Merging mk branch rev 199, new Population event for initialization (introducing final Strings for the event names), renaming Indy.SetData to putData, replacing static rank-mu-CMA parameters by a population-associated structure; oh and the "Description" button has made room for a "Show Solution" button.

src/eva2/gui/JModuleGeneralPanel.java

@@ -21,9 +21,16 @@ import java.awt.event.ActionEvent;
 import java.awt.event.ActionListener;
 import javax.swing.JButton;
 import javax.swing.JComponent;
+import javax.swing.JFrame;
 import javax.swing.JOptionPane;
 import javax.swing.JPanel;
 
+import eva2.server.go.InterfaceGOParameters;
+import eva2.server.go.individuals.AbstractEAIndividual;
+import eva2.server.go.problems.AbstractOptimizationProblem;
+import eva2.server.go.problems.InterfaceOptimizationProblem;
+import eva2.server.go.strategies.InterfaceOptimizer;
+import eva2.server.modules.AbstractModuleAdapter;
 import eva2.server.modules.ModuleAdapter;
 
 import wsi.ra.jproxy.RMIProxyLocal;
@@ -44,6 +51,7 @@ public class JModuleGeneralPanel implements RemoteStateListener, Serializable  {
 	private JButton m_actStop;
 //	private JButton m_actExitMod;
 	private JButton m_JHelpButton;
+	private JButton m_ShowSolButton;
 	private JPanel m_Panel;
 	private String m_HelperFileName;
 
@@ -90,10 +98,10 @@ public class JModuleGeneralPanel implements RemoteStateListener, Serializable  {
 					m_RunButton.setEnabled(false);
 					m_PPButton.setEnabled(false);
 //					m_RestartButton.setEnabled(false);
-					m_JHelpButton.setEnabled(true);
+//					m_JHelpButton.setEnabled(true);
 				} catch (Exception ee) {
 					ee.printStackTrace();
-					System.out.print ("Error in run: " +ee +" : " + ee.getMessage() );
+					System.err.print ("Error in run: " +ee +" : " + ee.getMessage() );
 				}
 			}
 		}
@@ -111,7 +119,7 @@ public class JModuleGeneralPanel implements RemoteStateListener, Serializable  {
 			public void actionPerformed(ActionEvent e){
 				try {
 					m_Adapter.stopOpt();	// this means user break
-				} catch (Exception ee) { System.out.print ("Error in stop: " + ee.getMessage() ); }
+				} catch (Exception ee) { System.err.print ("Error in stop: " + ee.getMessage() ); }
 			}
 		}
 		);
@@ -132,7 +140,7 @@ public class JModuleGeneralPanel implements RemoteStateListener, Serializable  {
 //					m_RunButton.setEnabled(false);
 				} catch (Exception ee) {
 					ee.printStackTrace();
-					System.out.println("Error in run: " +ee +" : " + ee.getMessage() );
+					System.err.println("Error in run: " +ee +" : " + ee.getMessage() );
 				}
 			}
 		}
@@ -140,8 +148,43 @@ public class JModuleGeneralPanel implements RemoteStateListener, Serializable  {
 		m_PPButton.setEnabled(m_StateRunning && m_Adapter.hasPostProcessing());
 		m_Panel.add(m_PPButton);
 		
+		makeHelpButton();
+		
+		if (m_Adapter instanceof AbstractModuleAdapter && (m_Adapter != null)) {
+		    /** This action listener, called by the "show" button will show the
+		     * currently best solution in a frame.
+		     */
+			m_ShowSolButton = new JButton("Show Solution");
+			m_ShowSolButton.addActionListener(new ActionListener() {
+		        public void actionPerformed(ActionEvent event) {
+					InterfaceGOParameters goParams = ((AbstractModuleAdapter)m_Adapter).getGOParameters();
+					InterfaceOptimizationProblem goProblem = goParams.getProblem();
+					InterfaceOptimizer opt = goParams.getOptimizer();
+					AbstractEAIndividual indy = opt.getPopulation().getBestEAIndividual();
+					if (indy != null) {
+			            JFrame frame = new JFrame();
+			            frame.setTitle("The current best solution for "+goProblem.getName());
+			            frame.setSize(400, 300);
+			            frame.setLocation(450, 250);
+			            frame.getContentPane().add(goProblem.drawIndividual(indy));
+			            frame.validate();
+			            frame.setVisible(true);
+					} else System.out.println("No current solution available.");
+		        }
+			}
+					);
+			m_ShowSolButton.setEnabled(false);
+			m_Panel.add(m_ShowSolButton);
+		}
+		
+//		m_actExitMod = new JButton("Exit Module");
+//		m_actExitMod.setToolTipText("todo !!.");// TODO
+		return m_Panel;
+	}
+	
+	private void makeHelpButton() {
 		///////////////////////////////////////////////////////////////
-		if (m_HelperFileName.equals("")== false) {
+		if (m_HelperFileName!=null && (!m_HelperFileName.equals(""))) {
 			m_JHelpButton= new JButton("Description");
 			m_JHelpButton.setToolTipText("Description of the current optimization algorithm.");
 			m_JHelpButton.addActionListener(new ActionListener() {
@@ -162,9 +205,6 @@ public class JModuleGeneralPanel implements RemoteStateListener, Serializable  {
 			);
 			m_Panel.add(m_JHelpButton);
 		}
-//		m_actExitMod = new JButton("Exit Module");
-//		m_actExitMod.setToolTipText("todo !!.");// TODO
-		return m_Panel;
 	}
     
 	/**
@@ -180,6 +220,7 @@ public class JModuleGeneralPanel implements RemoteStateListener, Serializable  {
 	}
 
 	public void performedStart(String infoString) {
+		m_ShowSolButton.setEnabled(true);
 	}
 
 
@@ -199,7 +240,22 @@ public class JModuleGeneralPanel implements RemoteStateListener, Serializable  {
 	 *
 	 */
 	public void setHelperFilename (String s) {
-		m_HelperFileName = s;
+		if ((s==null) && (s==m_HelperFileName)) return; // both are null, do nothing
+		if (s!=null) {
+			if (m_HelperFileName != null) {
+				if (!m_HelperFileName.equals(s)) {
+					m_Panel.remove(m_JHelpButton);
+					m_HelperFileName = s;
+					makeHelpButton();					
+				} //else // both are equal, do nothing
+			} else { // only old is null, nothing to be removed
+				m_HelperFileName = s;
+				makeHelpButton();
+			}
+		} else { // s is null, so just remove
+			m_Panel.remove(m_JHelpButton);
+			m_HelperFileName=s;
+		}
 	}
 }
 

src/eva2/server/go/GOStandaloneVersion.java

@@ -555,41 +555,43 @@ public class GOStandaloneVersion implements InterfaceGOStandalone, InterfacePopu
      * @param name          Could be used to indicate the nature of the event.
      */
     public void registerPopulationStateChanged(Object source, String name) {
-        Population population = ((InterfaceOptimizer)source).getPopulation();
+    	if (name.equals(Population.nextGenerationPerformed)) {
-        double x = 100/this.m_MultiRuns;
+    		Population population = ((InterfaceOptimizer)source).getPopulation();
-        if (this.m_GO.getTerminator() instanceof EvaluationTerminator) {
+    		double x = 100/this.m_MultiRuns;
-            double y = x/(double)((EvaluationTerminator)this.m_GO.getTerminator()).getFitnessCalls();
+    		if (this.m_GO.getTerminator() instanceof EvaluationTerminator) {
-            currentProgress = (int)(this.currentRun * x + population.getFunctionCalls()*y);
+    			double y = x/(double)((EvaluationTerminator)this.m_GO.getTerminator()).getFitnessCalls();
-        } else {
+    			currentProgress = (int)(this.currentRun * x + population.getFunctionCalls()*y);
-            currentProgress = (int)(this.currentRun * x);
+    		} else {
-        }
+    			currentProgress = (int)(this.currentRun * x);
-        updateStatus(currentProgress);
+    		}
+    		updateStatus(currentProgress);
 
-        // data to be stored in file
+    		// data to be stored in file
-        double tmpd = 0;
+    		double tmpd = 0;
-        StringBuffer  tmpLine = new StringBuffer("");
+    		StringBuffer  tmpLine = new StringBuffer("");
-        tmpLine.append(population.getFunctionCalls());
+    		tmpLine.append(population.getFunctionCalls());
-        tmpLine.append("\t");
+    		tmpLine.append("\t");
-        tmpLine.append(population.getBestEAIndividual().getFitness(0));
+    		tmpLine.append(population.getBestEAIndividual().getFitness(0));
-        tmpLine.append("\t");
+    		tmpLine.append("\t");
-        for (int i = 0; i < population.size(); i++) tmpd += ((AbstractEAIndividual)population.get(i)).getFitness(0)/(double)population.size();
+    		for (int i = 0; i < population.size(); i++) tmpd += ((AbstractEAIndividual)population.get(i)).getFitness(0)/(double)population.size();
-        tmpLine.append("\t");
+    		tmpLine.append("\t");
-        tmpLine.append(tmpd);
+    		tmpLine.append(tmpd);
-        tmpLine.append("\t");
+    		tmpLine.append("\t");
-        tmpLine.append(population.getWorstEAIndividual().getFitness(0));
+    		tmpLine.append(population.getWorstEAIndividual().getFitness(0));
-        //tmpLine.append("\t");
+    		//tmpLine.append("\t");
-        //tmpLine.append(this.m_GO.getProblem().getAdditionalFileStringValue(population));
+    		//tmpLine.append(this.m_GO.getProblem().getAdditionalFileStringValue(population));
-        this.writeToFile(tmpLine.toString());
+    		this.writeToFile(tmpLine.toString());
 
-        Double[] tmpData = new Double[2];
+    		Double[] tmpData = new Double[2];
-        tmpData[0] = new Double(population.getFunctionCalls());
+    		tmpData[0] = new Double(population.getFunctionCalls());
-        // instead of adding simply the best fitness value i'll ask the problem what to show
+    		// instead of adding simply the best fitness value i'll ask the problem what to show
-        tmpData[1] = this.m_GO.getProblem().getDoublePlotValue(population);
+    		tmpData[1] = this.m_GO.getProblem().getDoublePlotValue(population);
-        if (this.m_Plot != null) {
+    		if (this.m_Plot != null) {
-            if (this.m_ContinueFlag) this.m_Plot.setConnectedPoint(tmpData[0].doubleValue()+this.m_RecentFC, tmpData[1].doubleValue(), 1000+this.currentRun);
+    			if (this.m_ContinueFlag) this.m_Plot.setConnectedPoint(tmpData[0].doubleValue()+this.m_RecentFC, tmpData[1].doubleValue(), 1000+this.currentRun);
-            else this.m_Plot.setConnectedPoint(tmpData[0].doubleValue(), tmpData[1].doubleValue(), 1000+this.currentRun);
+    			else this.m_Plot.setConnectedPoint(tmpData[0].doubleValue(), tmpData[1].doubleValue(), 1000+this.currentRun);
-        }
+    		}
-        this.m_TmpData.add(tmpData);
+    		this.m_TmpData.add(tmpData);
+    	}
     }
 
     /** This method writes Data to file.

src/eva2/server/go/MOCCOStandalone.java

@@ -498,19 +498,21 @@ public class MOCCOStandalone implements InterfaceGOStandalone, InterfacePopulati
      * @param name          Could be used to indicate the nature of the event.
      */
     public void registerPopulationStateChanged(Object source, String name) {
-        int currentProgress;
+    	int currentProgress;
-        if (this.m_State.isVisible) {
+    	if (name.equals(Population.nextGenerationPerformed)) {
-            Population population = ((InterfaceOptimizer)source).getPopulation();
+    		if (this.m_State.isVisible) {
-            double x = 100;
+    			Population population = ((InterfaceOptimizer)source).getPopulation();
-            if (this.m_State.m_Terminator instanceof EvaluationTerminator) {
+    			double x = 100;
-                double y = x/(double)((EvaluationTerminator)this.m_State.m_Terminator).getFitnessCalls();
+    			if (this.m_State.m_Terminator instanceof EvaluationTerminator) {
-                currentProgress = (int)(population.getFunctionCalls()*y);
+    				double y = x/(double)((EvaluationTerminator)this.m_State.m_Terminator).getFitnessCalls();
-            } else {
+    				currentProgress = (int)(population.getFunctionCalls()*y);
-                currentProgress = (int)(0);
+    			} else {
-            }
+    				currentProgress = (int)(0);
-            updateStatus("Optimizing...",currentProgress);
+    			}
-        } else {
+    			updateStatus("Optimizing...",currentProgress);
-            // perhaps i could write it to file!?
+    		} else {
-        }
+    			// perhaps i could write it to file!?
+    		}
+    	}
     }
 }

src/eva2/server/go/individuals/AbstractEAIndividual.java

@@ -749,7 +749,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
      * @param name      The identifying name.
      * @param obj       The object that is to be stored.
      */
-    public void SetData(String name, Object obj) {
+    public void putData(String name, Object obj) {
    		m_dataHash.put(name, obj);
     }
 

src/eva2/server/go/operators/archiving/ArchivingNSGAII.java

@@ -118,7 +118,7 @@ public class ArchivingNSGAII extends ArchivingNSGA implements java.io.Serializab
             tmpNonDom   = new Population();
             for (int i = 0; i < tmpPop.size(); i++) {
                 if (this.isDominant((AbstractEAIndividual) tmpPop.get(i), tmpPop)) {
-                    ((AbstractEAIndividual)tmpPop.get(i)).SetData("ParetoLevel", new Integer(level));
+                    ((AbstractEAIndividual)tmpPop.get(i)).putData("ParetoLevel", new Integer(level));
                     tmpDom.add(tmpPop.get(i));
 
                 } else {
@@ -130,7 +130,7 @@ public class ArchivingNSGAII extends ArchivingNSGA implements java.io.Serializab
                 System.out.println("Problem NSGA II at level " + level + ".");
                 tmpDom.addPopulation(tmpNonDom);
                 for (int i = 0; i < tmpDom.size(); i++)
-                    ((AbstractEAIndividual)tmpDom.get(i)).SetData("ParetoLevel", new Integer(level));
+                    ((AbstractEAIndividual)tmpDom.get(i)).putData("ParetoLevel", new Integer(level));
                 tmpPop.clear();
 //                System.out.println(""+tmpPop.getStringRepresentation());
 //                tmpPop.removeDoubleInstancesUsingFitness();
@@ -158,7 +158,7 @@ public class ArchivingNSGAII extends ArchivingNSGA implements java.io.Serializab
             }
             hyperCube = heidi.calculateHyperCubeVolumes(fitness);
             for (int j = 0; j < fronts[i].size(); j++) {
-                ((AbstractEAIndividual)fronts[i].get(j)).SetData("HyperCube", new Double(hyperCube[j]));
+                ((AbstractEAIndividual)fronts[i].get(j)).putData("HyperCube", new Double(hyperCube[j]));
             }
         }
     }

src/eva2/server/go/operators/archiving/ArchivingPESAII.java

@@ -249,8 +249,8 @@ public class ArchivingPESAII extends AbstractArchiving implements java.io.Serial
                 for (int j = 0; j < coll.size(); j++) {
                     result[((Integer)coll.get(j)).intValue()] = coll.size();
                     tmpIndy = (AbstractEAIndividual)pop.get(((Integer)coll.get(j)).intValue());
-                    tmpIndy.SetData("SqueezeFactor", new Integer(coll.size()));
+                    tmpIndy.putData("SqueezeFactor", new Integer(coll.size()));
-                    tmpIndy.SetData("GridBox", curGrid);
+                    tmpIndy.putData("GridBox", curGrid);
                 }
             }
         }

src/eva2/server/go/operators/archiving/ArchivingSPEAII.java

@@ -512,8 +512,8 @@ public class ArchivingSPEAII extends AbstractArchiving implements java.io.Serial
         for (int i = 0; i < SPEAResult.length; i++) {
             if (1/(2+D[i]) >= 1) System.out.println("d " +1/(2+D[i]));
             SPEAResult[i] = SPEAFitness[i] + (1/(2+D[i]));
-            ((AbstractEAIndividual)pop.get(i)).SetData("RawFit", new Double(SPEAFitness[i]));
+            ((AbstractEAIndividual)pop.get(i)).putData("RawFit", new Double(SPEAFitness[i]));
-            ((AbstractEAIndividual)pop.get(i)).SetData("SPEAFit", new Double(SPEAResult[i]));
+            ((AbstractEAIndividual)pop.get(i)).putData("SPEAFit", new Double(SPEAResult[i]));
         }
 
         if (this.m_Debug && this.m_Plot != null) {

src/eva2/server/go/operators/archiving/RemoveSurplusIndividualsStaticHyperCube.java

@@ -44,7 +44,7 @@ public class RemoveSurplusIndividualsStaticHyperCube extends RemoveSurplusIndivi
         }
         space = this.calculateHyperCubeVolumes(fitness);
         for (int i = 0; i < archive.size(); i++) {
-            ((AbstractEAIndividual)archive.get(i)).SetData("HyperCube", new Double(space[i]));
+            ((AbstractEAIndividual)archive.get(i)).putData("HyperCube", new Double(space[i]));
         }
 
         while(archive.size() > archive.getPopulationSize()) {

src/eva2/server/go/operators/moso/MOSODynamicallyWeightedFitness.java

@@ -51,7 +51,7 @@ public class MOSODynamicallyWeightedFitness implements InterfaceMOSOConverter, j
         double[]    weights;
 
         tmpFit = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         weights = new double[tmpFit.length];
 
         // calculate the dynamic weights

src/eva2/server/go/operators/moso/MOSOEpsilonConstraint.java

@@ -53,7 +53,7 @@ public class MOSOEpsilonConstraint implements InterfaceMOSOConverter, java.io.Se
         double[]    tmpFit;
 
         tmpFit = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         resultFit[0] = tmpFit[this.m_EpsilonConstraint.m_OptimizeObjective];
         for (int i = 0; i < this.m_EpsilonConstraint.m_TargetValue.length; i++) {
             if (i != this.m_EpsilonConstraint.m_OptimizeObjective) {

src/eva2/server/go/operators/moso/MOSOEpsilonThreshold.java

@@ -56,7 +56,7 @@ public class MOSOEpsilonThreshold implements InterfaceMOSOConverter, java.io.Ser
         double[]    tmpFit;
 
         tmpFit = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         for (int i = 0; i < tmpFit.length; i++) {
             if (new Double(tmpFit[i]).isNaN()) System.out.println("Fitness is NaN");
             if (new Double(tmpFit[i]).isInfinite()) System.out.println("Fitness is Infinite");

src/eva2/server/go/operators/moso/MOSOGoalProgramming.java

@@ -50,7 +50,7 @@ public class MOSOGoalProgramming implements InterfaceMOSOConverter, java.io.Seri
         double[]    tmpFit;
 
         tmpFit = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         resultFit[0] = 0;
         for (int i = 0; (i < this.m_Goals.m_DoubleArray.length) && (i < tmpFit.length) ; i++)
             resultFit[0] += tmpFit[i]-this.m_Goals.m_DoubleArray[i];

src/eva2/server/go/operators/moso/MOSOLpMetric.java

@@ -52,7 +52,7 @@ public class MOSOLpMetric implements InterfaceMOSOConverter, java.io.Serializabl
         double[]    tmpFit;
 
         tmpFit = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         if (m_P >= 1) {
             // standard Lp Metric
             resultFit[0] = 0;

src/eva2/server/go/operators/moso/MOSOMOGARankBased.java

@@ -41,7 +41,7 @@ public class MOSOMOGARankBased implements InterfaceMOSOConverter, java.io.Serial
             }
         }
         for (int i = 0; i < pop.size(); i++) {
-            ((AbstractEAIndividual)pop.get(i)).SetData("MOGARank", new Integer(MOGARank[i]));
+            ((AbstractEAIndividual)pop.get(i)).putData("MOGARank", new Integer(MOGARank[i]));
         }
         for (int i = 0; i < pop.size(); i++) {
              this.convertSingleIndividual((AbstractEAIndividual)pop.get(i));
@@ -56,7 +56,7 @@ public class MOSOMOGARankBased implements InterfaceMOSOConverter, java.io.Serial
         double[]    tmpFit;
 
         tmpFit = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         resultFit[0] = ((Integer)indy.getData("MOGARank")).doubleValue();
         indy.SetFitness(resultFit);
     }

src/eva2/server/go/operators/moso/MOSOMaxiMin.java

@@ -66,7 +66,7 @@ public class MOSOMaxiMin implements InterfaceMOSOConverter, java.io.Serializable
             // write the result to the individuals
             tmpIndy     = (AbstractEAIndividual) pop.get(i);
             tmpFit      = tmpIndy.getFitness();
-            tmpIndy.SetData("MOFitness", tmpFit);
+            tmpIndy.putData("MOFitness", tmpFit);
             resultFit   = new double[1];
             resultFit[0] = result[i];
             tmpIndy.SetFitness(resultFit);
@@ -107,7 +107,7 @@ public class MOSOMaxiMin implements InterfaceMOSOConverter, java.io.Serializable
         double[]    tmpFit;
 
         tmpFit          = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         System.out.println("The MaxiMin MOSO can not be applied to single individuals! I default to random criterion.");
         resultFit[0]    = tmpFit[RNG.randomInt(0, tmpFit.length)];
         indy.SetFitness(resultFit);

src/eva2/server/go/operators/moso/MOSONoConvert.java

@@ -41,7 +41,7 @@ public class MOSONoConvert implements InterfaceMOSOConverter, java.io.Serializab
         double[]    tmpFit;
 
         tmpFit          = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
 //        resultFit[0]    = tmpFit[RNG.randomInt(0, tmpFit.length)];
 //        indy.SetFitness(resultFit);
     }    

src/eva2/server/go/operators/moso/MOSORandomChoice.java

@@ -45,7 +45,7 @@ public class MOSORandomChoice  implements InterfaceMOSOConverter, java.io.Serial
         double[]    tmpFit;
 
         tmpFit          = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         resultFit[0]    = tmpFit[RNG.randomInt(0, tmpFit.length-1)];
         indy.SetFitness(resultFit);
     }

src/eva2/server/go/operators/moso/MOSORandomWeight.java

@@ -44,7 +44,7 @@ public class MOSORandomWeight implements InterfaceMOSOConverter, java.io.Seriali
         double      sum = 0;
 
         tmpFit = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         tmpWeight = new double[tmpFit.length];
         for (int i = 0; i < tmpWeight.length; i++) {
             tmpWeight[i] = RNG.randomDouble(0,1);

src/eva2/server/go/operators/moso/MOSORankbased.java

@@ -45,7 +45,7 @@ public class MOSORankbased implements InterfaceMOSOConverter, java.io.Serializab
         double[]    tmpFit;
 
         tmpFit = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         resultFit[0] = ((Integer)indy.getData("ParetoLevel")).doubleValue();
         indy.SetFitness(resultFit);
     }

src/eva2/server/go/operators/moso/MOSOUtilityFunction.java

@@ -46,7 +46,7 @@ public class MOSOUtilityFunction implements InterfaceMOSOConverter, java.io.Seri
         double[]    tmpFit;
 
         tmpFit = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         resultFit[0] = 0;
 
         /**********************************************************************************************

src/eva2/server/go/operators/moso/MOSOWeightedFitness.java

@@ -51,7 +51,7 @@ public class MOSOWeightedFitness implements InterfaceMOSOConverter, java.io.Seri
         double[]    tmpFit;
 
         tmpFit = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         for (int i = 0; (i < this.m_Weights.m_DoubleArray.length) && (i < tmpFit.length) ; i++)
             resultFit[0] += tmpFit[i]*this.m_Weights.m_DoubleArray[i];
         indy.SetFitness(resultFit);

src/eva2/server/go/operators/moso/MOSOWeightedLPTchebycheff.java

@@ -55,7 +55,7 @@ public class MOSOWeightedLPTchebycheff implements InterfaceMOSOConverter, java.i
         double[]    tmpFit;
 
         tmpFit = indy.getFitness();
-        indy.SetData("MOFitness", tmpFit);
+        indy.putData("MOFitness", tmpFit);
         resultFit[0] = 0;
         for (int i = 0; i < tmpFit.length; i++) {
             if (this.m_WLPT.m_P == 0) {

src/eva2/server/go/operators/mutation/MutateESRankMuCMA.java

@@ -8,68 +8,230 @@ import wsi.ra.math.Jama.EigenvalueDecomposition;
 import wsi.ra.math.Jama.Matrix;
 import eva2.gui.BeanInspector;
 import eva2.gui.GenericObjectEditor;
+import eva2.server.go.InterfacePopulationChangedEventListener;
 import eva2.server.go.enums.ESMutationInitialSigma;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.InterfaceDataTypeDouble;
 import eva2.server.go.populations.Population;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
+import eva2.server.go.strategies.EvolutionStrategies;
 import eva2.tools.EVAERROR;
 import eva2.tools.Mathematics;
 import eva2.tools.Pair;
 
+/**
+ * The collection of all population specific data required for a rank-mu-CMA optimization.
+ * Besides methods for initialization, this class implements the InterfacePopulationChangedEventListener
+ * and is added as listener to the population it is initialized on. The reason for this is that the
+ * CMA parameters must be reinitialized whenever the population itself is initialized, so the reinit
+ * event is caught and handled. As there are numerous mutator instances but only one CMA parameter
+ * set per population, this way is the good one.
+ *  
+ * @author mkron
+ *
+ */
+class CMAParamSet implements InterfacePopulationChangedEventListener, Serializable {
+	private static final long serialVersionUID = -1940875082544233819L;
+	protected double firstSigma = -1.;
+	protected double sigma;
+	protected double d_sig, c_sig;
+	protected double[] meanX, pathC, pathS, eigenvalues;
+	protected double[] weights = null;
+	protected double[][] range = null;
+	protected Matrix              mC;
+	protected Matrix              mB;
+	protected boolean firstAdaptionDone = false;
+	
+	public String toString() {
+		return "d_sig " + d_sig + ", c_sig" + ", sigma " + sigma + ", firstSigma " + firstSigma+ ", firstAdaptionDone " + firstAdaptionDone 
+			+ ",\n meanX " + Arrays.toString(meanX) + ", pathC " + Arrays.toString(pathC)+ ", pathS " + Arrays.toString(pathS)+ ", eigenvalues " + Arrays.toString(eigenvalues)
+			+ ", weights " + Arrays.toString(weights)+ ",\n mC " + mC.toString() + ",\n mB " + mB.toString();
+	}
+
+	/**
+	 * Initializes a new CMAParamSet from scratch.
+	 * 
+	 * @see #initCMAParams(CMAParamSet, int, int, Population, double)
+	 * @param mu
+	 * @param lambda
+	 * @param oldGen
+	 * @param initialSigma
+	 * @return
+	 */
+	public static CMAParamSet initCMAParams(int mu, int lambda, Population oldGen, double initialSigma) {
+		return initCMAParams(new CMAParamSet(), mu, lambda, oldGen, initialSigma);
+	}
+	
+	/**
+	 * Initializes the CMA parameter set for given mu, lambda and a population.
+	 * The initialSigma parameter is used as initial sigma directly unless it is <0, in
+	 * that case the average range is used as initial sigma.
+	 * The parameter instance is also added as listener to the population.
+	 * 
+	 * @param params	the CMA parameter set to be used - its data are overwritten
+	 * @param mu	ES mu parameter
+	 * @param lambda	ES lambda parameter
+	 * @param pop	associated Population
+	 * @param initialSigma	initial sigma or -1 to indicate the usage of average range
+	 * @return
+	 */
+	public static CMAParamSet initCMAParams(CMAParamSet params, int mu, int lambda, Population pop, double initialSigma) {
+		// those are from init:
+		params.firstAdaptionDone = false;
+		params.range = ((InterfaceDataTypeDouble)pop.getEAIndividual(0)).getDoubleRange();
+
+		int dim = params.range.length;
+//		if (TRACE_1) System.out.println("WCMA init " + dim);
+//		if (TRACE_1) System.out.println("WCMA static init " + dim);
+		params.eigenvalues = new double[dim];
+		Arrays.fill(params.eigenvalues, 1.);
+		params.meanX = new double[dim];
+		params.pathC = new double[dim];
+		params.pathS = new double[dim];
+
+		params.mC = Matrix.identity(dim, dim);
+		params.mB = Matrix.identity(dim, dim);
+
+		// from adaptAfterSel
+		params.weights = initWeights(mu, lambda);
+		double muEff = getMuEff(params.weights, mu);
+		params.c_sig = (muEff+2)/(muEff+dim+3);
+//		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);
+		params.sigma = initialSigma;
+//		System.out.println("INitial sigma: "+sigma);
+		params.firstSigma = params.sigma;
+		params.meanX = pop.getCenter(); // this might be ok?
+		pop.addPopulationChangedEventListener(params);
+		return params;
+	}
+
+	/**
+	 * Initialize the default weights for the mu update using a log scale.
+	 * 
+	 * @param mu
+	 * @param lambda
+	 * @return
+	 */
+	public static double[] initWeights(int mu, int lambda) {
+		double[] theWeights = new double[mu];
+		double sum = 0;
+		int type = 0; // zero is default log scale
+		for (int i=0; i<mu; i++) {
+			if (type == 0) {
+				theWeights[i] = (Math.log((lambda+1)/2.)-Math.log(i+1));
+			} else theWeights[i] = 1.;
+			sum+=theWeights[i];
+		}
+		for (int i=0; i<mu; i++) theWeights[i] /= sum; 
+		return theWeights;
+	}
+
+	/**
+	 * So-called effective mu value.
+	 * 
+	 * @param weights
+	 * @param mu
+	 * @return
+	 */
+	public static double getMuEff(double[] weights, int mu) {
+		double res = 0, u;
+		for (int i=0; i<mu;i++) {
+			u = weights[i];
+			res += u*u;
+		}
+		return 1./res;
+	}
+
+	/**
+	 * The default mu_cov is equal to the mu_eff value.
+	 * 
+	 * @param weights
+	 * @param mu
+	 * @return
+	 */
+	public static double getMuCov(double[] weights, int mu) {
+		// 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)
+	 * if a population is reinitialized.
+	 * 
+	 * @see InterfacePopulationChangedEventListener
+	 */
+	public void registerPopulationStateChanged(Object source, String name) {
+		if (name.equals(Population.populationInitialized)) {
+			Population pop = (Population)source;
+			if (MutateESRankMuCMA.TRACE_1) System.out.println("Event " + name + " arrived in CMAParamSet!!!");
+			CMAParamSet params = (CMAParamSet)(pop.getData(MutateESRankMuCMA.cmaParamsKey));
+			int mu;
+			if (pop.hasData(EvolutionStrategies.esMuParam)) {
+				mu = (Integer)pop.getData(EvolutionStrategies.esMuParam); 
+			} else {
+				System.err.println("Unknown mu in reinit! using lambda/2...");
+				mu = pop.size()/2;
+			}
+			pop.putData(MutateESRankMuCMA.cmaParamsKey, CMAParamSet.initCMAParams(params, mu, pop.size(), pop, params.firstSigma));
+		}
+	}
+}
 
 /**
  * Implementing CMA ES with rank-mu-update and weighted recombination. More information can be found here:
  * - http://www.bionik.tu-berlin.de/user/niko/cmaesintro.html
  * - N.Hansen & S.Kern 2004: Evaluating the CMA Evolution Strategy on Multimodal Test Functions.
  *   Parallel Problem Solving from Nature 2004.
+ * - For the stopping criteria: Auger&Hansen, CEC '05, A Restart CMA ES with increasing population size. 
+ * 
+ * The implementation uses a structure for keeping all adaptive parameters, CMAParamSet, which is stored
+ * in the populations, so that in principle, multi-modal optimization with several populations becomes possible.
+ * This of course requires proper handling of the generational cycle, i.e., new generations should be cloned from
+ * the former ones (without individuals is ok) so that the parameters are taken over.
  * 
  * @author mkron
  *
  */
 public class MutateESRankMuCMA implements InterfaceMutationGenerational, Serializable {
-	int dim;
+//	int dim;
 	private double c_c, expRandStepLen;
-	private double[] z, zCor;
+	
+	// The static member lastParams is used to store the parameter set last seen in an adaption step, which
+	// is then later used for the individual mutations. Necessary because each individual has its own mutator
+	// instance. It would be cleaner to keep all parameters within the individual and have just one mutation
+	// instance, however this would create quite a big baustelle.
+	private static transient CMAParamSet lastParams=null;
 
 	private ESMutationInitialSigma initializeSig = ESMutationInitialSigma.avgInitialDistance;
 	private double userDefInitSig = 0.2;
-	private static double firstSigma = -1.;
+	public static final String cmaParamsKey = "RankMuCMAParameters";
-	private static double sigma;
+	
-	private static double d_sig, c_sig;
+	public static boolean TRACE_1 = false;
-	private static double[] meanX, pathC, pathS, eigenvalues;
+	public static boolean TRACE_2 = false;
-	private static double[] weights = null;
+	public static boolean TRACE_TEST = false;
-	private static double[][] range = null;
-	private static Matrix              mC;
-	private static Matrix              mB;
-//	private static double[]				mBD;
-	private static boolean firstAdaptionDone = false;
-	private static boolean TRACE_1 = false;
-	private static boolean TRACE_2 = false;
-	private static boolean TRACE_TEST = false;
-//	private Matrix				BD;
 
 	public MutateESRankMuCMA() {
-		firstAdaptionDone = false;
 	}
 
 	public MutateESRankMuCMA(MutateESRankMuCMA mutator) {
 		this.c_c            = mutator.c_c;
-//		this.c_sig            = mutator.c_sig;
-//		this.c_u_sig            = mutator.c_u_sig;
-//		this.d_sig            = mutator.d_sig;
 		this.expRandStepLen     = mutator.expRandStepLen;
-		this.dim			= mutator.dim;
 		this.initializeSig = mutator.initializeSig;
-
-//		if (mutator.meanX != null)    this.meanX    = (double[]) mutator.meanX.clone();
-//		if (mutator.pathC != null)  this.pathC  = (double[]) mutator.pathC.clone();
-//		if (mutator.pathS != null)     this.pathS     = (double[]) mutator.pathS.clone();
-		if (mutator.z != null)    this.z    = (double[]) mutator.z.clone();
-		if (mutator.zCor != null)    this.zCor    = (double[]) mutator.zCor.clone();
-//		if (mutator.eigenvalues != null) this.eigenvalues = (double[]) mutator.eigenvalues.clone();
-//		if (mutator.mC != null)    this.mC    = (Matrix) mutator.mC.clone();
-//		if (mutator.mB != null)      this.mB      = (Matrix) mutator.mB.clone();
 	}
 
 	public Object clone() {
@@ -79,6 +241,7 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 
 	/**
 	 * Retrieve the initial sigma for the given population and the user defined method.
+	 * For the halfRange case, -1 is returned, as the range is not available here.
 	 * @param initGen
 	 * @return
 	 */
@@ -89,12 +252,20 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 			//return initGen.getPopulationMeasures(null)[0]*getAvgRange();
 			// use euclidian measures without normation and scaling
 			return initGen.getPopulationMeasures(null)[0];
-		case halfRange: return getAvgRange()/2.;
+		//case halfRange: return getAvgRange(range)/2.;
 		case userDefined: return userDefInitSig ;
-		default: return 0.2;
+		default: return -1.;
 		}
 	}
 	
+	/**
+	 * Perform the main adaption of sigma and C using evolution paths.
+	 * The evolution path is deduced from the center of the selected population compared to the old
+	 * mean value.
+	 * See Hansen&Kern 04 for further information.
+	 * @param oldGen
+	 * @param selectedP
+	 */
 	public void adaptAfterSelection(Population oldGen, Population selectedP) {
 		Population selectedSorted = selectedP.getSortedBestFirst();
 		
@@ -105,58 +276,57 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 			EVAERROR.errorMsgOnce("Warning: invalid mu/lambda ratio (" + mu + "/" + lambda + ") ! Setting mu to lambda/2.");
 			mu = lambda/2;
 		}
-		if (!firstAdaptionDone) {
+		CMAParamSet params;
-			initWeights(mu, lambda);
+		if (oldGen.getGeneration() <= 1) { // init new param set. At gen < 1 we shouldnt be called, but better do it once too often
-			double muEff = getMuEff(mu);
+			if (oldGen.hasData(cmaParamsKey)) params = CMAParamSet.initCMAParams((CMAParamSet)oldGen.getData(cmaParamsKey), mu, lambda, oldGen, getInitSigma(oldGen));
-			c_sig = (muEff+2)/(muEff+dim+3);
+			else params = CMAParamSet.initCMAParams(mu, lambda, oldGen, getInitSigma(oldGen));			
-//			c_u_sig = Math.sqrt(c_sig * (2.-c_sig));
+		} else {
-			d_sig = c_sig+1+2*Math.max(0, Math.sqrt((muEff-1)/(dim+1)) - 1);
+			if (!oldGen.hasData(cmaParamsKey)) {
-			sigma = getInitSigma(oldGen);
+				if (oldGen.getGeneration() > 1) System.err.println("pop had no params at gen " + oldGen.getGeneration());
-//			System.out.println("INitial sigma: "+sigma);
+				params = CMAParamSet.initCMAParams(mu, lambda, oldGen, getInitSigma(oldGen));
-			firstSigma = sigma;
+			} else params = (CMAParamSet)oldGen.getData(cmaParamsKey);
-			meanX = oldGen.getCenter(); // this might be ok?
 		}
 		
 		int generation = oldGen.getGeneration();
 		
 		if (TRACE_1) {
-			System.out.println("WCMA adaptGenerational");
+			System.out.println("WCMA adaptGenerational **********");
 //			System.out.println("newPop measures: " + BeanInspector.toString(newPop.getPopulationMeasures()));
-			System.out.println("mu_eff: " + getMuEff(mu));
+			System.out.println("mu_eff: " + CMAParamSet.getMuEff(params.weights, mu));
-			System.out.println("meanX: " + BeanInspector.toString(meanX));
+			System.out.println(params.toString());
-			System.out.println("pathC: " + BeanInspector.toString(pathC));
+			System.out.println("*********************************");
-			System.out.println("pathS: " + BeanInspector.toString(pathS));
 		}
 
-		double[] newMeanX = calcMeanX(selectedSorted);
+		double[] newMeanX = calcMeanX(params.weights, selectedSorted);
 		if (TRACE_1) System.out.println("newMeanX:  " + BeanInspector.toString(newMeanX));
 		
+		int dim=params.meanX.length;
         double[] BDz = new double[dim];
         for (int i=0; i<dim; i++) { /* calculate xmean and BDz~N(0,C) */
         	// Eq. 4 from HK04, most right term
-        	BDz[i] = Math.sqrt(getMuEff(mu)) * (newMeanX[i] - meanX[i]) / getSigma(i);
+        	BDz[i] = Math.sqrt(CMAParamSet.getMuEff(params.weights, mu)) * (newMeanX[i] - params.meanX[i]) / getSigma(params, i);
         }
 //        if (TRACE_2) System.out.println("BDz is " + BeanInspector.toString(BDz));
 
-		double[] newPathS = pathS.clone();
+		double[] newPathS = params.pathS.clone();
-		double[] newPathC = pathC.clone();
+		double[] newPathC = params.pathC.clone();
 		
 		double[] zVect = new double[dim];
         /* calculate z := D^(-1) * B^(-1) * BDz into artmp, we could have stored z instead */
         for (int i = 0; i < dim; ++i) {
         	double sum=0.;
             for (int j = 0; j < dim; ++j) {
-                sum += mB.get(j,i) * BDz[j]; // times B transposed, (Eq 4) in HK04
+                sum += params.mB.get(j,i) * BDz[j]; // times B transposed, (Eq 4) in HK04
             }
-            zVect[i] = sum / Math.sqrt(eigenvalues[i]);
+            zVect[i] = sum / Math.sqrt(params.eigenvalues[i]);
         }
 
         /* cumulation for sigma (ps) using B*z */
 		for (int i = 0; i < dim; ++i) {
 			double sum = 0.;
-			for (int j = 0; j < dim; ++j) sum += mB.get(i,j) * zVect[j];
+			for (int j = 0; j < dim; ++j) sum += params.mB.get(i,j) * zVect[j];
-			newPathS[i] = (1. - getCs()) * pathS[i]
+			newPathS[i] = (1. - params.c_sig) * params.pathS[i]
-			              + Math.sqrt(getCs() * (2. - getCs())) * sum;
+			              + Math.sqrt(params.c_sig * (2. - params.c_sig)) * sum;
 		}
 //		System.out.println("pathS diff: " + BeanInspector.toString(Mathematics.vvSub(newPathS, pathS)));
 //		System.out.println("newPathS is " + BeanInspector.toString(newPathS));
@@ -164,12 +334,12 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 		double psNorm = Mathematics.norm(newPathS);
 		
         double hsig = 0;
-        if (psNorm / Math.sqrt(1. - Math.pow(1. - getCs(), 2. * generation))
+        if (psNorm / Math.sqrt(1. - Math.pow(1. - params.c_sig, 2. * generation))
                 / expRandStepLen < 1.4 + 2. / (dim + 1.)) {
             hsig = 1;
         }
         for (int i = 0; i < dim; ++i) {
-            newPathC[i] = (1. - getCc()) * pathC[i] + hsig
+            newPathC[i] = (1. - getCc()) * params.pathC[i] + hsig
             * Math.sqrt(getCc() * (2. - getCc())) * BDz[i];
         }
 		
@@ -180,36 +350,39 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 			System.out.println("newPathS: " + BeanInspector.toString(newPathS));
 		}
 
-		if (TRACE_1) System.out.println("Bef: C is \n" + mC.toString());
+		if (TRACE_1) System.out.println("Bef: C is \n" + params.mC.toString());
-		if (meanX == null) meanX = newMeanX;
+		if (params.meanX == null) params.meanX = newMeanX;
 		
-		updateCov(newPathC, newMeanX, hsig, mu, selectedSorted);
+		updateCov(params, newPathC, newMeanX, hsig, mu, selectedSorted);
-		updateBD();
+		updateBD(params);
 			
-		if (TRACE_2) System.out.println("Aft: C is \n" + mC.toString());
+		if (TRACE_2) System.out.println("Aft: C is \n" + params.mC.toString());
 
         /* update of sigma */
-		double sigFact = Math.exp(((psNorm / expRandStepLen) - 1) * getCs()
+		double sigFact = Math.exp(((psNorm / expRandStepLen) - 1) * params.c_sig
-                / getDamps());
+                / params.d_sig);
-		if (Double.isInfinite(sigFact)) sigma *= 10.; // in larger search spaces sigma tends to explode after init.  
+		if (Double.isInfinite(sigFact)) params.sigma *= 10.; // in larger search spaces sigma tends to explode after init.  
-		else sigma *= sigFact;
+		else params.sigma *= sigFact;
 
-        if (Double.isInfinite(sigma) || Double.isNaN(sigma)) {
+        if (Double.isInfinite(params.sigma) || Double.isNaN(params.sigma)) {
         	System.err.println("Error, unstable sigma!");
         }
-		testAndCorrectNumerics(generation, selectedSorted);
+		testAndCorrectNumerics(params, generation, selectedSorted);
         
-//		System.out.println("sigma=" + sigma + " psLen=" + (psNorm) + " chiN="+expRandStepLen + " cs="+getCs()+ " damps="+getDamps() + " diag " + BeanInspector.toString(eigenvalues));
 		if (TRACE_1) {
 			System.out.print("psLen=" + (psNorm) + " ");
-			outputParams(mu);
+			outputParams(params, mu);
 		}
 		
 		// take over data
-		meanX = newMeanX;
+		params.meanX = newMeanX;
-		pathC = newPathC;
+		params.pathC = newPathC;
-		pathS = newPathS;
+		params.pathS = newPathS;
-		firstAdaptionDone = true;
+		params.firstAdaptionDone = true;
+		
+		lastParams = params;
+		oldGen.putData(cmaParamsKey, params);
+		selectedP.putData(cmaParamsKey, params);
 //		if (TRACE_2) System.out.println("sampling around " + BeanInspector.toString(meanX));
 	}
 
@@ -218,7 +391,13 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 	 */
 	public void adaptGenerational(Population oldPop, Population selectedPop, 
 			Population newPop, boolean updateSelected) {
-		// nothing to do?	
+		// nothing to do? Oh yes, we can easily transfer the cma-params from the old to the new population. 
+		if (!newPop.hasData(cmaParamsKey)) {
+			if (!oldPop.hasData(cmaParamsKey)) System.err.println("warning: no cma param set found (MutateESRankMuCMA!");
+			else newPop.putData(cmaParamsKey, oldPop.getData(cmaParamsKey));
+		}
+		//newPop.addData(cmaParamsKey, oldPop.getData(cmaParamsKey));
+		//newPop.addPopulationChangedEventListener(this); // listen to every population to be informed about reinit events
 	}
 
 	/**
@@ -227,13 +406,13 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 	 * @param iterations
 	 * @param selected
 	 */
-    void testAndCorrectNumerics(int iterations, Population selected) { // not much left here
+    void testAndCorrectNumerics(CMAParamSet params, int iterations, Population selected) { // not much left here
     	/* Flat Fitness, Test if function values are identical */
     	if (iterations > 1) {
     		// selected pop is sorted
     		if (nearlySame(selected.getEAIndividual(0).getFitness(),selected.getEAIndividual(selected.size()-1).getFitness())) {
     			if (TRACE_1) System.err.println("flat fitness landscape, consider reformulation of fitness, step-size increased");
-    			sigma *= Math.exp(0.2+getCs()/getDamps());
+    			params.sigma *= Math.exp(0.2+params.c_sig/params.d_sig);
 //    			sigma=0.1;
     		}
     	}
@@ -243,20 +422,20 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
     	double fac = 1.;
     	double minEig = 1e-12;
     	double maxEig = 1e8;
-    	if (Mathematics.max(eigenvalues) < minEig) 
+    	if (Mathematics.max(params.eigenvalues) < minEig) 
-    		fac = 1./Math.sqrt(Mathematics.max(eigenvalues));
+    		fac = 1./Math.sqrt(Mathematics.max(params.eigenvalues));
-    	else if (Mathematics.min(eigenvalues) > maxEig)
+    	else if (Mathematics.min(params.eigenvalues) > maxEig)
-    		fac = 1./Math.sqrt(Mathematics.min(eigenvalues));
+    		fac = 1./Math.sqrt(Mathematics.min(params.eigenvalues));
 
     	if (fac != 1.) {
     		System.err.println("Scaling by " + fac);
-    		sigma /= fac;
+    		params.sigma /= fac;
-    		for(int i = 0; i < dim; ++i) {
+    		for(int i = 0; i < params.meanX.length; ++i) {
-    			pathC[i] *= fac;
+    			params.pathC[i] *= fac;
-    			eigenvalues[i] *= fac*fac;
+    			params.eigenvalues[i] *= fac*fac;
     			for (int j = 0; j <= i; ++j) {
-    				mC.set(i, j, mC.get(i,j)*fac*fac);
+    				params.mC.set(i, j, params.mC.get(i,j)*fac*fac);
-    				if (i!=j) mC.set(j, i, mC.get(i,j));
+    				if (i!=j) params.mC.set(j, i, params.mC.get(i,j));
     			}
     		}
     	}
@@ -274,70 +453,67 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 	 * @param i
 	 * @return
 	 */
-	private double getSigma(int i) {
+	private double getSigma(CMAParamSet params, int i) {
-		return sigma;
+		return params.sigma;
-	}
-
-	private double getDamps() {
-		return d_sig;
 	}
+//
+//	private double getDamps() {
+//		return d_sig;
+//	}
 
 	private double getCc() {
 		return c_c;
 	}
 
-	private double getCs() {
+//	private double getCs() {
-		return c_sig;
+//		return c_sig;
-	}
+//	}
 
-	private double calcExpRandStepLen() {
+	private double calcExpRandStepLen(int dim) {
 		// scale by avg range?
 		return Math.sqrt(dim)*(1.-(1./(4*dim))+(1./(21*dim*dim)));
 	}
 
-    private double getAvgRange() {
-		double sum = 0.;
-		for (int i=0; i<dim; i++) sum+=(range[i][1]-range[i][0]);
-		return sum/dim;
-	}
-
 	/* update C */
-	private void updateCov(double[] newPathC, double[] newMeanX, double hsig, int mu, Population selected) {
+	private void updateCov(CMAParamSet params, double[] newPathC, double[] newMeanX, double hsig, int mu, Population selected) {
 		double newVal = 0;
-        if (getCCov(mu) > 0) {
+    	int dim = newMeanX.length;
+		double ccv = getCCov(params.weights, mu, dim);
+        if (ccv > 0) {
+        	double mcv = CMAParamSet.getMuCov(params.weights, mu);
         	/* (only upper triangle!) */
             /* update covariance matrix */
         	//System.out.println("CCov " + getCCov(selected) + " Cc " + getCc() + " muCov " + getMuCov(selected));
             for (int i = 0; i < dim; ++i)
                 for (int j = 0; j <= i; ++j) {
 //                	oldVal = mC.get(i,j);
-                	newVal = (1 - getCCov(mu)) * mC.get(i,j)
+                	newVal = (1 - ccv) * params.mC.get(i,j)
-                    + getCCov(mu)
+                    + ccv
-                    * (1. / getMuCov(mu))
+                    * (1. / mcv)
                     * (newPathC[i] * newPathC[j] + (1 - hsig) * getCc()
-                            * (2. - getCc()) * mC.get(i,j));
+                            * (2. - getCc()) * params.mC.get(i,j));
-                    mC.set(i,j,newVal);
+                	params.mC.set(i,j,newVal);
                     for (int k = 0; k < mu; ++k) { /*
                     * additional rank mu
                     * update
                     */
                     	double[] x_k = ((InterfaceDataTypeDouble)selected.getEAIndividual(k)).getDoubleData();
-                    	newVal = mC.get(i,j)+ getCCov(mu) * (1 - 1. / getMuCov(mu))
+                    	newVal = params.mC.get(i,j)+ ccv * (1 - 1. / mcv)
-                        * getWeight(k)	* (x_k[i] - meanX[i])
+                        * params.weights[k]	* (x_k[i] - params.meanX[i])
-                        							* (x_k[j] - meanX[j]) / (getSigma(i) * getSigma(j)); // TODO right sigmas?
+                        							* (x_k[j] - params.meanX[j]) / (getSigma(params, i) * getSigma(params, j)); // TODO right sigmas?
-                        mC.set(i,j, newVal);
+                    	params.mC.set(i,j, newVal);
                     }
                 }
             // fill rest of C
             for (int i = 0; i < dim; ++i) {
                 for (int j = i+1; j < dim; ++j) {
 
-                	mC.set(i, j, mC.get(j,i));
+                	params.mC.set(i, j, params.mC.get(j,i));
                 }
                 
             }
-        	if (mC.get(0,1) != mC.get(1,0)) {
+        	if (params.mC.get(0,1) != params.mC.get(1,0)) {
-        		System.err.println("WARNING");
+        		System.err.println("WARNING, C is not symmetric!");
         	}            
 //            maxsqrtdiagC = Math.sqrt(math.max(math.diag(C)));
 //            minsqrtdiagC = Math.sqrt(math.min(math.diag(C)));
@@ -345,32 +521,22 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
         
 	}
 
-	private double getMuCov(int mu) {
+	private double getCCov(double[] weights, int mu, int dim) {
-		// default parameter value ( HK03, sec. 2)
-		return getMuEff(mu);
-	}
-
-	private double getCCov(int mu) {
 		// ( HK03, sec. 2)
 		//return Math.min(1., 2*getMuEff(selected)/(dim*dim));
-		double ccov = (2./(getMuCov(mu)*Math.pow(dim+Math.sqrt(2.), 2)))+(1.-(1./getMuCov(mu)))*Math.min(1., (2*getMuEff(mu)-1.)/(dim*dim+2*dim+4+getMuEff(mu)));
+		double muC=CMAParamSet.getMuCov(weights, mu);
+		double muE=CMAParamSet.getMuEff(weights,mu);
+		double ccov = (2./(muC*Math.pow(dim+Math.sqrt(2.), 2)))+(1.-(1./muC))*Math.min(1., (2*muE-1.)/(dim*dim+2*dim+4+muE));
 		return ccov;
 	}
 	
-	private double getMuEff(int mu) {
+	private void outputParams(CMAParamSet params, int mu) {
-		double res = 0, u;
+		System.out.println("sigma=" + params.sigma + " chiN="+expRandStepLen + " cs="+params.c_sig
-		for (int i=0; i<mu;i++) {
+				+ " damps="+params.d_sig + " Cc=" + getCc() + " Ccov=" + getCCov(params.weights, mu, params.meanX.length) 
-			u = getWeight(i);
+				+ " mueff=" + CMAParamSet.getMuEff(params.weights, mu) + " mucov="+CMAParamSet.getMuCov(params.weights, mu));
-			res += u*u;
-		}
-		return 1./res;
 	}
 
-	private void outputParams(int mu) {
+	private void updateBD(CMAParamSet params) {
-		System.out.println("sigma=" + sigma + " chiN="+expRandStepLen + " cs="+getCs()+ " damps="+getDamps() + " Cc=" + getCc() + " Ccov=" + getCCov(mu) + " mueff=" + getMuEff(mu) + " mucov="+getMuCov(mu));
-	}
-
-	private void updateBD() {
 //		C=triu(C)+transpose(triu(C,1)); % enforce symmetry
 //		[B,D] = eig(C);
 //		% limit condition of C to 1e14 + 1
@@ -382,13 +548,13 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 //		D = diag(sqrt(diag(D))); % D contains standard deviations now
 //		BD = B*D; % for speed up only
 		////////////////////////////////////////////7
-		mC = (mC.plus(mC.transpose()).times(0.5)); // MAKE C SYMMETRIC
+		params.mC = (params.mC.plus(params.mC.transpose()).times(0.5)); // MAKE C SYMMETRIC
 
 		EigenvalueDecomposition helper;
 //		this.m_Counter      = 0;
-		helper              = new EigenvalueDecomposition(mC);
+		helper              = new EigenvalueDecomposition(params.mC);
-		mB              = helper.getV(); // Return the eigenvector matrix
+		params.mB              = helper.getV(); // Return the eigenvector matrix
-		eigenvalues  = helper.getRealEigenvalues();
+		params.eigenvalues  = helper.getRealEigenvalues();
 		
 //		double[] sqrtEig = eigenvalues.clone();
 //		for (int i = 0; i < sqrtEig.length; i++) {
@@ -417,27 +583,10 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 	 * @param selectedPop
 	 * @return
 	 */
-	private double[] calcMeanX(Population selectedPop) {
+	private double[] calcMeanX(double[] weights, Population selectedPop) {
 		return selectedPop.getCenterWeighted(weights);
 	}
 	
-	private double getWeight(int i) {
-		return weights[i];
-	}
-
-	private void initWeights(int mu, int lambda) {
-		weights = new double[mu];
-		double sum = 0;
-		int type = 0; // zero is default log scale
-		for (int i=0; i<mu; i++) {
-			if (type == 0) {
-				weights[i] = (Math.log((lambda+1)/2.)-Math.log(i+1));
-			} else weights[i] = 1.;
-			sum+=weights[i];
-		}
-		for (int i=0; i<mu; i++) weights[i] /= sum; 
-	}
-	
 	public void crossoverOnStrategyParameters(AbstractEAIndividual indy1,
 			Population partners) {
 		// nothing to do
@@ -458,29 +607,30 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 	
 	public void init(AbstractEAIndividual individual,
 			InterfaceOptimizationProblem opt) {
-		// TODO recheck all this; some is handled in adaptGeneration on the first call
+//		firstAdaptionDone = false;
-		firstAdaptionDone = false;
+//		range = ((InterfaceDataTypeDouble)individual).getDoubleRange();
-		range = ((InterfaceDataTypeDouble)individual).getDoubleRange();
+//		dim = range.length;
-		dim = range.length;
+//		if (TRACE_1) System.out.println("WCMA init " + dim);
-		if (TRACE_1) System.out.println("WCMA init " + dim);
+//		c_c = (4./(dim+4));
+//		c_sig = Double.NaN; // mark as not yet initialized
+////		c_u_sig = Double.NaN;
+//		d_sig = Double.NaN; // init in first adaption step!  
+//		if (TRACE_1) System.out.println("WCMA static init " + dim);
+//		eigenvalues = new double[dim];
+//		Arrays.fill(eigenvalues, 1.);
+//		
+//		meanX = new double[dim];
+//		pathC = new double[dim];
+//		pathS = new double[dim];
+////		mBD = new double[dim];
+//		mC = Matrix.identity(dim, dim);
+//		mB = Matrix.identity(dim, dim);
+//		sigma = 0.2;
+		// ^^^ moved to CMAParamSet initialization
+		double[][] range = ((InterfaceDataTypeDouble)individual).getDoubleRange();
+		int dim = range.length;
 		c_c = (4./(dim+4));
-		c_sig = Double.NaN; // mark as not yet initialized
+		expRandStepLen = calcExpRandStepLen(dim);
-//		c_u_sig = Double.NaN;
-		d_sig = Double.NaN; // init in first adaption step!  
-		if (TRACE_1) System.out.println("WCMA static init " + dim);
-		eigenvalues = new double[dim];
-		Arrays.fill(eigenvalues, 1.);
-		
-		meanX = new double[dim];
-		pathC = new double[dim];
-		pathS = new double[dim];
-//		mBD = new double[dim];
-		mC = Matrix.identity(dim, dim);
-		mB = Matrix.identity(dim, dim);
-		sigma = 0.2;
-		z = new double[dim];
-		zCor = new double[dim];
-		expRandStepLen = calcExpRandStepLen();
 
 	}
 
@@ -494,36 +644,42 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 //			if (TRACE) System.out.println("WCMA mutate, bef: " + BeanInspector.toString(x));
 			double[][]  range  = ((InterfaceDataTypeDouble)individual).getDoubleRange();
 			
-			((InterfaceDataTypeDouble)individual).SetDoubleGenotype(mutate(x, range, 0));
+			// this is a critical point: where do the CMA parameters for this individual's mutation come from?
+			// for GA and ES we can expect that selection occured directly before the mutation cycle,
+			// so we take the parameter set from the last adpation step.
+			((InterfaceDataTypeDouble)individual).SetDoubleGenotype(mutate(lastParams, x, range, 0));
 			
 //			if (TRACE) System.out.println("WCMA mutate, aft: " + BeanInspector.toString(x));
 		} else System.err.println("Error, expecting InterfaceDataTypeDouble");
 	}
 
-	private double[] mutate(double[] x, double[][] range, int count) {
+	private double[] mutate(CMAParamSet params, double[] x, double[][] range, int count) {
-		if (firstAdaptionDone) {
+		int dim = range.length;
-			double[] sampl = new double[x.length]; // generate scaled random vector (D * z)
+		if (params!=null && (params.firstAdaptionDone)) {
+			double[] sampl = new double[dim]; // generate scaled random vector (D * z)
 			for (int i = 0; i < dim; ++i) {
-				sampl[i] = Math.sqrt(eigenvalues[i]) * RNG.gaussianDouble(1.);
+				sampl[i] = Math.sqrt(params.eigenvalues[i]) * RNG.gaussianDouble(1.);
 			}
 //			System.out.println("Sampling around " + BeanInspector.toString(meanX));
 			/* add mutation (sigma * B * (D*z)) */
 			for (int i = 0; i < dim; ++i) {
 				double sum = 0.;
 				for (int j = 0; j < dim; ++j)
-					sum += mB.get(i,j) * sampl[j];
+					sum += params.mB.get(i,j) * sampl[j];
-				x[i] = meanX[i]+getSigma(i)*sum;
+				x[i] = params.meanX[i]+getSigma(params, i)*sum;
 			}
 		} else {
-			// no valid meanX yet, so just do a gaussian jump with sigma
+			if (params==null) {
+				System.err.println("Error in MutateESRankMuCMA: parameter set was null! Skipping mutation...");
+			}			// no valid meanX yet, so just do a gaussian jump with sigma
 			for (int i = 0; i < dim; ++i) {
-				x[i] += RNG.gaussianDouble(getSigma(i));
+				x[i] += RNG.gaussianDouble(getSigma(params, i));
 			}
 		}
 		if (isInRange(x, range)) return x;
 		else {
 			if (count > 5) return repairMutation(x, range); // allow some nice tries before using brute force
-			else return mutate(x, range, count+1); // for really bad initial deviations this might be a quasi infinite loop
+			else return mutate(params, x, range, count+1); // for really bad initial deviations this might be a quasi infinite loop
 		}
 	}
 	
@@ -553,8 +709,8 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 	 * 
 	 * @return the initial sigma value actually employed 
 	 */
-	public double getFirstSigma() {
+	public double getFirstSigma(Population pop) {
-		return firstSigma;
+		return ((CMAParamSet)pop.getData(cmaParamsKey)).firstSigma;
 	}
 	
 	public void hideHideable() {
@@ -586,12 +742,13 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 	 * @param tolX
 	 * @return
 	 */
-	public boolean testAllDistBelow(double tolX) {
+	public boolean testAllDistBelow(Population pop, double tolX) {
 //		if all(sigma*(max(abs(pc), sqrt(diag(C)))) < stopTolX) 
 		boolean res = true;
+		CMAParamSet params = (CMAParamSet)pop.getData(cmaParamsKey);
 		int i=0;
-		while (res && i<dim) {
+		while (res && i<params.meanX.length) {
-			res = res && (getSigma(i)*Math.max(Math.abs(pathC[i]), Math.sqrt(mC.get(i,i))) < tolX);
+			res = res && (getSigma(params, i)*Math.max(Math.abs(params.pathC[i]), Math.sqrt(params.mC.get(i,i))) < tolX);
 			i++;
 		}
 		if (TRACE_TEST) if (res) System.out.println("testAllDistBelow hit");
@@ -604,19 +761,20 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 	 * @param gen
 	 * @return
 	 */
-	public boolean testNoChangeAddingDevAxis(double d, int gen) {
+	public boolean testNoChangeAddingDevAxis(Population pop, double d, int gen) {
 //		  if all(xmean == xmean + 0.1*sigma*BD(:,1+floor(mod(countiter,N))))
 //	    i = 1+floor(mod(countiter,N));
 //		stopflag(end+1) = {'warnnoeffectaxis'};
-		
+		CMAParamSet params = (CMAParamSet)pop.getData(cmaParamsKey);
+		int dim=params.meanX.length;
 		int k = gen%dim;
-		double[] ev_k = mB.getColumn(k);
+		double[] ev_k = params.mB.getColumn(k);
-		Mathematics.svMult(Math.sqrt(eigenvalues[k]), ev_k, ev_k); // this is now e_k*v_k = BD(:,...)
+		Mathematics.svMult(Math.sqrt(params.eigenvalues[k]), ev_k, ev_k); // this is now e_k*v_k = BD(:,...)
 		
 		int i=0;
 		boolean res = true;
 		while (res && (i<dim)) {
-			res = res && (meanX[i] == (meanX[i] + d*getSigma(i)*ev_k[i]));
+			res = res && (params.meanX[i] == (params.meanX[i] + d*getSigma(params, i)*ev_k[i]));
 			i++;
 		}
 		if (TRACE_TEST) if (res) System.out.println("testNoChangeAddingDevAxis hit");
@@ -628,13 +786,14 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 	 * @param d
 	 * @return
 	 */
-	public boolean testNoEffectCoord(double d) {
+	public boolean testNoEffectCoord(Population pop, double d) {
 //		if any(xmean == xmean + 0.2*sigma*sqrt(diag(C))) 
 //		stopflag(end+1) = {'warnnoeffectcoord'};
 		boolean ret = false;
+		CMAParamSet params = (CMAParamSet)pop.getData(cmaParamsKey);
 		int i=0;
-		while ((i<dim) && !ret) {
+		while ((i<params.meanX.length) && !ret) {
-			ret = ret || (meanX[i]==(meanX[i] + d*getSigma(i)*Math.sqrt(mC.get(i, i))));
+			ret = ret || (params.meanX[i]==(params.meanX[i] + d*getSigma(params, i)*Math.sqrt(params.mC.get(i, i))));
 			i++;
 		}
 		if (TRACE_TEST) if (ret) System.out.println("testNoEffectCoord hit");
@@ -648,10 +807,11 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 	 * @param d
 	 * @return true, if a diagonal entry is <= 0 or >= d, else false
 	 */
-	public boolean testCCondition(double d) {
+	public boolean testCCondition(Population pop, double d) {
 //	    if (min(diag(D)) <= 0) || (max(diag(D)) > 1e14*min(diag(D))) 
 //		  stopflag(end+1) = {'warnconditioncov'};
-		Pair<Double,Double> minMax = mC.getMinMaxDiag();
+		CMAParamSet params = (CMAParamSet)pop.getData(cmaParamsKey);
+		Pair<Double,Double> minMax = params.mC.getMinMaxDiag();
 		if ((minMax.head <= 0) || (minMax.tail >= d)) {		
 			if (TRACE_TEST) System.out.println("testCCondition hit");
 			return true;

src/eva2/server/go/populations/Population.java

@@ -2,7 +2,9 @@ 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;
 import java.util.PriorityQueue;
 import java.util.Set;
@@ -39,13 +41,16 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     protected Population    m_Archive       = null;
 
     transient private ArrayList<AbstractEAIndividual> sortedArr = null;
-    transient protected InterfacePopulationChangedEventListener	m_Listener = null;
+    transient private ArrayList<InterfacePopulationChangedEventListener> listeners = null;
+//    transient protected InterfacePopulationChangedEventListener	m_Listener = null;
 
     // the evaluation interval at which listeners are notified
     protected int 			notifyEvalInterval	= 0;
     protected HashMap<String, Object>		additionalPopData = null;
     
-    public static String funCallIntervalReached = "FunCallIntervalReached";
+    public static final String funCallIntervalReached = "FunCallIntervalReached";
+    public static final String populationInitialized = "PopulationReinitOccured";
+    public static final String nextGenerationPerformed = "NextGenerationPerformed";
     
     boolean useHistory						= false;
     public ArrayList<AbstractEAIndividual>  m_History       = new ArrayList<AbstractEAIndividual>();
@@ -95,7 +100,9 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         this.m_Size             = population.m_Size;
         this.useHistory 		= population.useHistory;
         this.notifyEvalInterval = population.notifyEvalInterval;
-        this.m_Listener			= population.m_Listener;
+//        this.m_Listener			= population.m_Listener;
+        if (population.listeners != null) this.listeners			= (ArrayList<InterfacePopulationChangedEventListener>)population.listeners.clone();
+        else listeners = null;
         if (population.additionalPopData != null) {
         	additionalPopData = new HashMap<String, Object>();
         	Set<String> keys = additionalPopData.keySet();
@@ -105,7 +112,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         }
     }
 
-    public void addData(String key, Object value) {
+    public void putData(String key, Object value) {
     	if (additionalPopData == null) additionalPopData = new HashMap<String, Object>();
     	additionalPopData.put(key, value);
     }
@@ -115,6 +122,11 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     	else return additionalPopData.get(key);
     }
     
+    public boolean hasData(String key) {
+    	if (additionalPopData != null) return (additionalPopData.get(key)!=null);
+    	else return false;
+    }
+    
     public Object clone() {
         return (Object) new Population(this);
     }
@@ -130,6 +142,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     	Population res = new Population();
     	res.setSameParams(this);
     	res.copyHistAndArchive(this);
+    	if (additionalPopData!=null) res.additionalPopData = (HashMap<String, Object>)(additionalPopData.clone());
     	return res;
     }
 
@@ -147,6 +160,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
             this.m_Archive.clear();
             this.m_Archive.init();
         }
+        firePropertyChangedEvent(Population.populationInitialized);
     }
 
     /** This method inits the population. Function and generation counters
@@ -215,11 +229,16 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     /** Something has changed
      */
     protected void firePropertyChangedEvent(String name) {
-        if (this.m_Listener != null) this.m_Listener.registerPopulationStateChanged(this, name);
+        if (listeners != null) {
+        	for (Iterator iterator = listeners.iterator(); iterator.hasNext();) {
+				InterfacePopulationChangedEventListener listener = (InterfacePopulationChangedEventListener) iterator.next();
+				listener.registerPopulationStateChanged(this, name);
+			}
+        }
     }
     
     private boolean doEvalNotify() {
-    	return ((this.m_Listener != null) && (notifyEvalInterval > 0));
+    	return ((listeners != null) && (listeners.size() > 0) && (notifyEvalInterval > 0));
     }
     
     /** This method return the current number of function calls performed.
@@ -260,7 +279,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         if (useHistory && (this.size() >= 1)) this.m_History.add(this.getBestEAIndividual());
         for (int i=0; i<size(); i++) ((AbstractEAIndividual)get(i)).incrAge(); 
         this.m_Generation++;
-        firePropertyChangedEvent("NextGenerationPerformed");
+        firePropertyChangedEvent(nextGenerationPerformed);
     }
 
     /** This method returns the current generation.
@@ -281,7 +300,16 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * @param ea
      */
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
-        this.m_Listener = ea;
+    	if (listeners == null) listeners = new ArrayList<InterfacePopulationChangedEventListener>(3);
+    	if (!listeners.contains(ea)) {
+    		listeners.add(ea);
+    	}
+    }
+    
+    public void removePopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
+    	if (listeners != null) {
+    		listeners.remove(ea);
+    	}
     }
     
     /** This method allows you to add a complete population to the current population.
@@ -350,12 +378,13 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 	
 	/** 
 	 * This method will return the index of the current best individual from the
-     * population.
+     * population. If the population is empty, -1 is returned.
      * 
      * @see getIndexOfBestOrWorstIndividual()
      * @return The index of the best individual.
      */
     public int getIndexOfBestIndividual() {
+    	if (size()<1) return -1;
     	return getIndexOfBestOrWorstIndividual(true, true);
     }
 	
@@ -422,15 +451,23 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         return result;
     }
 
-    /** This method returns the current best individual from the population
+    /** 
+	 * This method returns the current best individual from the population.
+	 * If the population is empty, null is returned.
+	 *
      * @return The best individual
      */
     public AbstractEAIndividual getBestEAIndividual() {
+    	if (size()<1) return null;
         int best = this.getIndexOfBestIndividual();
-        if (best == -1) System.err.println("This shouldnt happen!");;
+        if (best == -1) {
-        AbstractEAIndividual result = (AbstractEAIndividual)this.get(best);
+        	System.err.println("This shouldnt happen!");
-        if (result == null) System.err.println("Serious Problem! Population Size: " + this.size());
+        	return null;
-        return result;
+        } else {
+        	AbstractEAIndividual result = (AbstractEAIndividual)this.get(best);
+        	if (result == null) System.err.println("Serious Problem! Population Size: " + this.size());
+        	return result;
+        }
     }
 
     /** 

src/eva2/server/go/problems/AbstractOptimizationProblem.java

@@ -51,22 +51,14 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
      */
     public void evaluate(Population population) {
         AbstractEAIndividual    tmpIndy;
-
+        evaluatePopulationStart(population);
-//        if (population.isEvaluated()) {
+        for (int i = 0; i < population.size(); i++) {
-//        	System.err.println("Population evaluation seems not required!");
+        	tmpIndy = (AbstractEAIndividual) population.get(i);
-//        } else {
+        	tmpIndy.resetConstraintViolation();
-	        // @todo This is the position to implement a granular
+        	this.evaluate(tmpIndy);
-	        // @todo paralleliziation scheme
+        	population.incrFunctionCalls();
-	        evaluatePopulationStart(population);
+        }
-	        for (int i = 0; i < population.size(); i++) {
+        evaluatePopulationEnd(population);
-	            tmpIndy = (AbstractEAIndividual) population.get(i);
-	            tmpIndy.resetConstraintViolation();
-	            this.evaluate(tmpIndy);
-	            population.incrFunctionCalls();
-	        }
-	        evaluatePopulationEnd(population);
-//	        population.setEvaluated();
-//        }
     }
     
     /**
@@ -103,20 +95,6 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
     	return AbstractEAIndividual.getDefaultStringRepresentation(individual);
     }
 
-    /** This method returns a single line representation of the solution
-     * @param individual  The individual
-     * @return The string
-     */
-//    public String getSolutionDataFor(IndividualInterface individual) {
-//    }
-
-//    /** This method returns a string describing the optimization problem.
-//     * @return The description.
-//     */
-//    public String getStringRepresentationF() {
-//        return "AbstractOptimizationProblem: programmer failed to give further details";
-//    }
-
     /** This method returns a double value that will be displayed in a fitness
      * plot. A fitness that is to be minimized with a global min of zero
      * would be best, since log y can be used. But the value can depend on the problem.
@@ -152,7 +130,9 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
         result.setLayout(new BorderLayout());
         JTextArea   area    = new JTextArea();
         JScrollPane scroll  = new JScrollPane(area);
-        area.setText("Best Solution:\n"+this.getSolutionRepresentationFor(indy));
+        String text = "Best Solution:\n"+this.getSolutionRepresentationFor(indy);
+        area.setLineWrap(true);
+        area.setText(text);
         area.setEditable(false);
         result.add(scroll, BorderLayout.CENTER);
         return result;

src/eva2/server/go/problems/InterfaceOptimizationProblem.java

@@ -1,13 +1,10 @@
 package eva2.server.go.problems;
 
-
-
 import javax.swing.*;
 
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.populations.Population;
 import eva2.server.go.strategies.InterfaceOptimizer;
-import eva2.server.stat.InterfaceStatistics;
 
 /**
  * Created by IntelliJ IDEA.
@@ -51,15 +48,6 @@ public interface InterfaceOptimizationProblem extends InterfaceAdditionalPopulat
      */
     public void evaluate(AbstractEAIndividual individual);
 
-//	DEPRECATED, use PhenotypeMetric    
-//** This method should be used to calculate the distance between two
-//     * individuals
-//     * @param indy1     The first individual.
-//     * @param indy2     The second individual.
-//     * @return The distance.
-//     */
-//    public double distanceBetween(AbstractEAIndividual indy1, AbstractEAIndividual indy2);
-
     /******************** Some output methods *******************************************/
 
     /** This method allows the CommonJavaObjectEditorPanel to read the

src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java

@@ -73,7 +73,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
         }
 
         this.evaluatePopulation(this.m_Population);
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method will init the optimizer with a given population
@@ -92,7 +92,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
 
         if (reset) {
         	this.evaluatePopulation(this.m_Population);
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
@@ -212,7 +212,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
             nextGeneration.addPopulation(tmp);
             this.m_Population = nextGeneration;
         }
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method allows you to add the LectureGUI as listener to the Optimizer

src/eva2/server/go/strategies/ClusterBasedNichingEA.java

@@ -614,7 +614,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
 //        		System.out.println("species distract best towards " + BeanInspector.toString(distVect));
 //        	}
 //        }
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /**

src/eva2/server/go/strategies/ClusteringHillClimbing.java

@@ -113,7 +113,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
         this.m_Problem.initPopulation(this.m_Population);
         m_Population.addPopulationChangedEventListener(null); // noone will be notified directly on pop changes
         this.m_Problem.evaluate(this.m_Population);
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method will init the optimizer with a given population
@@ -127,7 +127,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
         if (reset) {
         	this.m_Population.init();
             this.m_Problem.evaluate(this.m_Population);
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
@@ -176,7 +176,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 			}
 		}
 //		System.out.println("funcalls: " + evalCnt);
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
 
 	}
 
@@ -189,7 +189,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 			// set funcalls to real value
 			m_Population.SetFunctionCalls(((Population)source).getFunctionCalls());
 //			System.out.println("FunCallIntervalReached at " + (((Population)source).getFunctionCalls()));
-			this.firePropertyChangedEvent("NextGenerationPerformed");
+			this.firePropertyChangedEvent(Population.nextGenerationPerformed);
 		} 
 		// do not react to NextGenerationPerformed
 		//else System.err.println("ERROR, event was " + name);

src/eva2/server/go/strategies/DifferentialEvolution.java

@@ -78,7 +78,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
         this.m_Problem.initPopulation(this.m_Population);
 //        children = new Population(m_Population.size());
         this.evaluatePopulation(this.m_Population);
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     public void hideHideable() {
@@ -94,7 +94,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
         if (reset) {
         	this.m_Population.init();
             this.evaluatePopulation(this.m_Population);
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
 //        if (reset) this.m_Population.init();
 //        else children = new Population(m_Population.size());
@@ -413,7 +413,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
 //        }
         m_Problem.evaluatePopulationEnd(m_Population);
         this.m_Population.incrGeneration();
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
     
     /**

src/eva2/server/go/strategies/DynamicParticleSwarmOptimization.java

@@ -375,7 +375,7 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
 			for (int i = 0; i < this.m_Population.size(); i++) {
 				AbstractEAIndividual indy = (AbstractEAIndividual)m_Population.get(i);
 		    	if (i>=quantumCount) {
-		    		indy.SetData(partTypeKey, quantumType);
+		    		indy.putData(partTypeKey, quantumType);
 		    		quantumCount += 1./quantumRatio;
 		    	}
 			}
@@ -437,8 +437,8 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
 	    // log the best individual of the population
 	    if (envHasChanged || (this.m_Population.getBestEAIndividual().isDominatingDebConstraints(this.m_BestIndividual))) {
 	        this.m_BestIndividual = (AbstractEAIndividual)this.m_Population.getBestEAIndividual().clone();
-	        this.m_BestIndividual.SetData(partBestFitKey, this.m_BestIndividual.getFitness());
+	        this.m_BestIndividual.putData(partBestFitKey, this.m_BestIndividual.getFitness());
-	        this.m_BestIndividual.SetData(partBestPosKey, ((InterfaceDataTypeDouble)this.m_BestIndividual).getDoubleData());
+	        this.m_BestIndividual.putData(partBestPosKey, ((InterfaceDataTypeDouble)this.m_BestIndividual).getDoubleData());
 	        //System.out.println("-- best ind set to " + ((InterfaceDataTypeDouble)this.m_BestIndividual).getDoubleData()[0] + "/" + ((InterfaceDataTypeDouble)this.m_BestIndividual).getDoubleData()[1]);
 	    }
     }

src/eva2/server/go/strategies/EvolutionStrategies.java

@@ -49,6 +49,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
 
     transient private String                m_Identifier = "";
     transient private InterfacePopulationChangedEventListener m_Listener;
+    public static final String esMuParam = "EvolutionStrategyMuParameter";
 
     public EvolutionStrategies() {
         this.m_Population.setPopulationSize(this.m_Lambda);
@@ -97,10 +98,11 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
 //        }
         //System.out.println("init");
     	checkPopulationConstraints();
+    	m_Population.putData(esMuParam, getMu());
         this.m_Problem.initPopulation(this.m_Population);
         this.evaluatePopulation(this.m_Population);
 //        this.m_Population.setPopulationSize(orgPopSize);
-//        this.firePropertyChangedEvent("NextGenerationPerformed");// not necessary if incrGeneration is called
+//        this.firePropertyChangedEvent(Population.nextGenerationPerformed);// not necessary if incrGeneration is called
     }
 
 
@@ -115,7 +117,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
         if (reset) {
         	this.m_Population.init();
             this.evaluatePopulation(this.m_Population);
-//            this.firePropertyChangedEvent("NextGenerationPerformed"); // not necessary if incrGeneration is called
+//            this.firePropertyChangedEvent(Population.nextGenerationPerformed); // not necessary if incrGeneration is called
         }
     }
 
@@ -203,7 +205,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
 //        System.out.println("Population size: " + this.m_Population.size());
 //        System.out.println("-- Best Fitness " + this.m_Population.getBestFitness()[0]);
         
-        this.firePropertyChangedEvent("NextGenerationPerformed"); // necessary here because evalPop was not called on m_Population
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed); // necessary here because evalPop was not called on m_Population
     }        
     
     /**

src/eva2/server/go/strategies/EvolutionStrategyIPOP.java

@@ -91,7 +91,7 @@ public class EvolutionStrategyIPOP extends EvolutionStrategies implements Interf
 //       
 //        setPop(getReplacePop(nextGeneration));
 //
-//        this.firePropertyChangedEvent("NextGenerationPerformed");
+//        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     	//////////////////////////
     	super.optimize();
     	
@@ -126,7 +126,7 @@ public class EvolutionStrategyIPOP extends EvolutionStrategies implements Interf
 		bestList.add(best);
 		best = null;
 		Population newPop = getPopulation().cloneWithoutInds();
-		getProblem().initPopulation(newPop);
+		getProblem().initPopulation(newPop); // this is where the reinit event of Pop is called, meaning that the rank-mu-cma matrix is reinitialized as well
 		double[] badFit = getPopulation().getBestFitness().clone();
 		Arrays.fill(badFit, Double.MAX_VALUE);
 		newPop.setAllFitnessValues(badFit);
@@ -137,7 +137,7 @@ public class EvolutionStrategyIPOP extends EvolutionStrategies implements Interf
     
     protected void firePropertyChangedEvent(String name) {
     	if (name.equals(Population.funCallIntervalReached)) {
-    		super.firePropertyChangedEvent("NextGenerationPerformed");
+    		super.firePropertyChangedEvent(Population.nextGenerationPerformed);
     	}
     	else {} // nothing, evt is produced in #registerPopulationStateChanged, dont forward original due to changing pop size
     }
@@ -196,16 +196,16 @@ public class EvolutionStrategyIPOP extends EvolutionStrategies implements Interf
 			// stop if the std dev of the normal distribution is smaller than TolX in all coords 
 			// and sigma p_c is smaller than TolX in all components; TolX = 10^-12 sigma_0
 	
-			if (rcmaMute.testAllDistBelow(10e-12*rcmaMute.getFirstSigma())) return true;
+			if (rcmaMute.testAllDistBelow(pop, 10e-12*rcmaMute.getFirstSigma(pop))) return true;
 			
 			// stop if adding a 0.1 std dev vector in a principal axis dir. of C does not change <x>_w^g
-			if (rcmaMute.testNoChangeAddingDevAxis(0.1, curGen)) return true;
+			if (rcmaMute.testNoChangeAddingDevAxis(pop, 0.1, curGen)) return true;
 			
 			// stop if adding a 0.2 std dev in each coordinate does (not???) change <x>_w^g
-			if (rcmaMute.testNoEffectCoord(0.2)) return true;
+			if (rcmaMute.testNoEffectCoord(pop, 0.2)) return true;
 	
 			// stop if the condition number of C exceeds 10^14
-			if (rcmaMute.testCCondition(10e14)) return true;		
+			if (rcmaMute.testCCondition(pop, 10e14)) return true;		
 		}
 		
 		return false;
@@ -231,7 +231,7 @@ public class EvolutionStrategyIPOP extends EvolutionStrategies implements Interf
 	public void registerPopulationStateChanged(Object source, String name) {
 		if (name.equals(Population.funCallIntervalReached)) {
     		getPopulation().SetFunctionCalls(((Population)source).getFunctionCalls()); // TODO this is ugly
-			super.firePropertyChangedEvent(name);
+			super.firePropertyChangedEvent(Population.nextGenerationPerformed);
 		} else {
 //			System.err.println("Not forwarding event " + name);
 		}

src/eva2/server/go/strategies/EvolutionaryProgramming.java

@@ -50,7 +50,7 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
             this.m_Problem.initPopulation(this.m_Population);
             this.evaluatePopulation(this.m_Population);
             this.m_PopulationSize = this.m_Population.size();
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
 
         /** This method will init the optimizer with a given population
@@ -61,7 +61,7 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
             if (reset) {
             	this.m_Population.init();
                 this.evaluatePopulation(this.m_Population);
-                this.firePropertyChangedEvent("NextGenerationPerformed");
+                this.firePropertyChangedEvent(Population.nextGenerationPerformed);
             }
         }
 
@@ -119,7 +119,7 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
             nextGeneration.addPopulation(this.m_Population);
             this.m_Population = nextGeneration;
 
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
 
     /** This method allows you to add the LectureGUI as listener to the Optimizer

src/eva2/server/go/strategies/FloodAlgorithm.java

@@ -59,7 +59,7 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         this.m_Problem.initPopulation(this.m_Population);
         this.m_Problem.evaluate(this.m_Population);
         this.m_CurrentFloodPeak = this.m_InitialFloodPeak;
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method will init the optimizer with a given population
@@ -70,7 +70,7 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         if (reset) {
         	this.m_Population.init();
             this.m_Problem.evaluate(this.m_Population);
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
         this.m_CurrentFloodPeak = this.m_InitialFloodPeak;
     }
@@ -99,7 +99,7 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         }
         this.m_CurrentFloodPeak = this.m_CurrentFloodPeak - this.m_DrainRate;
         this.m_Population.incrGeneration();
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method calculates the difference between the fitness values

src/eva2/server/go/strategies/GeneticAlgorithm.java

@@ -59,7 +59,7 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
         public void init() {
             this.m_Problem.initPopulation(this.m_Population);
             this.evaluatePopulation(this.m_Population);
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
 
         /** This method will init the optimizer with a given population
@@ -70,7 +70,7 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
             if (reset) {
             	this.m_Population.init();
                 this.evaluatePopulation(this.m_Population);
-                this.firePropertyChangedEvent("NextGenerationPerformed");
+                this.firePropertyChangedEvent(Population.nextGenerationPerformed);
             }
         }
 
@@ -162,7 +162,7 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
 //            }
 //        }
 //        this.m_Population.incrGeneration();
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
 
         /** This method allows you to add the LectureGUI as listener to the Optimizer

src/eva2/server/go/strategies/GradientDescentAlgorithm.java

@@ -56,7 +56,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
     if (reset) {
     	this.getPopulation().init();
         this.m_Problem.evaluate(this.getPopulation());
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
     //System.out.println("initByPopulation() called");
     indyhash = new Hashtable();
@@ -247,7 +247,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
     }
 
 
-    this.firePropertyChangedEvent("NextGenerationPerformed");
+    this.firePropertyChangedEvent(Population.nextGenerationPerformed);
   }
 
   private double momentumweigth = 0.1;

src/eva2/server/go/strategies/HillClimbing.java

@@ -55,7 +55,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
     public void init() {
         this.m_Problem.initPopulation(this.m_Population);
         this.m_Problem.evaluate(this.m_Population);
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     public void initByPopulation(Population pop, boolean reset) {
@@ -63,7 +63,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
         if (reset) {
         	this.m_Population.init();
             this.m_Problem.evaluate(this.m_Population);
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
@@ -107,7 +107,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
 //            }
 //        }
 //        this.m_Population.incrGeneration();
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     public InterfaceMutation getMutationOperator() {

src/eva2/server/go/strategies/IslandModelEA.java

@@ -135,7 +135,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
             this.m_Population.addPopulation(pop);
             this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
         }
-        this.firePropertyChangedEvent("NextGenerationPerformed", this.m_Optimizer.getPopulation());
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed, this.m_Optimizer.getPopulation());
     }
 
     /** This method will init the optimizer with a given population
@@ -197,7 +197,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
             this.m_Population.addPopulation(pop);
             this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
         }
-        this.firePropertyChangedEvent("NextGenerationPerformed", this.m_Optimizer.getPopulation());
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed, this.m_Optimizer.getPopulation());
     }
 
     /** The optimize method will compute an 'improved' and evaluated population
@@ -252,7 +252,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
             this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
         }
 //        System.out.println("Fitnesscalls :" + this.m_Population.getFunctionCalls());
-        this.firePropertyChangedEvent("NextGenerationPerformed", this.m_Optimizer.getPopulation());
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed, this.m_Optimizer.getPopulation());
         double plotValue = (this.m_Problem.getDoublePlotValue(this.m_Population)).doubleValue();
         if (this.m_Show) this.m_Plot.setConnectedPoint(this.m_Population.getFunctionCalls(), plotValue, 0);
         // now they are synchronized

src/eva2/server/go/strategies/MemeticAlgorithm.java

@@ -92,7 +92,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 		if (reset) {
 			this.getPopulation().init();
 			this.m_Problem.evaluate(this.getPopulation());
-			this.firePropertyChangedEvent("NextGenerationPerformed");
+			this.firePropertyChangedEvent(Population.nextGenerationPerformed);
 		}
 	}
 
@@ -101,7 +101,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 		this.m_GlobalOptimizer.SetProblem(this.m_Problem);
 		this.m_GlobalOptimizer.init();
 		this.evaluatePopulation(this.m_GlobalOptimizer.getPopulation());
-		this.firePropertyChangedEvent("NextGenerationPerformed");
+		this.firePropertyChangedEvent(Population.nextGenerationPerformed);
 	}
 
 	/**
@@ -192,7 +192,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 		if (TRACE)
 		  System.out.println("function calls"
 		      + this.m_GlobalOptimizer.getPopulation().getFunctionCalls());
-		this.firePropertyChangedEvent("NextGenerationPerformed");
+		this.firePropertyChangedEvent(Population.nextGenerationPerformed);
 	}
 
 	/**

src/eva2/server/go/strategies/MonteCarloSearch.java

@@ -55,7 +55,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
     public void init() {
         this.m_Problem.initPopulation(this.m_Population);
         this.m_Problem.evaluate(this.m_Population);
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method will init the optimizer with a given population
@@ -67,7 +67,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
         if (reset) {
         	this.m_Population.init();
             this.m_Problem.evaluate(this.m_Population);
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
@@ -86,7 +86,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
             }
         }
         this.m_Population.incrGeneration();
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
 

src/eva2/server/go/strategies/MultiObjectiveEA.java

@@ -76,7 +76,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
     public void init() {
         this.m_Optimizer.init();
         this.m_Archiver.addElementsToArchive(this.m_Optimizer.getPopulation());
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
 
@@ -87,7 +87,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
     public void initByPopulation(Population pop, boolean reset) {
         this.m_Optimizer.initByPopulation(pop, reset);
         this.m_Archiver.addElementsToArchive(this.m_Optimizer.getPopulation());
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** The optimize method will compute a 'improved' and evaluated population
@@ -127,7 +127,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
 
         System.gc();
 
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     private double[][] showMay(Population pop) {

src/eva2/server/go/strategies/ParticleSwarmOptimization.java

@@ -183,8 +183,8 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		for (int j = 0; j < writeData.length; j++) {
 			writeData[j]    = (writeData[j]/relSpeed)*this.m_InitialVelocity;
 		}
-		indy.SetData(partTypeKey, defaultType);
+		indy.putData(partTypeKey, defaultType);
-		indy.SetData(partVelKey, writeData);
+		indy.putData(partVelKey, writeData);
 	}
 
 	/**
@@ -197,12 +197,12 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		double[] tmpD        = indy.getFitness();
 		double[] writeData   = new double[tmpD.length];
 		System.arraycopy(tmpD, 0, writeData, 0, tmpD.length);
-		indy.SetData(partBestFitKey, writeData);
+		indy.putData(partBestFitKey, writeData);
 		// init best position
 		tmpD        = ((InterfaceDataTypeDouble)indy).getDoubleData();
 		writeData   = new double[tmpD.length];
 		System.arraycopy(tmpD, 0, writeData, 0, tmpD.length);
-		indy.SetData(partBestPosKey, writeData);
+		indy.putData(partBestPosKey, writeData);
 	}
 
 	/**
@@ -380,7 +380,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 
 		this.m_BestIndividual = (AbstractEAIndividual)this.m_Population.getBestEAIndividual().clone();
 
-		if (reset) this.firePropertyChangedEvent("NextGenerationPerformed");
+		if (reset) this.firePropertyChangedEvent(Population.nextGenerationPerformed);
 		
 		treeLevels = 0;
 		// the HPSO tree will contain layers 0...HPSOLevels, the last one is "incomplete" with only HPSOOrphans number of nodes
@@ -405,7 +405,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 			if (indy instanceof InterfaceDataTypeDouble) {
 				initIndividualDefaults(indy);
 			}
-			indy.SetData(indexKey, i);
+			indy.putData(indexKey, i);
 			indy.setIndividualIndex(i);
 		}
 	}
@@ -473,7 +473,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		if (indy instanceof InterfaceDataTypeDouble) {
 			indy.setParents(null);
 			indy.defaultInit();
-			indy.SetData(partTypeKey, defaultType); // turn into default type
+			indy.putData(partTypeKey, defaultType); // turn into default type
 			initIndividualDefaults(indy);
 			initIndividualMemory(indy);
 			plotIndy(((InterfaceDataTypeDouble)indy).getDoubleData(), null, (Integer)indy.getData(indexKey));
@@ -506,7 +506,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	protected void defaultIndividualUpdate(int index, AbstractEAIndividual indy, Population pop) {
 		InterfaceDataTypeDouble endy = (InterfaceDataTypeDouble) indy;
 		
-		indy.SetData(partTypeKey, defaultType);
+		indy.putData(partTypeKey, defaultType);
 		// default update
 		double[] personalBestPos   = (double[]) indy.getData(partBestPosKey);
 		double[] velocity       = (double[]) indy.getData(partVelKey);
@@ -598,8 +598,8 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	 * @param indy	the individual to update
 	 */
 	protected void updateIndProps(AbstractEAIndividual indy) {
-		indy.SetData(partBestFitKey, indy.getFitness());
+		indy.putData(partBestFitKey, indy.getFitness());
-		indy.SetData(partBestPosKey, ((InterfaceDataTypeDouble)indy).getDoubleData());
+		indy.putData(partBestPosKey, ((InterfaceDataTypeDouble)indy).getDoubleData());
 	}
 
 	/**
@@ -1025,7 +1025,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 			if (!m_CheckConstraints) System.err.println("warning, checkbounds will be forced by InterfaceESIndividual!");
 		}
 
-		indy.SetData(partVelKey, curVelocity);
+		indy.putData(partVelKey, curVelocity);
 //		((InterfaceESIndividual) indy).SetDGenotype(newPosition);
 	}
 
@@ -1135,7 +1135,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 
 //		System.out.println(">>> " + m_Population.getBestEAIndividual().getStringRepresentation());
 		
-		this.firePropertyChangedEvent("NextGenerationPerformed");
+		this.firePropertyChangedEvent(Population.nextGenerationPerformed);
 
 		if (sleepTime > 0 ) try { Thread.sleep(sleepTime); } catch(Exception e) {}
 		
@@ -1176,8 +1176,8 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	protected void logBestIndividual() {
 		if (this.m_Population.getBestEAIndividual().isDominatingDebConstraints(this.m_BestIndividual)) {
 			this.m_BestIndividual = (AbstractEAIndividual)this.m_Population.getBestEAIndividual().clone();
-			this.m_BestIndividual.SetData(partBestFitKey, this.m_BestIndividual.getFitness());
+			this.m_BestIndividual.putData(partBestFitKey, this.m_BestIndividual.getFitness());
-			this.m_BestIndividual.SetData(partBestPosKey, ((InterfaceDataTypeDouble)this.m_BestIndividual).getDoubleData());
+			this.m_BestIndividual.putData(partBestPosKey, ((InterfaceDataTypeDouble)this.m_BestIndividual).getDoubleData());
 //			System.out.println("new best: "+m_BestIndividual.toString());
 		}
 	}
@@ -1224,7 +1224,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		for (int i=0; i<pop.size(); i++) {
 			// cross-link the sorted list for faster access
 			origIndex = (Integer)((AbstractEAIndividual)sortedPopulation[i]).getData(indexKey);
-			((AbstractEAIndividual)pop.get(origIndex)).SetData(sortedIndexKey, new Integer(i));
+			((AbstractEAIndividual)pop.get(origIndex)).putData(sortedIndexKey, new Integer(i));
 		}
 	}
 
@@ -1260,22 +1260,22 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 						} else {
 							found = true;
 							// assign to leader, update swarm size
-							((AbstractEAIndividual)sortedPop[cur]).SetData(multiSwTypeKey, leaders.get(i));
+							((AbstractEAIndividual)sortedPop[cur]).putData(multiSwTypeKey, leaders.get(i));
-							((AbstractEAIndividual)sortedPop[cur]).SetData(multiSwSizeKey, new Integer(-1));
+							((AbstractEAIndividual)sortedPop[cur]).putData(multiSwSizeKey, new Integer(-1));
-							leaders.get(i).SetData(multiSwSizeKey, 1+sSize);
+							leaders.get(i).putData(multiSwSizeKey, 1+sSize);
 							break;
 						}
 					}
 				}
 				if (!found) { // new leader is found
 					leaders.add(((AbstractEAIndividual)sortedPop[cur]));
-					((AbstractEAIndividual)sortedPop[cur]).SetData(multiSwTypeKey, sortedPop[cur]);
+					((AbstractEAIndividual)sortedPop[cur]).putData(multiSwTypeKey, sortedPop[cur]);
-					((AbstractEAIndividual)sortedPop[cur]).SetData(multiSwSizeKey, new Integer(1));
+					((AbstractEAIndividual)sortedPop[cur]).putData(multiSwSizeKey, new Integer(1));
 				} else if (superfluous) {
 					//System.out.println("reinitializing " + cur);
-					((AbstractEAIndividual)sortedPop[cur]).SetData(partTypeKey, resetType);
+					((AbstractEAIndividual)sortedPop[cur]).putData(partTypeKey, resetType);
-					((AbstractEAIndividual)sortedPop[cur]).SetData(multiSwTypeKey, sortedPop[cur]);
+					((AbstractEAIndividual)sortedPop[cur]).putData(multiSwTypeKey, sortedPop[cur]);
-					((AbstractEAIndividual)sortedPop[cur]).SetData(multiSwSizeKey, new Integer(1));
+					((AbstractEAIndividual)sortedPop[cur]).putData(multiSwSizeKey, new Integer(1));
 				}
 				cur++;
 			}
@@ -1432,7 +1432,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 			if (indy.hasData(partTypeKey)) {
 				initIndividualDefaults(indy);
 				initIndividualMemory(indy);
-				indy.SetData(indexKey, i);
+				indy.putData(indexKey, i);
 				indy.setIndividualIndex(i);
 				if (TRACE) System.err.println("init indy " + i + " " + AbstractEAIndividual.getDefaultDataString(indy));
 			}

src/eva2/server/go/strategies/PopulationBasedIncrementalLearning.java

@@ -60,7 +60,7 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
     public void init() {
         this.m_Problem.initPopulation(this.m_Population);
         this.evaluatePopulation(this.m_Population);
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method will init the optimizer with a given population
@@ -78,7 +78,7 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
             this.evaluatePopulation(this.m_Population);
         }
         ((PBILPopulation)this.m_Population).buildProbabilityVector();
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method will evaluate the current population using the
@@ -121,7 +121,7 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
         } else {
             this.m_Population = nextGeneration;
         }
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method will set the problem that is to be optimized

src/eva2/server/go/strategies/SimulatedAnnealing.java

@@ -58,7 +58,7 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
         this.m_Problem.initPopulation(this.m_Population);
         this.m_Problem.evaluate(this.m_Population);
         this.m_CurrentTemperature = this.m_InitialTemperature;
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method will init the optimizer with a given population
@@ -71,7 +71,7 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
         if (reset) {
         	this.m_Population.init();
             this.m_Problem.evaluate(this.m_Population);
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
@@ -105,7 +105,7 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
         }
         this.m_CurrentTemperature = this.m_Alpha * this.m_CurrentTemperature;
         this.m_Population.incrGeneration();
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method calculates the difference between the fitness values

src/eva2/server/go/strategies/SteadyStateGA.java

@@ -54,7 +54,7 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
         public void init() {
             this.m_Problem.initPopulation(this.m_Population);
             this.evaluatePopulation(this.m_Population);
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
 
         /** This method will init the optimizer with a given population
@@ -65,7 +65,7 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
             if (reset) {
             	this.m_Population.init();
                 this.evaluatePopulation(this.m_Population);
-                this.firePropertyChangedEvent("NextGenerationPerformed");
+                this.firePropertyChangedEvent(Population.nextGenerationPerformed);
             }
         }
 
@@ -111,7 +111,7 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
             for (int i = 0; i < this.m_Population.size(); i++) this.generateChildren();
             this.m_Population.incrFunctionCallsBy(this.m_Population.size());
             this.m_Population.incrGeneration();
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
 
         /** This method allows you to add the LectureGUI as listener to the Optimizer

src/eva2/server/go/strategies/ThresholdAlgorithm.java

@@ -56,7 +56,7 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
         this.m_Problem.initPopulation(this.m_Population);
         this.m_Problem.evaluate(this.m_Population);
         this.m_CurrentT = this.m_InitialT;
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method will init the optimizer with a given population
@@ -69,7 +69,7 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
         if (reset) {
         	this.m_Population.init();
             this.m_Problem.evaluate(this.m_Population);
-            this.firePropertyChangedEvent("NextGenerationPerformed");
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
@@ -97,7 +97,7 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
         }
         this.m_CurrentT = this.m_Alpha * this.m_CurrentT;
         this.m_Population.incrGeneration();
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method calculates the difference between the fitness values

src/eva2/server/go/strategies/Tribes.java

@@ -736,7 +736,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
 		population.incrFunctionCalls();
 		if (notifyAfter(population.getFunctionCalls())) {
 //			System.out.println("Notifying after " + population.getFunctionCalls());
-			firePropertyChangedEvent("NextGenerationPerformed");
+			firePropertyChangedEvent(Population.nextGenerationPerformed);
 		}
 	}
 

src/eva2/server/go/strategies/WingedMultiObjectiveEA.java

@@ -92,7 +92,7 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
             this.m_SOOptimizer.init();
         }
         this.communicate();
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
 
@@ -130,7 +130,7 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
             this.m_SOOptimizer.initByPopulation(pop, reset);
         }
         this.communicate();
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** The optimize method will compute a 'improved' and evaluated population
@@ -148,7 +148,7 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
 
         System.gc();
 
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
     /** This method will manage comunication between the
@@ -171,7 +171,7 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
         oldFunctionCalls = this.m_Population.getFunctionCalls();
         this.m_Problem.evaluate(this.m_Population);
         this.m_Population.SetFunctionCalls(oldFunctionCalls);
-        this.firePropertyChangedEvent("NextGenerationPerformed");
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         double plotValue = (this.m_Problem.getDoublePlotValue(this.m_Population)).doubleValue();
         // now they are synchronized lets migrate
         this.migrate();

src/eva2/server/modules/AbstractModuleAdapter.java

@@ -16,12 +16,12 @@ import java.io.Serializable;
 import java.net.InetAddress;
 import java.util.Vector;
 
-import eva2.gui.JTabbedModuleFrame;
-import eva2.gui.LogPanel;
-import eva2.server.go.InterfaceProcessor;
-
 import wsi.ra.jproxy.MainAdapterClient;
 import wsi.ra.jproxy.RemoteStateListener;
+import eva2.gui.JTabbedModuleFrame;
+import eva2.gui.LogPanel;
+import eva2.server.go.InterfaceGOParameters;
+import eva2.server.go.InterfaceProcessor;
 /*==========================================================================*
 * ABSTRACT CLASS DECLARATION
 *==========================================================================*/
@@ -120,6 +120,12 @@ abstract public class AbstractModuleAdapter implements ModuleAdapter, Serializab
 	  } else return false;
   }
   
+  public InterfaceGOParameters getGOParameters() {
+	  if ((m_Processor != null) && (m_Processor instanceof Processor)) {
+		  return ((Processor)m_Processor).getGOParams();
+	  } else return null;
+  }
+  
   /**
    *
    */

src/eva2/server/modules/GOModuleAdapter.java

@@ -29,6 +29,6 @@ public class GOModuleAdapter extends GenericModuleAdapter implements ModuleAdapt
    * @param Client	the client instance
    */
   public GOModuleAdapter(String adapterName, MainAdapterClient client) {
-       super (adapterName, "GO.html", client, GOParameters.getInstance(), false);
+       super (adapterName, "", client, GOParameters.getInstance(), false);
    }
 }

src/eva2/server/modules/Processor.java

@@ -251,17 +251,19 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
      * @param name          Could be used to indicate the nature of the event.
      */
     public void registerPopulationStateChanged(Object source, String name) {
-		m_Statistics.createNextGenerationPerformed(
+    	if (name.equals(Population.nextGenerationPerformed)) {
-				(PopulationInterface)this.goParams.getOptimizer().getPopulation(), 
+    		m_Statistics.createNextGenerationPerformed(
-				this.goParams.getProblem());
+    				(PopulationInterface)this.goParams.getOptimizer().getPopulation(), 
-		if (m_ListenerModule != null) {
+    				this.goParams.getProblem());
-			m_ListenerModule.updateProgress(
+    		if (m_ListenerModule != null) {
-					getStatusPercent(
+    			m_ListenerModule.updateProgress(
-							goParams.getOptimizer().getPopulation(), 
+    					getStatusPercent(
-							runCounter,
+    							goParams.getOptimizer().getPopulation(), 
-							m_Statistics.getStatisticsParameter().getMultiRuns()), 
+    							runCounter,
-							null);
+    							m_Statistics.getStatisticsParameter().getMultiRuns()), 
-		}
+    							null);
+    		}
+    	}
     }
 
     /** This method writes Data to file.