Move InterfacePopulationChangedEventListener to

eva2.optimization.population.

src/eva2/OptimizerFactory.java

@@ -4,7 +4,7 @@ import eva2.optimization.enums.DEType;
 import eva2.optimization.enums.MutateESCrossoverType;
 import eva2.optimization.enums.PSOTopology;
 import eva2.optimization.enums.PostProcessMethod;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.*;
 import eva2.optimization.modules.OptimizationParameters;
 import eva2.optimization.operator.archiving.ArchivingNSGAII;
@@ -572,7 +572,7 @@ public class OptimizerFactory {
     }
 
     /**
-     * Return a simple String showing the accessible optimizers. For external
+     * Return a eva2.problems.simple String showing the accessible optimizers. For external
      * access."
      *
      * @return a String listing the accessible optimizers
@@ -1303,7 +1303,7 @@ public class OptimizerFactory {
     }
 
     /**
-     * Create a standard clustering hill climbing employing simple ES mutation
+     * Create a standard clustering hill climbing employing eva2.problems.simple ES mutation
      * with adaptive step size, starting in parallel 100 local searches and
      * clustering intermediate populations to avoid optima being found several
      * times by the same population (density based clustering with sigma =
@@ -1338,9 +1338,9 @@ public class OptimizerFactory {
     }
 
     /**
-     * Create a custom clustering hillclimber using ES mutation (simple or CMA)
+     * Create a custom clustering hillclimber using ES mutation (eva2.problems.simple or CMA)
      * or nelder mead. The parameters hcInitialStep and hcStepThresh are only
-     * relevant for the simple mutation based hc method.
+     * relevant for the eva2.problems.simple mutation based hc method.
      *
      * @param problem
      * @param evalCycle

src/eva2/OptimizerRunnable.java

@@ -20,7 +20,7 @@ import java.util.BitSet;
 
 
 /**
- * This Runnable class just encapsulates the Processor class with some simple ways to access a solution.
+ * This Runnable class just encapsulates the Processor class with some eva2.problems.simple ways to access a solution.
  *
  * @author mkron
  */

src/eva2/cli/Main.java

@@ -5,7 +5,7 @@ import eva2.OptimizerFactory;
 import eva2.optimization.OptimizationStateListener;
 import eva2.optimization.enums.DEType;
 import eva2.optimization.enums.PSOTopology;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.modules.OptimizationParameters;
 import eva2.optimization.operator.crossover.CrossoverESDefault;
 import eva2.optimization.operator.crossover.InterfaceCrossover;
@@ -29,7 +29,6 @@ import java.util.Map;
 import java.util.Set;
 import java.util.TreeMap;
 import java.util.logging.Level;
-import java.util.logging.LogManager;
 import java.util.logging.Logger;
 
 /**

src/eva2/examples/TestingCbnPostProc.java

@@ -11,7 +11,7 @@ import java.util.List;
 
 public class TestingCbnPostProc {
     public static void main(String[] args) {
-        // a simple bimodal target function, two optima near (1.7,0) and (-1.44/0)
+        // a eva2.problems.simple bimodal target function, two optima near (1.7,0) and (-1.44/0)
         FM0Problem fm0 = new FM0Problem();
         AbstractEAIndividual best;
         List<AbstractEAIndividual> ppSols;

src/eva2/examples/TestingPlusCmaEs.java

@@ -12,7 +12,7 @@ import eva2.optimization.strategies.EvolutionStrategies;
 public class TestingPlusCmaEs {
 
     public static void main(String[] args) {
-        // a simple bimodal target function, two optima near (1.7,0) and (-1.44/0)
+        // a eva2.problems.simple bimodal target function, two optima near (1.7,0) and (-1.44/0)
         FM0Problem fm0 = new FM0Problem();
         AbstractEAIndividual bestIndy;
         // create standard ES parameters                

src/eva2/gui/editor/GenericDoubleArrayEditor.java

@@ -10,7 +10,7 @@ import java.beans.PropertyChangeSupport;
 import java.beans.PropertyEditor;
 
 /**
- * A simple focus listener with an object ID and callback.
+ * A eva2.problems.simple focus listener with an object ID and callback.
  *
  * @author mkron
  */

src/eva2/optimization/go/MOCCOStandalone.java

@@ -7,6 +7,7 @@ import eva2.optimization.mocco.paretofrontviewer.MOCCOViewer;
 import eva2.optimization.operator.moso.InterfaceMOSOConverter;
 import eva2.optimization.operator.moso.MOSOWeightedFitness;
 import eva2.optimization.operator.terminators.EvaluationTerminator;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.population.Population;
 import eva2.problems.*;
 import eva2.optimization.strategies.InterfaceOptimizer;

src/eva2/optimization/go/StandaloneOptimization.java

@@ -14,6 +14,7 @@ import eva2.optimization.operator.mutation.MutateESFixedStepSize;
 import eva2.optimization.operator.mutation.MutateESLocal;
 import eva2.optimization.operator.selection.SelectTournament;
 import eva2.optimization.operator.terminators.EvaluationTerminator;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.population.Population;
 import eva2.problems.F1Problem;
 import eva2.optimization.strategies.EvolutionStrategies;
@@ -135,7 +136,7 @@ public class StandaloneOptimization implements InterfaceStandaloneOptimization,
         // build the Options Panel
         JParaPanel paraPanel = new JParaPanel(this, "MyGUI");
         Class object = null, editor = null;
-        String tmp = "eva2.optimization.go.Tools.InterfaceTest";
+        String tmp = "eva2.optimization.tools.InterfaceTest";
         try {
             object = Class.forName(tmp);
         } catch (java.lang.ClassNotFoundException e) {
@@ -316,7 +317,7 @@ public class StandaloneOptimization implements InterfaceStandaloneOptimization,
                 break;
             }
             case 1: {
-                // use the simple ES Local
+                // use the eva2.problems.simple ES Local
                 this.outputFileName = "X360_StandardES";
                 EvolutionStrategies es = new EvolutionStrategies();
                 this.optimizationParameters.setOptimizer(es);

src/eva2/optimization/individuals/ESIndividualBinaryData.java

@@ -288,7 +288,7 @@ public class ESIndividualBinaryData extends AbstractEAIndividual implements Inte
     }
 
     /**
-     * This method performs a simple one element mutation on the double vector
+     * This method performs a eva2.problems.simple one element mutation on the double vector
      */
     @Override
     public void defaultMutate() {

src/eva2/optimization/individuals/ESIndividualDoubleData.java

@@ -348,7 +348,7 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
     }
 
     /**
-     * This method performs a simple one element mutation on the double vector
+     * This method performs a eva2.problems.simple one element mutation on the double vector
      */
     @Override
     public void defaultMutate() {

src/eva2/optimization/individuals/ESIndividualIntegerData.java

@@ -303,7 +303,7 @@ public class ESIndividualIntegerData extends AbstractEAIndividual implements Int
     }
 
     /**
-     * This method performs a simple one element mutation on the double vector
+     * This method performs a eva2.problems.simple one element mutation on the double vector
      */
     @Override
     public void defaultMutate() {

src/eva2/optimization/individuals/GAIndividualBinaryData.java

@@ -88,7 +88,7 @@ public class GAIndividualBinaryData extends AbstractEAIndividual implements Inte
     }
 
     /**
-     * This method evaluates the GAIndividual as simple minimize number
+     * This method evaluates the GAIndividual as eva2.problems.simple minimize number
      * of bits problem.
      *
      * @return The number of true bits
@@ -218,7 +218,7 @@ public class GAIndividualBinaryData extends AbstractEAIndividual implements Inte
     }
 
     /**
-     * This method performs a simple one point mutation in the genotype
+     * This method performs a eva2.problems.simple one point mutation in the genotype
      */
     @Override
     public void defaultMutate() {

src/eva2/optimization/individuals/GAIndividualDoubleData.java

@@ -342,7 +342,7 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
     }
 
     /**
-     * This method performs a simple one point mutation in the genotype
+     * This method performs a eva2.problems.simple one point mutation in the genotype
      */
     @Override
     public void defaultMutate() {

src/eva2/optimization/individuals/GAIndividualIntegerData.java

@@ -398,7 +398,7 @@ public class GAIndividualIntegerData extends AbstractEAIndividual implements Int
     }
 
     /**
-     * This method performs a simple one point mutation in the genotype
+     * This method performs a eva2.problems.simple one point mutation in the genotype
      */
     @Override
     public void defaultMutate() {

src/eva2/optimization/individuals/GEIndividualProgramData.java

@@ -674,7 +674,7 @@ public class GEIndividualProgramData extends AbstractEAIndividual implements Int
     }
 
     /**
-     * This method performs a simple one point mutation in the genotype
+     * This method performs a eva2.problems.simple one point mutation in the genotype
      */
     @Override
     public void defaultMutate() {

src/eva2/optimization/individuals/GIIndividualIntegerData.java

@@ -317,7 +317,7 @@ public class GIIndividualIntegerData extends AbstractEAIndividual implements Int
     }
 
     /**
-     * This method performs a simple one point mutation in the genotype
+     * This method performs a eva2.problems.simple one point mutation in the genotype
      */
     @Override
     public void defaultMutate() {

src/eva2/optimization/individuals/GPIndividualProgramData.java

@@ -313,7 +313,7 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
     }
 
     /**
-     * This method performs a simple one element mutation on the program
+     * This method performs a eva2.problems.simple one element mutation on the program
      */
     @Override
     public void defaultMutate() {

src/eva2/optimization/individuals/codings/ga/GAGrayCodingDouble.java

@@ -85,7 +85,7 @@ public class GAGrayCodingDouble implements InterfaceGADoubleCoding, java.io.Seri
     }
 
     /**
-     * A simple test method
+     * A eva2.problems.simple test method
      *
      * @param args no args needed
      */

src/eva2/optimization/individuals/codings/ga/GAStandardCodingDouble.java

@@ -135,7 +135,7 @@ public class GAStandardCodingDouble implements InterfaceGADoubleCoding, java.io.
     }
 
     /**
-     * A simple test function without arguments
+     * A eva2.problems.simple test function without arguments
      *
      * @param args
      */

src/eva2/optimization/individuals/codings/gp/GPNodeAdd.java

@@ -4,7 +4,7 @@ package eva2.optimization.individuals.codings.gp;
 import eva2.problems.InterfaceProgramProblem;
 
 /**
- * A simple add node with two arguments.
+ * A eva2.problems.simple add node with two arguments.
  * Created by IntelliJ IDEA.
  * User: streiche
  * Date: 04.04.2003

src/eva2/optimization/individuals/codings/gp/GPNodeConst.java

@@ -3,7 +3,7 @@ package eva2.optimization.individuals.codings.gp;
 import eva2.problems.InterfaceProgramProblem;
 
 /**
- * A simple constant node with the value 1.
+ * A eva2.problems.simple constant node with the value 1.
  */
 public class GPNodeConst extends AbstractGPNode implements java.io.Serializable {
     double value = 1.;

src/eva2/optimization/individuals/codings/gp/GPNodeOne.java

@@ -2,7 +2,7 @@ package eva2.optimization.individuals.codings.gp;
 
 
 /**
- * A simple constant node with the value 1.
+ * A eva2.problems.simple constant node with the value 1.
  */
 public class GPNodeOne extends GPNodeConst implements java.io.Serializable {
     public GPNodeOne() {

src/eva2/optimization/individuals/codings/gp/GPNodePi.java

@@ -2,7 +2,7 @@ package eva2.optimization.individuals.codings.gp;
 
 
 /**
- * A simple constant node with the value 1.
+ * A eva2.problems.simple constant node with the value 1.
  */
 public class GPNodePi extends GPNodeConst implements java.io.Serializable {
     public GPNodePi() {

src/eva2/optimization/individuals/codings/gp/GPNodeProd.java

@@ -5,7 +5,7 @@ import eva2.problems.InterfaceProgramProblem;
 import eva2.tools.math.Mathematics;
 
 /**
- * A simple product node with a single, possibly vectorial (array), argument.
+ * A eva2.problems.simple product node with a single, possibly vectorial (array), argument.
  */
 public class GPNodeProd extends AbstractGPNode implements java.io.Serializable {
 

src/eva2/optimization/individuals/codings/gp/GPNodeSum.java

@@ -5,7 +5,7 @@ import eva2.problems.InterfaceProgramProblem;
 import eva2.tools.math.Mathematics;
 
 /**
- * A simple sum node with a single, possibly vectorial (array), argument.
+ * A eva2.problems.simple sum node with a single, possibly vectorial (array), argument.
  */
 public class GPNodeSum extends AbstractGPNode implements java.io.Serializable {
 

src/eva2/optimization/mocco/paretofrontviewer/MOCCOViewer.java

@@ -351,7 +351,7 @@ public class MOCCOViewer extends JPanel implements InterfaceRefSolutionListener,
     }
 
     /**
-     * This method will plot a simple fitness plot, using the iterations a x-axis
+     * This method will plot a eva2.problems.simple fitness plot, using the iterations a x-axis
      */
     public void plot1DFitnessPlot() {
         double xmin = 0, ymin = Double.POSITIVE_INFINITY, xmax = Double.NEGATIVE_INFINITY, ymax = Double.NEGATIVE_INFINITY, fitness;

src/eva2/optimization/mocco/paretofrontviewer/ParetoFrontView2D.java

@@ -176,7 +176,7 @@ public class ParetoFrontView2D extends JPanel implements InterfaceParetoFrontVie
     }
 
     /**
-     * This method will plot a simple fitness plot, using the iterations a x-axis
+     * This method will plot a eva2.problems.simple fitness plot, using the iterations a x-axis
      */
     public void plot2DParetoFront() {
         double xmin = Double.POSITIVE_INFINITY, ymin = Double.POSITIVE_INFINITY, xmax = Double.NEGATIVE_INFINITY, ymax = Double.NEGATIVE_INFINITY;

src/eva2/optimization/modules/AbstractOptimizationParameters.java

@@ -3,7 +3,7 @@ package eva2.optimization.modules;
 import eva2.gui.BeanInspector;
 import eva2.optimization.go.InterfaceNotifyOnInformers;
 import eva2.optimization.go.InterfaceOptimizationParameters;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.operator.postprocess.InterfacePostProcessParams;
 import eva2.optimization.operator.postprocess.PostProcessParams;
 import eva2.optimization.operator.terminators.InterfaceTerminator;

src/eva2/optimization/modules/Processor.java

@@ -4,7 +4,7 @@ import eva2.gui.BeanInspector;
 import eva2.optimization.OptimizationStateListener;
 import eva2.optimization.go.InterfaceNotifyOnInformers;
 import eva2.optimization.go.InterfaceOptimizationParameters;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.operator.paramcontrol.ConstantParameters;
 import eva2.optimization.operator.paramcontrol.InterfaceParameterControl;
 import eva2.optimization.operator.postprocess.PostProcess;

src/eva2/optimization/operator/archiving/ArchivingAllDominating.java

@@ -6,7 +6,7 @@ import eva2.optimization.population.Population;
 import eva2.util.annotation.Description;
 
 /**
- * This simple strategy simply archives all Pareto optimal solutions. This method is
+ * This eva2.problems.simple strategy simply archives all Pareto optimal solutions. This method is
  * very prone to OutOfMemory errors!
  */
 @Description("This is a straightforward strategy, which selects all dominating individuals (very prone to generate OutOfMemory errors).")

src/eva2/optimization/operator/archiving/ArchivingMaxiMin.java

@@ -11,7 +11,7 @@ import eva2.util.annotation.Description;
 
 
 /**
- * Another simple archiving strategy not based on dominance but on the MaxiMin
+ * Another eva2.problems.simple archiving strategy not based on dominance but on the MaxiMin
  * criterion. Doesn't work well on non-convex Pareto fronts.
  */
 @Description("Maxi Min Archiving.")

src/eva2/optimization/operator/classification/ClassificationSelfOrganizingMaps.java

@@ -10,7 +10,7 @@ import java.awt.event.WindowAdapter;
 import java.awt.event.WindowEvent;
 
 /**
- * Self-organizing maps, a simple, but easy to visualize method
+ * Self-organizing maps, a eva2.problems.simple, but easy to visualize method
  * for classification. The Dikel flag is an undocumented extension,
  * which seems to work but is not published.
  */

src/eva2/optimization/operator/mutation/CMAParamSet.java

@@ -1,6 +1,6 @@
 package eva2.optimization.operator.mutation;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;
 import eva2.optimization.population.Population;
 import eva2.optimization.strategies.EvolutionStrategies;

src/eva2/optimization/operator/selection/probability/SelProbStandard.java

@@ -5,7 +5,7 @@ import eva2.optimization.population.Population;
 import eva2.util.annotation.Description;
 
 /**
- * A simple sum to calculate the selection probability.
+ * A eva2.problems.simple sum to calculate the selection probability.
  * <p/>
  * p(i is selected) = exp(-fitness(i))/sum_j(exp(-fitness(j)))
  */

src/eva2/optimization/operator/selection/probability/SelProbStandardScaling.java

@@ -5,7 +5,7 @@ import eva2.optimization.population.Population;
 import eva2.util.annotation.Description;
 
 /**
- * A simple sum with a scaling factor.
+ * A eva2.problems.simple sum with a scaling factor.
  */
 @Description("This is a standard normation method with scaling.")
 public class SelProbStandardScaling extends AbstractSelProb implements java.io.Serializable {

src/eva2/optimization/population/InterfacePopulationChangedEventListener.java

@@ -1,12 +1,8 @@
-package eva2.optimization.go;
+package eva2.optimization.population;
 
 
 /**
- * Created by IntelliJ IDEA.
+ *
- * User: streiche
- * Date: 24.04.2003
- * Time: 18:09:47
- * To change this template use Options | File Templates.
  */
 public interface InterfacePopulationChangedEventListener {
 

src/eva2/optimization/population/Population.java

@@ -1,7 +1,6 @@
 package eva2.optimization.population;
 
 import eva2.gui.editor.GenericObjectEditor;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.*;
 import eva2.optimization.operator.distancemetric.EuclideanMetric;
 import eva2.optimization.operator.distancemetric.InterfaceDistanceMetric;
@@ -2191,7 +2190,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     /**
      * Calculate the average of the distance of each individual to its closest
      * neighbor in the population. The boolean parameter switches between
-     * range-normalized and simple euclidian distance. If calcVariance is true,
+     * range-normalized and eva2.problems.simple euclidian distance. If calcVariance is true,
      * the variance is calculated and returned as second entry
      *
      * @param normalizedPhenoMetric

src/eva2/optimization/statistics/AbstractStatistics.java

@@ -27,7 +27,7 @@ import java.util.*;
  * The method plotCurrentResults should be implemented to plot further results per iteration.
  * <p/>
  * All displayable data is now routed through a single pipeline, which consists in a
- * list of Objects assembled in the getOutputValues method. This allows all simple data types which are
+ * list of Objects assembled in the getOutputValues method. This allows all eva2.problems.simple data types which are
  * provided by the external informer instances to be handled uniformly to the internally collected data, and
  * thus they can be plotted and text-dumped in the same manner.
  * Basic fields are identified by the enum GraphSelectionEnum and are available independently of additional
@@ -221,7 +221,7 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
     }
 
     /**
-     * Return a simple String describing the current date and time.
+     * Return a eva2.problems.simple String describing the current date and time.
      *
      * @return
      */
@@ -807,7 +807,7 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
     }
 
     /**
-     * Return all simple data fields collected internally. This must correspond to the
+     * Return all eva2.problems.simple data fields collected internally. This must correspond to the
      * method {@link #getSimpleOutputHeader()}.
      *
      * @return

src/eva2/optimization/statistics/InterfaceTextListener.java

@@ -1,7 +1,7 @@
 package eva2.optimization.statistics;
 
 /**
- * A very simple interface class to receive raw String data.
+ * A very eva2.problems.simple interface class to receive raw String data.
  *
  * @author mkron
  */

src/eva2/optimization/statistics/StatisticsStandalone.java

@@ -8,7 +8,7 @@ import java.util.ArrayList;
 import java.util.List;
 
 /**
- * This simple statistics implementation can collect all Object data available during runs.
+ * This eva2.problems.simple statistics implementation can collect all Object data available during runs.
  * Be aware that the memory requirements can be excessive depending on the data produced by
  * the additional informers, and depending on the selected fields to be collected.
  * Therefore, the default is not to log the data but just print it using the super class.

src/eva2/optimization/strategies/AbstractOptimizer.java

@@ -1,6 +1,6 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.population.Population;
 import eva2.problems.F1Problem;
 import eva2.problems.InterfaceOptimizationProblem;

src/eva2/optimization/strategies/AdaptiveDifferentialEvolution.java

@@ -1,15 +1,11 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
-import eva2.optimization.operator.selection.replacement.ReplacementNondominatedSortingDistanceCrowding;
 import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
-import eva2.problems.AbstractMultiObjectiveOptimizationProblem;
 import eva2.problems.AbstractOptimizationProblem;
 import eva2.problems.F1Problem;
-import eva2.problems.InterfaceOptimizationProblem;
-import eva2.tools.math.RNG;
 import eva2.util.annotation.Description;
 import eva2.util.annotation.Parameter;
 

src/eva2/optimization/strategies/BinaryScatterSearch.java

@@ -1,7 +1,7 @@
 package eva2.optimization.strategies;
 
 import eva2.gui.BeanInspector;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.InterfaceDataTypeBinary;
 import eva2.optimization.individuals.InterfaceGAIndividual;
@@ -12,7 +12,6 @@ import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
 import eva2.problems.AbstractOptimizationProblem;
 import eva2.problems.B1Problem;
-import eva2.problems.InterfaceOptimizationProblem;
 import eva2.tools.Pair;
 import eva2.tools.math.RNG;
 import eva2.util.annotation.Description;

src/eva2/optimization/strategies/CHCAdaptiveSearchAlgorithm.java

@@ -1,6 +1,5 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.InterfaceGAIndividual;
 import eva2.optimization.operator.selection.InterfaceSelection;
@@ -9,7 +8,6 @@ import eva2.optimization.operator.selection.SelectRandom;
 import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
-import eva2.problems.B1Problem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.RNG;
 

src/eva2/optimization/strategies/ClusterBasedNichingEA.java

@@ -5,7 +5,7 @@ import eva2.gui.editor.GenericObjectEditor;
 import eva2.gui.plot.GraphPointSet;
 import eva2.gui.plot.Plot;
 import eva2.gui.plot.TopoPlot;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.EAIndividualComparator;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;

src/eva2/optimization/strategies/ClusteringHillClimbing.java

@@ -2,7 +2,7 @@ package eva2.optimization.strategies;
 
 import eva2.gui.editor.GenericObjectEditor;
 import eva2.optimization.enums.PostProcessMethod;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.operator.mutation.MutateESFixedStepSize;
 import eva2.optimization.operator.postprocess.PostProcess;
 import eva2.optimization.population.InterfaceSolutionSet;

src/eva2/optimization/strategies/DifferentialEvolution.java

@@ -2,7 +2,6 @@ package eva2.optimization.strategies;
 
 import eva2.gui.BeanInspector;
 import eva2.gui.editor.GenericObjectEditor;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;
 import eva2.optimization.operator.selection.replacement.ReplacementCrowding;
@@ -12,8 +11,6 @@ import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
 import eva2.problems.AbstractMultiObjectiveOptimizationProblem;
 import eva2.problems.AbstractOptimizationProblem;
-import eva2.problems.F1Problem;
-import eva2.problems.InterfaceOptimizationProblem;
 import eva2.tools.EVAERROR;
 import eva2.tools.math.Mathematics;
 import eva2.tools.math.RNG;

src/eva2/optimization/strategies/EsDpiNiching.java

@@ -2,7 +2,7 @@ package eva2.optimization.strategies;
 
 import eva2.gui.editor.GenericObjectEditor;
 import eva2.gui.plot.TopoPlot;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;
 import eva2.optimization.operator.cluster.ClusteringDynPeakIdent;

src/eva2/optimization/strategies/EvolutionStrategyIPOP.java

@@ -1,7 +1,7 @@
 package eva2.optimization.strategies;
 
 import eva2.gui.editor.GenericObjectEditor;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.operator.mutation.MutateESRankMuCMA;
 import eva2.optimization.operator.terminators.FitnessConvergenceTerminator;

src/eva2/optimization/strategies/EvolutionaryProgramming.java

@@ -1,13 +1,12 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.operator.selection.InterfaceSelection;
 import eva2.optimization.operator.selection.SelectEPTournaments;
 import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
-import eva2.problems.F1Problem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
 

src/eva2/optimization/strategies/FloodAlgorithm.java

@@ -1,12 +1,10 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.GAIndividualBinaryData;
 import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
-import eva2.problems.B1Problem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
 import eva2.util.annotation.Parameter;
@@ -148,7 +146,7 @@ public class FloodAlgorithm extends AbstractOptimizer implements java.io.Seriali
     }
 
     /**
-     * This main method will start a simple hillclimber. No arguments necessary.
+     * This main method will start a eva2.problems.simple hillclimber. No arguments necessary.
      *
      * @param args
      */

src/eva2/optimization/strategies/GeneticAlgorithm.java

@@ -1,6 +1,6 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.operator.mutation.InterfaceAdaptOperatorGenerational;
 import eva2.optimization.operator.selection.InterfaceSelection;
@@ -9,7 +9,6 @@ import eva2.optimization.operator.selection.SelectXProbRouletteWheel;
 import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
-import eva2.problems.F1Problem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
 

src/eva2/optimization/strategies/HillClimbing.java

@@ -16,7 +16,7 @@ import eva2.util.annotation.Description;
  */
 @Description("The Hill Climber uses the default EA mutation and initializing operators. If the population size is bigger than one a multi-start Hill Climber is performed.")
 public class HillClimbing extends AbstractOptimizer implements java.io.Serializable {
-    // These variables are necessary for the simple testcase
+    // These variables are necessary for the eva2.problems.simple testcase
 
     private InterfaceOptimizationProblem optimizationProblem = new B1Problem();
     private InterfaceMutation mutator = null;

src/eva2/optimization/strategies/InterfaceOptimizer.java

@@ -1,6 +1,6 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
 import eva2.problems.InterfaceOptimizationProblem;

src/eva2/optimization/strategies/IslandModelEA.java

@@ -1,7 +1,7 @@
 package eva2.optimization.strategies;
 
 import eva2.gui.plot.Plot;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.ESIndividualDoubleData;
 import eva2.optimization.operator.migration.*;
 import eva2.optimization.population.InterfaceSolutionSet;

src/eva2/optimization/strategies/LTGA.java

@@ -1,7 +1,7 @@
 package eva2.optimization.strategies;
 
 import eva2.gui.BeanInspector;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.InterfaceDataTypeBinary;
 import eva2.optimization.individuals.InterfaceGAIndividual;
@@ -10,7 +10,6 @@ import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
 import eva2.problems.AbstractOptimizationProblem;
 import eva2.problems.BKnapsackProblem;
-import eva2.problems.InterfaceOptimizationProblem;
 import eva2.tools.Pair;
 import eva2.tools.math.SpecialFunction;
 import eva2.util.annotation.Description;

src/eva2/optimization/strategies/MLTGA.java

@@ -1,7 +1,7 @@
 package eva2.optimization.strategies;
 
 import eva2.gui.BeanInspector;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.InterfaceDataTypeBinary;
 import eva2.optimization.individuals.InterfaceGAIndividual;

src/eva2/optimization/strategies/MemeticAlgorithm.java

@@ -1,6 +1,6 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.operator.selection.InterfaceSelection;
 import eva2.optimization.operator.selection.SelectBestIndividuals;

src/eva2/optimization/strategies/MonteCarloSearch.java

@@ -1,17 +1,15 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.GAIndividualBinaryData;
 import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
-import eva2.problems.B1Problem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
 
 /**
- * The simple random or Monte-Carlo search, simple but useful to evaluate the
+ * The eva2.problems.simple random or Monte-Carlo search, eva2.problems.simple but useful to evaluate the
  * complexity of the search space. This implements a Random Walk Search using
  * the initialization method of the problem instance, meaning that the random
  * characteristics may be problem dependent.
@@ -118,7 +116,7 @@ public class MonteCarloSearch extends AbstractOptimizer implements java.io.Seria
     }
 
     /**
-     * This main method will start a simple hillclimber. No arguments necessary.
+     * This main method will start a eva2.problems.simple hillclimber. No arguments necessary.
      *
      * @param args
      */

src/eva2/optimization/strategies/MultiObjectiveCMAES.java

@@ -1,7 +1,6 @@
 package eva2.optimization.strategies;
 
 import eva2.gui.editor.GenericObjectEditor;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.operator.archiving.ArchivingNSGAII;
 import eva2.optimization.operator.archiving.ArchivingNSGAIISMeasure;
@@ -10,7 +9,6 @@ import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
 import eva2.problems.AbstractOptimizationProblem;
-import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
 
 import java.io.Serializable;

src/eva2/optimization/strategies/MultiObjectiveEA.java

@@ -1,6 +1,6 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.EAIndividualComparator;
 import eva2.optimization.operator.archiving.ArchivingNSGAII;

src/eva2/optimization/strategies/NelderMeadSimplex.java

@@ -1,7 +1,7 @@
 package eva2.optimization.strategies;
 
 import eva2.gui.BeanInspector;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;
 import eva2.optimization.population.InterfaceSolutionSet;
@@ -14,7 +14,6 @@ import eva2.tools.math.Mathematics;
 import eva2.util.annotation.Description;
 
 import java.io.Serializable;
-import java.util.Vector;
 
 /**
  * Nelder-Mead-Simplex does not guarantee an equal number of evaluations within

src/eva2/optimization/strategies/NichePSO.java

@@ -4,7 +4,6 @@ import eva2.OptimizerFactory;
 import eva2.gui.editor.GenericObjectEditor;
 import eva2.gui.plot.TopoPlot;
 import eva2.optimization.enums.PSOTopology;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;
 import eva2.optimization.modules.OptimizationParameters;

src/eva2/optimization/strategies/ParticleFilterOptimization.java

@@ -2,7 +2,6 @@ package eva2.optimization.strategies;
 
 import eva2.gui.editor.GenericObjectEditor;
 import eva2.gui.plot.Plot;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;
 import eva2.optimization.operator.mutation.MutateESCorrVector;
@@ -13,7 +12,6 @@ import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
 import eva2.problems.AbstractOptimizationProblem;
-import eva2.problems.F1Problem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
 import eva2.util.annotation.Hidden;

src/eva2/optimization/strategies/ParticleSwarmOptimization.java

@@ -5,7 +5,7 @@ import eva2.gui.editor.GenericObjectEditor;
 import eva2.gui.plot.Plot;
 import eva2.gui.plot.TopoPlot;
 import eva2.optimization.enums.PSOTopology;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.EAIndividualComparator;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;

src/eva2/optimization/strategies/PopulationBasedIncrementalLearning.java

@@ -1,6 +1,6 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.InterfaceGAIndividual;
 import eva2.optimization.operator.selection.InterfaceSelection;
@@ -20,7 +20,7 @@ import java.util.logging.Logger;
 /**
  * Population based incremental learning in the PSM by Monmarche version with
  * also allows to simulate ant systems due to the flexible update rule of V. But
- * both are limited to binary genotypes. This is a simple implementation of
+ * both are limited to binary genotypes. This is a eva2.problems.simple implementation of
  * Population Based Incremental Learning.
  * <p/>
  * Nicolas Monmarché , Eric Ramat , Guillaume Dromel , Mohamed Slimane , Gilles
@@ -31,7 +31,7 @@ import java.util.logging.Logger;
 public class PopulationBasedIncrementalLearning extends AbstractOptimizer implements java.io.Serializable {
 
     private final static Logger LOGGER = Logger.getLogger(PopulationBasedIncrementalLearning.class.getName());
-    // These variables are necessary for the simple testcase
+    // These variables are necessary for the eva2.problems.simple testcase
     private InterfaceOptimizationProblem optimizationProblem = new B1Problem();
     private boolean useElitism = true;
     private InterfaceSelection selectionOperator = new SelectBestIndividuals();

src/eva2/optimization/strategies/ScatterSearch.java

@@ -4,7 +4,7 @@ import eva2.OptimizerFactory;
 import eva2.OptimizerRunnable;
 import eva2.gui.BeanInspector;
 import eva2.gui.editor.GenericObjectEditor;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;
 import eva2.optimization.modules.OptimizationParameters;

src/eva2/optimization/strategies/SimulatedAnnealing.java

@@ -10,7 +10,7 @@ import eva2.tools.math.RNG;
 import eva2.util.annotation.Description;
 
 /**
- * Simulated Annealing by Nelder and Mead, a simple yet efficient local search
+ * Simulated Annealing by Nelder and Mead, a eva2.problems.simple yet efficient local search
  * method. But to become less prone to premature convergence the cooling rate
  * has to be tuned to the optimization problem at hand. Again the population
  * size gives the number of multi-starts.
@@ -149,7 +149,7 @@ public class SimulatedAnnealing extends AbstractOptimizer implements java.io.Ser
     }
 
     /**
-     * This main method will start a simple hillclimber. No arguments necessary.
+     * This main method will start a eva2.problems.simple hillclimber. No arguments necessary.
      *
      * @param args
      */

src/eva2/optimization/strategies/SteadyStateGA.java

@@ -14,7 +14,7 @@ import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
 
 /**
- * A simple implementation of the steady-state GA with variable replacement
+ * A eva2.problems.simple implementation of the steady-state GA with variable replacement
  * schemes. To reduce the logging effort population.size() optimization steps
  * are performed each time optimize() is called.
  */

src/eva2/optimization/strategies/ThresholdAlgorithm.java

@@ -1,12 +1,10 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.GAIndividualBinaryData;
 import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
-import eva2.problems.B1Problem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
 
@@ -142,7 +140,7 @@ public class ThresholdAlgorithm extends AbstractOptimizer implements java.io.Ser
     }
 
     /**
-     * This main method will start a simple hillclimber. No arguments necessary.
+     * This main method will start a eva2.problems.simple hillclimber. No arguments necessary.
      *
      * @param args
      */

src/eva2/optimization/strategies/Tribes.java

@@ -2,7 +2,7 @@ package eva2.optimization.strategies;
 
 import eva2.gui.editor.GenericObjectEditor;
 import eva2.gui.plot.Plot;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;
 import eva2.optimization.population.InterfaceSolutionSet;

src/eva2/optimization/strategies/WingedMultiObjectiveEA.java

@@ -1,14 +1,12 @@
 package eva2.optimization.strategies;
 
 import eva2.gui.PropertyDoubleArray;
-import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.operator.moso.MOSOWeightedFitness;
 import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
 import eva2.problems.AbstractMultiObjectiveOptimizationProblem;
-import eva2.problems.FM0Problem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
 

src/eva2/problems/AbstractMultiModalProblemKnown.java

@@ -82,7 +82,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
     }
 
     /**
-     * Ths method allows you to evaluate a simple bit string to determine the fitness
+     * Ths method allows you to evaluate a eva2.problems.simple bit string to determine the fitness
      *
      * @param x The n-dimensional input vector
      * @return The m-dimensional output vector.

src/eva2/problems/AbstractProblemInteger.java

@@ -70,7 +70,7 @@ public abstract class AbstractProblemInteger extends AbstractOptimizationProblem
     }
 
     /**
-     * Evaluate a simple integer array to determine the fitness.
+     * Evaluate a eva2.problems.simple integer array to determine the fitness.
      *
      * @param x The n-dimensional input vector
      * @return The m-dimensional output vector.

src/eva2/problems/B1Problem.java

@@ -40,7 +40,7 @@ public class B1Problem extends AbstractProblemBinary implements java.io.Serializ
     }
 
     /**
-     * This is a simple method that evaluates a given Individual. The fitness
+     * This is a eva2.problems.simple method that evaluates a given Individual. The fitness
      * values of the individual will be set inside this method.
      *
      * @param b The BitSet that is to be evaluated.

src/eva2/problems/BKnapsackProblem.java

@@ -258,7 +258,7 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S
     }
 
     /**
-     * This is a simple method that evaluates a given Individual. The fitness
+     * This is a eva2.problems.simple method that evaluates a given Individual. The fitness
      * values of the individual will be set inside this method.
      *
      * @param b The BitSet that is to be evaluated.

src/eva2/problems/ConstrHimmelblauProblem.java

@@ -9,7 +9,7 @@ import java.io.Serializable;
 import java.util.Vector;
 
 /**
- * Himmelblau's nonlinear optimization problem with 5 simple boundary constraints and 3 nonlinear boundary constraints.
+ * Himmelblau's nonlinear optimization problem with 5 eva2.problems.simple boundary constraints and 3 nonlinear boundary constraints.
  */
 @Description("Himmelblau's nonlinear optimization problem")
 public class ConstrHimmelblauProblem extends AbstractProblemDouble implements Serializable {

src/eva2/problems/F1Problem.java

@@ -41,7 +41,7 @@ public class F1Problem extends AbstractProblemDoubleOffset implements Interface2
     }
 
     /**
-     * This method allows you to evaluate a simple bit string to determine the fitness
+     * This method allows you to evaluate a eva2.problems.simple bit string to determine the fitness
      *
      * @param x The n-dimensional input vector
      * @return The m-dimensional output vector.

src/eva2/problems/F22Problem.java

@@ -40,7 +40,7 @@ public class F22Problem extends AbstractProblemDoubleOffset implements Interface
     }
 
     /**
-     * This method allows you to evaluate a simple bit string to determine the fitness
+     * This method allows you to evaluate a eva2.problems.simple bit string to determine the fitness
      *
      * @param x The n-dimensional input vector
      * @return The m-dimensional output vector.

src/eva2/problems/FLensProblem.java

@@ -327,7 +327,7 @@ public class FLensProblem extends AbstractOptimizationProblem
     }
 
     /**
-     * Ths method allows you to evaluate a simple bit string to determine the fitness
+     * Ths method allows you to evaluate a eva2.problems.simple bit string to determine the fitness
      *
      * @param x The n-dimensional input vector
      * @return The m-dimensional output vector.

src/eva2/problems/I1Problem.java

@@ -28,7 +28,7 @@ public class I1Problem extends AbstractProblemInteger implements java.io.Seriali
     }
 
     /**
-     * Ths method allows you to evaluate a simple bit string to determine the fitness
+     * Ths method allows you to evaluate a eva2.problems.simple bit string to determine the fitness
      *
      * @param x The n-dimensional input vector
      * @return The m-dimensional output vector.

src/eva2/problems/TF1Problem.java

@@ -152,7 +152,7 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem
     }
 
     /**
-     * Ths method allows you to evaluate a simple bit string to determine the fitness
+     * Ths method allows you to evaluate a eva2.problems.simple bit string to determine the fitness
      *
      * @param x The n-dimensional input vector
      * @return The m-dimensional output vector.

src/eva2/problems/simple/InterfaceSimpleProblem.java

@@ -1,7 +1,7 @@
 package eva2.problems.simple;
 
 /**
- * A simple interface to easily include new optimization problems in Java into the
+ * A eva2.problems.simple interface to easily include new optimization problems in Java into the
  * EvA framework.
  */
 public interface InterfaceSimpleProblem<T> {

src/eva2/tools/MultirunRefiner.java

@@ -332,7 +332,7 @@ public class MultirunRefiner {
 //    }
 
     /**
-     * A simple method to read doubles from a string.
+     * A eva2.problems.simple method to read doubles from a string.
      *
      * @param searchme The string to be searched.
      * @return The array of doubles found.

src/eva2/tools/TXTFileFilter.java

@@ -8,7 +8,7 @@ package eva2.tools;
 
 import javax.swing.filechooser.FileFilter;
 
-/**A simple File Filter for *.txt files.
+/**A eva2.problems.simple File Filter for *.txt files.
  */
 
 public class TXTFileFilter extends FileFilter {

src/eva2/tools/chart2d/DPointIcon.java

@@ -3,7 +3,7 @@ package eva2.tools.chart2d;
 import java.awt.*;
 
 /**
- * A simple interface which can be used to paint certain icons at DPoints
+ * A eva2.problems.simple interface which can be used to paint certain icons at DPoints
  * ( @see chart2d.DPoint.setIcon or chart2d.DPointSet.setIcon ).
  * Different points may be easier recognized in a complex graph.
  * The container does not guarantee that the whole icon is visible in the graph

src/eva2/tools/matlab/JMatLink.java

@@ -1710,7 +1710,7 @@ public class JMatLink extends Thread {
 
     // this is a concurrent situation.
 
-    // The solution is simple: I always use a locking-mechanism to wait for the
+    // The solution is eva2.problems.simple: I always use a locking-mechanism to wait for the
 
     // data. The main thread will release the lock and the calling method can
 

test/eva2/tools/SerializerTest.java

@@ -22,7 +22,7 @@ public class SerializerTest {
 
     @Before
     public void setUp() {
-        // Create a simple object graph
+        // Create a eva2.problems.simple object graph
         dataStructObject = new ExampleDataStruct();
         dataStructObject.message = "hello world";
         dataStructObject.data = new int[]{1, 2, 3, 4};
@@ -90,7 +90,7 @@ public class SerializerTest {
 }
 
 /**
- * This is a simple serializable data structure that we use below for testing the methods above
+ * This is a eva2.problems.simple serializable data structure that we use below for testing the methods above
  *
  */
 class ExampleDataStruct implements Serializable {