Merging MK branch revs. 214,447-448,451,452,456-459.

src/eva2/OptimizerFactory.java

@@ -21,9 +21,11 @@ import eva2.server.go.operators.archiving.InformationRetrievalInserting;
 import eva2.server.go.operators.archiving.InterfaceArchiving;
 import eva2.server.go.operators.archiving.InterfaceInformationRetrieval;
 import eva2.server.go.operators.cluster.ClusteringDensityBased;
+import eva2.server.go.operators.cluster.InterfaceClustering;
 import eva2.server.go.operators.crossover.CrossoverESDefault;
 import eva2.server.go.operators.crossover.InterfaceCrossover;
 import eva2.server.go.operators.crossover.NoCrossover;
+import eva2.server.go.operators.distancemetric.IndividualDataMetric;
 import eva2.server.go.operators.mutation.InterfaceMutation;
 import eva2.server.go.operators.mutation.MutateESCovarianceMatrixAdaption;
 import eva2.server.go.operators.mutation.MutateESFixedStepSize;
@@ -50,6 +52,7 @@ import eva2.server.go.strategies.HillClimbing;
 import eva2.server.go.strategies.InterfaceOptimizer;
 import eva2.server.go.strategies.MonteCarloSearch;
 import eva2.server.go.strategies.MultiObjectiveEA;
+import eva2.server.go.strategies.NelderMeadSimplex;
 import eva2.server.go.strategies.ParticleSwarmOptimization;
 import eva2.server.go.strategies.PopulationBasedIncrementalLearning;
 import eva2.server.go.strategies.SimulatedAnnealing;
@@ -117,6 +120,12 @@ public class OptimizerFactory {
 
 	private static OptimizerRunnable lastRunnable = null;
 
+	private static final int cbnDefaultHaltingWindowLength=new ClusterBasedNichingEA().getHaltingWindow();
+	private static final double cbnDefaultHaltingWindowEpsilon=new ClusterBasedNichingEA().getEpsilonBound();
+	private static final double cbnDefaultClusterSigma = 0.1;
+	private static final int cbnDefaultMinGroupSize = 5;
+	private static final int cbnDefaultMaxGroupSize = -1;
+	
 	/**
 	 * This method optimizes the given problem using differential evolution.
 	 *
@@ -424,7 +433,7 @@ public class OptimizerFactory {
 	 * @param popsize
 	 * @param phi1
 	 * @param phi2
-	 * @param k
+	 * @param speedLim
 	 * @param listener
 	 * @param topology
 	 * @see ParticleSwarmOpimization
@@ -433,9 +442,8 @@ public class OptimizerFactory {
 	 */
 	public static final ParticleSwarmOptimization createParticleSwarmOptimization(
 			AbstractOptimizationProblem problem, int popsize, double phi1,
-			double phi2, double k,
+			double phi2, double speedLim, PSOTopologyEnum selectedTopology, int topologyRange, 
-			InterfacePopulationChangedEventListener listener,
+			InterfacePopulationChangedEventListener listener) {
-			PSOTopologyEnum selectedTopology) {
 
 		problem.initProblem();
 
@@ -450,9 +458,10 @@ public class OptimizerFactory {
 		pso.getPopulation().setTargetSize(popsize);
 		pso.setPhi1(phi1);
 		pso.setPhi2(phi2);
-		pso.setSpeedLimit(k);
+		pso.setSpeedLimit(speedLim);
 //		pso.getTopology().setSelectedTag(selectedTopology);
 		pso.setTopology(selectedTopology);
+		pso.setTopologyRange(topologyRange);
 		pso.addPopulationChangedEventListener(listener);
 		pso.init();
 
@@ -581,13 +590,13 @@ public class OptimizerFactory {
 		case HILLCL:
 			return hillClimbing(problem);
 		case CBN_ES:
-			return cbnES(problem);
+			return standardCbnES(problem);
 		case CL_HILLCL:
 			return stdClusteringHillClimbing(problem);
 		case CMA_ES_IPOP: 
 			return cmaESIPOP(problem);
 		case CBN_GA:
-			return cbnGA(problem);
+			return standardCbnGA(problem);
 		case PBIL:
 			return standardPBIL(problem);
 		default:
@@ -1171,31 +1180,99 @@ public class OptimizerFactory {
 		return makeParams(new MonteCarloSearch(), 50, problem, randSeed, makeDefaultTerminator());
 	}
 	
-	public static final GOParameters cbnES(AbstractOptimizationProblem problem) {
+	/**
+	 * Create a generic Clustering-based Niching EA with given parameters. Uses ClusteringDensityBased as
+	 * a default clustering algorithm.
+	 * 
+	 * @param problem
+	 * @param opt
+	 * @param clusterSigma
+	 * @param minClustSize
+	 * @param haltingWindowLength
+	 * @param haltingWindowEpsilon
+	 * @param popSize
+	 * @return
+	 */
+	public static final GOParameters createCbn(AbstractOptimizationProblem problem, InterfaceOptimizer opt,
+			double clusterSigma, int minClustSize, int maxSpecSize, int haltingWindowLength, double haltingWindowEpsilon, int popSize) {
+		return createCbn(problem, opt, new ClusteringDensityBased(clusterSigma, minClustSize), maxSpecSize,
+				new ClusteringDensityBased(clusterSigma, minClustSize), haltingWindowLength, haltingWindowEpsilon, popSize);
+	}
+	
+	/**
+	 * Create a generic Clustering-based Niching EA with given parameters. 
+	 * 
+	 * @param problem
+	 * @param opt
+	 * @param clustDifferentiate
+	 * @param clustMerge
+	 * @param haltingWindowLength
+	 * @param haltingWindowEpsilon
+	 * @param popSize
+	 * @return
+	 */
+	public static final GOParameters createCbn(AbstractOptimizationProblem problem, InterfaceOptimizer opt,
+			InterfaceClustering clustDifferentiate, int maxSpecSize, InterfaceClustering clustMerge, int haltingWindowLength, 
+			double haltingWindowEpsilon, int popSize) {
 		ClusterBasedNichingEA cbn = new ClusterBasedNichingEA();
+		cbn.setOptimizer(opt);
+		cbn.setMergingCA(clustMerge);
+		cbn.setMaxSpeciesSize(maxSpecSize);
+		cbn.setDifferentiationCA(clustDifferentiate);
+		if (clustMerge!=null) cbn.setUseMerging(true);
+		cbn.setShowCycle(0); // don't do graphical output
+		cbn.setHaltingWindow(haltingWindowLength);
+		cbn.setEpsilonBound(haltingWindowEpsilon);
+		return makeParams(cbn, popSize, problem, randSeed, makeDefaultTerminator());
+	}
+	
+	/**
+	 * A standard CBNES which employs a (15,50)-ES with further parameters as set by the EvA2 framework.
+	 *  
+	 * @param problem
+	 * @return
+	 */
+	public static final GOParameters standardCbnES(AbstractOptimizationProblem problem) {
 		EvolutionStrategies es = new EvolutionStrategies();
 		es.setMu(15);
 		es.setLambda(50);
 		es.setPlusStrategy(false);
-		cbn.setOptimizer(es);
+		return createCbn(problem, es, cbnDefaultClusterSigma, cbnDefaultMinGroupSize, cbnDefaultMaxGroupSize, cbnDefaultHaltingWindowLength, cbnDefaultHaltingWindowEpsilon, 100);
-		ClusteringDensityBased clustering = new ClusteringDensityBased(0.1);
-		cbn.setMergingCA((ClusteringDensityBased) clustering.clone());
-		cbn.setDifferentiationCA(clustering);
-		cbn.setShowCycle(0); // don't do graphical output
-
-		return makeParams(cbn, 100, problem, randSeed, makeDefaultTerminator());
 	}
 
-	public static final GOParameters cbnGA(AbstractOptimizationProblem problem) {
+	/**
-		ClusterBasedNichingEA cbn = new ClusterBasedNichingEA();
+	 * A standard CBNES which employs a CMA-ES, see {@link #cmaES(AbstractOptimizationProblem)}.
-		GeneticAlgorithm ga = new GeneticAlgorithm();
+	 *  
-		cbn.setOptimizer(ga);
+	 * @param problem
-		ClusteringDensityBased clustering = new ClusteringDensityBased(0.1);
+	 * @return
-		cbn.setMergingCA((ClusteringDensityBased) clustering.clone());
+	 */
-		cbn.setDifferentiationCA(clustering);
+	public static final GOParameters standardCbnCmaES(AbstractOptimizationProblem problem) {
-		cbn.setShowCycle(0); // don't do graphical output
+		GOParameters cmaEsParams = cmaES(problem);
-
+		EvolutionStrategies cmaES = (EvolutionStrategies)cmaEsParams.getOptimizer();
-		return makeParams(cbn, 100, problem, randSeed, makeDefaultTerminator());
+		return createCbn(problem, cmaES, cbnDefaultClusterSigma, cbnDefaultMinGroupSize, cbnDefaultMaxGroupSize, cbnDefaultHaltingWindowLength, cbnDefaultHaltingWindowEpsilon, 100);
+	}
+	
+	/**
+	 * A standard CBNGA with a GA and further parameters as set by the EvA2 framework.
+	 * @param problem
+	 * @return
+	 */
+	public static final GOParameters standardCbnGA(AbstractOptimizationProblem problem) {
+		GeneticAlgorithm ga = new GeneticAlgorithm();	
+		return createCbn(problem, ga, cbnDefaultClusterSigma, cbnDefaultMinGroupSize, cbnDefaultMaxGroupSize, cbnDefaultHaltingWindowLength, cbnDefaultHaltingWindowEpsilon, 100);
+	}
+	
+	/**
+	 * A standard CBNPSO with density based clustering working on personal best positions.
+	 * 
+	 * @param problem
+	 * @return
+	 */
+	public static final GOParameters standardCbnPSO(AbstractOptimizationProblem problem) {
+		GOParameters psoParams = standardPSO(problem);
+		ParticleSwarmOptimization pso = (ParticleSwarmOptimization)psoParams.getOptimizer();
+		ClusteringDensityBased clust = new ClusteringDensityBased(cbnDefaultClusterSigma, cbnDefaultMinGroupSize, new IndividualDataMetric(ParticleSwarmOptimization.partBestPosKey));
+		return createCbn(problem, pso, clust, cbnDefaultMaxGroupSize, new ClusteringDensityBased(clust), cbnDefaultHaltingWindowLength, cbnDefaultHaltingWindowEpsilon, 100);
 	}
 	
 	public static final GOParameters standardPBIL(AbstractOptimizationProblem problem) {
@@ -1306,6 +1383,10 @@ public class OptimizerFactory {
 	 * @return
 	 */
 	public static final GOParameters cmaESIPOP(AbstractOptimizationProblem problem) {
+		return createCmaEsIPop(problem, 2.);
+	}
+	
+	public static final GOParameters createCmaEsIPop(AbstractOptimizationProblem problem, double incLambdaFact) {
 		EvolutionStrategies es = new EvolutionStrategyIPOP();
 
 		AbstractEAIndividual indyTemplate = problem.getIndividualTemplate();
@@ -1317,6 +1398,7 @@ public class OptimizerFactory {
 			int lambda = (int) (4.0 + 3.0 * Math.log(dim));
 			es.setGenerationStrategy((int)Math.floor(lambda/2.),lambda, false);
 			es.setForceOrigPopSize(false);
+			((EvolutionStrategyIPOP)es).setIncPopSizeFact(incLambdaFact);
 			// Set CMA operator for mutation
 			AbstractEAIndividual indy = (AbstractEAIndividual) indyTemplate;
 			MutateESRankMuCMA cmaMut = new MutateESRankMuCMA();
@@ -1330,6 +1412,11 @@ public class OptimizerFactory {
 		return makeESParams(es, problem);
 	}
 	
+	public static final GOParameters standardNMS(AbstractOptimizationProblem problem) {
+		NelderMeadSimplex nms = NelderMeadSimplex.createNelderMeadSimplex(problem, null);
+		return makeParams(nms, 50, problem, randSeed, makeDefaultTerminator());
+	}
+	
 	public static final GOParameters standardDE(
 			AbstractOptimizationProblem problem) {
 		DifferentialEvolution de = new DifferentialEvolution();
@@ -1370,13 +1457,14 @@ public class OptimizerFactory {
 
 		return makeParams(ga, 100, problem, randSeed, makeDefaultTerminator());
 	}
-
+	
 	public static final GOParameters standardPSO(
 			AbstractOptimizationProblem problem) {
 		ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
 		pso.setPhiValues(2.05, 2.05);
 //		pso.getTopology().setSelectedTag("Grid");
 		pso.setTopology(PSOTopologyEnum.grid);
+		pso.setTopologyRange(1);
 		return makeParams(pso, 30, problem, randSeed, makeDefaultTerminator());
 	}
 

src/eva2/OptimizerRunnable.java

@@ -37,6 +37,7 @@ public class OptimizerRunnable implements Runnable {
 	private boolean doRestart = false; // indicate whether start or restart should be done --> whether pop will be reinitialized.
 	private boolean postProcessOnly = false;
 	private InterfaceTextListener listener = null;
+	private String ident="OptimizerRunnable";
 	
 	/**
 	 * Construct an OptimizerRunnable with given parameters and a StatisticsStandalone instance without restart,
@@ -87,6 +88,14 @@ public class OptimizerRunnable implements Runnable {
 		doRestart = restart;
 	}
 	
+	public void setIdentifier(String id) {
+		ident=id;
+	}
+	
+	public String getIdentifier() {
+		return ident;
+	}
+	
 	public InterfaceGOParameters getGOParams() {
 		return proc.getGOParams();
 	}

src/eva2/gui/BeanInspector.java

@@ -22,6 +22,7 @@ import java.util.ArrayList;
 import java.util.List;
 
 import eva2.server.go.populations.Population;
+import eva2.tools.Pair;
 import eva2.tools.SelectedTag;
 import eva2.tools.Tag;
 
@@ -131,7 +132,9 @@ public class BeanInspector {
 		return true;
 	}
 
-
+	public static String toString(Object Target) {
+		return toString(Target, ';', false);
+	}
 	/**
 	 * Collect the accessible properties of an object and their values in a string.
 	 * Special cases: Arrays and Lists are concatenations of their elements, Population is excepted from lists.
@@ -141,7 +144,7 @@ public class BeanInspector {
 	 * @param  Target  Description of the Parameter
 	 * @return         Description of the Return Value
 	 */
-	public static String toString(Object Target) {
+	public static String toString(Object Target, char delim, boolean tight) {
 		String ret = "";
 		if (Target == null) return "null";
 		// try the object itself
@@ -149,13 +152,18 @@ public class BeanInspector {
 		Class<? extends Object> type = Target.getClass();
 
 		if (type.isArray()) { // handle the array case
-			StringBuffer sbuf = new StringBuffer("[ ");
+			StringBuffer sbuf = new StringBuffer("[");
+			if (!tight) sbuf.append(" ");
 			int len = Array.getLength(Target);
 			for (int i=0; i<len; i++) {
 				sbuf.append(toString(Array.get(Target, i)));
-				if (i<len-1) sbuf.append("; ");
+				if (i<len-1) {
+					sbuf.append(delim);
+					if (!tight) sbuf.append(" ");
+				}
 			}
-			sbuf.append(" ]");
+			if (!tight) sbuf.append(" ");
+			sbuf.append("]");
 			return sbuf.toString();
 		}
 		
@@ -164,13 +172,15 @@ public class BeanInspector {
 		}
 
 		if (Target instanceof List && !(Target instanceof Population)) { // handle the list case
-			StringBuffer sbuf = new StringBuffer("[  ");
+			StringBuffer sbuf = new StringBuffer("[");
+			if (!tight) sbuf.append(" ");
 			List<?> lst = (List<?>)Target;
 			for (Object o : lst) {
 				sbuf.append(o.toString());
-				sbuf.append("; ");
+				sbuf.append(delim);
+				if (!tight) sbuf.append(" ");
 			}
-			sbuf.setCharAt(sbuf.length()-2, ' ');
+			if (!tight) sbuf.setCharAt(sbuf.length()-2, ' ');
 			sbuf.setCharAt(sbuf.length()-1, ']');
 			return sbuf.toString();
 		}
@@ -192,20 +202,48 @@ public class BeanInspector {
 		}
 
 		// otherwise try introspection and collect all public properties as strings
+
+		Pair<String[],Object[]> nameVals = getPublicPropertiesOf(Target, true); 
+
+		StringBuffer sbuf = new StringBuffer(type.getName());
+		sbuf.append("{");
+		for (int i=0; i<nameVals.head.length; i++) {
+			if (nameVals.head[i]!=null) {
+				sbuf.append(nameVals.head[i]);
+				sbuf.append("=");
+				sbuf.append(toString(nameVals.tail[i]));
+				sbuf.append(delim);
+				if (!tight) sbuf.append(" ");
+			}
+		}
+
+		sbuf.append("}");
+		return sbuf.toString();
+	}
+
+	/**
+	 * Retrieve names and values of instance fields which are accessible by getter method, optionally
+	 * by both getter and setter method. The returned arrays may contain null entries.
+	 * Properties marked as hidden or expert are skipped.
+	 *   
+	 * @param target
+	 * @return
+	 */
+	public static Pair<String[],Object[]> getPublicPropertiesOf(Object target, boolean requireSetter) {
 		BeanInfo Info = null;
 		PropertyDescriptor[] Properties = null;
 //		MethodDescriptor[] Methods = null;
 		try {
-			Info = Introspector.getBeanInfo(Target.getClass());
+			Info = Introspector.getBeanInfo(target.getClass());
 			Properties = Info.getPropertyDescriptors();
 			Info.getMethodDescriptors();
 		} catch (IntrospectionException ex) {
 			System.err.println("BeanTest: Couldn't introspect");
-			return ret;
+			return null;
 		}
 
-		StringBuffer sbuf = new StringBuffer(type.getName());
+		String[] nameArray = new String[Properties.length];
-		sbuf.append("{");
+		Object[] valArray = new Object[Properties.length];
 		for (int i = 0; i < Properties.length; i++) {
 			if (Properties[i].isHidden() || Properties[i].isExpert()) {
 				continue;
@@ -217,7 +255,7 @@ public class BeanInspector {
 			Method getter = Properties[i].getReadMethod();
 			Method setter = Properties[i].getWriteMethod();
 			// Only display read/write properties.
-			if (getter == null || setter == null) {
+			if (getter == null || (setter == null && requireSetter)) {
 				continue;
 			}
 			//System.out.println("name = "+name );
@@ -226,20 +264,16 @@ public class BeanInspector {
 			//System.out.println("m_Target"+m_Target.toString());
 
 			try {
-				Object value = getter.invoke(Target, args);
+				nameArray[i]=name;
-				sbuf.append(name);
+				valArray[i] = getter.invoke(target, args);
-				sbuf.append("=");
-				sbuf.append(toString(value));
-				sbuf.append("; ");
 			} catch (Exception e) {
 				System.err.println("BeanTest ERROR +"+ e.getMessage());
-				return sbuf.toString();
 			}
 		}
-		sbuf.append("}");
+		Pair<String[],Object[]> nameVals = new Pair<String[],Object[]>(nameArray, valArray);
-		return sbuf.toString();
+		return nameVals;
 	}
-
+	
 
 	/**
 	 *@param  Target  Description of the Parameter

src/eva2/gui/GenericArrayEditor.java

@@ -20,6 +20,7 @@ import java.awt.Dimension;
 import java.awt.Font;
 import java.awt.FontMetrics;
 import java.awt.Graphics;
+import java.awt.GridLayout;
 import java.awt.Insets;
 import java.awt.Rectangle;
 import java.awt.event.ActionEvent;
@@ -31,7 +32,6 @@ import java.beans.PropertyChangeListener;
 import java.beans.PropertyChangeSupport;
 import java.beans.PropertyEditor;
 import java.lang.reflect.Array;
-import java.lang.reflect.Method;
 
 import javax.swing.DefaultListCellRenderer;
 import javax.swing.DefaultListModel;
@@ -71,15 +71,26 @@ implements PropertyEditor {
 	private JButton m_DeleteBut = new JButton("Delete");
 	/** Click to add the current object configuration to the array */
 	private JButton m_AddBut = new JButton("Add");
+	private JButton m_SetBut = new JButton("Set");
+	private JButton m_SetAllBut = new JButton("Set all");
+	private Component m_View = null;
 	/** Listens to buttons being pressed and taking the appropriate action */
 	private ActionListener m_InnerActionListener =
 		new ActionListener() {
 		//
 		public void actionPerformed(ActionEvent e) {
+			boolean consistentView = true; // be optimistic...
+			if (m_View instanceof PropertyText) { // check consistency!
+				consistentView = ((PropertyText)m_View).checkConsistency();
+				if (!consistentView) {
+//					System.err.println("Warning, inconsistent view!");
+					((PropertyText)m_View).updateFromEditor();
+				}
+			}
 			if (e.getSource() == m_DeleteBut) {
 				int [] selected = m_ElementList.getSelectedIndices();
 				if (selected != null) {
-					for (int i = 0; i < selected.length; i++) {
+					for (int i = selected.length-1; i>=0; i--) {
 						int current = selected[i];
 						m_ListModel.removeElementAt(current);
 						if (m_ListModel.size() > current) {
@@ -113,6 +124,27 @@ implements PropertyEditor {
 				} catch (Exception ex) {
 					JOptionPane.showMessageDialog(GenericArrayEditor.this,"Could not create an object copy",null,JOptionPane.ERROR_MESSAGE);
 				}
+			} else if (e.getSource() == m_SetAllBut) {
+				Object addObj = m_ElementEditor.getValue();
+				for (int i=0; i<m_ListModel.size(); i++) {
+					try {
+						m_ListModel.setElementAt(new SerializedObject(addObj).getObject(), i);
+					} catch (Exception e1) {
+						JOptionPane.showMessageDialog(GenericArrayEditor.this,"Could not create an object copy",null,JOptionPane.ERROR_MESSAGE);
+					}
+				}
+				m_Support.firePropertyChange("", null, null);
+			} else if (e.getSource() == m_SetBut) {
+				int selected = m_ElementList.getSelectedIndex();
+				Object addObj = m_ElementEditor.getValue();
+				if (selected>=0 && (selected <m_ListModel.size())) {
+					try {
+						m_ListModel.setElementAt(new SerializedObject(addObj).getObject(), selected);
+					} catch (Exception e1) {
+						JOptionPane.showMessageDialog(GenericArrayEditor.this,"Could not create an object copy",null,JOptionPane.ERROR_MESSAGE);
+					}
+					m_Support.firePropertyChange("", null, null);
+				}
 			}
 		}
 	};
@@ -136,6 +168,7 @@ implements PropertyEditor {
 				if (m_ElementList.getSelectedIndex() != -1) {
 					m_DeleteBut.setEnabled(true);
 					m_ElementEditor.setValue(m_ElementList.getSelectedValue());
+					if (m_View instanceof PropertyText) ((PropertyText)m_View).updateFromEditor();
 				}
 			}
 		}
@@ -150,6 +183,8 @@ implements PropertyEditor {
 		add(m_Label, BorderLayout.CENTER);
 		m_DeleteBut.addActionListener(m_InnerActionListener);
 		m_AddBut.addActionListener(m_InnerActionListener);
+		m_SetAllBut.addActionListener(m_InnerActionListener);
+		m_SetBut.addActionListener(m_InnerActionListener);
 		m_ElementList.addListSelectionListener(m_InnerSelectionListener);
 		m_AddBut.setToolTipText("Add the current item to the list");
 		m_DeleteBut.setToolTipText("Delete the selected list item");
@@ -248,7 +283,7 @@ implements PropertyEditor {
 			Class elementClass = arrayInstance.getClass().getComponentType();
 			PropertyEditor editor = PropertyEditorProvider.findEditor(elementClass);
 			if (editor instanceof EnumEditor) editor.setValue(obj);
-			Component view = null;
+			m_View = null;
 			ListCellRenderer lcr = new DefaultListCellRenderer();
 			if (editor != null) {
 				if (editor instanceof GenericObjectEditor) {
@@ -256,15 +291,15 @@ implements PropertyEditor {
 					((GenericObjectEditor) editor).setClassType(elementClass);
 				}
 				if (editor.isPaintable() && editor.supportsCustomEditor()) {
-					view = new PropertyPanel(editor);
+					m_View = new PropertyPanel(editor);
 					lcr = new EditorListCellRenderer(editor.getClass(), elementClass);
 				} else if (editor.getTags() != null) {
-					view = new PropertyValueSelector(editor);
+					m_View = new PropertyValueSelector(editor);
 				} else if (editor.getAsText() != null) {
-					view = new PropertyText(editor);
+					m_View = new PropertyText(editor);
 				}
 			}
-			if (view == null) {
+			if (m_View == null) {
 				System.err.println("No property editor for class: "
 						+ elementClass.getName());
 			} else {
@@ -296,12 +331,18 @@ implements PropertyEditor {
 						}
 					}
 
-					setPreferredSize(new Dimension(300,300));
+					setPreferredSize(new Dimension(300,400));
-					JPanel panel = new JPanel();
+//					JPanel panel = new JPanel();
-					panel.setLayout(new BorderLayout());
+//					panel.setLayout(new BorderLayout());
-					panel.add(view, BorderLayout.CENTER);
+//					panel.add(view, BorderLayout.CENTER);
-					panel.add(m_AddBut, BorderLayout.EAST);
+//					panel.add(m_AddBut, BorderLayout.EAST);
-					add(panel, BorderLayout.NORTH);
+//					JPanel buttonPanel=new JPanel(new FlowLayout());
+					JPanel combinedPanel = new JPanel(new GridLayout(1,3));
+					combinedPanel.add(m_View );
+					combinedPanel.add(m_AddBut);
+					combinedPanel.add(m_SetBut);
+					combinedPanel.add(m_SetAllBut);
+					add(combinedPanel, BorderLayout.NORTH);
 					add(new JScrollPane(m_ElementList), BorderLayout.CENTER);
 					add(m_DeleteBut, BorderLayout.SOUTH);
 					m_ElementEditor.addPropertyChangeListener(new PropertyChangeListener() {
@@ -454,7 +495,7 @@ implements PropertyEditor {
 			editor.setValue(initial);
 			PropertyDialog pd = new PropertyDialog(editor,EVAHELP.cutClassName(editor.getClass().getName())
 					, 100, 100);
-			pd.setSize(200,200);
+//			pd.setSize(200,200);
 			pd.addWindowListener(new WindowAdapter() {
 				public void windowClosing(WindowEvent e) {
 					System.exit(0);

src/eva2/gui/PropertyDialog.java

@@ -15,8 +15,6 @@ package eva2.gui;
 import java.awt.BorderLayout;
 import java.awt.Component;
 import java.awt.Toolkit;
-import java.awt.event.WindowAdapter;
-import java.awt.event.WindowEvent;
 import java.beans.PropertyEditor;
 
 import eva2.EvAInfo;
@@ -55,7 +53,9 @@ public class PropertyDialog extends JEFrame {
   }
   
   protected static String getFrameNameFromEditor(PropertyEditor editor) {
-	  return EVAHELP.cutClassName(editor.getValue().getClass().getName());
+	  if (editor.getValue().getClass().isArray()) {
+		  return "Array of " + EVAHELP.cutClassName(editor.getValue().getClass().getComponentType().getName());
+	  } else return EVAHELP.cutClassName(editor.getValue().getClass().getName());
   }
   
   /**

src/eva2/gui/TopoPlot.java

@@ -84,6 +84,7 @@ public class TopoPlot extends Plot {
     double deltaY = sizeXY[1]/gridy;
     double maxDeriv=0;
     double[] pos = new double[2];
+    boolean TRACEMETH=false;
     //double fitRange = java.lang.Math.abs(problem.getMinFitness()-problem.getMaxFitness() );
     double fitRange = 0, max = -Double.MAX_VALUE, min = Double.MAX_VALUE, tmp;
     for (int x=0; x<gridx; x++) {
@@ -91,12 +92,14 @@ public class TopoPlot extends Plot {
     		pos[0] = border[0][0]+x*deltaX;
     		pos[1] = border[1][0]+y*deltaY;
     		tmp = (float)(problem.functionValue(pos));
+    		if (TRACEMETH) System.out.println(pos[0] + " " + pos[1] + " " + tmp);
     		if (tmp < min) min = tmp;
     		if (tmp > max) max = tmp;
-    		if (problem instanceof InterfaceFirstOrderDerivableProblem) {
+    		if (withGradientsIfAvailable && (problem instanceof InterfaceFirstOrderDerivableProblem)) {
     			double[] deriv = ((InterfaceFirstOrderDerivableProblem)problem).getFirstOrderGradients(pos);
     			for (int i=0; i<2;i++) maxDeriv=Math.max(maxDeriv, Math.abs(deriv[i])); // maximum deriv of first 2 dims
     		}
+
     	} // for y
     } // for x
     fitRange = java.lang.Math.abs(max - min);

src/eva2/server/go/enums/ESMutationInitialSigma.java

@@ -1,5 +1,5 @@
 package eva2.server.go.enums;
 
 public enum ESMutationInitialSigma {
-		halfRange, avgInitialDistance, userDefined;
+		halfRange, quarterRange, avgInitialDistance, userDefined;
 }

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

@@ -48,7 +48,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
 
     protected double[]                      m_Fitness               = new double[1];
     private double                          m_ConstraintViolation   = 0;
-    public boolean                          m_AreaConst4ParallelViolated = false;
+    public boolean                          m_AreaConst4ParallelViolated = false; // no idea what felix used this for...
     public boolean                          m_Marked                = false;    // is for GUI only!
     public boolean							m_isPenalized			= false;	// may be set true for penalty based constraints
 
@@ -143,7 +143,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
 //        m_Name              = new String(individual.m_Name);
     	m_dataHash			= (HashMap<String,Object>)(individual.m_dataHash.clone());
         m_ConstraintViolation = individual.m_ConstraintViolation;
-        m_AreaConst4ParallelViolated = individual.m_AreaConst4ParallelViolated;
+//        m_AreaConst4ParallelViolated = individual.m_AreaConst4ParallelViolated;
         m_Marked            = individual.m_Marked;
         m_isPenalized		= individual.m_isPenalized;
         individualIndex = individual.individualIndex;
@@ -181,7 +181,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
             // checking in mutation/crossover operators
             if (!this.m_MutationOperator.equals(indy.m_MutationOperator)) return false;
             if (!this.m_CrossoverOperator.equals(indy.m_CrossoverOperator)) return false;
-
+            System.err.println("Check whether this is semantically meant by equality!!! (AbstractEAIndividual.equals())");
             return true;
         } else {
             return false;
@@ -640,6 +640,28 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
     	return result;
     }
     
+    /** 
+     * This method will allow you to compare two individuals regarding the constraint violation only, 
+     * and only if the specific tag has been set. (For, e.g., additive penalty there is no way of discriminating
+     * fitness and penalty after assigning).
+     * The one with lesser violation is regarded better. If the instance is better than the given
+     * indy, 1 is returned. If it is worse than the given indy, -1 is returned. 
+     * If they both violate them equally, 0 is returned. 
+     * This means that if both do not violate the constraints, 0 is returned.
+     * 
+     * @param indy      The individual to compare to.
+     * @return 1 if the instance is better (regarding constraints only), -1 if is worse, 0 if they are equal in that respect.
+     */
+    public int compareConstraintViolation(AbstractEAIndividual indy) {
+        if ((this.m_ConstraintViolation > 0) && (indy.m_ConstraintViolation <= 0)) return -1;
+        if ((this.m_ConstraintViolation <= 0) && (indy.m_ConstraintViolation > 0)) return 1;
+        else {  // both violate: ((this.m_ConstraintViolation > 0) && (indy.m_ConstraintViolation > 0)) {
+            if (this.m_ConstraintViolation < indy.m_ConstraintViolation) return 1 ;
+            else if (this.m_ConstraintViolation > indy.m_ConstraintViolation) return -1;
+            else return 0;
+        }
+    }
+    
     /** This method will allow you to compare two individuals regarding the dominance.
      * Note this is dominance! If the individuals are not comparable this method will
      * return false!
@@ -647,16 +669,9 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
      * @return True if the own fitness dominates the other indy's
      */
     public boolean isDominatingDebConstraints(AbstractEAIndividual indy) {
-        double[]    tmpFitness  = indy.getFitness();
+        int constrViolComp = compareConstraintViolation(indy);
-        if (this.m_AreaConst4ParallelViolated) return false;
+        if (constrViolComp==0) return isDominatingFitness(getFitness(), indy.getFitness());
-        if (indy.m_AreaConst4ParallelViolated) return true;
+        else return (constrViolComp > 0);
-        if ((this.m_ConstraintViolation > 0) && (indy.m_ConstraintViolation == 0)) return false;
-        if ((this.m_ConstraintViolation == 0) && (indy.m_ConstraintViolation > 0)) return true;
-        if ((this.m_ConstraintViolation > 0) && (indy.m_ConstraintViolation > 0)) {
-            if (this.m_ConstraintViolation > indy.m_ConstraintViolation) return false;
-            else return true;
-        }
-        return isDominatingFitness(getFitness(), tmpFitness);
 //        for (int i = 0; (i < this.m_Fitness.length) && (i < tmpFitness.length); i++) {
 //            if (this.m_Fitness[i] <= tmpFitness[i]) result &= true;
 //            else result &= false;
@@ -677,7 +692,8 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
         return isDominatingFitnessNotEqual(getFitness(), indy.getFitness());
     }
 
-    /** This method will allow you to compare two individuals regarding the dominance.
+    /** 
+     * This method will allow you to compare two individuals regarding the dominance.
      * Note this is dominance! If the individuals are not comparable this method will
      * return false!
      *
@@ -686,19 +702,10 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
      */
     public boolean isDominatingDebConstraintsEqual(AbstractEAIndividual indy) {
     	// TODO: should this method really be called "..Equal"?
-        if (this.m_AreaConst4ParallelViolated) return false;
+        int constrViolComp = compareConstraintViolation(indy);
-        if (indy.m_AreaConst4ParallelViolated) return true;
+        if (constrViolComp==0) return isDominatingFitnessNotEqual(getFitness(), indy.getFitness());
-        if ((this.m_ConstraintViolation > 0) && (indy.m_ConstraintViolation == 0)) return false;
+        else return (constrViolComp > 0);
-        if ((this.m_ConstraintViolation == 0) && (indy.m_ConstraintViolation > 0)) return true;
+//        return isDominatingFitnessNotEqual(getFitness(), indy.getFitness());
-        if ((this.m_ConstraintViolation > 0) && (indy.m_ConstraintViolation > 0)) {
-            if (this.m_ConstraintViolation > indy.m_ConstraintViolation) return false;
-            else return true;
-        }
-//        for (int i = 0; (i < this.m_Fitness.length) && (i < tmpFitness.length); i++) {
-//            if (this.m_Fitness[i] < tmpFitness[i]) result &= true;
-//            else result &= false;
-//        }
-        return isDominatingFitnessNotEqual(getFitness(), indy.getFitness());
     }
 
     /** This method can be used to read the current selection probability of the individual.
@@ -869,14 +876,15 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
     public static String getDefaultStringRepresentation(AbstractEAIndividual individual) {
     	// Note that changing this method might change the hashcode of an individual 
     	// which might interfere with some functionality.
-        StringBuffer sb = new StringBuffer(getDefaultDataString(individual));
+        StringBuffer sb = new StringBuffer("Fit.: ");
-
-        sb.append(", fitness: ");
         sb.append(BeanInspector.toString(individual.getFitness()));
         if (individual.isMarkedPenalized() || individual.violatesConstraint()) 
         	sb.append(", X"); 
         sb.append(", ID: ");
         sb.append(individual.getIndyID());
+        sb.append(", ");
+        sb.append(getDefaultDataString(individual));
+
         if (individual.getParentIDs()!=null) {
         	sb.append(", parents: ");
             sb.append(BeanInspector.toString(individual.getParentIDs()));
@@ -996,10 +1004,35 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
 			for (int i=0; i<intData.length; i++) pos[i] = (double)intData[i];
 			return pos;
 		} // TODO check some more types here?
-		EVAERROR.errorMsgOnce("Unhandled case in AbstractEAIndividual.getPosition()!");
+		EVAERROR.errorMsgOnce("Unhandled case in AbstractEAIndividual.getDoublePosition()!");
 		return null;
 	}
     
+	/**
+	 * For any AbstractEAIndividual try to convert its position to double[] and return it.
+	 * Returns null if there is no conversion available.
+	 * 
+	 * @param indy
+	 * @return double valued position of an individual or null
+	 */
+	public static boolean setDoublePosition(AbstractEAIndividual indy, double[] pos) {
+		if (indy instanceof InterfaceESIndividual) {
+			((InterfaceESIndividual)indy).SetDGenotype(pos);
+			return true;
+		} else if (indy instanceof InterfaceDataTypeDouble) {
+			((InterfaceDataTypeDouble)indy).SetDoubleGenotype(pos);
+			return true;
+		} else if (indy instanceof InterfaceDataTypeInteger) {
+			EVAERROR.errorMsgOnce("Warning, double position truncated to integer! (AbstractEAIndividual.setDoublePosition)");
+			int[] intData = new int[pos.length];
+			for (int i=0; i<intData.length; i++) intData[i] = (int)pos[i];
+			((InterfaceDataTypeInteger)indy).SetIntGenotype(intData);
+			return true;
+		} // TODO check some more types here?
+		EVAERROR.errorMsgOnce("Unhandled case in AbstractEAIndividual.setDoublePosition()!");
+		return false;
+	}
+	
 	/**
 	 * Try to convert the individuals position to double[] and return it.
 	 * Returns null if there is no conversion available.

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

@@ -23,31 +23,31 @@ import java.util.Comparator;
 public class AbstractEAIndividualComparator implements Comparator<Object>, Serializable {
 
 	// flag whether a data field should be used.
-	String indyDataKey = "";
+	private String indyDataKey = "";
-	int fitCriterion = -1;
+	private int fitCriterion = -1;
+	private boolean preferFeasible = true;
 	
 	/**
 	 * Comparator implementation which compares two individuals based on their fitness.
-	 * The default version calls isDominatingDebConstraints() of the AbstractEAIndividual
+	 * The default version calls compares based on dominance with priority of feasibility if there are constraints.
-	 * class and assigns -1 if first is dominant, 1 if second is dominant, 0 if the two ind.s
+	 * It assigns -1 if first is better, 1 if second is better, 0 if the two ind.s are not comparable.
-	 * are not comparable.
 	 *
 	 */
 	public AbstractEAIndividualComparator() {
-		this("", -1);
+		this("", -1, true);
 	}
 	
 	/**
 	 * Constructor with data key. A data field of the individuals may be used to retrieve
 	 * the double array used for comparison. Both individuals must have a data field with
-	 * the given key and return a double array of the same dimension. No constraints are 
+	 * the given key and return a double array of the same dimension. Constraints are 
-	 * regarded for this comparison.
+	 * also regarded by default.
 	 * If indyDataKey is null, the default comparison is used.
 	 * 
 	 * @param indyDataKey
 	 */
 	public AbstractEAIndividualComparator(String indyDataKey) {
-		this(indyDataKey, -1);		
+		this(indyDataKey, -1, true);		
 	}
 	
 	/**
@@ -57,7 +57,11 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
 	 * @param fitnessCriterion
 	 */
 	public AbstractEAIndividualComparator(int fitnessCriterion) {
-		this("", fitnessCriterion);
+		this("", fitnessCriterion, true);
+	}
+	
+	public AbstractEAIndividualComparator(int fitIndex, boolean preferFeasible) {
+		this("", fitIndex, preferFeasible);
 	}
 	
 	/**
@@ -67,16 +71,20 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
 	 * @see #AbstractEAIndividualComparator(String)
 	 * @param indyDataKey
 	 * @param fitnessCriterion
+	 * @param priorizeConstraints
 	 */
-	public AbstractEAIndividualComparator(String indDataKey, int fitnessCriterion) {
+	public AbstractEAIndividualComparator(String indDataKey, int fitnessCriterion, boolean preferFeasible) {
-		indyDataKey = indDataKey;
+		this.indyDataKey = indDataKey;
-		fitCriterion = fitnessCriterion;	
+		this.fitCriterion = fitnessCriterion;
+		this.preferFeasible = preferFeasible;
 	}
 
 	public AbstractEAIndividualComparator(AbstractEAIndividualComparator other) {
 		indyDataKey = other.indyDataKey;
+		fitCriterion = other.fitCriterion;
+		preferFeasible = other.preferFeasible;
 	}
-	
+
 	public Object clone() {
 		return new AbstractEAIndividualComparator(this);
 	}
@@ -92,9 +100,16 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
 	public int compare(Object o1, Object o2) {
 		boolean o1domO2, o2domO1;
 		
-		if (indyDataKey != null && (indyDataKey.length()>0)) {
+		if (preferFeasible) { // check constraint violation first?
+			int constrViolComp = ((AbstractEAIndividual) o1).compareConstraintViolation((AbstractEAIndividual) o2);
+			if (constrViolComp>0) return -1;
+			else if (constrViolComp < 0) return 1;
+			// otherwise both do not violate, so regard fitness
+		}
+		if (indyDataKey != null && (indyDataKey.length()>0)) { // check specific key
 			double[] fit1 = (double[])((AbstractEAIndividual)o1).getData(indyDataKey);
 			double[] fit2 = (double[])((AbstractEAIndividual)o2).getData(indyDataKey);
+			if ((fit1==null) || (fit2==null)) throw new RuntimeException("Unknown individual data key " + indyDataKey + ", unable to compare individuals ("+this.getClass().getSimpleName()+")");
 			if (fitCriterion < 0) {
 				o1domO2 = AbstractEAIndividual.isDominatingFitness(fit1, fit2);
 				o2domO1 = AbstractEAIndividual.isDominatingFitness(fit2, fit1);
@@ -104,15 +119,15 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
 			}
 		} else {
 			if (fitCriterion < 0) {
-				o1domO2 = ((AbstractEAIndividual) o1).isDominatingDebConstraints((AbstractEAIndividual) o2);
+				o1domO2 = ((AbstractEAIndividual) o1).isDominating((AbstractEAIndividual) o2);
-				o2domO1 = ((AbstractEAIndividual) o2).isDominatingDebConstraints((AbstractEAIndividual) o1);
+				o2domO1 = ((AbstractEAIndividual) o2).isDominating((AbstractEAIndividual) o1);
 			} else {
 				if (((AbstractEAIndividual) o1).getFitness()[fitCriterion] == ((AbstractEAIndividual) o2).getFitness()[fitCriterion]) return 0;
 				return (((AbstractEAIndividual) o1).getFitness()[fitCriterion] < ((AbstractEAIndividual) o2).getFitness()[fitCriterion]) ? -1 : 1;
 			}
 		}
-		if (o1domO2 ^ o2domO1) return (o1domO2 ? -1 : 1);
+		if (o1domO2 ^ o2domO1) return (o1domO2 ? -1 : 1); // they are comparable
-		else return 0; // these are not comparable
+		else return 0; // they are not comparable
 	}
 
 	public String getIndyDataKey() {
@@ -135,6 +150,16 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
 		return "If -1, dominance is used, otherwise the indexed fitness criterion (for multiobjective problems)"; 
 	}
 	
+	public boolean isPreferFeasible() {
+		return preferFeasible;
+	}
+	public void setPreferFeasible(boolean priorConst) {
+		preferFeasible = priorConst;
+	}
+	public String preferFeasibleTipText() {
+		return "Activate preference of feasible individuals in any comparison acc. to Deb's rules.";
+	}
+	
 	public String globalInfo() {
 		return "A comparator class for general EA individuals. Compares individuals based on their fitness in context of minimization.";
 	}

src/eva2/server/go/operators/cluster/ClusteringDensityBased.java

@@ -46,6 +46,16 @@ public class ClusteringDensityBased implements InterfaceClustering, java.io.Seri
     	m_MinimumGroupSize = minGSize;
     }
     
+    /**
+     * Directly set the minimum cluster distance sigma and minimum group size.
+     * @param sigma the minimum cluster distance
+     */
+    public ClusteringDensityBased(double sigma, int minGSize, InterfaceDistanceMetric metric) {
+    	m_ClusterDistance = sigma;
+    	m_MinimumGroupSize = minGSize;
+    	m_Metric = metric;
+    }
+    
     public ClusteringDensityBased(ClusteringDensityBased a) {
         if (a.m_Metric != null) this.m_Metric           = (InterfaceDistanceMetric)a.m_Metric.clone();
         this.m_TestConvergingSpeciesOnBestOnly  = a.m_TestConvergingSpeciesOnBestOnly;

src/eva2/server/go/operators/cluster/ClusteringNearestBetter.java

@@ -11,6 +11,8 @@ import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.AbstractEAIndividualComparator;
 import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
 import eva2.server.go.operators.distancemetric.PhenotypeMetric;
+import eva2.server.go.operators.paramcontrol.ParamAdaption;
+import eva2.server.go.operators.paramcontrol.ParameterControlManager;
 import eva2.server.go.populations.Population;
 import eva2.tools.Pair;
 
@@ -34,7 +36,8 @@ public class ClusteringNearestBetter implements InterfaceClustering, Serializabl
     private double							currentMeanDistance = -1.;
     private int                         	minimumGroupSize  = 3;
 	private boolean 						testConvergingSpeciesOnBestOnly = true; // if two species are tested for convergence, only the best indies may be compared regarding the distance threshold 
-
+	protected ParameterControlManager		paramControl = new ParameterControlManager();
+	
 	private int[]                 			uplink;
     private double[]						uplinkDist;
     private AbstractEAIndividualComparator comparator = new AbstractEAIndividualComparator();
@@ -42,7 +45,7 @@ public class ClusteringNearestBetter implements InterfaceClustering, Serializabl
 	private static final String initializedForKey = "initializedClustNearestBetterOnHash";
 	private static final String initializedRefData = "initializedClustNearestBetterData";
 	
-	private static boolean TRACE = true;
+	private static boolean TRACE = false;
 
 	public ClusteringNearestBetter() {
 	}
@@ -58,9 +61,35 @@ public class ClusteringNearestBetter implements InterfaceClustering, Serializabl
     	this.testConvergingSpeciesOnBestOnly = o.testConvergingSpeciesOnBestOnly;
 	}
     
-    public void hideHideable() {
+    /**
+     * Set the mean distance factor in the adaptive case or the absolute distance
+     * threshold in the non-adaptive case.
+     * 
+     * @param adaptive
+     * @param thresholdOrFactor
+     * 
+     */
+    public ClusteringNearestBetter(boolean adaptive, double thresholdOrFactor) {
+    	setAdaptiveThreshold(adaptive);
+    	if (adaptive) setMeanDistFactor(thresholdOrFactor);
+    	else setDistThreshold(thresholdOrFactor);
+	}
+
+	public void hideHideable() {
     	setAdaptiveThreshold(isAdaptiveThreshold());
     }
+    
+	public ParameterControlManager getParamControl() {
+		return paramControl;
+	}
+	
+	public ParamAdaption[] getParameterControl() {
+		return paramControl.getSingleAdapters();
+	}
+	
+	public void setParameterControl(ParamAdaption[] paramControl) {
+		this.paramControl.setSingleAdapters(paramControl);
+	}
 
 	/** This method allows you to make a deep clone of
      * the object
@@ -289,7 +318,10 @@ public class ClusteringNearestBetter implements InterfaceClustering, Serializabl
 					// so add it to the cluster, mark it, and proceed recursively.
 					currentClust.add(sorted.get(children[current].get(i)));
 					clustered[children[current].get(i)]=true;
+					if (TRACE) System.out.println("Assigned " + current);
 					addChildren(children[current].get(i), clustered, sorted, currentClust);
+				} else {
+					if (TRACE) System.out.println("Not assigned " + current);
 				}
 			}
 		} else {

src/eva2/server/go/operators/crossover/CrossoverESDefault.java

@@ -7,7 +7,8 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.RNG;
 
 /**
- * Created by IntelliJ IDEA.
+ * Default 1-point-Crossover on InterfaceESIndividual instances.
+ * 
  * User: streiche
  * Date: 25.03.2003
  * Time: 11:16:39

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

@@ -1,5 +1,6 @@
 package eva2.server.go.operators.mutation;
 
+import eva2.gui.BeanInspector;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.InterfaceESIndividual;
 import eva2.server.go.populations.Population;
@@ -34,7 +35,7 @@ public class MutateESCovarianceMatrixAdaption implements InterfaceMutation, java
     protected Matrix              m_C;
     protected Matrix              B;
     protected boolean             m_CheckConstraints  = false;
-    protected int                 m_constraint        = 20;
+    protected int                 m_constraintMaxTries        = 50;
     protected int                 m_Counter;
     protected int                 m_frequency         = 1;
     protected double[]            m_Eigenvalues;
@@ -46,7 +47,7 @@ public class MutateESCovarianceMatrixAdaption implements InterfaceMutation, java
         this.m_Counter          = mutator.m_Counter;
         this.m_frequency        = mutator.m_frequency;
         this.m_InitSigmaScalar  = mutator.m_InitSigmaScalar;
-        this.m_constraint       = mutator.m_constraint;
+        this.m_constraintMaxTries       = mutator.m_constraintMaxTries;
         this.m_CheckConstraints = mutator.m_CheckConstraints;
         this.m_D                = mutator.m_D;
         this.m_SigmaGlobal      = mutator.m_SigmaGlobal;
@@ -181,7 +182,7 @@ public class MutateESCovarianceMatrixAdaption implements InterfaceMutation, java
             this.m_PathS[i]   = (1.0 - this.m_c) * this.m_PathS[i] + this.m_c * this.cu * Bz_d;
             pathLen          = pathLen + this.m_PathS[i] * this.m_PathS[i];
         }
-        this.m_SigmaGlobal = this.m_SigmaGlobal * Math.exp(this.Beta * this.m_c * (Math.sqrt(pathLen) - this.xi_dach));;
+        this.m_SigmaGlobal = this.m_SigmaGlobal * Math.exp(this.Beta * this.m_c * (Math.sqrt(pathLen) - this.xi_dach));
   }
 
     protected void evaluateNewObjectX(double[] x,double[][] range) {
@@ -218,8 +219,10 @@ public class MutateESCovarianceMatrixAdaption implements InterfaceMutation, java
 //            }
 //        }
 //        for (int i = 0; i < N; i++) x[i] = tmpD[i];
-        // conservation of mutaion direction:
+        // conservation of mutation direction:
-        double[] old = (double[]) this.m_Z.clone();
+        //double[] oldZ = (double[]) this.m_Z.clone();
+        double[] oldX = (double[])x.clone();
+        
         for (int i = 0; i < this.m_D; i++) this.m_Z[i] = RNG.gaussianDouble(1.0);
 
         this.m_C = (this.m_C.plus(this.m_C.transpose()).times(0.5)); // MAKE C SYMMETRIC
@@ -232,9 +235,9 @@ public class MutateESCovarianceMatrixAdaption implements InterfaceMutation, java
             this.m_Eigenvalues  = helper.getRealEigenvalues();
 
         }
-        boolean constraint  = false;
+        boolean isNewPosFeasible  = false;
         int     counter     = 0;
-        while (constraint == false && counter < this.m_constraint) {
+        while (!isNewPosFeasible && counter < this.m_constraintMaxTries) {
             for (int i = 0; i < this.m_D; i++) {
                 this.Bz[i] = 0;
                 for (int j = 0; j < this.m_D; j++) {
@@ -242,22 +245,28 @@ public class MutateESCovarianceMatrixAdaption implements InterfaceMutation, java
                 }
                 x[i] = x[i] + this.m_SigmaGlobal * this.Bz[i]; // here is the new value
             }
-            constraint = true;
+            isNewPosFeasible = true;
             if (this.m_CheckConstraints == true) {
                 for (int i = 0; i < m_D; i++) {
                     if (x[i] < range[i][0] || x[i] > range[i][1]) {
                     	// undo the step and try new Z
-                        for (int j = 0; j < this.m_D; j++) x[j] = x[j] - this.m_SigmaGlobal * this.Bz[j];
+                        for (int j = 0; j < this.m_D; j++) x[j] = oldX[j] - this.m_SigmaGlobal * this.Bz[j];
-                        this.m_Z[i] = RNG.gaussianDouble(1.0);
+                        this.m_Z[i] = RNG.gaussianDouble(1.0); // TODO is this feasible? mal mit rank-mu testen
-                        constraint = false;
+                        isNewPosFeasible = false;
                         counter++;
                         break;
                     }
                 }
             }
         }
-        if (this.m_CheckConstraints) { // CSpieth
+        if (counter>0) {
+//        	System.out.print("CMA ES Req " + counter + " ");
+//        	if (counter > 15) System.out.println(BeanInspector.toString(x));
+//        	else System.out.println();
+        }
+        if (this.m_CheckConstraints && !isNewPosFeasible) { // use force
         	Mathematics.projectToRange(x, range);
+//        	System.err.println("PROJECTING BY FORCE");
         }
     }
 

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

@@ -9,6 +9,7 @@ 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.operators.distancemetric.EuclideanMetric;
 import eva2.server.go.populations.Population;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.server.go.strategies.EvolutionStrategies;
@@ -139,7 +140,10 @@ class CMAParamSet implements InterfacePopulationChangedEventListener, Serializab
 //		c_u_sig = Math.sqrt(c_sig * (2.-c_sig));
 		params.d_sig = params.c_sig+1+2*Math.max(0, Math.sqrt((muEff-1)/(dim+1)) - 1);
 		
-		if (initialSigma<0) initialSigma = Mathematics.getAvgRange(params.range);
+		if (initialSigma<0) { // this means we scale the average range
+			if (initialSigma!=-0.25 && (initialSigma!=-0.5)) EVAERROR.errorMsgOnce("Warning, unexpected initial sigma in CMAParamSet!");
+			initialSigma = -initialSigma*Mathematics.getAvgRange(params.range);
+		}
 		if (initialSigma <= 0) {
 			EVAERROR.errorMsgOnce("warning: initial sigma <= zero! Working with converged population?");
 			initialSigma = 10e-10;
@@ -248,7 +252,7 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 	// instance, however this would create quite a big baustelle.
 	private static transient CMAParamSet lastParams=null;
 
-	private ESMutationInitialSigma initializeSig = ESMutationInitialSigma.avgInitialDistance;
+	private ESMutationInitialSigma initializeSig = ESMutationInitialSigma.quarterRange;
 	private double userDefInitSig = 0.2;
 	public static final String cmaParamsKey = "RankMuCMAParameters";
 	
@@ -263,6 +267,7 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 		this.c_c            = mutator.c_c;
 		this.expRandStepLen     = mutator.expRandStepLen;
 		this.initializeSig = mutator.initializeSig;
+		this.userDefInitSig = mutator.userDefInitSig;
 	}
 
 	public Object clone() {
@@ -272,7 +277,8 @@ 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.
+	 * For the halfRange case, -1 is returned, as the range is not available here but set 
+	 * in initializing the CMAParams.
 	 * @param initGen
 	 * @return
 	 */
@@ -282,11 +288,13 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 			// scaled by average range as the measures are normed
 			//return initGen.getPopulationMeasures(null)[0]*getAvgRange();
 			// use euclidian measures without normation and scaling
-			return initGen.getPopulationMeasures()[0];
+			return initGen.getPopulationMeasures(new EuclideanMetric())[0]; // use euclidean metric which is not normed by range instead of phenotype metric
 		//case halfRange: return getAvgRange(range)/2.;
 		case userDefined: return userDefInitSig ;
-		default: return -1.;
+		case halfRange: return -0.5;
+		case quarterRange: return -0.25;
 		}
+		throw new RuntimeException("Unknown initial sigma type!");
 	}
 	
 	/**
@@ -554,7 +562,8 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
                     * additional rank mu
                     * update
                     */
-                    	double[] x_k = ((InterfaceDataTypeDouble)selected.getEAIndividual(k)).getDoubleData();
+//                    	double[] x_k = ((InterfaceDataTypeDouble)selected.getEAIndividual(k)).getDoubleData();
+                    	double[] x_k = AbstractEAIndividual.getDoublePositionShallow(selected.getEAIndividual(k));
                     	newVal = params.mC.get(i,j)+ ccv * (1 - 1. / mcv)
                         * params.weights[k]	* (x_k[i] - params.meanX[i])
                         							* (x_k[j] - params.meanX[j]) / (getSigma(params, i) * getSigma(params, j)); // TODO right sigmas?
@@ -753,7 +762,7 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 		}
 		if (Mathematics.isInRange(x, range)) return x;
 		else {
-			if (count > 5) return repairMutation(x, range); // allow some nice tries before using brute force
+			if (count > 2*x.length) return repairMutation(x, range); // allow some nice tries before using brute force
 			else return mutate(params, x, range, count+1); // for really bad initial deviations this might be a quasi infinite loop
 		}
 	}
@@ -764,6 +773,7 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 //	    % (the latter will decrease the overall step size) and
 //	    % recalculate arx accordingly. Do not change arx or arz in any
 //	    % other way.
+		EVAERROR.errorMsgOnce("Warning, brute-forcing constraints! Too large initial sigma? (pot. multiple errors)");
 		Mathematics.projectToRange(x, range);
 		return x;	
 	}
@@ -896,4 +906,8 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 	public void setUserDefInitSig(double userDefInitSig) {
 		this.userDefInitSig = userDefInitSig;
 	}
+	
+	public String userDefInitSigTipText() {
+		return "Set a manual initial sigma which should be related to the initial individual distribution.";
+	}
 }

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

@@ -113,7 +113,7 @@ public class MutateGIOrdinal implements InterfaceMutation, java.io.Serializable
      * @return description
      */
     public String globalInfo() {
-        return "The ordinal mutation alters n element of the int attributes based on an oridinal ordering.";
+        return "The ordinal mutation alters n element of the int attributes based on an ordinal ordering.";
     }
 
     /** This method allows you to set the mean step size.

src/eva2/server/go/operators/paramcontrol/ParameterControlManager.java

@@ -1,11 +1,13 @@
 package eva2.server.go.operators.paramcontrol;
 
 import java.io.Serializable;
+import java.util.ArrayList;
 import java.util.Vector;
 
 import eva2.gui.BeanInspector;
 import eva2.server.go.populations.Population;
 import eva2.server.modules.Processor;
+import eva2.tools.Pair;
 
 /**
  * The ParameterControlManager handles an array of ParamAdaption instances which dynamically adapt
@@ -144,4 +146,25 @@ public class ParameterControlManager implements InterfaceParameterControl, Seria
 	public String getName() {
 		return "ParameterControlManager";
 	}
+
+	/**
+	 * Retrieve a list of objects which are properties of the given target object (retrievable by a 
+	 * getter method) and which implement the getParamControl method.
+	 * 
+	 * @param target
+	 * @return
+	 */
+	public static Object[] arrayOfControllables(
+			Object target) {
+		Pair<String[],Object[]> propsNamesVals = BeanInspector.getPublicPropertiesOf(target, true);
+		ArrayList<Object> controllables = new ArrayList<Object>();
+		Object[] objs = propsNamesVals.tail;
+		for (int i=0; i<objs.length; i++) {
+			if (objs[i]!=null) {
+				// TODO avoid hasMethod recreate some interface for this??
+				if (BeanInspector.hasMethod(objs[i], "getParamControl")!=null) controllables.add(objs[i]);
+			}
+		}
+		return controllables.toArray(new Object[controllables.size()]);
+	}
 }

src/eva2/server/go/operators/postprocess/PostProcess.java

@@ -34,8 +34,11 @@ import eva2.server.go.problems.AbstractMultiModalProblemKnown;
 import eva2.server.go.problems.AbstractOptimizationProblem;
 import eva2.server.go.problems.FM0Problem;
 import eva2.server.go.problems.Interface2DBorderProblem;
+import eva2.server.go.problems.InterfaceFirstOrderDerivableProblem;
+import eva2.server.go.problems.InterfaceInterestingHistogram;
 import eva2.server.go.problems.InterfaceMultimodalProblemKnown;
 import eva2.server.go.strategies.EvolutionStrategies;
+import eva2.server.go.strategies.GradientDescentAlgorithm;
 import eva2.server.go.strategies.HillClimbing;
 import eva2.server.go.strategies.NelderMeadSimplex;
 import eva2.server.modules.GOParameters;
@@ -61,6 +64,8 @@ public class PostProcess {
 	private static double minMutationStepSize = 0.0000000000000001;
 	// used for hill climbing post processing and only alive during that period
 	private static OptimizerRunnable ppRunnable = null;
+	public static final String movedDistanceKey = "PostProcessingMovedBy";
+	public static final String movedToPositionKey = "PostProcessingMovedTo";
 	
 	public static final int BEST_ONLY = 1;
 	public static final int BEST_RAND = 2;
@@ -157,9 +162,9 @@ public class PostProcess {
      * Cluster the population. Return for every cluster a subset of representatives which are the best individuals 
      * or a random subset of the cluster or the best and a random subset. Returns shallow copies!
      * returnQuota should be in  [0,1] and defines, which percentage of individuals of each cluster is kept, however
-     * if returnQuota is > 0, at least one is kept.
+     * at least one is kept per cluster.
      * lonerMode defines whether loners are discarded, kept, or treated as clusters, meaning they are kept if returnQuota > 0.
-     * takOverMode defines whether, of a cluster with size > 1, which n individuals are kept. Either the n best only,
+     * takeOverMode defines whether, of a cluster with size > 1, which n individuals are kept. Either the n best only,
      * or the single best and random n-1, or all n random. 
      * 
      * @param pop
@@ -231,9 +236,9 @@ public class PostProcess {
      * @param upper
      * @return
      */
-    public static Population filterFitnessIn(Population pop, double lower, double upper) {
+    public static Population filterFitnessIn(Population pop, double lower, double upper, int crit) {
-    	Population result = filterFitness(pop, upper, true);
+    	Population result = filterFitness(pop, upper, true, crit);
-    	return filterFitness(result, lower, false);
+    	return filterFitness(result, lower, false, crit);
     }
     
     /**
@@ -245,7 +250,7 @@ public class PostProcess {
      * @param bSmaller if true, return individuals with lower or equal, else with higher fitness norm only
      * @return
      */
-    public static Population filterFitness(Population pop, double fitNorm, boolean bSmallerEq) {
+    public static Population filterFitnessNormed(Population pop, double fitNorm, boolean bSmallerEq) {
     	AbstractEAIndividual indy;
     	Population result = new Population();
     	for (int i=0; i<pop.size(); i++) {
@@ -257,31 +262,33 @@ public class PostProcess {
     }
     
     /**
-     * Create a fitness histogram of an evaluated population within the given interval and nBins number of bins.
+     * Filter the individuals of a population which have a fitness below a given value.
-     * Therefore a bin is of size (upperBound-lowerBound)/nBins, and bin 0 starts at lowerBound.
+     * If the single fitness criterion is valid (within the range of the fitness array),
-     * Returns an integer array with the number of individuals in each bin.  
+     * the absolute value of that criterion is compared, otherwise (e.g. crit=-1) the
+     * phenotypic norm of the full vector is used.
+     * Returns shallow copies!
      * 
-     * @param pop	the population to scan.
+     * @param pop
-     * @param lowerBound	lower bound of the fitness interval
+     * @param fitNorm
-     * @param upperBound	upper bound of the fitness interval
+     * @param bSmaller if true, return individuals with lower or equal, else with higher fitness only
-     * @param nBins	number of bins
+     * @param crit index of the fitness criterion or -1 to use the norm
-     * @return	an integer array with the number of individuals in each bin
+     * @return
-     * @see filterFitnessIn()
      */
-    public static int[] createFitNormHistogram(Population pop, double lowerBound, double upperBound, int nBins) {
+    public static Population filterFitness(Population pop, double fitThresh, boolean bSmallerEq, int crit) {
-    	int[] res = new int[nBins];
+    	double curFit;
-    	double lower = lowerBound;
+    	AbstractEAIndividual indy;
-    	double step = (upperBound - lowerBound) / nBins;
+    	Population result = new Population();
-    	for (int i=0; i<nBins; i++) {
+    	for (int i=0; i<pop.size(); i++) {
-//    		if (TRACE) System.out.println("checking between " + lower + " and " + (lower+step));
+			indy = pop.getEAIndividual(i);
-    		res[i] = filterFitnessIn(pop, lower, lower+step).size();
+			if ((crit>=0) && (crit<indy.getFitness().length)) curFit = indy.getFitness(crit);
-//    		if (TRACE) System.out.println("found " + res[i]);
+			else curFit = PhenotypeMetric.norm(indy.getFitness());
-    		lower += step;
+			
-    	}
+			if (bSmallerEq && (curFit<=fitThresh)) result.add(indy);
-    	return res;
+			else if (!bSmallerEq && (curFit>fitThresh)) result.add(indy);
+		}
+    	return result;
     }
     
-    
 //    /** 
 //     * This method returns a set of individuals corresponding to an optimum in a given list.
 //     * The individuals returned are to be nearer than epsilon to a given optimum. It is not
@@ -399,6 +406,28 @@ public class PostProcess {
 		runPP();
 	}
 	
+	// TODO ! test this
+	public static int processWithGDA(Population pop, AbstractOptimizationProblem problem, InterfaceTerminator term, int baseEvals, double minStepSize, double maxStepSize) {
+		GradientDescentAlgorithm gda = new GradientDescentAlgorithm();
+		gda.setAdaptStepSizeLocally(true);
+		gda.SetProblem(problem);
+		gda.setLocalMinStepSize(minStepSize);
+		gda.setLocalMaxStepSize(maxStepSize);
+		gda.setRecovery(false);
+		gda.initByPopulation(pop, false);
+
+		int funCallsBefore = pop.getFunctionCalls();
+		pop.SetFunctionCalls(baseEvals);
+
+		ppRunnable = new OptimizerRunnable(OptimizerFactory.makeParams(gda, pop, problem, 0, term), true);
+		ppRunnable.getStats().createNextGenerationPerformed(gda.getPopulation(), gda, null);
+
+		int funCallsDone = pop.getFunctionCalls()-baseEvals;
+		pop.SetFunctionCalls(funCallsBefore);
+
+		return funCallsDone;
+	}
+
 	/**
 	 * Search for a local minimum using nelder mead and return the solution found and the number of steps
 	 * (evaluations) actually performed. This uses the whole population as starting population for nelder mead
@@ -426,7 +455,17 @@ public class PostProcess {
 		// as nms creates a new population and has already evaluated them, send a signal to stats
 		ppRunnable.getStats().createNextGenerationPerformed(nms.getPopulation(), nms, null);
 		
+//		if (problem instanceof InterfaceFirstOrderDerivableProblem) {
+//			double[] x = pop.getBestEAIndividual().getDoublePosition();
+//			System.out.println("grads: " + BeanInspector.toString(((InterfaceFirstOrderDerivableProblem)problem).getFirstOrderGradients(x)));
+//		}
+		
 		runPP();
+		
+//		if (problem instanceof InterfaceFirstOrderDerivableProblem) {
+//			double[] x = pop.getBestEAIndividual().getDoublePosition();
+//			System.out.println("grads: " + BeanInspector.toString(((InterfaceFirstOrderDerivableProblem)problem).getFirstOrderGradients(x)));
+//		}
 
 		int funCallsDone = pop.getFunctionCalls()-baseEvals;
 		pop.SetFunctionCalls(funCallsBefore);
@@ -520,25 +559,46 @@ public class PostProcess {
 	 * @param maxPerturbation
 	 * @param includeCand
 	 */
-	public static Population createLSSupPopulation(PostProcessMethod method, AbstractOptimizationProblem problem, Population candidates, int index, double maxPerturbation, boolean includeCand) {
+	public static Population createLSSupPopulation(PostProcessMethod method, AbstractOptimizationProblem problem, Population candidates, int index, double maxRelativePerturbation, boolean includeCand) {
 		Population subPop = null;
 		switch (method) {
 		case cmaES:
-			subPop = createPopInSubRange(maxPerturbation, problem, candidates.getEAIndividual(index));
+			// target size minus one because indy is added later
+			subPop = new Population();
+			createPopInSubRange(subPop, maxRelativePerturbation, getDefCMAPopSize(candidates.getEAIndividual(index))-1, candidates.getEAIndividual(index));
 			break;
 		case hillClimber:
 			System.err.println("INVALID in createLSSupPopulation");
 			break;
 		case nelderMead:
 			double[][] range = ((InterfaceDataTypeDouble)candidates.getEAIndividual(index)).getDoubleRange();
-			double perturb = findNMSPerturb(candidates, index, maxPerturbation);
+			double perturb = findNMSPerturb(candidates, index, relToAbsPerturb(maxRelativePerturbation, range));
 			if (TRACE) System.out.println("perturb " + index + " is " + perturb);
-			subPop = NelderMeadSimplex.createNMSPopulation(candidates.getEAIndividual(index), perturb, range, false); 
+			subPop = NelderMeadSimplex.createNMSPopulation(candidates.getEAIndividual(index), absToRelPerturb(perturb, range), range, false); 
 		}
 //		subPop.setSameParams(candidates);
 		return subPop;
 	}
 	
+	public static double relToAbsPerturb(double maxRelativePerturbation, double[][] range) {
+		return maxRelativePerturbation*(Mathematics.getAvgRange(range));
+	}
+	
+	public static double absToRelPerturb(double maxAbsPerturbation, double[][] range) {
+		return maxAbsPerturbation/(Mathematics.getAvgRange(range));
+	}
+	
+	private static int getDefCMAPopSize(AbstractEAIndividual template) {
+		if (isDoubleCompliant(template)) {
+			double[][] range = getDoubleRange(template);
+			int targetSize = (int) (4.0 + 3.0 * Math.log(range.length)); // minus one because indy is added later
+			return targetSize;
+		} else {
+			System.err.println("Warning, invalid individual for PostProcess.getDefCMAPopSize");
+			return 10;
+		}
+	}
+
 	/**
 	 * Employ hill-climbing directly or NM/CMA on the candidates. The candidate population
 	 * must not be empty and candidates must implement InterfaceDataTypeDouble.
@@ -588,7 +648,8 @@ public class PostProcess {
 	 * improved by checking the convergence state in the future. The given terminator will be applied to each
 	 * candidate sub-population anew. If the terminator is null, 10*n steps will be performed on each candidate.
 	 * 
-	 * A double value is added to each solution individual that replaces its ancestor candidate, using the key "PostProcessingMovedBy".
+	 * A double value is added to each solution individual that replaces its ancestor candidate, using the key 
+	 * PostProcess.movedDistanceKey.
 	 * It indicates the phenotype distance the found solution has moved relatively to the original candidate.
 	 * 
 	 * @see #findNMSPerturb(Population, int, double)
@@ -598,17 +659,17 @@ public class PostProcess {
 	 * @param method	NM or CMA is allowed here
 	 * @param candidates
 	 * @param term
-	 * @param maxPerturbation perturbation for the sub population
+	 * @param maxRelativePerturbation perturbation for the sub population relative to problem range
 	 * @param prob
 	 * @return the number of evaluations performed
 	 */
-	public static int processSingleCandidatesNMCMA(PostProcessMethod method, Population candidates, InterfaceTerminator term, double maxPerturbation, AbstractOptimizationProblem prob) {
+	public static int processSingleCandidatesNMCMA(PostProcessMethod method, Population candidates, InterfaceTerminator term, double maxRelativePerturbation, AbstractOptimizationProblem prob) {
 		ArrayList<Population> nmPops = new ArrayList<Population>();
 		int stepsPerf = 0;
 		Population subPop;
 		
 		for (int i=0; i<candidates.size(); i++) { // create all subPopulations
-			subPop = createLSSupPopulation(method, prob, candidates, i, maxPerturbation, false);
+			subPop = createLSSupPopulation(method, prob, candidates, i, maxRelativePerturbation, false);
 			
 			prob.evaluate(subPop);
 			stepsPerf += subPop.size();
@@ -642,7 +703,9 @@ public class PostProcess {
 //				if (subPop.getBestEAIndividual().isDominant(candidates.getEAIndividual(i))) { // TODO Multiobjective???
 				if (subPop.getBestEAIndividual().getFitness(0)<candidates.getEAIndividual(i).getFitness(0)) {
 //					System.out.println("moved by "+ PhenotypeMetric.dist(candidates.getEAIndividual(i), subPop.getBestEAIndividual()));
-					subPop.getBestEAIndividual().putData("PostProcessingMovedBy", new Double(PhenotypeMetric.dist(candidates.getEAIndividual(i), subPop.getBestEAIndividual())));
+					subPop.getBestEAIndividual().putData(movedDistanceKey, new Double(PhenotypeMetric.dist(candidates.getEAIndividual(i), subPop.getBestEAIndividual())));
+//					subPop.getBestEAIndividual().putData(movedToPositionKey, subPop.getBestEAIndividual().getDoublePosition());
+					// ^ this makes no sense here since the new position is returned anyways by replacing the candidate individual
 					candidates.set(i, subPop.getBestEAIndividual());
 				}
 			} else {
@@ -689,36 +752,33 @@ public class PostProcess {
 	 * problem constraints, meaning that the box may be smaller at the brim of the problem-defined search range.
 	 * 
 	 * @param searchBoxLen
-	 * @param prob
 	 * @param indy
 	 * @return
 	 */
-	private static Population createPopInSubRange(double searchBoxLen,
+	public static void createPopInSubRange(Population destPop, double searchBoxLen,
-			AbstractOptimizationProblem prob,
+			int targetSize,
 			AbstractEAIndividual indy) {
 		if (isDoubleCompliant(indy)) {
 			double[][] range = getDoubleRange(indy);
 			double[] data = getDoubleData(indy);
-			int lambda= (int) (4.0 + 3.0 * Math.log(range.length));
 			double[][] newRange = new double[range.length][2];
 			for (int dim=0; dim<range.length; dim++) {
 				// create a small range array around the expected local optimum 
 				newRange[dim][0] = Math.max(range[dim][0], data[dim]-(searchBoxLen/2.));
 				newRange[dim][1] = Math.min(range[dim][1], data[dim]+(searchBoxLen/2.));
 			}
-			Population pop = new Population();
+//			Population pop = new Population();
-			for (int i=0; i<lambda-1; i++) { // minus one because indy is added later
+			destPop.clear();
+			for (int i=0; i<targetSize; i++) { 
 				AbstractEAIndividual tmpIndy = (AbstractEAIndividual)indy.clone();
 				data = getDoubleData(tmpIndy);
 				ESIndividualDoubleData.defaultInit(data, newRange);
 				setDoubleData(tmpIndy, data);
-				pop.addIndividual(tmpIndy);
+				destPop.addIndividual(tmpIndy);
 			}
-			pop.synchSize();
+			destPop.synchSize();
-			return pop;
 		} else {
 			System.err.println("invalid individual type!");
-			return null;
 		}
 	}
 
@@ -760,7 +820,7 @@ public class PostProcess {
 		if (plot == null) {
 			plot = new TopoPlot("PostProcessing: " + title, "x", "y",range[0],range[1]);
 		    if (prob instanceof Interface2DBorderProblem) {
-				plot.setParams(60, 60);
+		    	plot.setParams(60, 60);
 		    	plot.setTopology((Interface2DBorderProblem)prob);
 		    }
 		}
@@ -860,9 +920,9 @@ public class PostProcess {
 		if (prob instanceof InterfaceMultimodalProblemKnown) {
 			InterfaceMultimodalProblemKnown mmkProb = (InterfaceMultimodalProblemKnown)prob;
 			listener.println("number of known optima is " + mmkProb.getRealOptima().size());
-			listener.println("default epsilon is " + mmkProb.getEpsilon());
+			listener.println("default epsilon is " + mmkProb.getDefaultAccuracy());
-			listener.println("optima found with default epsilon: " + getFoundOptima(solutions, mmkProb.getRealOptima(), mmkProb.getEpsilon(), true).size());
+			listener.println("optima found with default epsilon: " + getFoundOptima(solutions, mmkProb.getRealOptima(), mmkProb.getDefaultAccuracy(), true).size());
-			listener.println("max peak ratio is " + mmkProb.getMaximumPeakRatio(getFoundOptima(solutions, mmkProb.getRealOptima(), mmkProb.getEpsilon(), true)));
+			listener.println("max peak ratio is " + mmkProb.getMaximumPeakRatio(getFoundOptima(solutions, mmkProb.getRealOptima(), mmkProb.getDefaultAccuracy(), true)));
 			for (double epsilon=0.1; epsilon > 0.00000001; epsilon/=10.) {
 				//	out.println("no optima found: " + ((InterfaceMultimodalProblemKnown)mmProb).getNumberOfFoundOptima(pop));
 				listener.println("found " + getFoundOptima(solutions, mmkProb.getRealOptima(), epsilon, true).size() + " for epsilon = " + epsilon + ", maxPeakRatio: " + AbstractMultiModalProblemKnown.getMaximumPeakRatio(mmkProb,solutions, epsilon));
@@ -885,6 +945,24 @@ public class PostProcess {
 		}
 	}
 	
+	/**
+	 * Cluster the potential optima from a population using {@link #getFoundOptima(Population, Population, double, boolean)} or
+	 * extractPotentialOptima {@link AbstractOptimizationProblem}. Overwrites the given histogram and returns the found solutions
+	 * within a new population.
+	 * 
+	 * @param pop
+	 * @param prob
+	 * @param hist
+	 * @param accuracy
+	 * @param maxPPEvalsPerIndy
+	 * @return
+	 */
+	public static Population clusterBestUpdateHistogram(Population pop, AbstractOptimizationProblem prob, SolutionHistogram hist, int crit, double accuracy) {
+		Population opts = clusterBest(pop, accuracy, 0, KEEP_LONERS, BEST_ONLY);
+		hist.updateFrom(opts, crit);
+		return opts;
+	}
+	
 	/**
 	 * General post processing method, receiving parameter instance for specification.
 	 * Optional clustering and HC step, output contains population measures, fitness histogram and
@@ -897,7 +975,7 @@ public class PostProcess {
 	 * @return the clustered, post-processed population
 	 */
 	public static Population postProcess(InterfacePostProcessParams params, Population inputPop, AbstractOptimizationProblem problem, InterfaceTextListener listener) {
-		if (params.isDoPostProcessing()) {
+		if (params.isDoPostProcessing() && (inputPop!=null)) {
 			Plot plot;
 			
 			Population clusteredPop, outputPop, stateBeforeLS;
@@ -945,11 +1023,17 @@ public class PostProcess {
 			double upBnd = PhenotypeMetric.norm(outputPop.getWorstEAIndividual().getFitness())*1.1;
 			upBnd = Math.pow(10,Math.floor(Math.log10(upBnd)+1));
 			double lowBnd = 0;
-			int[] sols = PostProcess.createFitNormHistogram(outputPop, lowBnd, upBnd, 20);
+			int fitCrit = 0; // use first fitness criterion
+			SolutionHistogram solHist = SolutionHistogram.createFitNormHistogram(outputPop, lowBnd, upBnd, 20, fitCrit);
 //			PostProcessInterim.outputResult((AbstractOptimizationProblem)goParams.getProblem(), outputPop, 0.01, System.out, 0, 2000, 20, goParams.getPostProcessSteps());
 			if (outputPop.size()>1) {
 				if (listener != null) listener.println("measures: " + BeanInspector.toString(outputPop.getPopulationMeasures()));
-				if (listener != null) listener.println("solution histogram in [" + lowBnd + "," + upBnd + "]: " + BeanInspector.toString(sols));
+				if (listener != null) listener.println("solution histogram: " + solHist + ", score " + solHist.getScore());
+				if ((listener != null) && (problem instanceof InterfaceInterestingHistogram)) {
+					SolutionHistogram pSolHist = ((InterfaceInterestingHistogram)problem).getHistogram();
+					pSolHist.updateFrom(outputPop, fitCrit);
+					listener.println("problem-defined histogram: " + pSolHist + ", score " + pSolHist.getScore());
+				}
 			}
 			
 			//////////// multimodal data output
@@ -1002,7 +1086,7 @@ public class PostProcess {
 	 * @param maxPerturb optional upper bound of the returned perturbation
 	 * @return
 	 */
-	public static double findNMSPerturb(Population candidates, int i, double maxPerturb) {
+	public static double findNMSPerturb(Population candidates, int i, double maxAbsPerturb) {
 		double minDistNeighbour = Double.MAX_VALUE;
 		AbstractEAIndividual indy = candidates.getEAIndividual(i);
 		boolean found=false;
@@ -1019,13 +1103,13 @@ public class PostProcess {
 		}
 		if (!found) {
 //			System.err.println("warning, equal candidates in PostProcess.findNMSPerturb - converged population?!");
-			if (maxPerturb>0) return maxPerturb;
+			if (maxAbsPerturb>0) return maxAbsPerturb;
 			else {
 				System.err.println("error, unable to select perturbance value in PostProcess.findNMSPerturb since all candidates are equal. Converged population?!");
 				return 0.01;
 			}
 		}
-		if (maxPerturb>0) return Math.min(maxPerturb, minDistNeighbour/3.);
+		if (maxAbsPerturb>0) return Math.min(maxAbsPerturb, minDistNeighbour/3.);
 		else return minDistNeighbour/3.;
 	}
 	

src/eva2/server/go/operators/postprocess/SolutionHistogram.java

@@ -0,0 +1,166 @@
+package eva2.server.go.operators.postprocess;
+
+import java.util.Arrays;
+
+import eva2.gui.BeanInspector;
+import eva2.server.go.populations.Population;
+import eva2.tools.math.Mathematics;
+
+public class SolutionHistogram {
+	private double lBound, uBound;
+	private int numBins;
+	private int[] histogram;
+	private int arity; // number of summed-up histograms
+	
+	public SolutionHistogram(double lower, double upper, int nBins) {
+		lBound=lower;
+		uBound=upper;
+		numBins=nBins;
+		histogram = new int[numBins];
+		arity = 0;
+	}
+	
+	public SolutionHistogram cloneEmpty() {
+		return new SolutionHistogram(getLowerBound(), getUpperBound(), getNumBins());
+	}
+	
+	public boolean isEmtpy() {
+		return arity==0;
+	}
+	
+	private void setEntry(int i, int v) {
+		histogram[i]=v;
+	}
+	
+	public int getEntry(int i) {
+		return histogram[i];
+	}
+	
+	public double lowerBoundOfEntry(int i) {
+		return lBound+(i*getStepSize());
+	}
+	public double upperBoundOfEntry(int i) {
+		return lBound+((i+1)*getStepSize());
+	}
+	public double getLowerBound() {
+		return lBound;
+	}
+
+	public double getUpperBound() {
+		return uBound;
+	}
+
+	public int getNumBins() {
+		return numBins;
+	}
+
+	public double getStepSize() {
+		return (uBound-lBound)/numBins;
+	}
+	
+	public boolean isCompatible(SolutionHistogram o) {
+		if (lBound==o.getLowerBound() && (uBound==o.getUpperBound()) && (numBins==o.getNumBins())) return true;
+		else return false;
+	}
+	
+	/**
+	 * Add another histogram to this one.
+	 * @param o
+	 */
+	public void addHistogram(SolutionHistogram o) {
+		if (o.isEmtpy()) System.err.println("Warning, adding empty histogram... (SolutionHistogram)");
+		if (isCompatible(o)) {
+			arity+=o.arity;
+			for (int i=0; i<numBins; i++) histogram[i]+=o.histogram[i];
+		}
+	}
+	
+	/**
+	 * Get the average of summed-up histograms.
+	 * @return
+	 */
+	public double[] getAverage() {
+		double[] avg = new double[numBins];
+		for (int i=0; i<numBins; i++) avg[i]=((double)histogram[i])/arity;
+		return avg;
+	}
+	
+	public int sum() {
+		return Mathematics.sum(histogram);
+	}
+	
+	public String toString() {
+		return "Hist("+arity+"):"+lBound+"/"+uBound+","+BeanInspector.toString(histogram) + ",Sc:"+getScore()+(arity>1 ? (",Avg.Sc:"+(getScore()/arity)) : (""));
+	}
+	
+	/**
+	 * Fill a given histogram from a population. Old entries are overwritten.
+	 * @param pop
+	 * @param hist
+	 */
+    public static void createFitNormHistogram(Population pop, SolutionHistogram hist, int crit) {
+    	hist.reset();
+    	for (int i=0; i<hist.getNumBins(); i++) {
+    		hist.setEntry(i, PostProcess.filterFitnessIn(pop, hist.lowerBoundOfEntry(i), hist.upperBoundOfEntry(i), crit).size());
+    	}
+    	hist.setSingularHist();
+    }
+    
+    /**
+     * Notify that a single histogram has been created.
+     */
+    private void setSingularHist() {
+		arity=1;
+	}
+
+	private void reset() {
+		arity=0;
+		Arrays.fill(histogram, 0);
+	}
+	
+	/**
+	 * Fill a given histogram from a population. Old entries are overwritten.
+	 * This resets the arity.
+	 * @param pop
+	 * @param hist
+	 */
+	public void updateFrom(Population pop, int crit) {
+    	SolutionHistogram.createFitNormHistogram(pop, this, crit);
+    }
+    
+    /**
+     * Create a fitness histogram of an evaluated population within the given interval and nBins number of bins.
+     * Therefore a bin is of size (upperBound-lowerBound)/nBins, and bin 0 starts at lowerBound.
+     * Returns an integer array with the number of individuals in each bin.  
+     * 
+     * @param pop	the population to scan.
+     * @param lowerBound	lower bound of the fitness interval
+     * @param upperBound	upper bound of the fitness interval
+     * @param nBins	number of bins
+     * @return	an integer array with the number of individuals in each bin
+     * @see filterFitnessIn()
+     */    
+    public static SolutionHistogram createFitNormHistogram(Population pop, double lowerBound, double upperBound, int nBins, int crit) {
+    	SolutionHistogram res = new SolutionHistogram(lowerBound, upperBound, nBins);
+    	createFitNormHistogram(pop, res, crit);
+    	return res;
+    }    
+    
+//    public void updateFrom(Population pop, double accuracy) {
+//    	
+//    }
+
+	public double getScore() {
+		double sc = 0; 
+		if (sum()>0) {
+			for (int i=numBins-1; i>=0; i--) {
+				sc += getScalingFactor(i)*((double)getEntry(i));
+			}
+			return sc;
+		} else return 0;
+	}
+
+	private double getScalingFactor(int i) {
+		return (numBins-i)/((double)numBins);
+	}
+}

src/eva2/server/go/operators/terminators/CombinedTerminator.java

@@ -88,7 +88,8 @@ public class CombinedTerminator implements InterfaceTerminator, Serializable {
 			// make sure that both terminators are triggered by every call, because some judge
 			// time-dependently and store information on the population.
 			ret = getTermState(t1, curPopOrSols);
-			ret = ret && getTermState(t2, curPopOrSols); 
+			boolean ret2 = getTermState(t2, curPopOrSols);
+			ret=ret && ret2;
 			if (ret) msg = "Terminated because both: " + t1.lastTerminationMessage() + " And " + t2.lastTerminationMessage();
 		} else { // OR
 			// make sure that both terminators are triggered on every call, because some judge

src/eva2/server/go/operators/terminators/FitnessConvergenceTerminator.java

@@ -91,11 +91,11 @@ Serializable {
 	public boolean isTerminated(PopulationInterface Pop) {
 		if (!firstTime && isStillConverged(Pop)) {
 			if (stagnationTimeHasPassed(Pop)) {
-				// population hasnt improved much for max time, criterion is met
+				// population hasnt changed much for max time, criterion is met
 				msg = getTerminationMessage(tagString);
 				return true;
 			} else {
-				// population hasnt improved much for i<max time, keep running
+				// population hasnt changed much for i<max time, keep running
 				return false;
 			}
 		} else {
@@ -131,7 +131,7 @@ Serializable {
 	}
 
 	/**
-	 * Return true if |oldFit - curFit| < |oldFit| * thresh% (relative case)
+	 * Return true if |oldFit - curFit| < |oldFit| * thresh (relative case)
 	 * and if |oldFit - curFit| < thresh (absolute case).
 	 *
 	 * @param curFit
@@ -160,13 +160,6 @@ Serializable {
 		}
 	}
 
-	/**
-	 *
-	 */
-//	public String toString() {
-//		return BeanTest.toString(this);
-//	}
-
 	/**
 	 *
 	 */

src/eva2/server/go/operators/terminators/HistoryConvergenceTerminator.java

@@ -0,0 +1,149 @@
+package eva2.server.go.operators.terminators;
+
+import java.io.Serializable;
+import java.util.List;
+
+import eva2.server.go.InterfaceTerminator;
+import eva2.server.go.PopulationInterface;
+import eva2.server.go.individuals.AbstractEAIndividual;
+import eva2.server.go.individuals.AbstractEAIndividualComparator;
+import eva2.server.go.operators.distancemetric.ObjectiveSpaceMetric;
+import eva2.server.go.populations.InterfaceSolutionSet;
+import eva2.server.go.populations.Population;
+import eva2.server.go.problems.InterfaceOptimizationProblem;
+
+/**
+ * A terminator regarding population history (the archive of a population). Since the population
+ * history is not in general accessible from the GUI, this terminator is hidden.
+ * @author mkron
+ *
+ */
+public class HistoryConvergenceTerminator implements InterfaceTerminator, Serializable {
+	int haltingWindowLen = 15;
+	int fitCrit = 0;
+	double convergenceThreshold;
+	boolean stdDevInsteadOfImprovement;
+	AbstractEAIndividualComparator indyImprovementComparator = new AbstractEAIndividualComparator("", -1, true);
+	String msg;
+	
+	public static final boolean hideFromGOE=true; // hide from GUI
+	
+	public HistoryConvergenceTerminator() {}
+	
+	public HistoryConvergenceTerminator(int windowLen, double convThreshold, int fitnessCrit, boolean stdDevInsteadOfImprovement) {
+		haltingWindowLen = windowLen;
+		convergenceThreshold=convThreshold;
+		fitCrit = fitnessCrit;
+		this.stdDevInsteadOfImprovement = stdDevInsteadOfImprovement;
+	}
+	
+	public String globalInfo() {
+		return "Converge based on a halting window on a population history.";
+	}
+	
+	public void init(InterfaceOptimizationProblem prob) {
+		msg = "Not terminated.";
+	}
+
+	public boolean isTerminated(PopulationInterface pop) {
+        int     histLen = (((Population)pop).getHistory()).size();
+        boolean res = false;
+        if (histLen >= haltingWindowLen) {
+        	List<AbstractEAIndividual> subHist = ((Population)pop).getHistory().subList(histLen-haltingWindowLen, histLen);
+        	if (stdDevInsteadOfImprovement) { // look at fitness std dev.
+        		double[] fitMeas = Population.getFitnessMeasures(subHist, fitCrit);
+        		res = (fitMeas[3]<convergenceThreshold);
+        		if (res) {
+        			msg="Historic fitness std.dev. below " + convergenceThreshold + " for " + histLen + " generations.";
+        		}
+        	} else { // look at improvements
+              AbstractEAIndividual historicHWAgo = subHist.get(0);
+              res = true;
+              for (int i = 1; i < haltingWindowLen; i++) {
+              	// if historic[-hW] is worse than historic[-hW+i] return false
+              	AbstractEAIndividual historicIter = subHist.get(i);
+              	// if the iterated indy (the later one in history) has improved, there is no convergence.
+              	boolean improvementHappened = (testSecondForImprovement(historicHWAgo, historicIter));
+              	if (improvementHappened) {
+              		res = false;
+              		break;
+              	}
+              }
+              if (res) {
+            	  msg = "History did not improve" + (convergenceThreshold>0 ? (" by more than " + convergenceThreshold) : "") + " for " + haltingWindowLen + " iterations.";
+              }
+        	}
+        }
+        return res;
+	}
+
+    /**
+     * Define the criterion by which individual improvement is judged. The original version defined
+     * improvement strictly, but for some EA this should be done more laxly. E.g. DE will hardly ever
+     * stop improving slightly, so optionally use an epsilon-bound: improvement only counts if it is
+     * larger than epsilon in case useEpsilonBound is true.
+     * 
+     * @param firstIndy
+     * @param secIndy
+     * @return true if the second individual has improved in relation to the first one
+     */
+    private boolean testSecondForImprovement(AbstractEAIndividual firstIndy, AbstractEAIndividual secIndy) {
+    	if (convergenceThreshold > 0) {
+    		double fitDiff = (new ObjectiveSpaceMetric()).distance(firstIndy, secIndy);
+    		boolean ret = (secIndy.isDominatingDebConstraints(firstIndy));
+    		ret = ret && (fitDiff > convergenceThreshold);  // there is improvement if the second is dominant and the fitness difference is larger than epsilon  
+    		return ret;
+    	} else {
+    		return (indyImprovementComparator.compare(firstIndy, secIndy)>0);
+    	}
+    }
+    
+	public boolean isTerminated(InterfaceSolutionSet sols) {
+		return isTerminated(sols.getCurrentPopulation());
+	}
+
+	public String lastTerminationMessage() {
+		return msg;
+	}
+
+	public int getHaltingWindowLen() {
+		return haltingWindowLen;
+	}
+	public void setHaltingWindowLen(int haltingWindowLen) {
+		this.haltingWindowLen = haltingWindowLen;
+	}
+	public String haltingWindowLenTipText() {
+		return "Number of generations regarded back in the history"; 
+	}
+
+	public int getFitCrit() {
+		return fitCrit;
+	}
+	public void setFitCrit(int fitCrit) {
+		this.fitCrit = fitCrit;
+	}
+	public String fitCritTipText() {
+		return "The index of the fitness criterion regarded (multi-objective case).";
+	}
+
+	public double getConvergenceThreshold() {
+		return convergenceThreshold;
+	}
+	public void setConvergenceThreshold(double convergenceThreshold) {
+		this.convergenceThreshold = convergenceThreshold;
+	}
+	public String convergenceThresholdTipText() {
+		return "Threshold below improvements (or deviations) are still seen as stagnation."; 
+	}
+
+	public boolean isStdDevInsteadOfImprovement() {
+		return stdDevInsteadOfImprovement;
+	}
+	public void setStdDevInsteadOfImprovement(boolean stdDevInsteadOfImprovement) {
+		this.stdDevInsteadOfImprovement = stdDevInsteadOfImprovement;
+	}
+	public String stdDevInsteadOfImprovementTipText() {
+		return "Look at the standard deviation of historic fitness values instead of absolute fitness.";
+	}
+	
+}

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

@@ -8,16 +8,17 @@ import java.util.List;
 import java.util.PriorityQueue;
 import java.util.Set;
 
+import eva2.gui.GenericObjectEditor;
 import eva2.server.go.IndividualInterface;
 import eva2.server.go.InterfacePopulationChangedEventListener;
 import eva2.server.go.PopulationInterface;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.AbstractEAIndividualComparator;
-import eva2.server.go.individuals.GAIndividualBinaryData;
 import eva2.server.go.individuals.InterfaceDataTypeDouble;
 import eva2.server.go.operators.distancemetric.EuclideanMetric;
 import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
 import eva2.server.go.operators.distancemetric.PhenotypeMetric;
+import eva2.server.go.operators.postprocess.PostProcess;
 import eva2.server.go.operators.selection.probability.AbstractSelProb;
 import eva2.tools.EVAERROR;
 import eva2.tools.Pair;
@@ -44,7 +45,6 @@ import eva2.tools.math.Jama.Matrix;
  */
 
 public class Population extends ArrayList implements PopulationInterface, Cloneable, java.io.Serializable {
-
     protected int           m_Generation    = 0;
     protected int           m_FunctionCalls = 0;
     protected int           m_TargetSize          = 50;
@@ -62,13 +62,16 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     public static final String nextGenerationPerformed = "NextGenerationPerformed";
     
     boolean useHistory						= false;
-    public ArrayList<AbstractEAIndividual>  m_History       = new ArrayList<AbstractEAIndividual>();
+    private transient ArrayList<AbstractEAIndividual>  m_History       = new ArrayList<AbstractEAIndividual>();
 
     // remember when the last sorted queue was prepared
     private int lastQModCount = -1;
     // a sorted queue (for efficiency)
     transient private ArrayList<AbstractEAIndividual> sortedArr = null;
     private int lastFitCrit = -1;
+	private AbstractEAIndividualComparator historyComparator = null;
+	private double[] seedPos = new double[10];
+	private double aroundDist=0.1;
 	private transient static boolean TRACE = false;
     
     // remember when the last evaluation was performed
@@ -138,6 +141,10 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 		}
 	}
 
+	public void hideHideable() {
+		setInitMethod(getInitMethod());
+	}
+	
 	public void copyHistAndArchive(Population population) {
     	if (population.m_Archive != null) this.m_Archive = (Population)population.m_Archive.clone();
     	if (population.m_History != null) this.m_History = (ArrayList<AbstractEAIndividual>)population.m_History.clone();
@@ -161,6 +168,8 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         this.m_TargetSize             = population.m_TargetSize;
         this.useHistory 		= population.useHistory;
         this.notifyEvalInterval = population.notifyEvalInterval;
+        this.initMethod			= population.initMethod;
+        if (population.seedPos!=null) this.seedPos = population.seedPos.clone();
 //        this.m_Listener			= population.m_Listener;
         if (population.listeners != null) this.listeners			= (ArrayList<InterfacePopulationChangedEventListener>)population.listeners.clone();
         else listeners = null;
@@ -236,22 +245,53 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         case randomLatinHypercube:
         	createRLHSampling(this, false);
         	break;
+        case aroundSeed:
+        	AbstractEAIndividual template = (AbstractEAIndividual) getEAIndividual(0).clone();
+        	if (template.getDoublePosition().length<=seedPos.length) {
+        		if (template.getDoublePosition().length<seedPos.length) {
+        			double[] smallerSeed = new double[template.getDoublePosition().length];
+        			System.arraycopy(seedPos, 0, smallerSeed, 0, smallerSeed.length);
+        			AbstractEAIndividual.setDoublePosition(template, smallerSeed);
+        		} else AbstractEAIndividual.setDoublePosition(template, seedPos);
+        		PostProcess.createPopInSubRange(this, aroundDist, this.getTargetSize(), template);
+        	} else System.err.println("Warning, skipping seed initialization: too small individual seed!");
+        	break;
         }
         firePropertyChangedEvent(Population.populationInitialized);
     }
+    
+    public double[] getInitPos() {
+    	return seedPos ;
+    }
+    public void setInitPos(double[] si) {
+    	seedPos=si;
+    }
+    public String initPosTipText() {
+    	return "Position around which the population will be (randomly) initialized. Be aware that the vector length must match (or exceed) problem dimension!";
+    }
 
+    public void setInitAround(double d) {
+    	aroundDist = d;
+    }
+    public double getInitAround() {
+    	return aroundDist;
+    }
+    public String initAroundTipText() {
+    	return "Lenght of hypercube within which individuals are initialized around the initial position.";
+    }
+    
 //    /** This method inits the population. Function and generation counters
 //     * are reset and m_Size default Individuals are created and initialized by
 //     * the GAIndividual default init() method.
 //      */
-//    public void defaultInit(AbstractEAIndividual template) { Were missing the optimization problem - thus no initial range can be specified. And since no one calls this method, I removed it for now
+//    public void defaultInit(AbstractEAIndividual template) {
 //        this.m_Generation       = 0;
 //        this.m_FunctionCalls    = 0;
 //        this.m_Archive          = null;
 //        this.clear();
 //        for (int i = 0; i < this.m_TargetSize; i++) {
 //            AbstractEAIndividual tmpIndy = (AbstractEAIndividual)template.clone();
-//            tmpIndy.defaultInit(null);
+//            tmpIndy.defaultInit();
 //            super.add(tmpIndy);
 //        }
 //    }
@@ -303,12 +343,38 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     
     /**
      * Activate or deactivate the history tracking, which stores the best individual in every
-     * generation in the incrGeneration() method.
+     * generation in the incrGeneration() method. This uses a default individual comparator
+     * which is based on fitness.
      * 
      * @param useHist
      */
     public void setUseHistory(boolean useHist) {
+    	this.setUseHistory(useHist, (useHist ? (new AbstractEAIndividualComparator()) : null));
+    }
+    
+    /**
+     * Activate or deactivate the history tracking, which stores the best individual in every
+     * generation in the incrGeneration() method.
+     * 
+     * @param useHist
+     * @param histComparator comparator to use for determining the best current individual
+     */
+    public void setUseHistory(boolean useHist, AbstractEAIndividualComparator histComparator) {
     	useHistory = useHist;
+    	this.historyComparator  = histComparator;
+    }
+    
+    public int getHistoryLength() {
+    	if (useHistory) return m_History.size();
+    	else return 0;
+    }
+    
+    public ArrayList<AbstractEAIndividual> getHistory() {
+    	return m_History;
+    }
+    
+    public void SetHistory(ArrayList<AbstractEAIndividual> theHist) {
+    	m_History = theHist;
     }
     
     /** This method will allow you to increment the current number of function calls.
@@ -396,7 +462,9 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * Stagnation measured etc. pp.
      */
     public void incrGeneration() {
-        if (useHistory && (this.size() >= 1)) this.m_History.add((AbstractEAIndividual)this.getBestEAIndividual().clone());
+        if (useHistory && (this.size() >= 1)) {
+        	this.m_History.add((AbstractEAIndividual)this.getBestEAIndividual().clone());// TODO
+        }
         for (int i=0; i<size(); i++) ((AbstractEAIndividual)get(i)).incrAge(); 
         this.m_Generation++;
         firePropertyChangedEvent(nextGenerationPerformed);
@@ -591,9 +659,9 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * @see getIndexOfBestOrWorstIndividual()
      * @return The index of the best individual.
      */
-    public int getIndexOfBestIndividual() {
+    public int getIndexOfBestIndividualPrefFeasible() {
     	if (size()<1) return -1;
-    	return getIndexOfBestOrWorstIndividual(true, true, -1);
+    	return getIndexOfBestOrWorstIndy(true, true, -1);
     }
 	
 	/** 
@@ -603,8 +671,8 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * @see getIndexOfBestOrWorstIndividual()
      * @return The index of the best individual.
      */
-    public int getIndexOfWorstIndividual() {
+    public int getIndexOfWorstIndividualNoConstr() {
-    	return getIndexOfBestOrWorstIndividual(false, false, -1);
+    	return getIndexOfBestOrWorstIndy(false, false, -1);
     }
     
 	/** 
@@ -615,9 +683,9 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * @see getIndexOfBestOrWorstIndividual()
      * @return The index of the best individual.
      */
-    public int getIndexOfBestIndividual(int fitIndex) {
+    public int getIndexOfBestIndividualPrefFeasible(int fitIndex) {
     	if (size()<1) return -1;
-    	return getIndexOfBestOrWorstIndividual(true, true, fitIndex);
+    	return getIndexOfBestOrWorstIndy(true, true, fitIndex);
     }
 	
 	/** 
@@ -627,8 +695,8 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * @see getIndexOfBestOrWorstIndividual()
      * @return The index of the best individual.
      */
-    public int getIndexOfWorstIndividual(int fitIndex) {
+    public int getIndexOfWorstIndividualNoConstr(int fitIndex) {
-    	return getIndexOfBestOrWorstIndividual(false, false, fitIndex);
+    	return getIndexOfBestOrWorstIndy(false, false, fitIndex);
     }
     
     /**
@@ -686,49 +754,104 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * @param indicate whether constraints should be regarded
      * @return The index of the best (worst) individual.
      */
-    public int getIndexOfBestOrWorstIndividual(boolean bBest, boolean checkConstraints, int fitIndex) {
+    public int getIndexOfBestOrWorstIndividual(boolean bBest, AbstractEAIndividualComparator comparator) {
-        int     result          = -1;
+    	ArrayList<AbstractEAIndividual> sorted = getSorted(comparator);
-        double[]  curSelFitness = null;
+    	if (bBest) return indexOf(sorted.get(0));
-        boolean allViolate = true;
+    	else return indexOfInstance(sorted.get(sorted.size()-1));
-
-        for (int i = 0; i < super.size(); i++) {
-            if (!checkConstraints || !(getEAIndividual(i).violatesConstraint())) {
-            	allViolate = false;
-            	if ((result<0) || (compareFit(bBest, getEAIndividual(i).getFitness(), curSelFitness, fitIndex))) {
-            		// fit i is better than remembered
-            		result          = i;
-            		curSelFitness  = getEAIndividual(i).getFitness(); // remember fit i
-            	}
-            }
-        }
-        if (result < 0) {
-        	if (checkConstraints && allViolate) {
-        		// darn all seem to violate the constraint
-        		// so lets search for the guy who is close to feasible
-        		
-        		// to avoid problems with NaN or infinite fitness value, preselect an ind.
-        		result          = 0;
-        		double minViol = getEAIndividual(0).getConstraintViolation();
-        		for (int i = 1; i < super.size(); i++) {
-        			if ((bBest && getEAIndividual(i).getConstraintViolation() < minViol) ||
-            			(!bBest && (getEAIndividual(i).getConstraintViolation() > minViol))) {
-        				result          = i;
-        				minViol  = ((AbstractEAIndividual)super.get(i)).getConstraintViolation();
-        			}
-        		}
-//        		System.err.println("Population reports: All individuals violate the constraints, choosing smallest constraint violation.");
-        		// this is now really reported nicer...
-        	} else {
-        		// not all violate, maybe all are NaN!
-        		// so just select a random one
-        		System.err.println("Population reports: All individuals seem to have NaN or infinite fitness!");
-        		result = RNG.randomInt(size());
-        	}
-        }
-        return result;
     }
 
+    public int getIndexOfBestOrWorstIndy(boolean bBest, boolean checkConstraints, int fitIndex) {
+    	return getIndexOfBestOrWorstIndividual(bBest, new AbstractEAIndividualComparator(fitIndex, checkConstraints));
+    }
+    
     /**
+     * Returns the index of the first occurrence of the specified element
+     * in this list, or -1 if this list does not contain the element.
+     * This only checks for pointer equality. Different instances which
+     * have equal properties will be considered different. 
+     */
+    public int indexOfInstance(Object o) {
+    	if (o == null) {
+    		System.err.println("Error, instance null should not be contained! (Population.indexOfInstance)");
+    	} else {
+    		for (int i = 0; i < size(); i++) {
+    			if (o==get(i)) {
+    				return i;
+    			}
+    		}
+    	}
+    	return -1;
+    }
+
+//	/** 
+//	 * This method will return the index of the current best (worst) individual from the
+//     * population. If indicated, only those are regarded which do not violate the constraints.
+//     * If all violate the constraints, the smallest (largest) violation is selected.
+//     * Comparisons are done multicriterial, but note that for incomparable sets (pareto fronts)
+//     * this selection will not be fair (always the lowest index of incomparable sets will be returned).
+//     * 
+//     * @param bBest if true, smallest fitness (regarded best) index is returned, else the highest one
+//     * @param indicate whether constraints should be regarded
+//     * @return The index of the best (worst) individual.
+//     */
+//    public int getIndexOfBestOrWorstIndividual(boolean bBest, boolean checkConstraints, int fitIndex) {
+//        int     result          = -1;
+//        double[]  curSelFitness = null;
+//        boolean allViolate = true;
+//
+//        for (int i = 0; i < super.size(); i++) {
+//            if (!checkConstraints || !(getEAIndividual(i).violatesConstraint())) {
+//            	allViolate = false;
+//            	if ((result<0) || (compareFit(bBest, getEAIndividual(i).getFitness(), curSelFitness, fitIndex))) {
+//            		// fit i is better than remembered
+//            		result          = i;
+//            		curSelFitness  = getEAIndividual(i).getFitness(); // remember fit i
+//            	}
+//            }
+//        }
+//        if (result < 0) {
+//        	if (checkConstraints && allViolate) {
+//        		// darn all seem to violate the constraint
+//        		// so lets search for the guy who is close to feasible
+//        		
+//        		// to avoid problems with NaN or infinite fitness value, preselect an ind.
+//        		result          = 0;
+//        		double minViol = getEAIndividual(0).getConstraintViolation();
+//        		for (int i = 1; i < super.size(); i++) {
+//        			if ((bBest && getEAIndividual(i).getConstraintViolation() < minViol) ||
+//            			(!bBest && (getEAIndividual(i).getConstraintViolation() > minViol))) {
+//        				result          = i;
+//        				minViol  = ((AbstractEAIndividual)super.get(i)).getConstraintViolation();
+//        			}
+//        		}
+////        		System.err.println("Population reports: All individuals violate the constraints, choosing smallest constraint violation.");
+//        		// this is now really reported nicer...
+//        	} else {
+//        		// not all violate, maybe all are NaN!
+//        		// so just select a random one
+//        		System.err.println("Population reports: All individuals seem to have NaN or infinite fitness!");
+//        		result = RNG.randomInt(size());
+//        	}
+//        }
+//        int testcmp = getIndexOfBestOrWorstIndy(bBest, checkConstraints, fitIndex);
+//        if (result!=testcmp) {
+//        	if (((getEAIndividual(result).getFitness(0)!=getEAIndividual(testcmp).getFitness(0)) && (!violatesConstraints(testcmp) || !violatesConstraints(result)))
+//        			|| (violatesConstraints(testcmp) && violatesConstraints(result) && (!equalConstraintViolation(testcmp, result)))) {
+//        		System.err.println("Error in comparison!!! " + getIndexOfBestOrWorstIndy(bBest, checkConstraints, fitIndex));
+//        	}
+//        } else System.err.println("Succ with " + bBest + " " + checkConstraints + " " + fitIndex);
+//        return result;
+//    }
+
+//    private boolean equalConstraintViolation(int a, int b) {
+//    	return (getEAIndividual(a).getConstraintViolation()==getEAIndividual(b).getConstraintViolation());
+//	}
+//
+//	private boolean violatesConstraints(int i) {
+//    	return (getEAIndividual(i).isMarkedPenalized() || getEAIndividual(i).violatesConstraint());
+//	}
+
+	/**
      * Search the population for the best (worst) considering the given criterion (or all criteria
      * for fitIndex<0) which also does not violate constraints.
      * Returns -1 if no feasible solution is found, else the index of the best feasible solution.
@@ -764,8 +887,8 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     }
     
     /** 
-     * This method returns the current best individual from the population 
+     * This method returns the currently best individual from the population 
-     * by a given fitness component.
+     * by a given fitness component while prioritizing feasible ones.
      * If the population is empty, null is returned.
      *
      * @param fitIndex the fitness criterion index or -1
@@ -773,7 +896,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      */
     public AbstractEAIndividual getBestEAIndividual(int fitIndex) {
     	if (size()<1) return null;
-    	int best = this.getIndexOfBestIndividual(fitIndex);
+    	int best = this.getIndexOfBestIndividualPrefFeasible(fitIndex);
     	if (best == -1) {
     		System.err.println("This shouldnt happen!");
     		return null;
@@ -1041,7 +1164,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     }
     
 	public AbstractEAIndividual getWorstEAIndividual(int fitIndex) {
-    	return getEAIndividual(getIndexOfWorstIndividual(fitIndex));
+    	return getEAIndividual(getIndexOfWorstIndividualNoConstr(fitIndex));
     }
 
     /** 
@@ -1135,14 +1258,14 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         this.m_Archive = a;
     }
 
-    /** This method will return a string description of the GAIndividal
+    /** 
-     * noteably the Genotype.
+     * This method will return a string description of the Population 
+     * including the individuals.
      * @return A descriptive string
      */
     public String getStringRepresentation() {
         StringBuilder strB = new StringBuilder(200);
         strB.append("Population:\nPopulation size: ");
-        strB.append("Population size: ");
         strB.append(this.size());
         strB.append("\nFunction calls : ");
         strB.append(this.m_FunctionCalls);
@@ -1268,7 +1391,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     	modCount++;
     	if (lastFitCrit==fitIndex && (lastQModCount==(modCount-1))) {
     		// if nothing happend between this event and the last sorting by the same criterion...
-    		sortedArr.remove(prev);
+    		if (!sortedArr.remove(prev)) System.err.println("Error in Population.set!");
     		int i=0;
     		AbstractEAIndividualComparator comp = new AbstractEAIndividualComparator(fitIndex);
     		while (i<sortedArr.size() && comp.compare(element, sortedArr.get(i))>0) {
@@ -1310,15 +1433,30 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     	return (IndividualInterface)set(index, ind);
     }
     
-    public void removeIndividual (IndividualInterface ind) {
+    /**
+     * 
+     * @param ind
+     * @return true if an indy was actually removed, else false
+     */
+    public boolean removeMember(IndividualInterface ind) {
+    	long id = ((AbstractEAIndividual)ind).getIndyID();
         for (int i = 0; i < this.size(); i++) {
-            if (ind.equals(this.get(i))) {
+            if (getEAIndividual(i).getIndyID()==id) {
-                this.remove(i);
+            	removeIndexSwitched(i);
-                return;
+            	return true;
             }
         }
+        return false;
     }
 
+	public void removeMembers(Population tmpPop, boolean errorOnMissing) {
+		for (int i=0; i<tmpPop.size(); i++) {
+			if (!removeMember(tmpPop.getEAIndividual(i))) {
+				if (errorOnMissing) throw new RuntimeException("Error, member to be removed was missing (Population.removeMembers)!");
+			}
+		}
+	}
+	
     public IndividualInterface getBestIndividual() {
         return (IndividualInterface)this.getBestEAIndividual();
     }
@@ -1382,7 +1520,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 //            }
 //    		return measures;
 //    	} 
-    	double[] res = calcPopulationMeasures(metric);
+    	double[] res = getPopulationMeasures(this, metric);
 //        putData(lastPopMeasuresFlagKey, new Integer(modCount));
 //        putData(lastPopMeasuresDatKey, res);
 //        System.out.println(getStringRepresentation());
@@ -1390,34 +1528,34 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         return res;
     }
 
-	private double[] calcPopulationMeasures(InterfaceDistanceMetric metric) {
+	public static double[] getPopulationMeasures(List<AbstractEAIndividual> pop, InterfaceDistanceMetric metric) {
 		double d;
     	double[] res = new double[3];
     	
-    	double meanDist = 0.;
+    	double distSum = 0.;
     	double maxDist = Double.MIN_VALUE;
     	double minDist = Double.MAX_VALUE;
     	
-        for (int i = 0; i < this.size(); i++) {
+        for (int i = 0; i < pop.size(); i++) {
-        	for (int j = i+1; j < this.size(); j++) {
+        	for (int j = i+1; j < pop.size(); j++) {
-        		if (metric == null) d = EuclideanMetric.euclideanDistance(AbstractEAIndividual.getDoublePositionShallow(getEAIndividual(i)), 
+        		if (metric == null) d = EuclideanMetric.euclideanDistance(AbstractEAIndividual.getDoublePositionShallow(pop.get(i)), 
-                		AbstractEAIndividual.getDoublePositionShallow(getEAIndividual(j)));
+                		AbstractEAIndividual.getDoublePositionShallow(pop.get(j)));
-        		else d = metric.distance((AbstractEAIndividual)this.get(i), (AbstractEAIndividual)this.get(j));
+        		else d = metric.distance((AbstractEAIndividual)pop.get(i), (AbstractEAIndividual)pop.get(j));
-                meanDist += d;
+                distSum += d;
                 if (d < minDist) minDist = d;
                 if (d > maxDist) maxDist = d;
             }
         }
         res[1] = minDist;
         res[2] = maxDist;
-        if (this.size() > 1) res[0] = meanDist / (this.size() * (this.size()-1) / 2);
+        if (pop.size() > 1) res[0] = distSum / (pop.size() * (pop.size()-1) / 2);
         else { // only one indy?
         	res[1]=0;
         	res[2]=0;
         }
 		return res;
 	}
-    
+
     /**
      * Returns the average, minimal and maximal individual fitness and std dev. for the population in the given criterion.
      *
@@ -1425,6 +1563,16 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * @return the average, minimal, maximal and std dev. of fitness of individuals in an array
      */
     public double[] getFitnessMeasures(int fitCrit) {
+    	return Population.getFitnessMeasures(this, fitCrit);
+    }
+    
+    /**
+     * Returns the average, minimal and maximal individual fitness and std dev. for the population in the given criterion.
+     *
+     * @param fitCrit fitness dimension to be used
+     * @return the average, minimal, maximal and std dev. of fitness of individuals in an array
+     */
+    public static double[] getFitnessMeasures(List<AbstractEAIndividual> pop, int fitCrit) {
     	double d;
     	double[] res = new double[4];
     	
@@ -1433,23 +1581,23 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     	res[2] = Double.MIN_VALUE;
     	res[3] = 0;
     	
-        for (int i = 0; i < this.size(); i++) {
+        for (int i = 0; i < pop.size(); i++) {
-        		d = this.getEAIndividual(i).getFitness(fitCrit);
+        		d = pop.get(i).getFitness(fitCrit);
         		res[0] += d;
                 if (d < res[1]) res[1] = d;
                 if (d > res[2]) res[2] = d;
         }
         
-        if (size()==0) {
+        if (pop.size()==0) {
         	res[0]=res[1]=res[2]=res[3]=Double.NaN;
         } else {
         	// calc standard deviation
-        	res[0] = res[0] / this.size();
+        	res[0] = res[0] / pop.size();
-        	for (int i=0; i< this.size(); i++) {
+        	for (int i=0; i< pop.size(); i++) {
-        		d = res[0]-this.getEAIndividual(i).getFitness(fitCrit);
+        		d = res[0]-pop.get(i).getFitness(fitCrit);
         		res[3]+=d*d;
         	}
-        	res[3] /= this.size();
+        	res[3] /= pop.size();
         	res[3] = Math.sqrt(res[3]);
         }
         
@@ -1564,33 +1712,33 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 	}
 	
 	/**
-	 * Search for the closest individual which is not equal to the given individual. Return
+	 * Search for the closest individual to the indexed individual within the population. Return
 	 * its index or -1 if none could be found.
 	 * 
 	 * @param indy
 	 * @return closest neighbor (euclidian measure) of the given individual in the given population 
 	 */
-	public int getNeighborIndex(AbstractEAIndividual indy) {
+	public int getNeighborIndex(int neighborIndex) {
 		// get the neighbor...
-		int index = -1;
+		int foundIndex = -1;
 		double mindist = Double.POSITIVE_INFINITY;
 
 		for (int i = 0; i < size(); ++i){ 
 			AbstractEAIndividual currentindy = getEAIndividual(i);
-			if (!indy.equals(currentindy)){ // dont compare particle to itself or a copy of itself
+			if (i!=neighborIndex){ // dont compare particle to itself or a copy of itself
-				double dist = EuclideanMetric.euclideanDistance(AbstractEAIndividual.getDoublePositionShallow(indy),
+				double dist = EuclideanMetric.euclideanDistance(AbstractEAIndividual.getDoublePositionShallow(getEAIndividual(neighborIndex)),
 						AbstractEAIndividual.getDoublePositionShallow(currentindy));
 				if (dist  < mindist){ 
 					mindist = dist;
-					index = i;
+					foundIndex = i;
 				}
 			}
 		}
-		if (index == -1){
+		if (foundIndex == -1){
 			System.err.println("Pop too small or all individuals in population are equal !?");
 			return -1;
 		}
-		return index;
+		return foundIndex;
 	}
 	
 	/**
@@ -1609,7 +1757,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 		double d=0;
 		for (int i = 0; i < size(); ++i){
 			AbstractEAIndividual neighbor, indy = getEAIndividual(i);
-			int neighborIndex = getNeighborIndex(indy);
+			int neighborIndex = getNeighborIndex(i);
 			if (neighborIndex >= 0) neighbor = getEAIndividual(neighborIndex);
 			else return null;
 			if (normalizedPhenoMetric){
@@ -1719,6 +1867,8 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 
 	public void setInitMethod(PopulationInitMethod initMethod) {
 		this.initMethod = initMethod;
+		GenericObjectEditor.setShowProperty(this.getClass(), "initAround", initMethod==PopulationInitMethod.aroundSeed);
+		GenericObjectEditor.setShowProperty(this.getClass(), "initPos", initMethod==PopulationInitMethod.aroundSeed);
 	}
 	
 	public String initMethodTipText() {
@@ -1792,6 +1942,16 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 		return Math.max(0, this.getTargetSize() - this.size());
 	}
 
+	public boolean assertMembers(Population pop) {
+		for (int i=0; i<pop.size(); i++) {
+			if (!isMemberByID(pop.getEAIndividual(i))) {
+				System.err.println("Warning, indy " + i + " is not a member of " + this);
+				return false;
+			}
+		}
+		return true;
+	}
+
 //	/**
 //	 * Mark the population at the current state as evaluated. Changes to the modCount or hashes of individuals
 //	 * will invalidate the mark.

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

@@ -1,5 +1,5 @@
 package eva2.server.go.populations;
 
 public enum PopulationInitMethod {
-	individualDefault, randomLatinHypercube;
+	individualDefault, randomLatinHypercube, aroundSeed;
 }

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

@@ -99,6 +99,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 		this.m_GlobalOpt = Double.NEGATIVE_INFINITY;
 		m_ListOfOptima = new Population();
 		this.initListOfOptima();
+		if (!fullListAvailable() && (Double.isInfinite(m_GlobalOpt))) m_GlobalOpt=0;
 	}
 
 	/** Ths method allows you to evaluate a simple bit string to determine the fitness
@@ -106,6 +107,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 	 * @return  The m-dimensional output vector.
 	 */
 	public double[] eval(double[] x) {
+		x = rotateMaybe(x);
 		double[] result = new double[1];
 		result[0]   = this.m_GlobalOpt - evalUnnormalized(x)[0];
 		return result;
@@ -124,7 +126,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 	 * @return String
 	 */
 	public String getAdditionalFileStringHeader(PopulationInterface pop) {
-		return "#Optima found \t Maximum Peak Ratio";
+		return "#Optima found \tMaximum Peak Ratio \t" + super.getAdditionalFileStringHeader(pop);
 	}
 
 	/** 
@@ -137,7 +139,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 //		result += AbstractEAIndividual.getDefaultDataString(pop.getBestIndividual()) +"\t";
 		result += this.getNumberOfFoundOptima((Population)pop)+"\t";
 		result += this.getMaximumPeakRatio((Population)pop);
-		return result;
+		return result +"\t"+ super.getAdditionalFileStringValue(pop);
 	}
 //
 //	/** This method returns a string describing the optimization problem.
@@ -212,6 +214,14 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 	public Population getRealOptima() {
 		return this.m_ListOfOptima;
 	}
+	
+    /**
+     * Return true if the full list of optima is available, else false.
+     * @return
+     */
+    public boolean fullListAvailable() {
+    	return ((getRealOptima()!=null) && (getRealOptima().size()>0));
+    }
 
 	/** This method returns the Number of Identified optima
 	 * @param pop       A population of possible solutions.
@@ -222,7 +232,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 	}
 	
 	public static int getNoFoundOptimaOf(InterfaceMultimodalProblemKnown mmProb, Population pop) {
-		List<AbstractEAIndividual> sols = PostProcess.getFoundOptima(pop, mmProb.getRealOptima(), mmProb.getEpsilon(), true);
+		List<AbstractEAIndividual> sols = PostProcess.getFoundOptima(pop, mmProb.getRealOptima(), mmProb.getDefaultAccuracy(), true);
 		return sols.size();
 	}
 
@@ -241,9 +251,19 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 		return getMaximumPeakRatio(this, pop, m_Epsilon);
 	}
 	
+	/**
+	 * Returns -1 if the full list is not available. Otherwise calculates the maximum peak ratio
+	 * based on the full list of known optima.
+	 * 
+	 * @param mmProb
+	 * @param pop
+	 * @param epsilon
+	 * @return
+	 */
 	public static double getMaximumPeakRatio(InterfaceMultimodalProblemKnown mmProb, Population pop, double epsilon) {
 		double          foundInvertedSum = 0, sumRealMaxima = 0;
-		Population 		realOpts = mmProb.getRealOptima();
+		if (!mmProb.fullListAvailable()) return -1;
+		Population realOpts = mmProb.getRealOptima();
 		double 			tmp, maxOpt = realOpts.getEAIndividual(0).getFitness(0);
 		sumRealMaxima = maxOpt;
 		for (int i=1; i<realOpts.size(); i++) {
@@ -306,23 +326,23 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 //	return range;
 //	}
 
-	/**
+//	/**
-	 * @return the m_Epsilon
+//	 * @return the m_Epsilon
-	 */
+//	 */
-	public double getEpsilon() {
+//	public double getEpsilon() {
-		return m_Epsilon;
+//		return m_Epsilon;
-	}
+//	}
 
 	/**
 	 * @param epsilon the m_Epsilon to set
 	 */
-	public void setEpsilon(double epsilon) {
+	public void setDefaultAccuracy(double epsilon) {
-		m_Epsilon = epsilon;
+		super.SetDefaultAccuracy(epsilon);
-	}
-
-	public String epsilonTipText() {
-		return "Epsilon criterion indicating whether an optimum was found";
 	}
+//
+//	public String epsilonTipText() {
+//		return "Epsilon criterion indicating whether an optimum was found";
+//	}
 
 	@Override
 	public int getProblemDimension() {

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

@@ -1,11 +1,11 @@
 package eva2.server.go.problems;
 
 import java.awt.BorderLayout;
+import java.io.Serializable;
+import java.util.Vector;
 import java.util.concurrent.ExecutorService;
 import java.util.concurrent.Executors;
 import java.util.concurrent.Semaphore;
-import java.io.Serializable;
-import java.util.Vector;
 
 import javax.swing.JComponent;
 import javax.swing.JPanel;
@@ -17,12 +17,16 @@ import eva2.server.go.PopulationInterface;
 import eva2.server.go.enums.PostProcessMethod;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.InterfaceDataTypeDouble;
+import eva2.server.go.operators.cluster.ClusteringDensityBased;
+import eva2.server.go.operators.cluster.InterfaceClustering;
+import eva2.server.go.operators.distancemetric.IndividualDataMetric;
 import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
 import eva2.server.go.operators.distancemetric.PhenotypeMetric;
 import eva2.server.go.operators.moso.MOSONoConvert;
 import eva2.server.go.operators.mutation.InterfaceMutation;
 import eva2.server.go.operators.mutation.MutateESFixedStepSize;
 import eva2.server.go.operators.postprocess.PostProcess;
+import eva2.server.go.operators.postprocess.SolutionHistogram;
 import eva2.server.go.operators.terminators.CombinedTerminator;
 import eva2.server.go.operators.terminators.EvaluationTerminator;
 import eva2.server.go.operators.terminators.PhenotypeConvergenceTerminator;
@@ -37,7 +41,8 @@ import eva2.tools.math.Mathematics;
  * Time: 13:40:12
  * To change this template use Options | File Templates.
  */
-public abstract class AbstractOptimizationProblem implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serializable {
+public abstract class AbstractOptimizationProblem 
+implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serializable {
 	class EvalThread extends Thread {
 		AbstractOptimizationProblem prob;
 		AbstractEAIndividual ind;
@@ -71,6 +76,8 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
     protected 	AbstractEAIndividual      m_Template;
 //    private transient ArrayList<ParamChangeListener> changeListeners = null;
 
+	private double defaultAccuracy = 0.01; // default accuracy for identifying optima.
+
     /** This method returns a deep clone of the problem.
      * @return  the clone
      */
@@ -88,7 +95,9 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
 		return "Set the number of threaded parallel function evaluations - interesting for slow functions and generational optimizers.";
 	}
 
-	/** This method inits the Problem to log multiruns
+	/** 
+	 * This method initializes the problem instance.
+	 * If you override it, make sure to call the super method!
      */
     public abstract void initProblem();
 
@@ -231,8 +240,8 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
      * @return String
      */
     public String getAdditionalFileStringHeader(PopulationInterface pop) {
-        return "Solution";
+    	if (this instanceof InterfaceInterestingHistogram) return "Solution \t Histogram(c0) \t Score";
-//    	return "";
+    	else return "Solution";
     }
 
     /** This method returns the additional data that is to be written into a file
@@ -240,8 +249,12 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
      * @return String
      */
     public String getAdditionalFileStringValue(PopulationInterface pop) {
-//    	return "";
+    	String solStr = AbstractEAIndividual.getDefaultDataString(pop.getBestIndividual()); 
-        return AbstractEAIndividual.getDefaultDataString(pop.getBestIndividual());
+    	if (this instanceof InterfaceInterestingHistogram) {
+    		SolutionHistogram hist = ((InterfaceInterestingHistogram)this).getHistogram();
+    		Population sols = PostProcess.clusterBestUpdateHistogram((Population)pop, this, hist, 0, getDefaultAccuracy());
+    		return solStr + " \t " + hist + "\t" + hist.getScore();
+    	} else return solStr; 
     }
 
     /**
@@ -328,21 +341,27 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
      * @param epsilonPhenoSpace maximal allowed improvement of an individual before considered premature (given as distance in the search space)
      * @param epsilonFitConv if positive: additional absolute convergence criterion (fitness space) as termination criterion of the local search  
      * @param clusterSigma minimum cluster distance
-     * @param numOfFailures 
+     * @param maxEvalsPerIndy 
      * @see #isPotentialOptimumNMS(AbstractEAIndividual, double, double, int)
      * @return 
      */
-    public Population extractPotentialOptima(Population pop, double epsilonPhenoSpace, double epsilonFitConv, double clusterSigma, int numOfFailures) {
+    public static Population extractPotentialOptima(AbstractOptimizationProblem prob, Population pop, double epsilonPhenoSpace, double epsilonFitConv, double clusterSigma, int maxEvalsPerIndy) {
     	Population potOptima = new Population();
     	for (int i = 0; i < pop.size(); ++i){
     		AbstractEAIndividual indy = pop.getEAIndividual(i);
 //    		System.out.println("bef: " + indy.toString());
-    		if (isPotentialOptimumNMS(indy, epsilonPhenoSpace, epsilonFitConv, numOfFailures)){ 
+    		boolean isConverged = AbstractOptimizationProblem.isPotentialOptimumNMS(prob, indy, epsilonPhenoSpace, epsilonFitConv, maxEvalsPerIndy);
-//       	if (isPotentialOptimum(indy, epsilon,-1,-1)){ 
+    		if (isConverged) {
     			potOptima.addIndividual(indy);
+    			if (!indy.hasData(PostProcess.movedDistanceKey)) {
+    				System.err.println("Error, missing distance information in individual (AbstractOptimizationProblem.extractPotentialOptimum)");
+    			}
     		}
     	}
-    	if (clusterSigma > 0) return (Population)PostProcess.clusterBest(potOptima, clusterSigma, 0, PostProcess.KEEP_LONERS, PostProcess.BEST_ONLY).clone();
+    	// cluster by the converged-to positions instead of the original ones
+    	InterfaceClustering clustering = new ClusteringDensityBased(clusterSigma, 2, new IndividualDataMetric(PostProcess.movedToPositionKey));
+    	clustering = new ClusteringDensityBased(clusterSigma, 2);
+    	if (clusterSigma > 0) return (Population)PostProcess.clusterBest(potOptima, clustering, 0, PostProcess.KEEP_LONERS, PostProcess.BEST_ONLY).clone();
     	else return potOptima;
     }
    
@@ -407,31 +426,33 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
     
     /**
      * Refine a given individual using Nelder-Mead-Simplex local search. Return true, if the refined result is within a given
-     * distance from the original individual in phenotype space. The numOfFailures parameter gives the maximum evaluations
+     * distance from the original individual in phenotype space. The maxEvaluations parameter gives the maximum evaluations
-     * for the local search Using the epsilonFitConv parameter may define a convergence criterion as PhenotypeConvergenceTerminator 
+     * for the local search. Using the epsilonFitConv parameter one may define a convergence criterion as PhenotypeConvergenceTerminator 
      * which is combined (using OR) with the evaluation counter.
-     * If numOfFailures is smaller than zero, 100*dim is used. Be aware that this may cost quite some runtime depending on the target
+     * If maxEvaluations is smaller than zero, 100*dim is used. Be aware that this may cost quite some runtime depending on the target
      * function.
+     * A double value for the distance by which it was moved is added to the individual using the key PostProcess.movedDistanceKey.
      * 
      * @param orig
-     * @param epsilonPhenoSpace
+     * @param epsilonPhenoSpace allowed distance for a solution moved by local search 
-     * @param epsilonFitConv
+     * @param epsilonFitConv	if the fitness changes below the threshold, the search is stopped earlier. Set to -1 to deactivate.
-     * @param numOfFailures
+     * @param maxEvaluations	maximal number of evaluations or -1
      * @return
      */
-    public boolean isPotentialOptimumNMS(AbstractEAIndividual orig, double epsilonPhenoSpace, double epsilonFitConv, int numOfFailures){
+    public static boolean isPotentialOptimumNMS(AbstractOptimizationProblem prob, AbstractEAIndividual orig, double epsilonPhenoSpace, double epsilonFitConv, int maxEvaluations){
     	
     	AbstractEAIndividual indy = (AbstractEAIndividual)orig.clone();
-    	this.evaluate(indy); // indy may be evaluated in a normalised way...
+    	prob.evaluate(indy); // indy may be evaluated in a normalised way...
     	
     	InterfaceDistanceMetric metric = new PhenotypeMetric();
     	double overallDist = 0;
-    	double initPerturb = -1;
+    	double initRelPerturb = -1;
     	int dim = -1;
     	if (orig instanceof InterfaceDataTypeDouble) {
-    		initPerturb = epsilonPhenoSpace/(2*(Mathematics.getAvgRange(((InterfaceDataTypeDouble)orig).getDoubleRange())));
+//    		initPerturb = epsilonPhenoSpace/(2*(Mathematics.getAvgRange(((InterfaceDataTypeDouble)orig).getDoubleRange())));
+    		initRelPerturb = epsilonPhenoSpace*0.5;
     		dim=((InterfaceDataTypeDouble)orig).getDoubleRange().length;
-        	if (numOfFailures<0) numOfFailures = 100*AbstractEAIndividual.getDoublePositionShallow(this.m_Template).length; // scales the effort with the number of problem dimensions
+        	if (maxEvaluations<0) maxEvaluations = 100*AbstractEAIndividual.getDoublePositionShallow(prob.m_Template).length; // scales the effort with the number of problem dimensions
     	} else {
     		System.err.println("Cannot initialize NMS on non-double valued individuals!");
     		return false;
@@ -439,14 +460,31 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
     	
     	Population pop = new Population(1);
     	pop.add(orig);
-    	InterfaceTerminator term = new EvaluationTerminator(numOfFailures); 
+    	InterfaceTerminator term = new EvaluationTerminator(maxEvaluations); 
     	if (epsilonFitConv > 0) term = new CombinedTerminator(new PhenotypeConvergenceTerminator(epsilonFitConv, 10*dim, true, true), term, false);
-    	int evalsPerf = PostProcess.processSingleCandidatesNMCMA(PostProcessMethod.nelderMead, pop, term, initPerturb, this);
+    	int evalsPerf = PostProcess.processSingleCandidatesNMCMA(PostProcessMethod.nelderMead, pop, term, initRelPerturb, prob);
     	overallDist = metric.distance(indy, pop.getBestEAIndividual());
 //    	System.out.println("aft: " + pop.getBestEAIndividual().toString() + ", evals performed: " + evalsPerf + ", opt moved by " + overallDist);
 //    	System.out.println("terminated because: " + term.lastTerminationMessage());
-        if (overallDist < epsilonPhenoSpace) return true;
+    	orig.putData(PostProcess.movedDistanceKey, overallDist);
-        else return false; 
+    	orig.putData(PostProcess.movedToPositionKey, pop.getBestEAIndividual().getDoublePosition());
+//    	if (overallDist==0) {
+//    		PostProcess.processSingleCandidatesNMCMA(PostProcessMethod.nelderMead, pop, term, initPerturb, this);
+//    	}
+    	return (overallDist < epsilonPhenoSpace);
+    }
+    
+    public double getDefaultAccuracy() {
+    	return defaultAccuracy ;
+    }
+    public void SetDefaultAccuracy(double defAcc) {
+    	defaultAccuracy = defAcc;
+    }    
+    public void setDefaultAccuracy(double defAcc) {
+    	defaultAccuracy = defAcc;
+    }
+    public String defaultAccuracyTipText() {
+    	return "A default threshold to identify optima - e.g. the assumed minimal distance between any two optima.";
     }
     
 //    /**********************************************************************************************************************

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

@@ -2,6 +2,7 @@ package eva2.server.go.problems;
 
 import eva2.gui.BeanInspector;
 import eva2.gui.GenericObjectEditor;
+import eva2.gui.TopoPlot;
 import eva2.server.go.PopulationInterface;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.ESIndividualDoubleData;
@@ -11,7 +12,10 @@ import eva2.server.go.operators.constraint.GenericConstraint;
 import eva2.server.go.populations.Population;
 import eva2.server.go.strategies.InterfaceOptimizer;
 import eva2.tools.Pair;
+import eva2.tools.diagram.ColorBarCalculator;
+import eva2.tools.math.Mathematics;
 import eva2.tools.math.RNG;
+import eva2.tools.math.Jama.Matrix;
 
 /**
  * For a double valued problem, there are two main methods to implement: {@link #getProblemDimension()}
@@ -37,10 +41,14 @@ import eva2.tools.math.RNG;
 public abstract class AbstractProblemDouble extends AbstractOptimizationProblem implements InterfaceProblemDouble, Interface2DBorderProblem/*, InterfaceParamControllable */{
 	private double m_DefaultRange = 10;
 	private double m_Noise = 0;
-	
+	private boolean doRotation = false; // should really be false by default
+    private Matrix 			rotation;
+    
 //	PropertySelectableList<AbstractConstraint> constraintList = new PropertySelectableList<AbstractConstraint>(new AbstractConstraint[]{new GenericConstraint()});
     private AbstractConstraint[] constraintArray = new AbstractConstraint[]{new GenericConstraint()};
-    private boolean withConstraints = false;  
+    private boolean withConstraints = false;
+	private transient boolean isShowing = false;
+	private double rotAngle = 22.5; // for default rotation along every axis   
     public static String rawFitKey="UnconstrainedFitnessValue";
     
 	public AbstractProblemDouble() {
@@ -66,12 +74,16 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 	protected void cloneObjects(AbstractProblemDouble o) {
 		this.m_DefaultRange = o.m_DefaultRange;
 		this.m_Noise = o.m_Noise;
+		this.SetDefaultAccuracy(o.getDefaultAccuracy());
 		if (o.m_Template != null) this.m_Template = (AbstractEAIndividual)o.m_Template.clone();
 		if (o.constraintArray!=null) {
 			this.constraintArray=o.constraintArray.clone();
 			for (int i=0; i<constraintArray.length; i++) constraintArray[i]=(AbstractConstraint)o.constraintArray[i].clone();
 		}
 		this.withConstraints=o.withConstraints;
+		this.doRotation = o.doRotation;
+		this.rotation = (Matrix)o.rotation.clone();
+		this.rotAngle = o.rotAngle;
 	}
 	
 	/**
@@ -111,6 +123,14 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
         	addConstraints(individual, x);
         }
 	}
+
+	protected double[] rotateMaybe(double[] x) {
+		if (isDoRotation()) {
+    		if (rotation==null) initProblem();
+	    	x = Mathematics.rotate(x, rotation);
+    	}
+		return x;
+	}
 	
 	/**
 	 * Add all constraint violations to the individual. Expect that the fitness has already been set.
@@ -140,6 +160,8 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 	
 	/**
 	 * Evaluate a double vector, representing the target function.
+	 * If you implement this, you should take care of the offsets and rotation,
+	 * e.g. by using x=rotateMaybe(x) before further evaluation.
 	 * 
 	 * @param x the vector to evaluate
 	 * @return	the target function value
@@ -202,6 +224,28 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 	@Override
 	public void initProblem() {
 		initTemplate();
+        if (isDoRotation()) {
+        	rotation = initDefaultRotationMatrix(rotAngle , getProblemDimension());
+        } else rotation = null;
+	}
+	
+	/**
+	 * Initialize rotation matrix which rotates around the given angle in every axis.
+	 * 
+	 * @param rotAngle
+	 * @param dim
+	 * @return
+	 */
+	public static Matrix initDefaultRotationMatrix(double rotAngle, int dim) {
+		Matrix rotation=null;
+//		Matrix vec = new Matrix(dim, 1);
+//		for (int i=0; i<dim; i++) vec.set(i,0, i+1);
+		//	System.out.println(BeanInspector.toString(eval(vec.getColumnPackedCopy())));
+//		rotation = new Matrix(dim, dim);
+//		rotation = Mathematics.getRotationMatrix(vec).transpose();
+		rotation = Mathematics.getRotationMatrix((rotAngle*Math.PI/180.), dim).transpose();
+		//	System.out.println(BeanInspector.toString(eval(vec.getColumnPackedCopy())));
+		return rotation;
 	}
 	
     /** 
@@ -267,6 +311,17 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 		return "Absolute limit for the symmetric range in any dimension";
 	}
 	
+	public void setDoRotation(boolean doRotation) {
+		this.doRotation = doRotation;
+		if (!doRotation) rotation=null;
+	}
+
+	public boolean isDoRotation() {
+		return doRotation;
+	}
+	public String doRotationTipText() {
+		return "If marked, the function is rotated by 22.5 degrees along every axis.";
+	}
     /**********************************************************************************************************************
      * These are for InterfaceParamControllable
      */
@@ -388,4 +443,19 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 		}
 		return new Pair<Integer,Double>(numViol, sum);
 	}
+	
+	public boolean isShowPlot() {
+		return isShowing ;
+	}
+	public void setShowPlot(boolean showP) {
+		if (!isShowing && showP) {
+			TopoPlot plot = new TopoPlot(getName(), "x1", "x2");
+			plot.setParams(60,60, ColorBarCalculator.BLUE_TO_RED);
+			plot.setTopology(this, makeRange(), true);
+		}
+		isShowing = showP;
+	}
+	public String showPlotTipText() {
+		return "Produce an exemplary 2D plot of the function (dimensional cut at x_i=0 for n>1).";
+	}
 }

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

@@ -39,13 +39,6 @@ public abstract class AbstractProblemDoubleOffset extends AbstractProblemDouble
     	setDefaultRange(defRange);
     }
 
-    /** This method inits the Problem to log multiruns
-     */
-    public void initProblem() {
-//        this.m_OverallBest = null;
-    	initTemplate();
-    }
-
 /**********************************************************************************************************************
  * These are for GUI
  */

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

@@ -51,6 +51,7 @@ public class ConstrHimmelblauProblem extends AbstractProblemDouble implements Se
 
 	@Override
 	public double[] eval(double[] x) {
+		x = rotateMaybe(x);
 		double v=5.3578547*x[2]*x[2]+0.8356891*x[0]*x[4]+37.293239*x[0]-40792.141;
 		if (useYOffset) v+=yOffset;
 		return new double[]{v};

src/eva2/server/go/problems/F10Problem.java

@@ -37,6 +37,7 @@ public class F10Problem extends AbstractProblemDoubleOffset implements Interface
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
         double[] result = new double[1];
         double c1 = this.calculateC(1);
         result[0]     = m_YOffSet;

src/eva2/server/go/problems/F11Problem.java

@@ -39,6 +39,7 @@ public class F11Problem extends AbstractProblemDoubleOffset implements Interface
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
         double[] result = new double[1];
         double tmpProd = 1;
         for (int i = 0; i < x.length; i++) {

src/eva2/server/go/problems/F12Problem.java

@@ -34,13 +34,14 @@ public class F12Problem extends AbstractProblemDoubleOffset implements java.io.S
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
         double[] result = new double[1];
-        double tmp = -5;
+        double tmp = 0;//-5;
         for (int i = 1; i < x.length-1; i++) {
             tmp += Math.pow(x[i]-m_XOffSet, 2);
         }
         double x0 = x[0]-m_XOffSet;
-        result[0] = m_YOffSet+(Math.exp(-5*x0*x0)+2*Math.exp(-5*Math.pow(1-x0, 2)))*Math.exp(tmp);
+        result[0] = m_YOffSet+((Math.exp(-5*x0*x0)+2*Math.exp(-5*Math.pow(1-x0, 2)))*Math.exp(-5*tmp));
         return result;
     }
 

src/eva2/server/go/problems/F13Problem.java

@@ -1,12 +1,14 @@
 package eva2.server.go.problems;
 
+import eva2.gui.GenericObjectEditor;
 import eva2.server.go.individuals.ESIndividualDoubleData;
+import eva2.server.go.operators.postprocess.SolutionHistogram;
 
 /**
  * Schwefels sine root function (1981) with a minimum at 420.9687^n of value 0.
  * Function f(x) = (418.9829 * n) - sum_n(x_i * sin(sqrt(abs(x_i)))) + (418.9829 * n);
  */
-public class F13Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem {
+public class F13Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, InterfaceInterestingHistogram {
 
     public F13Problem() {
         this.m_Template         = new ESIndividualDoubleData();
@@ -27,28 +29,37 @@ public class F13Problem extends AbstractProblemDoubleOffset implements Interface
         return (Object) new F13Problem(this);
     }
     
-//    public double[][] makeRange() {
+    @Override
-//	    double[][] range = new double[this.m_ProblemDimension][2];
+    public double getRangeLowerBound(int dim) {
-//	    for (int i = 0; i < range.length; i++) {
+    	return -512.03;
-//	        range[i][0] = -512.03;
+	}
-//	        range[i][1] = 511.97;
+    
-//	    }
+	@Override
-//	    return range;
+	public double getRangeUpperBound(int dim) {
-//    }
+		return 511.97;
-
+	}
+	
+	@Override
+	public void hideHideable() {
+		super.hideHideable();
+		GenericObjectEditor.setHideProperty(this.getClass(), "defaultRange", true);
+	}
+ 
     /** Ths method allows you to evaluate a double[] to determine the fitness
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
         double[] result = new double[1];
         result[0] = m_YOffSet;
         
         for (int i=0; i<x.length; i++) {
-        	double xi = x[i]-m_XOffSet;
+        	double xi = (x[i]-m_XOffSet);
         	result[0] -= xi*Math.sin(Math.sqrt(Math.abs(xi)));
         }
         result[0] += (418.9829 * m_ProblemDimension);
+        // res = cn-sum_i(xi*sin(sqrt(abs(xi))))
         return result;
     }
 
@@ -67,6 +78,11 @@ public class F13Problem extends AbstractProblemDoubleOffset implements Interface
         return result;
     }
 
+	public SolutionHistogram getHistogram() {
+		if (getProblemDimension() < 15) return new SolutionHistogram(0, 800, 16);
+		else if (getProblemDimension() < 25) return new SolutionHistogram(0, 1600, 16);
+		else return new SolutionHistogram(0, 3200, 12);
+	}
 /**********************************************************************************************************************
  * These are for GUI
  */
@@ -85,4 +101,7 @@ public class F13Problem extends AbstractProblemDoubleOffset implements Interface
         return "Schwefels sine-root Function (multimodal, 1981). Remember to use range check!";
     }
 
+    public void setDefaultAccuracy(double v) {
+    	super.SetDefaultAccuracy(v);
+    }
 }

src/eva2/server/go/problems/F15Problem.java

@@ -0,0 +1,102 @@
+package eva2.server.go.problems;
+
+import static java.lang.Math.PI;
+import static java.lang.Math.sin;
+
+public class F15Problem extends AbstractProblemDoubleOffset {
+	int iterations = 100;
+	
+    public F15Problem(F15Problem f15Problem) {
+		iterations = f15Problem.iterations;
+	}
+
+	public Object clone() {
+        return (Object) new F15Problem(this);
+    }
+
+    /** This method allows you to evaluate a double[] to determine the fitness
+     * @param x     The n-dimensional input vector
+     * @return  The m-dimensional output vector.
+     */
+    public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
+    	double[] c = new double[2];
+    	
+//    	c[0]= (x[0])*getXOffSet();
+//    	c[1]= (x[1])*Math.sin((Math.PI/2)*getYOffSet());
+    	
+		c[0] = x[0]*x[2];
+		c[1] = x[1] * sin( PI / 2.0 * x[3]);
+		
+//    	c[0]= (x[0]+(x[2]/10))*getXOffSet();
+//    	c[1]= (x[1]+(x[3]/10))*Math.sin(Math.PI/2*getYOffSet());
+//    	c[0]= (x[0]*(1-x[2]/10));
+//    	c[1]= (x[1]*(1-x[3]/10));
+        double[] result = new double[1];
+        result[0] = flatten(evalRec(x, c, iterations));
+        return result;
+    }
+    
+    private double[] evalRec(double[] x, double[] c, int n) {
+        if (n==0) return x;
+        else return evalRec(addComplex(squareComplex(x),c), c, n-1);
+    }
+    
+    private double[] squareComplex(double[] x) {
+    	double[] result = new double[2];
+    	result[0] = (x[0]*x[0])-(x[1]*x[1]);
+    	result[1] = (2*x[0]*x[1]);
+    	return result;
+    }
+    
+    private double[] addComplex(double[] x, double[] y) {
+    	double[] result = new double[2];
+    	result[0] = x[0] + y[0];
+    	result[1] = x[1] + y[1];
+    	return result;
+    }
+    
+    private double flatten(double[] x) {
+    	
+    	double len = Math.sqrt((x[0]*x[0])+(x[1]*x[1]));
+    	double ang = Math.atan2(x[1],x[0]);
+    	if (Double.isNaN(len) || (len > 1000.)) len = 1000.;
+    	return len;
+//    	return 0.5+0.5*t*(2*b*a*u(x/a));
+//    	return 1/(2*Math.PI*ang);
+//    	return +Math.abs(x[0])+Math.abs(x[1]);
+    }
+    
+    public int getProblemDimension() {
+    	return 4;
+    }
+    
+    public String getName() {
+    	return "F15-Problem";
+    }
+    
+    public double getRangeLowerBound(int dim) {
+//    	return -1;
+    	if (dim == 0) return -2.5;
+    	else return -1.5;
+    }
+
+    public double getRangeUpperBound(int dim) {
+//    	return 1;
+    	return 1.5;
+    }
+
+	/**
+	 * @return the iterations
+	 */
+	public int getIterations() {
+		return iterations;
+	}
+
+	/**
+	 * @param iterations the iterations to set
+	 */
+	public void setIterations(int iterations) {
+		this.iterations = iterations;
+	}
+}

src/eva2/server/go/problems/F16Problem.java

@@ -13,6 +13,7 @@ public class F16Problem extends AbstractProblemDouble implements InterfaceMultim
 	
 	@Override
 	public double[] eval(double[] x) {
+		x = rotateMaybe(x);
 		double[] res = new double[1];
 		double sum = 0;
 		
@@ -50,6 +51,6 @@ public class F16Problem extends AbstractProblemDouble implements InterfaceMultim
 	}
 	
 	public String globalInfo() {
-		return "Multiple optima with increasing densitiy near the lower bounds, there for decreasing attractor size.";
+		return "Multiple optima with increasing densitiy near the lower bounds, therefore decreasing attractor size.";
 	}
 }

src/eva2/server/go/problems/F17Problem.java

@@ -1,18 +1,33 @@
 package eva2.server.go.problems;
 
+import eva2.server.go.operators.postprocess.SolutionHistogram;
+/**
+ * Bohachevsky function, numerous optima on an oval hyperparabola with similar attractor sizes 
+ * but decreasing fitness towards the bounds. Described e.g. in Shir&Bäck, PPSN 2006, 
+ * "Niche radius adaption in the CMA-ES Niching Algorithm".
+ * 
+ */
 public class F17Problem extends AbstractProblemDouble implements
-		InterfaceMultimodalProblem {
+		InterfaceMultimodalProblem, InterfaceInterestingHistogram{
 	int dim = 10;
 	
 	public F17Problem() {
+		setDefaultRange(10.);
 		dim=10;
 	}
 	
+	public F17Problem(int dimension) {
+		this();
+		setProblemDimension(dimension);
+	}
+	
 	public F17Problem(F17Problem other) {
+		super(other);
 		dim=other.dim;
 	}
 
 	public double[] eval(double[] x) {
+		x = rotateMaybe(x);
 		double[] res = new double[1];
 		double sum = 0;
 		for (int i=0; i<getProblemDimension()-1; i++) {
@@ -30,7 +45,7 @@ public class F17Problem extends AbstractProblemDouble implements
 	public void setProblemDimension(int newDim) {
 		dim = newDim;
 	}
-
+	
 	public Object clone() {
 		return new F17Problem(this);
 	}
@@ -42,4 +57,9 @@ public class F17Problem extends AbstractProblemDouble implements
 	public String globalInfo() {
 		return "Bohachevsky function, numerous optima on an oval hyperparabola with similar attractor sizes but decreasing fitness towards the bounds.";
 	}
+
+	public SolutionHistogram getHistogram() {
+		if (getProblemDimension()<15) return new SolutionHistogram(-0.5, 7.5, 16);
+		else return new SolutionHistogram(-0.5, 15.5, 16);
+	}
 }

src/eva2/server/go/problems/F18Problem.java

@@ -14,6 +14,7 @@ InterfaceMultimodalProblem {
 	}
 
 	public double[] eval(double[] x) {
+		x = rotateMaybe(x);
 		double[] res = new double[1];
 		double sum = 0;
 		for (int i=0; i<getProblemDimension(); i++) {

src/eva2/server/go/problems/F19Problem.java

@@ -1,14 +1,23 @@
 package eva2.server.go.problems;
 
 import java.util.Arrays;
+import java.util.Random;
 
-import wsi.ra.math.RNG;
+import eva2.server.go.operators.postprocess.SolutionHistogram;
+import eva2.tools.math.RNG;
 
+/**
+ * Fletcher-Powell function with up to 2^n optima from Shir&Baeck, PPSN 2006, 
+ * after Bäck 1996. Alphas and Matrices A and B are randomly created with a fixed seed.
+ * @author mkron
+ *
+ */
 public class F19Problem extends AbstractProblemDouble implements
-InterfaceMultimodalProblem {
+InterfaceMultimodalProblem, InterfaceInterestingHistogram {
 	int dim = 10;
 	transient private double[] alphas, As;
 	transient private int[] A,B;
+	private long randSeed = 23; 
 	
 	public F19Problem() {
 		alphas=null;
@@ -21,15 +30,21 @@ InterfaceMultimodalProblem {
 		alphas=null;
 	}
 
+	public F19Problem(int d) {
+		this();
+		setProblemDimension(d);
+	}
+
 	public void initProblem() {
 		super.initProblem();
-		if (alphas==null) {
+//		if (alphas==null) {
 			// create static random data
-			alphas = RNG.randomVector(dim, -Math.PI, Math.PI);
+			Random rand = new Random(randSeed);
-			A = RNG.randomVector(dim*dim, -100, 100);
+			alphas = RNG.randomDoubleArray(rand, -Math.PI, Math.PI, dim);
-			B = RNG.randomVector(dim*dim, -100, 100);
+			A = RNG.randomIntArray(rand, -100, 100, dim*dim);
+			B = RNG.randomIntArray(rand, -100, 100, dim*dim);
 			As = transform(alphas);
-		}
+//		}
 	}
 
 	private double[] transform(double[] x) {
@@ -55,6 +70,7 @@ InterfaceMultimodalProblem {
 	}
 	
 	public double[] eval(double[] x) {
+		x = rotateMaybe(x);
 		double[] res = new double[1];
 		double[] Bs = transform(x);
 		
@@ -88,7 +104,12 @@ InterfaceMultimodalProblem {
 	}
 
 	public String globalInfo() {
-		return "Fletcher-Powell function with up to 2^n optima from Shir&Baeck, PPSN 2006, after Bäck 1996. Alphas and Matrices A and B are randomly created but kept when the dimension is decreased.";
+		return "Fletcher-Powell function with up to 2^n optima from Shir&Baeck, PPSN 2006, after Bäck 1996. Alphas and Matrices A and B are randomly created with a fixed seed.";
+	}
+
+	public SolutionHistogram getHistogram() {
+		if (getProblemDimension()<15) return new SolutionHistogram(0, 8, 16);
+		else return new SolutionHistogram(0, 40000, 16);
 	}
 }
 

src/eva2/server/go/problems/F1Problem.java

@@ -44,6 +44,7 @@ public class F1Problem extends AbstractProblemDoubleOffset implements Interface2
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
         double[] result = new double[1];
         result[0]     = m_YOffSet;
         // add an offset in solution space
@@ -89,6 +90,7 @@ public class F1Problem extends AbstractProblemDoubleOffset implements Interface2
     }
 
 	public double[] getFirstOrderGradients(double[] x) {
+		x = rotateMaybe(x);
 		// first order partial derivation in direction x_i is 2*x_i
 		double[] grads=new double[x.length];
 		for (int i=0; i<x.length; i++) {

src/eva2/server/go/problems/F20Problem.java

@@ -0,0 +1,117 @@
+package eva2.server.go.problems;
+
+import java.io.Serializable;
+
+import eva2.server.go.operators.postprocess.SolutionHistogram;
+
+/**
+ * The multi-modal, multi-funnel Rana function, f_rana = sum_{i=0}^{n-2} (g(x_i, x_{i+1})
+ * with g(x,y) = x sin(a)cos(b)+(y+1)cos(a)sin(b), a=sqrt(|-x+y+1|), b=sqrt(|x+y+1|).
+ * 
+ * For gnuplot: x *sin(sqrt(abs(-x+y+1)))*cos(sqrt(abs(x+y+1)))+(y+1)*cos(sqrt(abs(-x+y+1)))*sin(sqrt(abs(x+y+1)))
+ * 
+ * @author mkron
+ *
+ */
+public class F20Problem extends AbstractProblemDouble implements Serializable, InterfaceInterestingHistogram{
+	private int dim=10;
+	private boolean shiftFit = false;
+	
+	public F20Problem() {
+		setDefaultRange(512.);
+	}
+	
+	public F20Problem(int dim, boolean rotate) {
+		this();
+		setProblemDimension(dim);
+		setDoRotation(rotate);
+	}
+	
+	public F20Problem(F20Problem o) {
+		super(o);
+		setDefaultRange(512);
+		this.dim = o.dim;
+		this.setShiftFit(o.isShiftFit());
+	}
+
+	@Override
+	public double[] eval(double[] x) {
+		x = rotateMaybe(x);
+		double sum=getYOffset();
+		
+		for (int i=0; i<x.length-1; i++) {
+			sum += g(x[i],x[i+1]);
+		}
+		return new double[] {sum};
+	}
+
+	private double getYOffset() {
+		if (isShiftFit()) return (getProblemDimension() - 1)*getDefaultRange();
+		else return 0;
+	}
+	
+	private double g(double x, double y) {
+		double a=beta(-x,y);
+		double b=beta(x,y);
+		return x*Math.sin(a)*Math.cos(b)+(y+1.)*Math.cos(a)*Math.sin(b);
+	}
+	
+//	private double alpha(double x, double y) {
+//		return Math.sqrt(Math.abs(-x+y+1));
+//	}
+	
+	private double beta(double x, double y) {
+		return Math.sqrt(Math.abs(x+y+1));
+	}
+	
+	@Override
+	public int getProblemDimension() {
+		return dim;
+	}
+	
+	public void setProblemDimension(int newDim) {
+		dim=newDim;
+	}
+
+	@Override
+	public Object clone() {
+		return new F20Problem(this);
+	}
+
+	public String getName() {
+		return "Rana"+(isDoRotation() ? "-rot" : "");
+	}
+	
+	public String globalInfo() {
+		return "The Rana function is non-separable, highly multi-modal and multi-funnel." +
+				" There are diagonal ridges across the search space and the optima are close to the bounds." +
+				"The minimum fitness f(x*) is close to (n-1)*r for dimension n and default range r, by which " +
+				"this implementation may be shifted to the positive domain.";  
+	}
+
+	public void setShiftFit(boolean shiftFit) {
+		this.shiftFit = shiftFit;
+	}
+
+	public boolean isShiftFit() {
+		return shiftFit;
+	}
+
+	public SolutionHistogram getHistogram() {
+		if (getProblemDimension()==10) {
+			if (getYOffset()==0) return new SolutionHistogram(-5000, -3400, 16);
+			else return new SolutionHistogram(0, 1600, 16);
+		}
+		if (getProblemDimension()==30) {
+			if (getYOffset()==0) return new SolutionHistogram(-15000, -8600, 16); 
+//			das passst wohl nicht für Multimodales... Also nochmal für 30D.
+			else return new SolutionHistogram(0, 6400, 16);
+		}
+		if (getProblemDimension() <= 5) {
+			double lower = getYOffset()-((getProblemDimension()-1)*getDefaultRange());
+			return new SolutionHistogram(lower, lower+160, 16);
+		} else if (getProblemDimension() < 15) {
+			return new SolutionHistogram(getYOffset()-5000, getYOffset()-3400, 16);
+		} else return new SolutionHistogram(getYOffset()-15000, getYOffset()-13400, 16);
+	}
+}

src/eva2/server/go/problems/F21Problem.java

@@ -0,0 +1,77 @@
+package eva2.server.go.problems;
+
+import java.io.Serializable;
+
+import eva2.server.go.operators.postprocess.SolutionHistogram;
+
+/**
+ * The Levy-function, from Levy, A., and Montalvo, A. (1985). Also described in 
+ * "A Trust-Region Algorithm for Global Optimization", Bernardetta Addisy and Sven Leyfferz, 2004/2006.
+ */
+
+public class F21Problem extends AbstractProblemDouble implements Serializable, InterfaceInterestingHistogram {
+	private int dim=10;
+    
+	public F21Problem() {
+		super();
+		super.SetDefaultAccuracy(0.00001);
+		setDefaultRange(10);
+	}
+	
+	public F21Problem(int d) {
+		this();
+		setProblemDimension(d);
+	}
+	
+	public F21Problem(F21Problem f21Problem) {
+		super(f21Problem);
+		dim=f21Problem.getProblemDimension();
+		setDefaultRange(f21Problem.getDefaultRange());
+	}
+	
+	@Override
+	public double[] eval(double[] x) {
+		x = rotateMaybe(x);
+		double t=0, s=0, sum=0;
+
+		for (int i=0; i<x.length-2; i++) {
+			s=(x[i]-1.);
+			t=Math.sin(Math.PI*x[i+1]);
+			sum += (s*s)*(1+10.*t*t);
+		}
+		
+		s=Math.sin(Math.PI*x[0]);
+		double[] y = new double[1];
+		y[0] = 10.*s*s+sum+dim*(x[dim-1]-1)*(x[dim-1]-1);
+		return y;
+	}
+
+	@Override
+	public int getProblemDimension() {
+		return dim;
+	}
+
+	public void setProblemDimension(int d) {
+		dim=d;
+	}
+	
+	@Override
+	public Object clone() {
+		return new F21Problem(this);
+	}
+
+	public SolutionHistogram getHistogram() {
+		if (getProblemDimension()<15) return new SolutionHistogram(0, 2, 16);
+		else if (getProblemDimension()<25) return new SolutionHistogram(0, 4, 16);
+		else return new SolutionHistogram(0, 8, 16);
+	}
+
+	public String getName() {
+		return "F21-Problem";
+	}
+	
+	public String globalInfo() {
+		return "The Levy-function, from Levy, A., and Montalvo, A. (1985)";
+	}
+	
+}

src/eva2/server/go/problems/F2Problem.java

@@ -1,6 +1,8 @@
 package eva2.server.go.problems;
 
 import eva2.server.go.individuals.ESIndividualDoubleData;
+import eva2.server.go.populations.Population;
+import eva2.server.go.strategies.GradientDescentAlgorithm;
 
 /**
  * Created by IntelliJ IDEA.
@@ -9,9 +11,12 @@ import eva2.server.go.individuals.ESIndividualDoubleData;
  * Time: 19:03:09
  * To change this template use File | Settings | File Templates.
  */
-public class F2Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, java.io.Serializable, InterfaceFirstOrderDerivableProblem {
+public class F2Problem extends AbstractProblemDoubleOffset implements InterfaceLocalSearchable, InterfaceMultimodalProblem, java.io.Serializable, InterfaceFirstOrderDerivableProblem {
 
-    public F2Problem() {
+    private transient GradientDescentAlgorithm localSearchOptimizer=null;
+
+
+	public F2Problem() {
         this.m_Template         = new ESIndividualDoubleData();
     }
     public F2Problem(F2Problem b) {
@@ -33,6 +38,7 @@ public class F2Problem extends AbstractProblemDoubleOffset implements InterfaceM
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
         double[] result = new double[1];
         result[0]     = m_YOffSet;
         double xi, xii;
@@ -46,6 +52,7 @@ public class F2Problem extends AbstractProblemDoubleOffset implements InterfaceM
     }
     
 	public double[] getFirstOrderGradients(double[] x) {
+		x = rotateMaybe(x);
         int dim = x.length;
         double[] result = new double[dim];
         double xi, xii;
@@ -93,4 +100,26 @@ public class F2Problem extends AbstractProblemDoubleOffset implements InterfaceM
     public String globalInfo() {
         return "Generalized Rosenbrock's function.";
     }
+
+    public void doLocalSearch(Population pop) {
+    	if (localSearchOptimizer == null) {
+    		initLS();
+    	}
+    	localSearchOptimizer.setPopulation(pop);
+    	localSearchOptimizer.optimize();
+    }
+
+    private void initLS() {
+		localSearchOptimizer = new GradientDescentAlgorithm();
+	    localSearchOptimizer.SetProblem(this);
+	    localSearchOptimizer.init();
+    }
+
+    public double getLocalSearchStepFunctionCallEquivalent() {
+    	double cost = 1;
+    	if (this.localSearchOptimizer instanceof GradientDescentAlgorithm) {
+    		cost = ((GradientDescentAlgorithm) localSearchOptimizer).getIterations();
+    	}
+    	return cost;
+    }
 }

src/eva2/server/go/problems/F3Problem.java

@@ -31,6 +31,7 @@ public class F3Problem extends AbstractProblemDoubleOffset implements java.io.Se
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
         double[] result = new double[1];
         result[0]     = m_YOffSet+6*x.length;
         for (int i = 0; i < x.length-1; i++) {

src/eva2/server/go/problems/F4Problem.java

@@ -35,6 +35,7 @@ public class F4Problem extends AbstractProblemDoubleOffset implements java.io.Se
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
         double[] result = new double[1];
         result[0]     = m_YOffSet;
         for (int i = 0; i < x.length-1; i++) {

src/eva2/server/go/problems/F5Problem.java

@@ -35,6 +35,7 @@ public class F5Problem extends AbstractProblemDoubleOffset implements java.io.Se
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
         double[]    result = new double[1];
         double      tmp;
         result[0]     = m_YOffSet;

src/eva2/server/go/problems/F6Problem.java

@@ -2,28 +2,26 @@ package eva2.server.go.problems;
 
 import eva2.server.go.individuals.ESIndividualDoubleData;
 import eva2.tools.math.Mathematics;
+import eva2.server.go.operators.postprocess.SolutionHistogram;
+import eva2.server.go.populations.Population;
+import eva2.server.go.strategies.GradientDescentAlgorithm;
 import eva2.tools.math.Jama.Matrix;
 
 /**
- * Created by IntelliJ IDEA.
+ * Generalized Rastrigin's function.
- * User: streiche
+ * 
- * Date: 30.06.2005
- * Time: 13:09:36
- * To change this template use File | Settings | File Templates.
  */
-public class F6Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, InterfaceFirstOrderDerivableProblem, java.io.Serializable {
+public class F6Problem extends AbstractProblemDoubleOffset
-
+implements InterfaceMultimodalProblem, InterfaceFirstOrderDerivableProblem, InterfaceLocalSearchable, java.io.Serializable, InterfaceInterestingHistogram {
-	private boolean 		doRotation = false;
     private double          m_A     = 10;
     private double          m_Omega = 2*Math.PI;
-    private Matrix 			rotation;
+	private transient GradientDescentAlgorithm localSearchOptimizer=null;
 
     public F6Problem() {
         this.m_Template         = new ESIndividualDoubleData();
     }
     public F6Problem(F6Problem b) {
-        super(b);       
+        super(b);
-        doRotation 				= b.doRotation;
         this.m_A                = b.m_A;
         this.m_Omega            = b.m_Omega;
     }
@@ -31,17 +29,6 @@ public class F6Problem extends AbstractProblemDoubleOffset implements InterfaceM
     public F6Problem(int dim) {
 		super(dim);
 	}
-	/** This method inits the Problem to log multiruns
-     */
-    public void initProblem() {
-        super.initProblem();
-        if (doRotation) {
-        	rotation = new Matrix(m_ProblemDimension, m_ProblemDimension);
-        	Matrix vec = new Matrix(m_ProblemDimension, 1);
-        	for (int i=0; i<m_ProblemDimension; i++) vec.set(i,0, i+1);
-        	rotation = Mathematics.getRotationMatrix(vec).transpose();
-        } else rotation = null;
-    }
     
     /** This method returns a deep clone of the problem.
      * @return  the clone
@@ -55,10 +42,7 @@ public class F6Problem extends AbstractProblemDoubleOffset implements InterfaceM
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
-    	if (doRotation) {
+    	x = rotateMaybe(x);
-	    	Matrix resVec = rotation.times(new Matrix(x, x.length));
-	    	x = resVec.getColumnPackedCopy();
-    	}
         double[] result = new double[1];
         result[0]     = x.length * this.m_A + m_YOffSet;
         for (int i = 0; i < x.length; i++) {
@@ -69,10 +53,7 @@ public class F6Problem extends AbstractProblemDoubleOffset implements InterfaceM
     }
     
 	public double[] getFirstOrderGradients(double[] x) {
-    	if (doRotation) {
+		x = rotateMaybe(x);
-	    	Matrix resVec = rotation.times(new Matrix(x, x.length));
-	    	x = resVec.getColumnPackedCopy();
-    	}
         double[] result = new double[x.length];        
         for (int j=0; j<x.length; j++) {
         	result[j]=0;
@@ -141,10 +122,36 @@ public class F6Problem extends AbstractProblemDoubleOffset implements InterfaceM
     public String omegaTipText() {
         return "Choose Omega.";
     }
-	public boolean isDoRotation() {
+    
-		return doRotation;
+    public void setDefaultAccuracy(double acc) {
-	}
+    	super.SetDefaultAccuracy(acc);
-	public void setDoRotation(boolean doRotation) {
+    }
-		this.doRotation = doRotation;
+
+	public SolutionHistogram getHistogram() {
+		if (getProblemDimension() < 15) return new SolutionHistogram(-0.5, 15.5, 16);
+		else if (getProblemDimension() < 25) return new SolutionHistogram(-0.5, 39.5, 16);
+		else return new SolutionHistogram(0, 80, 16);
 	}
+	
+    public void doLocalSearch(Population pop) {
+    	if (localSearchOptimizer == null) {
+    		initLS();
+    	}
+    	localSearchOptimizer.setPopulation(pop);
+    	localSearchOptimizer.optimize();
+    }
+
+    private void initLS() {
+		localSearchOptimizer = new GradientDescentAlgorithm();
+	    localSearchOptimizer.SetProblem(this);
+	    localSearchOptimizer.init();
+    }
+
+    public double getLocalSearchStepFunctionCallEquivalent() {
+    	double cost = 1;
+    	if (this.localSearchOptimizer instanceof GradientDescentAlgorithm) {
+    		cost = ((GradientDescentAlgorithm) localSearchOptimizer).getIterations();
+    	}
+    	return cost;
+    }
 }

src/eva2/server/go/problems/F7Problem.java

@@ -70,6 +70,7 @@ public class F7Problem extends AbstractProblemDoubleOffset implements java.io.Se
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
         double[] result = new double[1];
         result[0]     = m_YOffSet;
         if ((Math.floor(this.m_CurrentTimeStamp / this.m_t)%2) == 0) {

src/eva2/server/go/problems/F8Problem.java

@@ -41,6 +41,7 @@ public class F8Problem extends AbstractProblemDoubleOffset implements InterfaceM
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
         double[]        result = new double[1];
         double          sum1 = 0, sum2 = 0, exp1, exp2;
 

src/eva2/server/go/problems/F9Problem.java

@@ -24,6 +24,7 @@ public class F9Problem extends AbstractProblemDoubleOffset implements java.io.Se
      * @return  The m-dimensional output vector.
      */
     public double[] eval(double[] x) {
+    	x = rotateMaybe(x);
         double[] result = new double[1];
         result[0]     = m_YOffSet;
         for (int i = 0; i < x.length; i++) {

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

@@ -87,6 +87,7 @@ public class GPFunctionProblem extends AbstractProblemDouble implements Interfac
 			EVAERROR.errorMsgOnce("mismatching dimension of GPFunctionProblem! Setting to " + x.length);
 			setProblemDimension(x.length);
 		}
+		x = rotateMaybe(x);
 		pos = x;
 		Double res = (Double) gpProblem.evaluate(this);
 		double[] fit = new double[1];

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

@@ -10,6 +10,8 @@ public interface InterfaceFirstOrderDerivableProblem {
 
 	/**
 	 * Calculate the first order gradients of this problem.
+	 * If you implement this, be aware that some types of AbstractProblemDouble may be rotated,
+	 * so you may have to use x = rotateMaybe(x) first.
 	 * @param x
 	 * @return the first order gradients of this problem
 	 */

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

@@ -0,0 +1,20 @@
+package eva2.server.go.problems;
+
+import eva2.server.go.operators.postprocess.SolutionHistogram;
+
+/**
+ * Target functions may provide an idea which fitness values are 
+ * interesting.
+ * 
+ * @author mkron
+ *
+ */
+public interface InterfaceInterestingHistogram {
+	/**
+	 * For this specific instance, provide an empty histogram defining in which
+	 * area interesting solutions would lie.
+	 * 
+	 * @return
+	 */
+	public SolutionHistogram getHistogram();
+}

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

@@ -19,6 +19,12 @@ public interface InterfaceMultimodalProblemKnown extends InterfaceMultimodalProb
      */
     public void initListOfOptima();
 
+    /**
+     * Return true if the full list of optima is available, else false.
+     * @return
+     */
+    public boolean fullListAvailable();
+    
     /** 
      * This method returns a list of all optima as population or null if 
      * the optima are unknown.
@@ -48,5 +54,5 @@ public interface InterfaceMultimodalProblemKnown extends InterfaceMultimodalProb
      *   
      * @return
      */
-    public double getEpsilon();
+    public double getDefaultAccuracy();
 }

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

@@ -267,9 +267,10 @@ public class SimpleProblemWrapper extends AbstractOptimizationProblem {
      * @return description
      */
     public String globalInfo() {
-    	Object maybeAdditionalString = BeanInspector.callIfAvailable(simProb, "globalInfo", null);
+    	return "Wrapping simple problem implementations.";
-    	if (maybeAdditionalString != null) {
+    }
-    		return "Wrapping a simple problem: " + (String)maybeAdditionalString;
+    
-    	} else return "Wrapping a simple problem.";
+    public String[] getGOEPropertyUpdateLinks() {
+    	return new String[] {"globalInfo", "simpleProblem"};
     }
 }

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

@@ -327,7 +327,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
      * @return True if converged.
      */
     private boolean testSpeciesForConvergence(Population pop) {
-        ArrayList<AbstractEAIndividual> speciesHistory = pop.m_History;
+        ArrayList<AbstractEAIndividual> speciesHistory = pop.getHistory();
         int     histLen = speciesHistory.size();
 
         if (histLen <= haltingWindow) {
@@ -762,7 +762,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
     	if (plot && (m_Topology!=null)) plotLine(m_Topology, spec1.getBestEAIndividual(), spec2.getBestEAIndividual());
     	spec1.addPopulation(spec2);
     	// keep longer history
-        if (spec2.m_History.size() > spec1.m_History.size()) spec1.m_History = spec2.m_History;
+        if (spec2.getHistoryLength() > spec1.getHistoryLength()) spec1.SetHistory(spec2.getHistory());
         if (spec2.getGeneration() > spec1.getGeneration()) spec1.setGenerationTo(spec2.getGeneration());
         // possibly notify the optimizer of the merging event to merge population based information
         if (m_Optimizer instanceof InterfaceSpeciesAware) ((InterfaceSpeciesAware)m_Optimizer).mergeToFirstPopulationEvent(spec1, spec2);
@@ -780,10 +780,10 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
 		newSp.setUseHistory(true);
     	if (startAtP1Gen) { // start explicitely as a child population of p1
     		newSp.setGenerationTo(parentSp.getGeneration());
-    		newSp.m_History = (ArrayList<AbstractEAIndividual>) parentSp.m_History.clone();
+    		newSp.SetHistory((ArrayList<AbstractEAIndividual>) parentSp.getHistory().clone());
     	} else { // start anew (from undiff)
         	newSp.setGenerationTo(0);
-            newSp.m_History = new ArrayList<AbstractEAIndividual>();
+            newSp.SetHistory(new ArrayList<AbstractEAIndividual>());
     	}
     	
         if (m_Optimizer instanceof InterfaceSpeciesAware) ((InterfaceSpeciesAware)m_Optimizer).splitFromFirst(parentSp, newSp);

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

@@ -138,8 +138,7 @@ public class EvolutionStrategyIPOP extends EvolutionStrategies implements Interf
     protected void firePropertyChangedEvent(String name) {
     	if (name.equals(Population.funCallIntervalReached)) {
     		super.firePropertyChangedEvent(Population.nextGenerationPerformed);
-    	}
+    	} else {} // nothing, evt is produced in #registerPopulationStateChanged, dont forward original due to changing pop size
-    	else {} // nothing, evt is produced in #registerPopulationStateChanged, dont forward original due to changing pop size
     }
     
     public void init() {
@@ -152,7 +151,7 @@ public class EvolutionStrategyIPOP extends EvolutionStrategies implements Interf
     	super.setLambda(initialLambda);
     	checkPopulationConstraints();
 		setForceOrigPopSize(false);
-    	getPopulation().setNotifyEvalInterval(initialLambda);
+    	getPopulation().setNotifyEvalInterval(Math.max(initialLambda, 100));
     	super.init();
     	bestList = new LinkedList<AbstractEAIndividual>();
     	best = getPopulation().getBestEAIndividual();
@@ -291,7 +290,6 @@ public class EvolutionStrategyIPOP extends EvolutionStrategies implements Interf
 	public double getStagThreshold() {
 		return stagThreshold;
 	}
-
 	/**
 	 * @param stagThreshold the stagThreshold to set
 	 */
@@ -299,6 +297,9 @@ public class EvolutionStrategyIPOP extends EvolutionStrategies implements Interf
 		this.stagThreshold = stagThreshold;
 		if (fitConvTerm != null) fitConvTerm.setConvergenceThreshold(stagThreshold);
 	}
+	public String getStagThresholdTipText() {
+		return "Trigger new aera if the fitness does not change more than this threshold within certain no. iterations."; 
+	}
 
 	/**
 	 * @return the stagTime

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

@@ -1,5 +1,6 @@
 package eva2.server.go.strategies;
 
+import eva2.gui.BeanInspector;
 import eva2.server.go.InterfacePopulationChangedEventListener;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.GAIndividualBinaryData;
@@ -111,7 +112,8 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
             this.m_ParentSelection.prepareSelection(this.m_Population);
             this.m_PartnerSelection.prepareSelection(this.m_Population);
             parents     = this.m_ParentSelection.selectFrom(this.m_Population, this.m_Population.getTargetSize());
-            //System.out.println("Parents:"+parents.getSolutionRepresentationFor());
+//            System.out.println("Parents:"+parents.getStringRepresentation());
+//            double[] meas = parents.getPopulationMeasures();
 
             if (parents.getEAIndividual(0).getMutationOperator() instanceof InterfaceMutationGenerational) {
             	((InterfaceMutationGenerational)parents.getEAIndividual(0).getMutationOperator()).adaptAfterSelection(m_Population, parents);
@@ -155,7 +157,8 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
                 for (int i = 0; i < this.m_Plague; i++) if (this.m_Population.size() > 2) this.m_Population.remove(this.m_Population.getWorstEAIndividual());
                 this.m_Population.setTargetSize(this.m_Population.size());
             }
-            //System.out.println("Population size: " + this.m_Population.size());
+//            System.out.println("Population size: " + this.m_Population.size());
+//            System.out.println("Population: " + m_Population.getStringRepresentation());
 //        if (this.m_Population.getArchive() != null) {
 //            if (this.m_Population.getArchive().getArchive() != null) {
 //                System.out.println("Zwei Archive!");

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

@@ -1,5 +1,6 @@
 package eva2.server.go.strategies;
 
+import eva2.gui.BeanInspector;
 import eva2.server.go.InterfacePopulationChangedEventListener;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.InterfaceDataTypeDouble;
@@ -50,6 +51,8 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
 	transient private String m_Identifier = "";
 	private Population m_Population;
 
+	private static boolean TRACE=false;
+
 	private static final String lockKey = "gdaLockDataKey";
 	private static final String lastFitnessKey = "gdaLastFitDataKey";
 	private static final String stepSizeKey = "gdaStepSizeDataKey";
@@ -74,6 +77,23 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
     this.m_Population = new Population();
     this.m_Population.setTargetSize(1);
   }
+  
+  /**
+   * GDA with locally adapted step size.
+   * 
+   * @param minStepSize
+   * @param maxStepSize
+   * @param maxAbsoluteChange
+   */
+  public GradientDescentAlgorithm(double minStepSize, double maxStepSize, double maxAbsoluteChange) {
+	  globalStepSizeAdaption=false;
+	  localStepSizeAdaption=true;
+	  localminstepsize = minStepSize; 
+	  globalminstepsize = minStepSize;
+	  localmaxstepsize = maxStepSize;
+	  globalmaxstepsize = maxStepSize;
+	  maximumabsolutechange = maxAbsoluteChange;
+  }
 
   public Object clone() {
     /**@todo Implement InterfaceOptimizer method*/
@@ -140,6 +160,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
           double[] oldchange = null;
 
           double[] gradient = ((InterfaceFirstOrderDerivableProblem) m_Problem).getFirstOrderGradients(params);
+          if (TRACE) System.out.println("GDA: " + BeanInspector.toString(params) + ", grad: " + BeanInspector.toString(gradient));
           if ((oldgradient != null) && (wstepsize != null)) { // LOCAL adaption
             for (int li = 0; li < wstepsize.length; li++) {
               double prod = gradient[li] * oldgradient[li];
@@ -214,7 +235,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
         indy = ((AbstractEAIndividual)this.m_Population.get(i));
 //        Hashtable history = (Hashtable) indyhash.get(indy);
         if (indy.getFitness()[0] > recoverythreshold) {
-          System.out.println("Gradient Descent: Fitness critical:" + indy.getFitness()[0]);
+          if (TRACE) System.out.println("Gradient Descent: Fitness critical:" + indy.getFitness()[0]);
           ((InterfaceDataTypeDouble) indy).SetDoublePhenotype((double[]) indy.getData(oldParamsKey));
           double[] changes = (double[]) indy.getData(changesKey);
           int[] lock = (int[]) indy.getData(lockKey);

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

@@ -120,7 +120,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 		if (TRACE) System.out.println("global search");
 		this.m_GlobalOptimizer.optimize();
 
-		if (((this.m_GlobalOptimizer.getPopulation().getGeneration() % this.globalSearchIterations) == 0)
+		if ((globalSearchIterations>0) && (((this.m_GlobalOptimizer.getPopulation().getGeneration() % this.globalSearchIterations) == 0))
 		    && (this.localSearchSteps > 0)
 		    && (this.m_Problem instanceof InterfaceLocalSearchable)) {
 			// here the local search is performed

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

@@ -83,8 +83,10 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 
 	public void addPopulationChangedEventListener(
 			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener == null) m_Listener = new Vector<InterfacePopulationChangedEventListener>();
+		if (ea!=null) {
-		if (!m_Listener.contains(ea)) m_Listener.add(ea);
+			if (m_Listener == null) m_Listener = new Vector<InterfacePopulationChangedEventListener>();
+			if (!m_Listener.contains(ea)) m_Listener.add(ea);
+		}
 	}
 	
 	public boolean removePopulationChangedEventListener(
@@ -270,7 +272,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 //					m_Problem.evaluate(ind);
 //					this.m_Population.incrFunctionCalls();
 				}
-				m_Population.set(m_Population.getIndexOfWorstIndividual(fitIndex), ind, fitIndex);
+				m_Population.set(m_Population.getIndexOfWorstIndividualNoConstr(fitIndex), ind, fitIndex);
 			}else{//keine Verbesserung gefunden shrink!!
 				
 				double[] u_1 = ((InterfaceDataTypeDouble) m_Population.getBestEAIndividual(fitIndex)).getDoubleData();
@@ -286,6 +288,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 			evalsDone =  m_Population.getFunctionCalls() - evalCntStart;
 		} while (evalsDone < generationCycle);
 		m_Problem.evaluatePopulationEnd(m_Population);
+		this.m_Population.incrGeneration();
 	}
 
 	public void setPopulation(Population pop) {
@@ -344,7 +347,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 		NelderMeadSimplex nms = new NelderMeadSimplex();
 		nms.setProblemAndPopSize(problem);
 		
-		nms.addPopulationChangedEventListener(listener);
+		if (listener!=null) nms.addPopulationChangedEventListener(listener);
 		nms.init();
 
 		if (listener!=null) listener.registerPopulationStateChanged(nms.getPopulation(), "");
@@ -400,18 +403,18 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 	 * also contains the initial candidate. However, the new candidates have not been evaluated.
 	 * 
 	 * @param candidate
-	 * @param perturbRatio
+	 * @param perturbRelative
 	 * @param range
 	 * @param includeCand
 	 * @return
 	 */
-	public static Population createNMSPopulation(AbstractEAIndividual candidate, double perturbRatio, double[][] range, boolean includeCand) {
+	public static Population createNMSPopulation(AbstractEAIndividual candidate, double perturbRelative, double[][] range, boolean includeCand) {
 		Population initPop = new Population();
 		if (includeCand) initPop.add(candidate);
-		if (perturbRatio >= 1. || (perturbRatio <= 0.)) {
+		if (perturbRelative >= 1. || (perturbRelative <= 0.)) {
 			System.err.println("Warning: perturbation ratio should lie between 0 and 1! (NelderMeadSimplex:createNMSPopulation)");
 		}
-		addPerturbedPopulation(perturbRatio, initPop, range, candidate);
+		addPerturbedPopulation(perturbRelative, initPop, range, candidate);
 		return initPop;
 	}
 

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

@@ -1,6 +1,8 @@
 package eva2.server.go.strategies;
 
+import java.util.ArrayList;
 import java.util.Arrays;
+import java.util.LinkedList;
 import java.util.Vector;
 
 import eva2.gui.BeanInspector;
@@ -13,6 +15,7 @@ import eva2.server.go.enums.PostProcessMethod;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.AbstractEAIndividualComparator;
 import eva2.server.go.individuals.InterfaceDataTypeDouble;
+import eva2.server.go.operators.distancemetric.EuclideanMetric;
 import eva2.server.go.operators.distancemetric.PhenotypeMetric;
 import eva2.server.go.operators.paramcontrol.ParamAdaption;
 import eva2.server.go.operators.paramcontrol.ParameterControlManager;
@@ -61,7 +64,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		
 	protected Population                      m_Population        = new Population();
 	Object[] 								sortedPop			= null;
-	protected AbstractEAIndividual            m_BestIndividual;
+	protected AbstractEAIndividual            m_BestIndividual = null;
 	protected InterfaceOptimizationProblem    m_Problem           = new F1Problem();
 	protected boolean                         m_CheckRange  = true;
 	protected boolean						  checkSpeedLimit 		= false;
@@ -127,6 +130,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	transient protected eva2.gui.Plot      m_Plot;
 
 	private boolean externalInitialPop = false;
+	private static String lastSuccessKey = "successfulUpdate";
 //	private double lsCandidateRatio=0.25;
 
 
@@ -137,7 +141,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		algType = new SelectedTag("Inertness", "Constriction");
 		algType.setSelectedTag(1);
 
-		setWithConstriction(getPhi1(), getPhi2());
+		setConstriction(getPhi1(), getPhi2());
 		hideHideable();
 	}
 
@@ -224,6 +228,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		// evaluation needs to be done here now, as its omitted if reset is false
 		initDefaults(this.m_Population);
 		this.evaluatePopulation(this.m_Population);
+		if (m_BestIndividual == null) m_BestIndividual = m_Population.getBestEAIndividual();
 		initByPopulation(null, false);
 		externalInitialPop = false;
 	}
@@ -673,8 +678,9 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 			AbstractEAIndividual indy = (AbstractEAIndividual)pop.get(i);
 			if (isIndividualToUpdate(indy)) {
 				updateIndProps(indy, indy);
+				indy.putData(lastSuccessKey , indy.getData(partVelKey));
 //				System.err.println("updated " + i + " - "+ getParticleInfo(indy));
-			}
+			} else indy.putData(lastSuccessKey, null);
 		}
 	}
 
@@ -768,7 +774,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		double[] accel, curVelocity    = new double[lastVelocity.length];
 		
 		if (useAlternative) {
-			accel	= getAccelerationAlternative(personalBestPos, neighbourBestPos, curPosition, range);
+			accel	= getAccelerationAlternative(index, personalBestPos, neighbourBestPos, curPosition, range);
 		} else {
 			accel	= getAcceleration(personalBestPos, neighbourBestPos, curPosition, range);
 		}
@@ -801,33 +807,75 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		return accel;
 	}
 	
-	protected double[] getAccelerationAlternative(double[] personalBestPos, double[] neighbourBestPos, double[] curPosition, double[][] range) {
+	protected double[] getAccelerationAlternative(int index, double[] personalBestPos, double[] neighbourBestPos, double[] curPosition, double[][] range) {
-		double[] accel    = new double[curPosition.length];
+		double[] accel    = getAcceleration(personalBestPos, neighbourBestPos, curPosition, range);
-		double chi;
+		double[] successfulVel = getSuccessfulVel(index);
-		
+//		double succW = 0.5;
-		Matrix cogVecB = new Matrix(curPosition.length, 1);
+		if (successfulVel!=null) {
-		Matrix socVecB = new Matrix(curPosition.length, 1);
+			Mathematics.vvAdd(accel, successfulVel, accel);
-		
+			Mathematics.svMult(0.5, accel, accel);
-		for (int i = 0; i < personalBestPos.length; i++) {
-			cogVecB.set(i, 0, (personalBestPos[i]-curPosition[i]));
-			socVecB.set(i, 0, (neighbourBestPos[i]-curPosition[i]));
-		}
-		Matrix cogRandB = getOrientedGaussianRandomVectorB(cogVecB, 5);
-		Matrix socRandB = getOrientedGaussianRandomVectorB(socVecB, 5);
-
-		for (int i = 0; i < curPosition.length; i++) {
-			if (algType.getSelectedTag().getID()==1) chi=m_InertnessOrChi;
-			else chi = 1.;
-			// the component from the cognition model
-//			accel[i]  = this.m_Phi1*chi*/*RNG.randomDouble(0,1)**/cogRand.getElement(i);
-			accel[i]  = this.m_Phi1*chi*/*RNG.randomDouble(0,1)**/cogRandB.get(i,0);
-			// the component from the social model
-//			accel[i]  += this.m_Phi2*chi*/*RNG.randomDouble(0,1)**/socRand.getElement(i);
-			accel[i]  += this.m_Phi2*chi*/*RNG.randomDouble(0,1)**/socRandB.get(i,0);
 		}
 		return accel;
 	}
 	
+	private double[] getSuccessfulVel(int index) {
+		if (true) {
+			return (double[])m_Population.getEAIndividual(index).getData(lastSuccessKey);
+		} else { // random one
+		ArrayList<Integer> successes = new ArrayList<Integer>();
+		for (int i = 0; i < this.m_Population.size(); i++) {
+			double[] succVel = (double[])m_Population.getEAIndividual(i).getData(lastSuccessKey);
+			if (succVel!=null) successes.add(new Integer(i));
+		}
+		if (successes.size()>0) {
+			int i = successes.get(RNG.randomInt(successes.size()));
+			return (double[])m_Population.getEAIndividual(i).getData(lastSuccessKey);
+		} else return null;
+		}
+	}
+
+//	protected double[] getAccelerationAlternative(double[] personalBestPos, double[] neighbourBestPos, double[] curPosition, double[][] range) {
+//		double[] accel    = new double[curPosition.length];
+//		double chi;
+//
+//		if (algType.getSelectedTag().getID()==1) chi=m_InertnessOrChi;
+//		else chi = 1.;
+//
+//		boolean rotatedVect=false;
+//		if (rotatedVect) {
+//			Matrix cogVecB = new Matrix(curPosition.length, 1);
+//			Matrix socVecB = new Matrix(curPosition.length, 1);
+//			for (int i = 0; i < personalBestPos.length; i++) {
+//				cogVecB.set(i, 0, (personalBestPos[i]-curPosition[i]));
+//				socVecB.set(i, 0, (neighbourBestPos[i]-curPosition[i]));
+//			}
+//			Matrix cogRandB = getOrientedGaussianRandomVectorB(cogVecB, 5);
+//			Matrix socRandB = getOrientedGaussianRandomVectorB(socVecB, 5);
+//
+//			for (int i = 0; i < curPosition.length; i++) {
+//				// the component from the cognition model
+//				//			accel[i]  = this.m_Phi1*chi*/*RNG.randomDouble(0,1)**/cogRand.getElement(i);
+//				accel[i]  = this.m_Phi1*chi*/*RNG.randomDouble(0,1)**/cogRandB.get(i,0);
+//				// the component from the social model
+//				//			accel[i]  += this.m_Phi2*chi*/*RNG.randomDouble(0,1)**/socRand.getElement(i);
+//				accel[i]  += this.m_Phi2*chi*/*RNG.randomDouble(0,1)**/socRandB.get(i,0);
+//			}
+//			return accel;
+//		} else {
+//			double sPB = RNG.randomDouble();
+//			double sNB = 1.-sPB;
+//			double[] mean= personalBestPos.clone();
+//			Mathematics.svMult(sPB, mean);
+//			Mathematics.svvAddScaled(sNB, neighbourBestPos, mean, mean); // middle position
+//			double stddev = chi*EuclideanMetric.euclideanDistance(personalBestPos, neighbourBestPos)/3.; // std.dev is one third of the distance
+//			double[] gausRnd = getGaussianVector(mean, stddev);
+//			for (int i=0; i<accel.length; i++) {
+//				accel[i] = (gausRnd[i] - curPosition[i]); 
+//			}
+//			return accel;
+//		}
+//	}
+	
 //	public static void main(String[] args) {
 //		ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
 //		GVector tmp, vec = new GVector(5);
@@ -886,6 +934,13 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		return resVec;
 	}
 	
+	protected double[] getGaussianVector(double[] mean, double dev) {
+		double[] res = new double[mean.length];
+		RNG.gaussianVector(dev, res, false);
+		Mathematics.vvAdd(mean, res, res);
+		return res;
+	}
+	
 	public static double project(double min, double max, double val) {
 		if (val < min) return min;
 		else if (val > max) return max;
@@ -1593,6 +1648,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 				if (TRACE) System.err.println("init indy " + i + " " + AbstractEAIndividual.getDefaultDataString(indy));
 			}
 		}
+		m_BestIndividual = pop.getBestEAIndividual();
 	}
 	public String populationTipText() {
 		return "Edit the properties of the population used.";
@@ -1648,8 +1704,8 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	}
 
 	/**
-	 * Set the inertness parameter so as to resemble the constriction scheme. Use this before
+	 * Set the phi values as well as the inertness parameter so as to resemble the constriction scheme. 
-	 * calling the setAsTau methods. The constriction scheme calculates the speed update in the following
+	 * The constriction scheme calculates the speed update in the following
 	 * way: v(t+1) = Chi * ( v(t) + tau1*u1*(p-x(t)) * tau2*u2*(g-x(t)))
 	 * with u1, u2 random variables in (0,1) and tau1 and tau2 usually set to 2.05. The sum tau1 and tau2
 	 * must be greater than 4. The Chi parameter (constriction) is set as in
@@ -1662,11 +1718,14 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	 * @param tau1
 	 * @param tau2
 	 */
-	protected void setWithConstriction(double tau1, double tau2) {
+	protected void setConstriction(double tau1, double tau2) {
 		double pSum = tau1+tau2;
 		if (pSum <= 4) {
 			System.err.println("error, invalid tauSum value in PSO::setWithConstriction");
 		} else {
+			if (!getAlgoType().isSelectedString("Constriction")) System.err.println("Warning, PSO algorithm variant constriction expected!");
+			m_Phi1=tau1;
+			m_Phi2=tau2;
 			setInertnessOrChi(2./(Math.abs(2-pSum-Math.sqrt((pSum*pSum)-(4*pSum)))));
 		}
 	}
@@ -1691,7 +1750,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	 */
 	public void setPhi1 (double l) {
 		this.m_Phi1 = l;
-		if (algType.getSelectedTag().getID() == 1) setWithConstriction(getPhi1(), getPhi2());
+		if (algType.getSelectedTag().getID() == 1) setConstriction(getPhi1(), getPhi2());
 	}
 	public double getPhi1() {
 		return this.m_Phi1;
@@ -1705,7 +1764,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	 */
 	public void setPhi2 (double l) {
 		this.m_Phi2 = l;
-		if (algType.getSelectedTag().getID() == 1) setWithConstriction(getPhi1(), getPhi2());
+		if (algType.getSelectedTag().getID() == 1) setConstriction(getPhi1(), getPhi2());
 	}
 	public double getPhi2() {
 		return this.m_Phi2;
@@ -1723,7 +1782,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	public void setPhiValues(double phi1, double phi2) {
 		m_Phi1 = phi1;
 		m_Phi2 = phi2;
-		if (algType.isSelectedString("Constriction")) setWithConstriction(phi1, phi2);
+		if (algType.isSelectedString("Constriction")) setConstriction(phi1, phi2);
 	}
 	
 	/**
@@ -1778,7 +1837,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	 */
 	public void setAlgoType(SelectedTag s) {
 		this.algType = s;
-		if (s.getSelectedTag().getID() == 1) setWithConstriction(getPhi1(), getPhi2());
+		if (s.getSelectedTag().getID() == 1) setConstriction(getPhi1(), getPhi2());
 	}
 
 	public SelectedTag getAlgoType() {
@@ -1956,7 +2015,10 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		for (int i=0; i<population.size(); i++) {
 			double[] personalBestPos = (double[]) population.getEAIndividual(i).getData(partBestPosKey);
 			double[] personalBestfit = (double[]) population.getEAIndividual(i).getData(partBestFitKey);
-			double relDiff = (personalBestfit[0]-((InterfaceProblemDouble)m_Problem).eval(personalBestPos)[0])/personalBestfit[0];
+			
+			double relDiff;
+			if (personalBestfit[0]!=0) relDiff = (personalBestfit[0]-((InterfaceProblemDouble)m_Problem).eval(personalBestPos)[0])/personalBestfit[0];
+			else relDiff=(personalBestfit[0]-((InterfaceProblemDouble)m_Problem).eval(personalBestPos)[0]); // absolute diff in this case
 //			if (personalBestfit[0]!=((InterfaceProblemDouble)m_Problem).eval(personalBestPos)[0]) {
 			if (Math.abs(relDiff)>1e-20) {
 				System.err.println("Warning: mismatching best fitness by " + relDiff);

src/eva2/server/stat/AbstractStatistics.java

@@ -1,5 +1,6 @@
 package eva2.server.stat;
 
+import java.io.File;
 import java.io.FileOutputStream;
 import java.io.PrintWriter;
 import java.text.SimpleDateFormat;
@@ -14,7 +15,6 @@ import eva2.server.go.IndividualInterface;
 import eva2.server.go.PopulationInterface;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
-import eva2.server.go.populations.InterfaceSolutionSet;
 import eva2.server.go.populations.Population;
 import eva2.server.go.problems.InterfaceAdditionalPopulationInformer;
 import eva2.server.go.strategies.InterfaceOptimizer;
@@ -107,13 +107,18 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 	 * @param infoString
 	 */
 	protected void initOutput(String infoString) {
-		SimpleDateFormat formatter = new SimpleDateFormat("E'_'yyyy.MM.dd'_at_'HH.mm.ss");
+		String startDate = getDateString();
-		String startDate = formatter.format(new Date());
 		// open the result file:
 		if (doFileOutput()  // not "text-window only" 
 				&& (m_StatsParams.getOutputVerbosity().getSelectedTagID() > StatsParameter.VERBOSITY_NONE)) { // verbosity accordingly high
 			//!resFName.equalsIgnoreCase("none") && !resFName.equals("")) {
-			String fname = makeOutputFileName(m_StatsParams.getResultFilePrefix(), infoString, startDate);
+			String fnameBase = makeOutputFileName(m_StatsParams.getResultFilePrefix(), infoString, startDate);
+			int cnt=0;
+			String fname = fnameBase;
+			while (new File(fname).exists()) {
+				cnt++;
+				fname=fnameBase+"."+cnt;
+			}
 			if (TRACE) System.out.println("FileName =" + fname);
 			try {
 				resultOut = new PrintWriter(new FileOutputStream(fname));
@@ -126,6 +131,16 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 		} else resultOut = null;
 	}
 	
+	/**
+	 * Return a simple String describing the current date and time.
+	 * @return
+	 */
+	public static String getDateString() {
+		SimpleDateFormat formatter = new SimpleDateFormat("E'_'yyyy.MM.dd'_at_'HH.mm.ss");
+		String dt = formatter.format(new Date());
+		return dt;
+	}
+	
 	protected boolean doFileOutput() {
 		return (m_StatsParams.getOutputTo().getSelectedTagID()!=1);  // not "text-window only" 
 	}
@@ -319,9 +334,7 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 		if (TRACE)
 			System.out.println("End of run");
 		if (resultOut != null) {
-			SimpleDateFormat formatter = new SimpleDateFormat(
+			String StopDate = getDateString();
-			"E'_'yyyy.MM.dd'_at_'hh:mm:ss");
-			String StopDate = formatter.format(new Date());
 			resultOut.println("StopDate:" + StopDate);
 			resultOut.close();
 		}
@@ -686,11 +699,13 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 		}
 //		meanCollection.set(pop.getGenerations()-1, means);
 		
-		lastSols  = (opt!=null) ? new Population(opt.getAllSolutions()) : pop;
+		lastSols  = (opt!=null) ? new Population(opt.getAllSolutions().getSolutions()) : pop;
 		if (doTextOutput()) {
 			Pair<String,Double[]> addInfo = getOutputLine(informerList, lastSols);
 
-			if (printLineByVerbosity(runIterCnt)) printToTextListener(addInfo.head()+'\n');
+			if (printLineByVerbosity(runIterCnt)) {
+				printToTextListener(addInfo.head()+'\n');
+			}
 //			updateAdditionalInfo(addInfo.tail());
 			if (addInfo.tail()!=null) {
 				additionalInfoSums = updateAdditionalInfo(additionalInfoSums, addInfo.tail());
@@ -728,7 +743,14 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 	}
 		
 	private boolean isKthRun(int i, int k) {
-		return (i % k) == 0;
+		// ingeniously shifting i by two since the stats counter starts at 0
+		// after two evaluations have already happened: initialization and first optimization
+		// this allows the last iteration to be within the displayed set if k is a divisor of whole iterations as expected 
+		if ((i==0) || (k==0)) return true;
+		else {
+			if (i<=2) return (i % k) == 0; // show more at the beginning (always first time)
+			else return ((i+2) % k) == 0;
+		}
 	}
 	
 	private boolean printHeaderByVerbosity() {

src/eva2/tools/StringTools.java

@@ -1,5 +1,6 @@
 package eva2.tools;
 
+import java.util.HashMap;
 import java.util.LinkedList;
 import java.util.StringTokenizer;
 
@@ -72,7 +73,7 @@ public class StringTools {
 	 * with their arities. Returns for each key a value depending on arity and whether it was found.
 	 * For any key, if it was not found null is returned as value. Its index in the keys array is contained
 	 * in the returned integer array.
-	 * For any key, if it was found, the corresponding value is either Boolean(true) for zero-arity-keys
+	 * For any key, if it was found, the corresponding value is a String containing "true" for zero-arity-keys
 	 * or a String array of length of the arity containing the arguments to the key. 
 	 * 
 	 * @param args
@@ -88,16 +89,30 @@ public class StringTools {
     	for (int i=0; i<args.length; i++) { // loop all arguments
     		boolean found=false;
     		for (int k=0; k<keys.length; k++) { // loop all keys
+    			if (found || (i>=args.length)) break; // if a key was found look at next argument
 	    		if ((ignoreCase && (args[i].equalsIgnoreCase(keys[k]))) 
 	    				|| (!ignoreCase && (args[i].equals(keys[k])))) { // if the key was found
 	    			found=true;
-	    			if (arities[k]==0) values[k]=new Boolean(true); // and its zero-arity, just return true as its value
+	    			if (arities[k]==0) values[k]=new String("true"); // and its zero-arity, just return true as its value
 	    			else { // else return an array of size arity with following strings
-	    				values[k]=new String[arities[k]];
+	    				try {
-	    				for (int j=0; j<arities[k]; j++) {
+	    				if (arities[k]==1) {
-	    					i++;
+	    					values[k]=args[i+1];
-	    					((String[])values[k])[j]=args[i];
+	    				} else { // create String array and fill with following args depending on arity
+	    					values[k]=new String[arities[k]];
+	    					if (arities[k]>0) {
+	    						for (int j=0; j<arities[k]; j++) {
+	    							((String[])values[k])[j]=args[i+j+1];
+	    						}
+	    						i+=(arities[k]-1); // jump one more for every arity beyond 1
+	    					}
 	    				}
+	    				} catch (ArrayIndexOutOfBoundsException e) {
+	    					String errMsg = "Not enough parameters for option "+ keys[k] + ", expected number of arguments: " + arities[k];
+	    					System.err.println(errMsg);
+	    					throw new RuntimeException(errMsg);
+	    				}
+	    				i++; // jump one cause we had at least arity 1
 	    			}
 	    		}
     		}
@@ -106,6 +121,31 @@ public class StringTools {
     	return unrecogs.toArray(new Integer[unrecogs.size()]);
     }
     
+    /**
+     * Store the arguments in a hash map.
+     * 
+     * @see #parseArguments(String[], String[], int[], Object[], boolean)
+     * @param args
+     * @param keys
+     * @param arities
+     * @param ignoreCase
+     * @return
+     */
+    public static HashMap<String, Object> parseArguments(String[] args, String[] keys, int[] arities, boolean ignoreCase, boolean printErrorsOnUnrecog) {
+    	Object[] values = new Object[keys.length];
+    	Integer[] unrecogs = parseArguments(args, keys, arities, values, ignoreCase);
+    	if (printErrorsOnUnrecog) {
+    		if (unrecogs.length>0) {
+    			System.err.println("Unrecognized command line options: ");
+    			for (int i=0; i<unrecogs.length; i++) System.err.println("   " + args[unrecogs[i]]);
+    		}
+    	}
+    	HashMap<String, Object> map = new HashMap<String, Object>();
+    	for (int i=0; i<keys.length; i++) {
+    		map.put(keys[i], values[i]);
+    	}
+    	return map;
+    }
 	
 	/**
 	 * Check whether an object is a valid String array and if so return the i-th String.

src/eva2/tools/chart2d/DArea.java

@@ -122,10 +122,10 @@ private static final boolean TRACE = false;
    */
   public DRectangle getDRectangle(){
     DRectangle rect = (DRectangle)visible_rect.clone();
-    if( min_x != null ) rect.x      = Math.max(rect.x,      getMinX()            );
+    if( min_x != null ) rect.setX(Math.max(rect.getX(),      getMinX()            ));
-    if( min_y != null ) rect.y      = Math.max(rect.y,      getMinY()            );
+    if( min_y != null ) rect.setY(Math.max(rect.getY(),      getMinY()            ));
-    if( max_x != null ) rect.width  = Math.min(rect.width,  getMaxX() - getMinX());
+    if( max_x != null ) rect.setWidth(Math.min(rect.getWidth(),  getMaxX() - getMinX()));
-    if( max_y != null ) rect.height = Math.min(rect.height, getMaxY() - getMinY());
+    if( max_y != null ) rect.setHeight(Math.min(rect.getHeight(), getMaxY() - getMinY()));
     return rect;
   }
 
@@ -135,7 +135,7 @@ private static final boolean TRACE = false;
    * @return DRectangle the size and position of the visible area
    */
   public SlimRect getSlimRectangle(){
-	  SlimRect srect = new SlimRect(visible_rect.x, visible_rect.y, visible_rect.width, visible_rect.height);
+	  SlimRect srect = new SlimRect(visible_rect.getX(), visible_rect.getY(), visible_rect.getWidth(), visible_rect.getHeight());
     if( min_x != null ) srect.x      = Math.max(srect.x,      getMinX()            );
     if( min_y != null ) srect.y      = Math.max(srect.y,      getMinY()            );
     if( max_x != null ) srect.width  = Math.min(srect.width,  getMaxX() - getMinX());
@@ -186,7 +186,7 @@ private static final boolean TRACE = false;
         "You shopuld never try to set an empty rectangle\n"
         + "as the visible rectangle of an DArea" );
 
-    if( !rect.equals( visible_rect ) && rect.width > 0 && rect.height > 0 ){
+    if( !rect.equals( visible_rect ) && rect.getWidth() > 0 && rect.getHeight() > 0 ){
       auto_focus = false;
       visible_rect = (DRectangle)rect.clone();
       repaint();
@@ -278,7 +278,7 @@ private static final boolean TRACE = false;
    * @param mix the minimal x-value
    */
   public void setMinX( double mix ){
-    if( mix > min_rect.x ) throw
+    if( mix > min_rect.getX() ) throw
       new IllegalArgumentException(
         "Mimimal y-value axes intersects minmal rectangle.");
     min_x = new Double( mix );
@@ -309,7 +309,7 @@ private static final boolean TRACE = false;
    * @param miy the minimal y-value
    */
   public void setMinY( double miy ){
-    if( miy > min_rect.y ) throw
+    if( miy > min_rect.getY() ) throw
       new IllegalArgumentException(
         "Mimimal y-value axes intersects minmal rectangle.");
     min_y = new Double( miy );
@@ -341,7 +341,7 @@ private static final boolean TRACE = false;
    * @param max the maximal x-value
    */
   public void setMaxX( double max ){
-    if( max < min_rect.x + min_rect.width ) throw
+    if( max < min_rect.getX() + min_rect.getWidth() ) throw
       new IllegalArgumentException(
         "Maximal x-value axes intersects minmal rectangle.");
     max_x = new Double( max );
@@ -373,7 +373,7 @@ private static final boolean TRACE = false;
    * @param may the maximal y-value
    */
   public void setMaxY( double may ){
-    if( may < min_rect.y + min_rect.height ) throw
+    if( may < min_rect.getY() + min_rect.getHeight() ) throw
       new IllegalArgumentException(
         "Maximal y-value axes intersects minmal rectangle.");
     max_y = new Double( may );
@@ -432,8 +432,10 @@ private static final boolean TRACE = false;
       new IllegalArgumentException("Cannot repaint a null DRectangle");
     if( r.isAll() || auto_focus ) repaint();
     else{
-      Point p1 = measures.getPoint( r.x, r.y ),
+        Point p1 = measures.getPoint( r.getX(), r.getY() ),
-            p2 = measures.getPoint( r.x + r.width, r.y + r.height);
+        p2 = measures.getPoint( r.getX() + r.getWidth(), r.getY() + r.getHeight());
+//        Point p1 = measures.getPoint( r.x, r.y ),
+//        p2 = measures.getPoint( r.x + r.width, r.y + r.height);
       if( p1 == null || p2 == null ) repaint();
       else {
         DBorder b = getDBorder();
@@ -690,8 +692,8 @@ private static final boolean TRACE = false;
         return;
       }
       else{
-    	  grid.setDistances(ScaledBorder.aBitBigger( grid.rectangle.width / max_grid ), 
+    	  grid.setDistances(ScaledBorder.aBitBigger( grid.rectangle.getWidth() / max_grid ), 
-    			  ScaledBorder.aBitBigger( grid.rectangle.height / max_grid ));
+    			  ScaledBorder.aBitBigger( grid.rectangle.getHeight() / max_grid ));
       }
     }
     grid.paint( m );

src/eva2/tools/chart2d/DGrid.java

@@ -96,8 +96,8 @@ public class DGrid extends DComponent
     DPoint p1, p2;
     DLine l;
 
-    minX=Mathematics.firstMultipleAbove(rectangle.x, hor_dist);
+    minX=Mathematics.firstMultipleAbove(rectangle.getX(), hor_dist);
-    minY=Mathematics.firstMultipleAbove(rectangle.y, ver_dist);
+    minY=Mathematics.firstMultipleAbove(rectangle.getY(), ver_dist);
 //    minX = (int)( rectangle.x / hor_dist );
 //    if( minX * hor_dist <= rectangle.x ) minX++;
 //    minX *= hor_dist;
@@ -105,18 +105,18 @@ public class DGrid extends DComponent
 //    if( minY * ver_dist <= rectangle.y ) minY++;
 //    minY *= ver_dist;
 
-    p1 = new DPoint( 0, rectangle.y );
+    p1 = new DPoint( 0, rectangle.getY() );
-    p2 = new DPoint( 0, rectangle.y + rectangle.height );
+    p2 = new DPoint( 0, rectangle.getY() + rectangle.getHeight() );
-    for( pos = minX; pos<=rectangle.x + rectangle.width; pos += hor_dist ){
+    for( pos = minX; pos<=rectangle.getX() + rectangle.getWidth(); pos += hor_dist ){
       p1.x = p2.x = pos;
       l = new DLine( p1, p2, color );
       l.paint( m );
     }
 
-    p1.x = rectangle.x;
+    p1.x = rectangle.getX();
-    p2.x = p1.x + rectangle.width;
+    p2.x = p1.x + rectangle.getWidth();
     pos = minY;
-    while ( pos<=rectangle.y + rectangle.height){
+    while ( pos<=rectangle.getY() + rectangle.getHeight()){
       p1.y = p2.y = pos;
       l = new DLine( p1, p2, color );
       l.paint( m );

src/eva2/tools/chart2d/DRectangle.java

@@ -24,7 +24,8 @@ import java.awt.* ;
 
 public class DRectangle extends DComponent
 {
-  public double x, y, width, height;
+  private double  x, y;
+  private double width, height;
   public static final int PART = 0, ALL = 1, EMPTY = 2;
   protected int status;
   protected Color fillColor;
@@ -38,11 +39,11 @@ public class DRectangle extends DComponent
     super(true);
     this.x = x;
     this.y = y;
-    if( width < 0 ) throw
+    if( width < 0  || Double.isInfinite(width) || Double.isNaN(width)) throw
-      new IllegalArgumentException("Width of a DRectangle has to be >= 0");
+      new IllegalArgumentException("Width of a DRectangle is invalid (" + width + ")");
     this.width = width;
-    if( height < 0 ) throw
+    if( height < 0 || Double.isInfinite(height) || Double.isNaN(height)) throw
-      new IllegalArgumentException("Height of a DRectangle has to be >= 0");
+      new IllegalArgumentException("Height of a DRectangle is invalid (" + height + ")");
     this.height = height;
     status = PART;
   }
@@ -78,6 +79,31 @@ public class DRectangle extends DComponent
     return true;
   }
   
+  public double getHeight() { return height; }
+  public double getWidth() { return width; }
+  public void setHeight(double h) {
+	  if (Double.isInfinite(h) || Double.isNaN(h)) {
+		  System.err.println("Warning, infinite vaule for height!");
+	  } else height = h;
+  }
+  public void setWidth(double w) {
+	  if (Double.isInfinite(w) || Double.isNaN(w)) {
+		  System.err.println("Warning, infinite vaule for width!");
+	  } else width = w;
+  }
+  public double getX() { return x; } 
+  public double getY() { return y; } 
+  public void setX(double v) {
+	  if (Double.isInfinite(v) || Double.isNaN(v)) {
+		  System.err.println("Warning, infinite vaule for x!");
+	  } else x = v;
+  }
+  public void setY(double v) {
+	  if (Double.isInfinite(v) || Double.isNaN(v)) {
+		  System.err.println("Warning, infinite vaule for y!");
+	  } else y = v;
+  }
+  
   /**
    * Faster contains withouth checking for ALL or EMPTY status.
    * 
@@ -135,7 +161,9 @@ public class DRectangle extends DComponent
    * @return true when the size of the rectangle changed
    */
   public boolean insert( DPoint p ){
-    if( p.x == Double.NaN || p.y == Double.NaN ) return false;
+    if( p.x == Double.NaN || p.y == Double.NaN || Double.isInfinite(p.x) || Double.isInfinite(p.y)) {
+    	return false;
+    }
     if( isAll() ) return false;
     if( contains( p ) ) return false;
     if( isEmpty() ){

src/eva2/tools/chart2d/ScaledBorder.java

@@ -69,7 +69,7 @@ public class ScaledBorder implements Border
    * the size of the source rectangle
    * that means before the values are mdified by scale functions
    */
-  SlimRect src_rect;
+  SlimRect src_rect = null;
 
   /**
    * the minimal increment of the scales
@@ -321,7 +321,7 @@ public class ScaledBorder implements Border
 			  g.drawLine( insets.left - marker_length, p.y, insets.left, p.y );
 		  }
 		  if (v+src_dY<= v) {
-			  System.err.println("Overflow error B in ScaledBorder!");
+			  System.err.println("Overflow error B in ScaledBorder! v,src_dY:" + v + ", " + src_dY);
 			  v*=1.01;
 		  }
 		  v += src_dY;
@@ -336,6 +336,9 @@ public class ScaledBorder implements Border
 	    if( under_construction ) System.out.println("ScaledBorder.getSrcdY()");
 	    if( (!do_refresh && src_dY != -1) || !auto_scale_y ) return src_dY;
 	    int max = componentHeight / fontMetricsHeight;
+	    if (Double.isInfinite(src_rect.height) || Double.isInfinite(src_rect.width)) {
+	    	System.err.println("Error, infinite value in ScaledBorder:getSrcdY !!");
+	    }
 	    double minsrc_dY = 2 * src_rect.height / (double)max; // die 2 einfach mal so eingesetzt   <--------------------------
 	    src_dY = aBitBigger( minsrc_dY );
 	    if( src_dY < minimal_increment ) src_dY = minimal_increment;
@@ -417,7 +420,7 @@ public class ScaledBorder implements Border
    * @return the displayable value
    */
   public static double aBitBigger( double min ){
-    if( min <= 0 ) return 1;
+    if( min <= 0 || Double.isInfinite(min) || Double.isNaN(min)) return 1;
     double d = 1;
     if( min < d ){
       while( d * .5 > min ) {

src/eva2/tools/chart2d/SlimRect.java

@@ -69,7 +69,7 @@ public class SlimRect {
 	 * @return A rectangle representing the intersection or null
 	 */
 	public SlimRect getIntersection( DRectangle r ){
-		return getIntersection(r.x, r.y, r.width, r.height);
+		return getIntersection(r.getX(), r.getY(), r.getWidth(), r.getHeight());
 	}
 	
 	/**
@@ -105,7 +105,7 @@ public class SlimRect {
 	 * @return true if the two rectangles do not intersect, else false
 	 */
 	public boolean hasEmptyIntersection(DRectangle r){
-		return (getIntersection(r.x, r.y, r.width, r.height)==null);
+		return (getIntersection(r.getX(), r.getY(), r.getWidth(), r.getHeight())==null);
 	}
 	
 	/**

src/eva2/tools/math/Mathematics.java

@@ -743,44 +743,82 @@ public class Mathematics {
 	public static void normalizeSum(double[] v, double[] res) {
 		svMult(1./sum(v), v, res);
 	}
+	
+    /**
+     * Return a matrix A which performs the rotation of vec to (1,0,0,...0) if forward is true, else
+     * return a matrix B which performs the reverted rotation, where B=A' (transposition).
+     *   
+     * @param vec
+     * @return
+     */
+    public static Matrix getRotationMatrix(Matrix vec) {
+            Matrix A = Matrix.identity(vec.getRowDimension(), vec.getRowDimension());
+            Matrix tmp = Matrix.identity(vec.getRowDimension(), vec.getRowDimension());
+            Matrix z = (Matrix)vec.clone();
+            
+            z.multi(1./z.norm2()); // normalize
+    
+            for (int i=1; i<vec.getRowDimension(); i++) {
+                    double w = Math.atan2(z.get(i,0), z.get(0,0));// calc angle between the projection of x and x0 in x0-xi-plane
+//                  System.out.println("deg: "+(w/Math.PI)*180);
 
-	/**
+                    // make partial rotation matrix
-	 * Return a matrix A which performs the rotation of vec to (1,0,0,...0) if forward is true, else
+                    getRotationEntriesSingleAxis(tmp, 0, i, w);
-	 * return a matrix B which performs the reverted rotation, where B=A' (transposition).
+    
-	 *   
+                    A = tmp.times(A);                       // add to resulting rotation
-	 * @param vec
+                    z = tmp.times(z);                       // z is now 0 in i-th component
-	 * @return
+    
-	 */
+                    // reset tmp matrix to unity
-	public static Matrix getRotationMatrix(Matrix vec) {
+                    resetRotationEntriesSingleAxis(tmp, 0, i);
-		Matrix A = Matrix.identity(vec.getRowDimension(), vec.getRowDimension());
+            }
-		Matrix tmp = Matrix.identity(vec.getRowDimension(), vec.getRowDimension());
+            return A;
-		Matrix z = (Matrix)vec.clone();
+    }
 	
-		double w, cosw, sinw;
+    public static Matrix getRotationMatrix(double w, int dim) {
-	
+        Matrix A = Matrix.identity(dim, dim);
-		z.multi(z.norm2()); // normalize
+        Matrix tmp = Matrix.identity(dim, dim);
-	
+
-	
+        for (int i=1; i<dim; i++) {
-		for (int i=1; i<vec.getRowDimension(); i++) {
+//              System.out.println("deg: "+(w/Math.PI)*180);
-			w = Math.atan2(z.get(i,0), z.get(0,0));// calc angle between the projection of x and x0 in x0-xi-plane
+                // make partial rotation matrix
-	
+                getRotationEntriesSingleAxis(tmp, i-1, i, w);
-			cosw = Math.cos(w);
+                A = tmp.times(A);                       // add to resulting rotation
-			sinw = Math.sin(w);
+                // reset tmp matrix to unity
-			tmp.set(0, 0, cosw);	// make partial rotation matrix
+                resetRotationEntriesSingleAxis(tmp, i-1, i);
-			tmp.set(0, i, sinw);
+        }
-			tmp.set(i, 0, -sinw);
+//      Matrix vec = new Matrix(dim, 1);
-			tmp.set(i, i, cosw);
+//      for (int i=0; i<dim; i++) vec.set(i,0, 1);
-	
+//      vec = A.times(vec);
-			A = tmp.times(A);			// add to resulting rotation
+//      vec = A.times(vec);
-			z = tmp.times(z);			// z is now 0 in i-th component
+        return A;
-	
+    }
-			tmp.set(0, 0, 1); // reset tmp matrix to unity
+
-			tmp.set(0, i, 0);
+    /**
-			tmp.set(i, 0, 0);
+     * Set rotation matrix entries along i/j axis. w is expected in radians.
-			tmp.set(i, i, 1);
+     * 
-		}
+     * @param tmp
-		return A;
+     * @param i
-	}
+     * @param j
+     * @param w
+     */
+    public static void getRotationEntriesSingleAxis(Matrix tmp, int i, int j, double w) {
+    	double cosw = Math.cos(w);
+    	double sinw = Math.sin(w);
+    	tmp.set(i, i, cosw);
+    	tmp.set(i, j, sinw);
+    	tmp.set(j, i, -sinw);
+    	tmp.set(j, j, cosw);
+    }
+
+    /**
+     *  Reset single axis rotation matrix to unity.
+     */
+    public static void resetRotationEntriesSingleAxis(Matrix tmp, int i, int j) {
+    	tmp.set(i, i, 1);
+    	tmp.set(i, j, 0);
+    	tmp.set(j, i, 0);
+    	tmp.set(j, j, 1);               
+    }
 
 	/**
 	 * Rotate the vector by angle alpha around axis i/j
@@ -796,7 +834,23 @@ public class Mathematics {
 		vect[i] = (xi*Math.cos(alpha))-(xj*Math.sin(alpha));
 		vect[j] = (xi*Math.sin(alpha))+(xj*Math.cos(alpha));
 	}
-	
+    
+    /**
+     * Rotate a given double vector using a rotation matrix. If the matrix
+     * is null, x will be returned unchanged. Matrix dimensions must fit.
+     * 
+     * @param x
+     * @param rotMatrix
+     * @return the rotated vector
+     */
+    public static double[] rotate(double[] x, Matrix rotMatrix) {
+            if (rotMatrix!=null) {
+                    Matrix resVec = rotMatrix.times(new Matrix(x, x.length));
+                    x = resVec.getColumnPackedCopy();
+                    return x;
+            } else return x;
+    }
+
 	/**
 	 * Rotate the vector along all axes by angle alpha or a uniform random value
 	 * in [-alpha, alpha] if randomize is true.

src/eva2/tools/math/RNG.java

@@ -4,27 +4,19 @@ import java.util.ArrayList;
 import java.util.Random;
 
 
-/**
+public class RNG {
- * 
- */
-public class RNG extends Random {
-	/**
-	 * 
-	 */
-	private static final long serialVersionUID = 1565216859128723844L;
 	private static Random random;
 	private static long randomSeed;
 	/**
 	 *
 	 */
 	static {
-		randomSeed = System.currentTimeMillis();
+		randomSeed=System.currentTimeMillis();
-		random = new Random(randomSeed);
+		random=new Random(randomSeed);
 	}
-
 	/**
-	  *
+	 *
-	  */
+	 */
 	public static void setRandomSeed(long new_seed) {
 		// counter++;
 		randomSeed = new_seed;
@@ -43,23 +35,23 @@ public class RNG extends Random {
 	}
 
 	/**
-	   *
+	 *
-	   */
+	 */
 	public static void setRandomSeed() {
 		randomSeed = System.currentTimeMillis();
 		random = new Random(randomSeed);
 	}
 
 	/**
-	   *
+	 *
-	   */
+	 */
 	public static void setRandom(Random base_random) {
 		random = base_random;
 	}
 
 	/**
-	   *
+	 *
-	   */
+	 */
 	public static long getRandomSeed() {
 		return randomSeed;
 	}
@@ -157,6 +149,25 @@ public class RNG extends Random {
 		return result;
 	}
 
+	/** This method returns a evenly distributed int value.
+	 * The boundarys are included.
+	 * @param lo         Lower bound.
+	 * @param hi         Upper bound.
+	 * @return int
+	 */
+	public static int randomInt(Random rand, int lo,int hi) {
+		if (hi<lo) {
+			System.err.println("Invalid boundary values! Returning zero.");
+			return -1;
+		}
+		int result = (Math.abs(rand.nextInt())%(hi-lo+1))+lo;
+		if ((result < lo) || (result > hi)) {
+			System.err.println("Error, invalid value " + result + " in RNG.randomInt! boundaries were lo/hi: " + lo + " / " + hi);
+			result = Math.abs(rand.nextInt()%(hi-lo+1))+lo;
+		}
+		return result;
+	}
+
 	/**
 	 * Returns a random long between 0 and Long.MAX_VALUE-1 (inclusively).
 	 */
@@ -172,15 +183,15 @@ public class RNG extends Random {
 	}
 
 	/**
-	   *
+	 *
-	   */
+	 */
 	public static float randomFloat() {
 		return random.nextFloat();
 	}
 
 	/**
-	   *
+	 *
-	   */
+	 */
 	public static float randomFloat(float lo, float hi) {
 		return (hi - lo) * random.nextFloat() + lo;
 	}
@@ -193,19 +204,23 @@ public class RNG extends Random {
 	}
 
 	/**
-	   *
+	 *
-	   */
+	 */
-	public static double randomDouble(double lo, double hi) {
+	public static double randomDouble(double lo,double hi) {
-		return (hi - lo) * random.nextDouble() + lo;
+		return (hi-lo)*random.nextDouble()+lo;
+	}
+
+	public static double randomDouble(Random rand, double lo,double hi) {
+		return (hi-lo)*rand.nextDouble()+lo;
 	}
 
 	/**
 	 * Create a uniform random vector within the given bounds.
 	 */
-	public static double[] randomDoubleArray(double[] lo, double[] hi) {
+	public static double[] randomDoubleArray(double[] lo,double[] hi) {
 		double[] xin = new double[lo.length];
-		for (int i = 0; i < lo.length; i++)
+		for (int i=0;i<lo.length;i++)
-			xin[i] = (hi[i] - lo[i]) * random.nextDouble() + lo[i];
+			xin[i] = (hi[i]-lo[i])*random.nextDouble()+lo[i];
 		return xin;
 	}
 
@@ -214,48 +229,50 @@ public class RNG extends Random {
 	 */
 	public static double[] randomDoubleArray(double[][] range) {
 		double[] xin = new double[range.length];
-		for (int i = 0; i < xin.length; i++)
+		for (int i=0;i<xin.length;i++)
-			xin[i] = (range[i][1] - range[i][0]) * random.nextDouble()
+			xin[i] = (range[i][1]-range[i][0])*random.nextDouble()+range[i][0];
-					+ range[i][0];
 		return xin;
 	}
 
 	/**
-	 * Create a uniform random double vector within the given bounds (inclusive)
+	 * Create a uniform random double vector within the given bounds (inclusive) in every dimension.
-	 * in every dimension.
 	 * 
 	 * @param lower
 	 * @param upper
 	 * @param size
 	 * @return
 	 */
-	public static double[] randomDoubleArray(double lower, double upper,
+	public static double[] randomDoubleArray(double lower, double upper, int size) {
-			int size) {
 		double[] result = new double[size];
 		for (int i = 0; i < result.length; i++) {
 			result[i] = RNG.randomDouble(lower, upper);
 		}
 		return result;
-		// double[] xin = new double[size];
+		//	    double[] xin = new double[size];
-		// for (int i=0;i<size;i++)
+		//	    for (int i=0;i<size;i++)
-		// xin[i] = (hi-lo)*random.nextDouble()+lo;
+		//	      xin[i] = (hi-lo)*random.nextDouble()+lo;
-		// return xin;
+		//	    return xin;
 	}
 
+	public static double[] randomDoubleArray(Random rand, double lower, double upper, int size) {
+		double[] result = new double[size];
+		for (int i = 0; i < result.length; i++) {
+			result[i] = RNG.randomDouble(rand, lower, upper);
+		}
+		return result;
+	}
 	/**
-	   *
+	 *
-	   */
+	 */
-	public static double[] randomDoubleArray(double[] lo, double[] hi,
+	public static double[] randomDoubleArray(double[] lo,double[] hi,double[] xin) {
-			double[] xin) {
+		//counter++;
-		// counter++;
+		for (int i=0;i<lo.length;i++)
-		for (int i = 0; i < lo.length; i++)
+			xin[i] = (hi[i]-lo[i])*random.nextDouble()+lo[i];
-			xin[i] = (hi[i] - lo[i]) * random.nextDouble() + lo[i];
 		return xin;
 	}
 
 	/**
-	 * Create a uniform random integer vector within the given bounds
+	 * Create a uniform random integer vector within the given bounds (inclusive) in every dimension.
-	 * (inclusive) in every dimension.
 	 * 
 	 * @param n
 	 * @param lower
@@ -270,17 +287,24 @@ public class RNG extends Random {
 		return result;
 	}
 
+	public static int[] randomIntArray(Random rand, int lower, int upper, int size) {
+		int[] result = new int[size];
+		for (int i = 0; i < result.length; i++) {
+			result[i] = RNG.randomInt(rand, lower, upper);
+		}
+		return result;
+	}
 	/**
-	   *
+	 *
-	   */
+	 */
 	public static boolean randomBoolean() {
 		// counter++;
 		return (randomInt() == 1);
 	}
 
 	/**
-	   *
+	 *
-	   */
+	 */
 	public static int randomBit() {
 		// counter++;
 		return randomInt();
@@ -298,8 +322,8 @@ public class RNG extends Random {
 	}
 
 	/**
-	   *
+	 *
-	   */
+	 */
 	public static float gaussianFloat(float dev) {
 		// counter++;
 		return (float) random.nextGaussian() * dev;
@@ -318,16 +342,16 @@ public class RNG extends Random {
 	}
 
 	/**
-	   *
+	 *
-	   */
+	 */
 	public static float exponentialFloat(float mean) {
 		// counter++;
 		return (float) (-mean * Math.log(randomDouble()));
 	}
 
 	/**
-	   *
+	 *
-	   */
+	 */
 	public static double exponentialDouble(double mean) {
 		// counter++;
 		return -mean * Math.log(randomDouble());