
 	
@@ -303,16 +311,16 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 	 *
 	 */
 	public synchronized void createNextGenerationPerformed(PopulationInterface
-			pop, InterfaceAdditionalPopulationInformer informer) {
+			pop, List<InterfaceAdditionalPopulationInformer> informerList) {
 		if (firstPlot) {
 			initPlots(m_StatsParams.getPlotDescriptions());
 //			if (doTextOutput()) printToTextListener(getOutputHeader(informer, pop)+'\n');
 			firstPlot = false;
 		}
-		if ((runIterCnt==0) && printHeaderByVerbosity()) printToTextListener(getOutputHeader(informer, pop)+'\n');
+		if ((runIterCnt==0) && printHeaderByVerbosity()) printToTextListener(getOutputHeader(informerList, pop)+'\n');
 
 		if (pop.getSpecificData() != null) {
-			plotSpecificData(pop, informer);
+			plotSpecificData(pop, informerList);
 			return;
 		}
 		// by default plotting only the best
@@ -361,7 +369,7 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 		}
 //		meanCollection.set(pop.getGenerations()-1, means);
 		
-		if (doTextOutput() && printLineByVerbosity(runIterCnt)) printToTextListener(getOutputLine(informer, pop)+'\n');
+		if (doTextOutput() && printLineByVerbosity(runIterCnt)) printToTextListener(getOutputLine(informerList, pop)+'\n');
 		plotCurrentResults();
 
 		runIterCnt++;
diff --git a/src/eva2/server/stat/InterfaceStatistics.java b/src/eva2/server/stat/InterfaceStatistics.java
index 103b28e1..b071a268 100644
--- a/src/eva2/server/stat/InterfaceStatistics.java
+++ b/src/eva2/server/stat/InterfaceStatistics.java
@@ -12,8 +12,9 @@ package eva2.server.stat;
 /*==========================================================================*
  * IMPORTS
  *==========================================================================*/
+import java.util.List;
+
 import eva2.server.go.IndividualInterface;
-import eva2.server.go.InterfaceGOParameters;
 import eva2.server.go.PopulationInterface;
 import eva2.server.go.problems.InterfaceAdditionalPopulationInformer;
 /*==========================================================================*
@@ -34,7 +35,7 @@ public interface InterfaceStatistics {
 	public void addTextListener(InterfaceTextListener listener);
 	public boolean removeTextListener(InterfaceTextListener listener);
 	public void printToTextListener(String s);
-	public void createNextGenerationPerformed(PopulationInterface Pop, InterfaceAdditionalPopulationInformer informer);
+	public void createNextGenerationPerformed(PopulationInterface Pop, List<InterfaceAdditionalPopulationInformer> informerList);
 	public void createNextGenerationPerformed(double[] bestfit,double[] worstfit,int calls);
 	public InterfaceStatisticsParameter getStatisticsParameter(); // called from moduleadapter
 	public IndividualInterface getBestSolution(); // returns the best overall solution
diff --git a/src/eva2/server/stat/StatisticsDummy.java b/src/eva2/server/stat/StatisticsDummy.java
index f3da7ae6..6c9f158d 100644
--- a/src/eva2/server/stat/StatisticsDummy.java
+++ b/src/eva2/server/stat/StatisticsDummy.java
@@ -1,5 +1,7 @@
 package eva2.server.stat;
 
+import java.util.List;
+
 import eva2.server.go.IndividualInterface;
 import eva2.server.go.PopulationInterface;
 import eva2.server.go.individuals.AbstractEAIndividual;
@@ -35,7 +37,7 @@ public class StatisticsDummy implements InterfaceStatistics, InterfaceTextListen
 	}
 
 	public void createNextGenerationPerformed(PopulationInterface pop,
-			InterfaceAdditionalPopulationInformer informer) {
+			List<InterfaceAdditionalPopulationInformer> informerList) {
 		bestCurrentIndividual = (AbstractEAIndividual)pop.getBestIndividual();
 		if ((bestIndividualAllover == null) || (AbstractStatistics.secondIsBetter(bestIndividualAllover, bestCurrentIndividual))) {
 			bestIndividualAllover = bestCurrentIndividual;
diff --git a/src/eva2/server/stat/StatisticsStandalone.java b/src/eva2/server/stat/StatisticsStandalone.java
index 4f7ddc39..92382edc 100644
--- a/src/eva2/server/stat/StatisticsStandalone.java
+++ b/src/eva2/server/stat/StatisticsStandalone.java
@@ -90,7 +90,7 @@ public class StatisticsStandalone extends AbstractStatistics implements Interfac
 		((ArrayList<double[]>[]) m_Result.get(0))[optRunsPerformed].add(new double[] {functionCalls, currentBestFit[0]});
 	}
 
-	public void plotSpecificData(PopulationInterface pop, InterfaceAdditionalPopulationInformer informer) {
+	public void plotSpecificData(PopulationInterface pop, List<InterfaceAdditionalPopulationInformer> informerList) {
 		if (TRACE) System.out.println(" m_SpecificData !!");
 		double[] specificData = pop.getSpecificData();
 		if (specificData != null) {
diff --git a/src/eva2/server/stat/StatisticsWithGUI.java b/src/eva2/server/stat/StatisticsWithGUI.java
index 6ac79669..6c8e3237 100644
--- a/src/eva2/server/stat/StatisticsWithGUI.java
+++ b/src/eva2/server/stat/StatisticsWithGUI.java
@@ -241,7 +241,7 @@ public class StatisticsWithGUI extends AbstractStatistics implements Serializabl
 	/**
 	 *
 	 */
-	public void plotSpecificData(PopulationInterface pop, InterfaceAdditionalPopulationInformer informer) {
+	public void plotSpecificData(PopulationInterface pop, List<InterfaceAdditionalPopulationInformer> informer) {
 		double[] specificData = pop.getSpecificData();
 		int calls = pop.getFunctionCalls();
 		ArrayList<String[]> description = new ArrayList<String[]>();
diff --git a/src/eva2/tools/Mathematics.java b/src/eva2/tools/Mathematics.java
index feec7d5c..94498713 100644
--- a/src/eva2/tools/Mathematics.java
+++ b/src/eva2/tools/Mathematics.java
@@ -727,4 +727,66 @@ public class Mathematics {
 		}
 		return viols;
 	}
+	
+	
+	/**
+	 * Calculate the average length of the range intervals over all dimensions.
+	 * 
+	 * @param range
+	 * @return the average length of the range intervals
+	 */
+    public static double getAvgRange(double[][] range) {
+		double sum = 0.;
+		for (int i=0; i<range.length; i++) sum+=(range[i][1]-range[i][0]);
+		return sum/range.length;
+	}
+
+    /**
+     * Reflect the entries of x which violate the bounds to within the range. 
+     * Return the number of violating dimensions.
+     * 
+     * @param x
+     * @param range
+     * @return the number of violating dimensions
+     */
+	public static int reflectBounds(double[] x, double[][] range) {
+		int viols=0;
+		double d = 0.;
+		for (int i=0; i<x.length; i++) {
+			double dimLen = range[i][1]-range[i][0];
+			if (dimLen <= 0.) System.err.println("Error in reflectBounds: empty range!");
+			if (x[i]<range[i][0]) {
+				viols++;
+				d = range[i][0]-x[i];
+				while (d > dimLen) d -= dimLen; // avoid violating the other bound immediately
+				x[i]=range[i][0]+d;
+			} else if (x[i]>range[i][1]) {
+				viols++;
+				d = x[i]-range[i][1];
+				while (d>dimLen) d -= dimLen; // avoid violating the other bound immediately
+				x[i]=range[i][1]-d;
+			}
+		}
+		return viols;
+	}
+	
+	/**
+	 * Simple version of reflection of a value moving by a step and bouncing
+	 * of min and max values like a pool ball. Precondition is min <= val <= max,
+	 * post condition is min <= retVal <= max.
+	 * 
+	 * @param val
+	 * @param step
+	 * @param min
+	 * @param max
+	 * @return
+	 */
+	public static double reflectValue(double val, double step, double min, double max) {
+		while (step > (max-min)) step -= (max-min);
+		if ((val + step) > max) 
+			return (2 * max - val - step);
+		if ((val + step) < min) 
+			 return (2 * min - val - step);
+		return (val += step);
+	}
 }
diff --git a/src/eva2/tools/SelectedTag.java b/src/eva2/tools/SelectedTag.java
index d9b7fc66..24ea1bc8 100644
--- a/src/eva2/tools/SelectedTag.java
+++ b/src/eva2/tools/SelectedTag.java
@@ -73,8 +73,9 @@ public class SelectedTag implements java.io.Serializable {
 	 * 
 	 * @param i     The new selected tag index
 	 */
-	public void setSelectedTag(int i) {
+	public SelectedTag setSelectedTag(int i) {
 		if ((i >= 0) && (i < this.m_Tags.length)) this.m_Selected = i;
+		return this;
 	}
 	
 	/**
@@ -84,14 +85,15 @@ public class SelectedTag implements java.io.Serializable {
 	 *  
 	 * @param str    The new selected tag name
 	 */
-	public void setSelectedTag(String str) {
+	public SelectedTag setSelectedTag(String str) {
 		for (int i=0; i<m_Tags.length; i++) {
 			if (m_Tags[i].m_String.compareTo(str) == 0) {
 				m_Selected = i;
-				return;
+				return this;
 			}
 		}
 		System.err.println("Warning, trying to select unknown string (SelectedTag::setSelectedTag(String)");
+		return this;
 	}
 	
 	/**