Fixed comment fuckup where simple was replaced with eva2.problems.simple

Implements YamlStatistics in cli.Main
A few fixes in the processor to allow GUI-less optimization

src/eva2/OptimizerFactory.java

@@ -572,7 +572,7 @@ public class OptimizerFactory {
     }
 
     /**
-     * Return a eva2.problems.simple String showing the accessible optimizers. For external
+     * Return a 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 eva2.problems.simple ES mutation
+     * Create a standard clustering hill climbing employing simple ES mutation
      * with adaptive step size, starting in parallel 100 local searches and
      * clustering intermediate populations to avoid optima being found several
      * times by the same population (density based clustering with sigma =
@@ -1338,9 +1338,9 @@ public class OptimizerFactory {
     }
 
     /**
-     * Create a custom clustering hillclimber using ES mutation (eva2.problems.simple or CMA)
+     * Create a custom clustering hillclimber using ES mutation (simple or CMA)
      * or nelder mead. The parameters hcInitialStep and hcStepThresh are only
-     * relevant for the eva2.problems.simple mutation based hc method.
+     * relevant for the 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 eva2.problems.simple ways to access a solution.
+ * This Runnable class just encapsulates the Processor class with some simple ways to access a solution.
  *
  * @author mkron
  */
@@ -255,7 +255,7 @@ public class OptimizerRunnable implements Runnable {
      * @param vLev
      */
     public void setVerbosityLevel(InterfaceStatisticsParameters.OutputVerbosity vLev) {
-        proc.getStatistics().getStatisticsParameter().setOutputVerbosity(vLev);
+        proc.getStatistics().getStatisticsParameters().setOutputVerbosity(vLev);
     }
 
     /**
@@ -264,7 +264,7 @@ public class OptimizerRunnable implements Runnable {
      * @param outp
      */
     public void setOutputTo(InterfaceStatisticsParameters.OutputTo outp) {
-        proc.getStatistics().getStatisticsParameter().setOutputTo(outp);
+        proc.getStatistics().getStatisticsParameters().setOutputTo(outp);
     }
 
     /**
@@ -273,7 +273,7 @@ public class OptimizerRunnable implements Runnable {
      * @param multis
      */
     public void setMultiRuns(int multis) {
-        proc.getStatistics().getStatisticsParameter().setMultiRuns(multis);
+        proc.getStatistics().getStatisticsParameters().setMultiRuns(multis);
     }
 
     /**
@@ -282,6 +282,6 @@ public class OptimizerRunnable implements Runnable {
      * @param addInfo
      */
     public void setOutputFullStatsToText(boolean addInfo) {
-        proc.getStatistics().getStatisticsParameter().setOutputAllFieldsAsText(addInfo);
+        proc.getStatistics().getStatisticsParameters().setOutputAllFieldsAsText(addInfo);
     }
 }

src/eva2/cli/Main.java

@@ -1,20 +1,26 @@
 package eva2.cli;
 
 import eva2.optimization.go.InterfaceOptimizationParameters;
-import eva2.optimization.individuals.InterfaceDataTypeDouble;
+import eva2.optimization.individuals.IndividualInterface;
+import eva2.optimization.modules.Processor;
 import eva2.optimization.operator.terminators.InterfaceTerminator;
+import eva2.optimization.population.Population;
+import eva2.optimization.population.PopulationInterface;
+import eva2.optimization.statistics.InterfaceStatistics;
+import eva2.optimization.statistics.InterfaceStatisticsListener;
 import eva2.optimization.statistics.InterfaceStatisticsParameters;
+import eva2.optimization.statistics.InterfaceTextListener;
 import eva2.optimization.strategies.InterfaceOptimizer;
+import eva2.problems.InterfaceAdditionalPopulationInformer;
 import eva2.problems.InterfaceOptimizationProblem;
-import org.yaml.snakeyaml.Yaml;
 
 import java.io.BufferedWriter;
 import java.io.FileWriter;
 import java.io.IOException;
 import java.util.ArrayList;
-import java.util.Arrays;
 import java.util.LinkedHashMap;
 import java.util.List;
+import java.util.logging.Logger;
 
 /**
  *
@@ -49,32 +55,91 @@ public class Main {
             System.exit(0);
         }
 
-        //optimizer.addPopulationChangedEventListener(new OptimizationLogger(parameters, ));
-        for (int i = 0; i < statisticsParameters.getMultiRuns(); i++) {
+        Processor optimizationProcessor = new Processor(new YamlStatistics(statisticsParameters), parameters);
+        optimizationProcessor.setSaveParams(false);
+        optimizationProcessor.startOptimization();
 
-            problem.initializeProblem();
-            problem.initializePopulation(optimizer.getPopulation());
 
-            optimizer.setProblem(problem);
-            terminator.initialize(problem);
-
-            /**
-             * This is the main optimization loop. We keep calling
-             * optimize() until a termination criterion is met or
-             * the user aborts the optimization manually.
-             */
-            do {
-                optimizer.optimize();
-            } while (!terminator.isTerminated(optimizer.getAllSolutions()));
-
-            System.out.println(Arrays.toString(((InterfaceDataTypeDouble)optimizer.getPopulation().getBestEAIndividual()).getDoubleData()));
-        }
-
-        try {
-            bw.write(new Yaml().dump(optimizationLog));
-            bw.close();
-        } catch (IOException ex) {
-            ex.printStackTrace();
+        optimizationProcessor.runOptimizationOnce();
     }
 }
+
+final class YamlStatistics implements InterfaceStatistics {
+    private static final Logger LOGGER = Logger.getLogger(YamlStatistics.class.getName());
+    private InterfaceStatisticsParameters statisticsParameters;
+
+    public YamlStatistics(InterfaceStatisticsParameters statisticsParameters) {
+        super();
+        this.statisticsParameters = statisticsParameters;
+    }
+
+    @Override
+    public void startOptimizationPerformed(String infoString, int runNumber, InterfaceOptimizationParameters params, List<InterfaceAdditionalPopulationInformer> informerList) {
+
+    }
+
+    @Override
+    public void stopOptimizationPerformed(boolean normal, String stopMessage) {
+
+    }
+
+    @Override
+    public void addDataListener(InterfaceStatisticsListener listener) {
+
+    }
+
+    @Override
+    public boolean removeDataListener(InterfaceStatisticsListener listener) {
+        return false;
+    }
+
+    @Override
+    public void addTextListener(InterfaceTextListener listener) {
+
+    }
+
+    @Override
+    public boolean removeTextListener(InterfaceTextListener listener) {
+        return false;
+    }
+
+    @Override
+    public void printToTextListener(String s) {
+        System.out.println(s);
+    }
+
+    @Override
+    public void createNextGenerationPerformed(PopulationInterface Pop, InterfaceOptimizer opt, List<InterfaceAdditionalPopulationInformer> informerList) {
+
+    }
+
+    @Override
+    public void createNextGenerationPerformed(double[] bestFit, double[] worstFit, int calls) {
+
+    }
+
+    @Override
+    public InterfaceStatisticsParameters getStatisticsParameters() {
+        return statisticsParameters;
+    }
+
+    @Override
+    public IndividualInterface getRunBestSolution() {
+        return null;
+    }
+
+    @Override
+    public IndividualInterface getBestSolution() {
+        return null;
+    }
+
+    @Override
+    public double[] getBestFitness() {
+        return new double[0];
+    }
+
+    @Override
+    public void postProcessingPerformed(Population resultPop) {
+
+    }
 }

src/eva2/examples/TestingCbnPostProc.java

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

@@ -602,7 +602,7 @@ public class Main extends JFrame implements OptimizationStateListener {
         // GUI is ready
         evaClient.addWindowListener(windowListener);
         // modify initial settings and activate output of all data:
-        evaClient.getStatistics().getStatisticsParameter().setOutputAllFieldsAsText(true);
+        evaClient.getStatistics().getStatisticsParameters().setOutputAllFieldsAsText(true);
         // add a data listener instance:
         evaClient.getStatistics().addDataListener(statisticsListener);
 
@@ -759,7 +759,7 @@ public class Main extends JFrame implements OptimizationStateListener {
     }
 
     public InterfaceStatisticsParameters getStatisticsParameter() {
-        return ((GenericModuleAdapter) currentModuleAdapter).getStatistics().getStatisticsParameter();
+        return ((GenericModuleAdapter) currentModuleAdapter).getStatistics().getStatisticsParameters();
     }
 
     private void loadSpecificModule(String selectedModule, InterfaceOptimizationParameters optimizationParameters) {

src/eva2/gui/editor/DoubleArrayEditor.java

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

src/eva2/optimization/go/StandaloneOptimization.java

@@ -317,7 +317,7 @@ public class StandaloneOptimization implements InterfaceStandaloneOptimization,
                 break;
             }
             case 1: {
-                // use the eva2.problems.simple ES Local
+                // use the 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 eva2.problems.simple one element mutation on the double vector
+     * This method performs a simple one element mutation on the double vector
      */
     @Override
     public void defaultMutate() {

src/eva2/optimization/individuals/ESIndividualDoubleData.java

@@ -339,7 +339,7 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
     }
 
     /**
-     * This method performs a eva2.problems.simple one element mutation on the double vector
+     * This method performs a 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 eva2.problems.simple one element mutation on the double vector
+     * This method performs a 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 eva2.problems.simple minimize number
+     * This method evaluates the GAIndividual as 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 eva2.problems.simple one point mutation in the genotype
+     * This method performs a simple one point mutation in the genotype
      */
     @Override
     public void defaultMutate() {

src/eva2/optimization/individuals/GAIndividualDoubleData.java

@@ -339,7 +339,7 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
     }
 
     /**
-     * This method performs a eva2.problems.simple one point mutation in the genotype
+     * This method performs a 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 eva2.problems.simple one point mutation in the genotype
+     * This method performs a 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 eva2.problems.simple one point mutation in the genotype
+     * This method performs a 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 eva2.problems.simple one point mutation in the genotype
+     * This method performs a 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 eva2.problems.simple one element mutation on the program
+     * This method performs a 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 eva2.problems.simple test method
+     * A simple test method
      *
      * @param args no args needed
      */

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

@@ -130,7 +130,7 @@ public class GAStandardCodingDouble implements InterfaceGADoubleCoding, java.io.
     }
 
     /**
-     * A eva2.problems.simple test function without arguments
+     * A simple test function without arguments
      */
     public static void main() {
         GAStandardCodingDouble t = new GAStandardCodingDouble();

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

@@ -4,7 +4,7 @@ package eva2.optimization.individuals.codings.gp;
 import eva2.problems.InterfaceProgramProblem;
 
 /**
- * A eva2.problems.simple add node with two arguments.
+ * A simple add node with two arguments.
  */
 public class GPNodeAdd extends AbstractGPNode implements java.io.Serializable {
 

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

@@ -3,7 +3,7 @@ package eva2.optimization.individuals.codings.gp;
 import eva2.problems.InterfaceProgramProblem;
 
 /**
- * A eva2.problems.simple constant node with the value 1.
+ * A 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 eva2.problems.simple constant node with the value 1.
+ * A 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 eva2.problems.simple constant node with the value 1.
+ * A 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 eva2.problems.simple product node with a single, possibly vectorial (array), argument.
+ * A 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 eva2.problems.simple sum node with a single, possibly vectorial (array), argument.
+ * A 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 eva2.problems.simple fitness plot, using the iterations a x-axis
+     * This method will plot a 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

@@ -175,7 +175,7 @@ public class ParetoFrontView2D extends JPanel implements InterfaceParetoFrontVie
     }
 
     /**
-     * This method will plot a eva2.problems.simple fitness plot, using the iterations a x-axis
+     * This method will plot a 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/GenericModuleAdapter.java

@@ -79,7 +79,7 @@ public class GenericModuleAdapter extends AbstractModuleAdapter implements Seria
         }
         EvATabbedFrameMaker frmMkr = new EvATabbedFrameMaker();
 
-        InterfaceStatisticsParameters Stat = statisticsModule.getStatisticsParameter();
+        InterfaceStatisticsParameters Stat = statisticsModule.getStatisticsParameters();
         EvAModuleButtonPanelMaker buttonPanel = new EvAModuleButtonPanelMaker(remoteModuleAdapter, ((Processor) processor).isOptimizationRunning());
         buttonPanel.setHelperFilename(helperFilename);
         frmMkr.addPanelMaker(buttonPanel);

src/eva2/optimization/modules/Processor.java

@@ -73,9 +73,9 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
 
         // the statistics want to be informed if the strategy or the optimizer (which provide statistical data as InterfaceAdditionalInformer) change.
         if (statistics != null && (optimizationParameters != null)) {
-            if (statistics.getStatisticsParameter() instanceof InterfaceNotifyOnInformers) {
+            if (statistics.getStatisticsParameters() instanceof InterfaceNotifyOnInformers) {
                 // 	addition for the statistics revision with selectable strings - make sure the go parameters are represented within the statistics
-                optimizationParameters.addInformableInstance((InterfaceNotifyOnInformers) (statistics.getStatisticsParameter()));
+                optimizationParameters.addInformableInstance((InterfaceNotifyOnInformers) (statistics.getStatisticsParameters()));
             }
         }
     }
@@ -232,8 +232,8 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
          * We keep the optimization running until it is aborted by the user or
          * the number of multiple runs has been reached.
          */
-        while (isOptimizationRunning() && (runCounter < statistics.getStatisticsParameter().getMultiRuns())) {
-            LOGGER.info(String.format("Starting Optimization %d/%d", runCounter + 1, statistics.getStatisticsParameter().getMultiRuns()));
+        while (isOptimizationRunning() && (runCounter < statistics.getStatisticsParameters().getMultiRuns())) {
+            LOGGER.info(String.format("Starting Optimization %d/%d", runCounter + 1, statistics.getStatisticsParameters().getMultiRuns()));
             statistics.startOptimizationPerformed(getInfoString(), runCounter, optimizationParameters, getInformerList());
 
             problem.initializeProblem();
@@ -246,7 +246,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
             }
 
             if (optimizationStateListener != null) {
-                optimizationStateListener.updateProgress(getStatusPercent(optimizer.getPopulation(), runCounter, statistics.getStatisticsParameter().getMultiRuns()), null);
+                optimizationStateListener.updateProgress(getStatusPercent(optimizer.getPopulation(), runCounter, statistics.getStatisticsParameters().getMultiRuns()), null);
             }
 
             /**
@@ -388,7 +388,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
                         getStatusPercent(
                                 optimizationParameters.getOptimizer().getPopulation(),
                                 runCounter,
-                                statistics.getStatisticsParameter().getMultiRuns()),
+                                statistics.getStatisticsParameters().getMultiRuns()),
                         null);
             }
         }
@@ -442,7 +442,12 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
     }
 
     public Population performPostProcessing() {
-        return performPostProcessing((PostProcessParams) optimizationParameters.getPostProcessParams(), (InterfaceTextListener) statistics);
+        PostProcessParams ppp = (PostProcessParams)optimizationParameters.getPostProcessParams();
+        if (ppp.isDoPostProcessing()) {
+            return performPostProcessing(ppp, (InterfaceTextListener) statistics);
+        } else {
+            return null;
+        }
     }
 
     /**

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

@@ -6,7 +6,7 @@ import eva2.optimization.population.Population;
 import eva2.util.annotation.Description;
 
 /**
- * This eva2.problems.simple strategy simply archives all Pareto optimal solutions. This method is
+ * This 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

@@ -10,7 +10,7 @@ import eva2.util.annotation.Description;
 
 
 /**
- * Another eva2.problems.simple archiving strategy not based on dominance but on the MaxiMin
+ * Another 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 eva2.problems.simple, but easy to visualize method
+ * Self-organizing maps, a 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/selection/probability/SelProbStandard.java

@@ -4,7 +4,7 @@ import eva2.optimization.population.Population;
 import eva2.util.annotation.Description;
 
 /**
- * A eva2.problems.simple sum to calculate the selection probability.
+ * A 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

@@ -4,7 +4,7 @@ import eva2.optimization.population.Population;
 import eva2.util.annotation.Description;
 
 /**
- * A eva2.problems.simple sum with a scaling factor.
+ * A 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/Population.java

@@ -2182,7 +2182,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
     /**
      * Calculate the average of the distance of each individual to its closest
      * neighbor in the population. The boolean parameter switches between
-     * range-normalized and eva2.problems.simple euclidian distance. If calcVariance is true,
+     * range-normalized and simple euclidian distance. If calcVariance is true,
      * the variance is calculated and returned as second entry
      *
      * @param normalizedPhenoMetric

src/eva2/optimization/statistics/AbstractStatistics.java

@@ -209,7 +209,7 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
     }
 
     /**
-     * Return a eva2.problems.simple String describing the current date and time.
+     * Return a simple String describing the current date and time.
      *
      * @return
      */
@@ -297,7 +297,7 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
         ToDo: Figure out if we need this. Right now it is just spamming the text output
         if (printRunIntroVerbosity()) {
             printToTextListener("\nStatistics parameters: ");
-            printToTextListener(BeanInspector.niceToString(getStatisticsParameter()) + '\n');
+            printToTextListener(BeanInspector.niceToString(getStatisticsParameters()) + '\n');
         }
         */
         functionCalls = 0;
@@ -645,7 +645,7 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
     }
 
     @Override
-    public InterfaceStatisticsParameters getStatisticsParameter() {
+    public InterfaceStatisticsParameters getStatisticsParameters() {
         return statisticsParameter;
     }
 
@@ -780,7 +780,7 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
     }
 
     /**
-     * Return all eva2.problems.simple data fields collected internally. This must correspond to the
+     * Return all simple data fields collected internally. This must correspond to the
      * method {@link #getSimpleOutputHeader()}.
      *
      * @return
@@ -1151,7 +1151,6 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
                 } // this adds up data of a single iteration across multiple runs
             }
         }
-
         plotCurrentResults();
         fireDataListeners();
         if (resultOut != null) {

src/eva2/optimization/statistics/InterfaceStatistics.java

@@ -37,7 +37,7 @@ public interface InterfaceStatistics {
 
     void createNextGenerationPerformed(double[] bestFit, double[] worstFit, int calls);
 
-    InterfaceStatisticsParameters getStatisticsParameter(); // called from moduleadapter
+    InterfaceStatisticsParameters getStatisticsParameters(); // called from moduleadapter
 
     IndividualInterface getRunBestSolution(); // return the best fitness of the last run (may not be equal to the last population)
 

src/eva2/optimization/statistics/InterfaceTextListener.java

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

src/eva2/optimization/statistics/OptimizationJobList.java

@@ -208,8 +208,8 @@ public class OptimizationJobList extends PropertySelectableList<OptimizationJob>
                     AbstractOptimizationParameters curParams = (AbstractOptimizationParameters) ((AbstractModuleAdapter) jobList.module).getOptimizationParameters();
                     curParams.setSameParams((AbstractOptimizationParameters) job.getOptimizationParameters());
                     ((GenericModuleAdapter) jobList.module).setOptimizationParameters(curParams);
-                    ((GenericModuleAdapter) jobList.module).getStatistics().getStatisticsParameter().setMultiRuns(job.getNumRuns());
-                    ((GenericModuleAdapter) jobList.module).getStatistics().getStatisticsParameter().setFieldSelection(job.getFieldSelection(((GenericModuleAdapter) jobList.module).getStatistics().getStatisticsParameter().getFieldSelection()));
+                    ((GenericModuleAdapter) jobList.module).getStatistics().getStatisticsParameters().setMultiRuns(job.getNumRuns());
+                    ((GenericModuleAdapter) jobList.module).getStatistics().getStatisticsParameters().setFieldSelection(job.getFieldSelection(((GenericModuleAdapter) jobList.module).getStatistics().getStatisticsParameters().getFieldSelection()));
                 } else {
                     JOptionPane.showMessageDialog(parent, "Select exactly one job to reuse!", "Error", JOptionPane.ERROR_MESSAGE);
                 }

src/eva2/optimization/statistics/StatisticsDummy.java

@@ -74,7 +74,7 @@ public class StatisticsDummy implements InterfaceStatistics, InterfaceTextListen
     }
 
     @Override
-    public InterfaceStatisticsParameters getStatisticsParameter() {
+    public InterfaceStatisticsParameters getStatisticsParameters() {
         return sParams;
     }
 

src/eva2/optimization/strategies/FloodAlgorithm.java

@@ -145,7 +145,7 @@ public class FloodAlgorithm extends AbstractOptimizer implements java.io.Seriali
     }
 
     /**
-     * This main method will start a eva2.problems.simple hillclimber. No arguments necessary.
+     * This main method will start a simple hillclimber. No arguments necessary.
      */
     public static void main() {
         FloodAlgorithm program = new FloodAlgorithm();

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 eva2.problems.simple testcase
+    // These variables are necessary for the simple testcase
 
     private InterfaceOptimizationProblem optimizationProblem = new B1Problem();
     private InterfaceMutation mutator = null;

src/eva2/optimization/strategies/MonteCarloSearch.java

@@ -8,7 +8,7 @@ import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
 
 /**
- * The eva2.problems.simple random or Monte-Carlo search, eva2.problems.simple but useful to evaluate the
+ * The simple random or Monte-Carlo search, 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.
@@ -115,7 +115,7 @@ public class MonteCarloSearch extends AbstractOptimizer implements java.io.Seria
     }
 
     /**
-     * This main method will start a eva2.problems.simple hillclimber. No arguments necessary.
+     * This main method will start a simple hillclimber. No arguments necessary.
      *
      * @param args
      */

src/eva2/optimization/strategies/PopulationBasedIncrementalLearning.java

@@ -16,7 +16,7 @@ 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 eva2.problems.simple implementation of
+ * both are limited to binary genotypes. This is a simple implementation of
  * Population Based Incremental Learning.
  * <p>
  * Nicolas Monmarché , Eric Ramat , Guillaume Dromel , Mohamed Slimane , Gilles
@@ -27,7 +27,7 @@ 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 eva2.problems.simple testcase
+    // These variables are necessary for the simple testcase
     private InterfaceOptimizationProblem optimizationProblem = new B1Problem();
     private boolean useElitism = true;
     private InterfaceSelection selectionOperator = new SelectBestIndividuals();

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 eva2.problems.simple yet efficient local search
+ * Simulated Annealing by Nelder and Mead, a 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 eva2.problems.simple hillclimber. No arguments necessary.
+     * This main method will start a 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 eva2.problems.simple implementation of the steady-state GA with variable replacement
+ * A 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

@@ -140,7 +140,7 @@ public class ThresholdAlgorithm extends AbstractOptimizer implements java.io.Ser
     }
 
     /**
-     * This main method will start a eva2.problems.simple hillclimber. No arguments necessary.
+     * This main method will start a simple hillclimber. No arguments necessary.
      *
      * @param args
      */

src/eva2/problems/AbstractMultiModalProblemKnown.java

@@ -82,7 +82,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
     }
 
     /**
-     * Ths method allows you to evaluate a eva2.problems.simple bit string to determine the fitness
+     * Ths method allows you to evaluate a 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 eva2.problems.simple integer array to determine the fitness.
+     * Evaluate a 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 eva2.problems.simple method that evaluates a given Individual. The fitness
+     * This is a 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 eva2.problems.simple method that evaluates a given Individual. The fitness
+     * This is a 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 eva2.problems.simple boundary constraints and 3 nonlinear boundary constraints.
+ * Himmelblau's nonlinear optimization problem with 5 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 eva2.problems.simple bit string to determine the fitness
+     * This method allows you to evaluate a 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

@@ -39,7 +39,7 @@ public class F22Problem extends AbstractProblemDoubleOffset implements Interface
     }
 
     /**
-     * This method allows you to evaluate a eva2.problems.simple bit string to determine the fitness
+     * This method allows you to evaluate a 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 eva2.problems.simple bit string to determine the fitness
+     * Ths method allows you to evaluate a 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 eva2.problems.simple bit string to determine the fitness
+     * Ths method allows you to evaluate a 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 eva2.problems.simple bit string to determine the fitness
+     * Ths method allows you to evaluate a 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 eva2.problems.simple interface to easily include new optimization problems in Java into the
+ * A 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 eva2.problems.simple method to read doubles from a string.
+     * A 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 eva2.problems.simple File Filter for *.txt files.
+/**A 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 eva2.problems.simple interface which can be used to paint certain icons at DPoints
+ * A 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

@@ -1674,7 +1674,7 @@ public class JMatLink extends Thread {
 
     // this is a concurrent situation.
 
-    // The solution is eva2.problems.simple: I always use a locking-mechanism to wait for the
+    // The solution is simple: I always use a locking-mechanism to wait for the
 
     // data. The main thread will release the lock and the calling method can