Fixing lots of smaller bugs and code inconsitencies.

Added several annotations and removed globalInfo/TipText methods.

src/eva2/cli/Main.java

@@ -324,6 +324,10 @@ public class Main implements OptimizationStateListener, InterfacePopulationChang
 
                 break;
             case "EvolutionStrategies":
+                //double cm, cr;
+                //int mu, lambda;
+                boolean plusStrategy;
+                //this.optimizer = OptimizerFactory.createEvolutionStrategy()
                 break;
             default:
                 throw new Exception("Unsupported Optimizer");
@@ -349,7 +353,7 @@ public class Main implements OptimizationStateListener, InterfacePopulationChang
             OptimizerFactory.setEvaluationTerminator(50000);
             OptimizerFactory.addTerminator(new FitnessValueTerminator(new double[]{0.00001}), CombinedTerminator.OR);
 
-            LOGGER.log(Level.INFO, "Running {0}", "Differential Evolution");
+            LOGGER.log(Level.INFO, "Running {0}", optimizer.getName());
 
             OptimizationParameters params = OptimizerFactory.makeParams(optimizer, this.populationSize, this.problem);
             double[] result = OptimizerFactory.optimizeToDouble(params);

src/eva2/optimization/go/InterfaceOptimizationParameters.java

@@ -7,35 +7,29 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.optimization.strategies.InterfaceOptimizer;
 
 /**
- * Created by IntelliJ IDEA.
+ *
- * User: streiche
- * Date: 11.05.2003
- * Time: 13:14:06
- * To change this template use Options | File Templates.
  */
 public interface InterfaceOptimizationParameters {
     /**
      * This method allows you to serialize the current parameters into a *.ser file
      */
-    public void saveInstance();
+    void saveInstance();
 
     /**
      * This method returns the name
      *
      * @return string
      */
-    public String getName();
+    String getName();
 
     /**
      * This methods allow you to set and get the Seed for the Random Number Generator.
      *
      * @param x Long seed.
      */
-    public void setSeed(long x);
+    void setSeed(long x);
 
-    public long getSeed();
+    long getSeed();
-
-    public String seedTipText();
 
     /**
      * This method allows you to choose a termination criteria for the
@@ -43,48 +37,33 @@ public interface InterfaceOptimizationParameters {
      *
      * @param term The new terminator
      */
-    public void setTerminator(InterfaceTerminator term);
+    void setTerminator(InterfaceTerminator term);
 
-    public InterfaceTerminator getTerminator();
+    InterfaceTerminator getTerminator();
-
-    public String terminatorTipText();
 
     /**
      * This method allows you to set the current optimizing algorithm
      *
      * @param optimizer The new optimizing algorithm
      */
-    public void setOptimizer(InterfaceOptimizer optimizer);
+    void setOptimizer(InterfaceOptimizer optimizer);
 
-    public InterfaceOptimizer getOptimizer();
+    InterfaceOptimizer getOptimizer();
-//    public String optimizerTipText();
 
     /**
      * This method will set the problem that is to be optimized
      *
      * @param problem
      */
-    public void setProblem(InterfaceOptimizationProblem problem);
+    void setProblem(InterfaceOptimizationProblem problem);
 
-    public InterfaceOptimizationProblem getProblem();
+    InterfaceOptimizationProblem getProblem();
 
-    public String problemTipText();
+    InterfacePostProcessParams getPostProcessParams();
 
-    /**
+    void setPostProcessParams(InterfacePostProcessParams ppp);
-     * This method will set the output filename
-     *
-     * @param name TODO invalidate these!
-     */
-//    public void setOutputFileName (String name);
-//    public String getOutputFileName ();
-//    public String outputFileNameTipText();
-    public InterfacePostProcessParams getPostProcessParams();
 
-    public void setPostProcessParams(InterfacePostProcessParams ppp);
+    void setDoPostProcessing(boolean doPP);
-
-    public String postProcessParamsTipText();
-
-    public void setDoPostProcessing(boolean doPP);
 
     /**
      * Give an instance which should be informed about elements which are additional informers.
@@ -92,7 +71,7 @@ public interface InterfaceOptimizationParameters {
      * @param o
      * @see InterfaceAdditionalPopulationInformer
      */
-    public void addInformableInstance(InterfaceNotifyOnInformers o);
+    void addInformableInstance(InterfaceNotifyOnInformers o);
 
-    public boolean removeInformableInstance(InterfaceNotifyOnInformers o);
+    boolean removeInformableInstance(InterfaceNotifyOnInformers o);
 }

src/eva2/optimization/modules/AbstractOptimizationParameters.java

@@ -11,6 +11,7 @@ import eva2.optimization.problems.InterfaceAdditionalPopulationInformer;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.optimization.strategies.InterfaceOptimizer;
 import eva2.tools.Serializer;
+import eva2.util.annotation.Parameter;
 
 import java.io.FileNotFoundException;
 import java.io.FileOutputStream;
@@ -23,13 +24,39 @@ import java.util.logging.Logger;
 
 public abstract class AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
     protected static final Logger LOGGER = Logger.getLogger(AbstractOptimizationParameters.class.getName());
+
+    @Parameter(name = "Random Seed", description = "Random number seed, set to zero to use current system time.")
     protected long randomSeed = (long) 0.0;
 
-    // Opt. Algorithms and Parameters
+    /**
+     * The optimizer to be executed.
+     */
+    @Parameter(name = "Optimizer", description = "Choose an optimization strategy.")
     protected InterfaceOptimizer optimizer;
+
+    /**
+     * The optimization problem to be optimized.
+     * When changed it is automatically applied to the
+     * selected optimizer.
+     */
+    @Parameter(name = "Problem", description = "Choose the problem that is to optimize and the EA individual parameters.")
     protected InterfaceOptimizationProblem problem;
+
+    /**
+     * The termination criteria that terminated an
+     * optimization run.
+     */
+    @Parameter(name = "Terminator", description = "Choose a termination criterion.")
     protected InterfaceTerminator terminator;
+
+    /**
+     * Post processing parameters.
+     * This can be enabled in the UI and will perform additional
+     * optimization e.g. with Hill Climbing.
+     */
+    @Parameter(name = "Post Processing", description = "Parameters for the post processing step.")
     protected InterfacePostProcessParams postProcessing = new PostProcessParams(false);
+
     transient protected InterfacePopulationChangedEventListener populationChangedEventListener;
     transient private List<InterfaceNotifyOnInformers> toInformAboutInformers = null;
 
@@ -181,10 +208,6 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
         return this.optimizer;
     }
 
-    public String optimizerTipText() {
-        return "Choose an optimization strategy.";
-    }
-
     @Override
     public String getName() {
         return "Optimization parameters";
@@ -207,11 +230,6 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
         return this.problem;
     }
 
-    @Override
-    public String problemTipText() {
-        return "Choose the problem that is to optimize and the EA individual parameters.";
-    }
-
     /**
      * This methods allow you to set and get the Seed for the Random Number Generator.
      *
@@ -232,11 +250,6 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
         return randomSeed;
     }
 
-    @Override
-    public String seedTipText() {
-        return "Random number seed, set to zero to use current system time.";
-    }
-
     /**
      * This method allows you to choose a termination criteria for the
      * evolutionary algorithm.
@@ -253,11 +266,6 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
         return this.terminator;
     }
 
-    @Override
-    public String terminatorTipText() {
-        return "Choose a termination criterion.";
-    }
-
     @Override
     public InterfacePostProcessParams getPostProcessParams() {
         return postProcessing;
@@ -268,11 +276,6 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
         postProcessing = ppp;
     }
 
-    @Override
-    public String postProcessParamsTipText() {
-        return "Parameters for the post processing step";
-    }
-
     @Override
     public void setDoPostProcessing(boolean doPP) {
         postProcessing.setDoPostProcessing(doPP);

src/eva2/optimization/modules/MOEAParameters.java

@@ -102,7 +102,6 @@ public class MOEAParameters extends AbstractOptimizationParameters implements In
         ((MultiObjectiveEA) this.optimizer).setOptimizer(b);
     }
 
-    @Override
     public String optimizerTipText() {
         return "Choose a population based optimizing technique to use.";
     }

src/eva2/optimization/modules/OptimizationParameters.java

@@ -8,6 +8,7 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.optimization.strategies.GeneticAlgorithm;
 import eva2.optimization.strategies.InterfaceOptimizer;
 import eva2.tools.Serializer;
+import eva2.util.annotation.Description;
 
 import java.io.FileInputStream;
 import java.io.FileNotFoundException;
@@ -16,16 +17,24 @@ import java.util.logging.Level;
 
 
 /**
- * OptimizationParamers for configuration of an
+ * OptimizationParameters for configuration of an
  * optimization run.
  * <p/>
  * This class is used to generate the default GUI
  * configuration panel for optimizations.
  */
+@Description("Select the optimization parameters.")
 public class OptimizationParameters extends AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
 
+    /**
+     * Should be removed and replaced by a more solid
+     * serialization. (EvAScript?)
+     *
+     * @deprecated
+     * @return
+     */
     public static OptimizationParameters getInstance() {
-        return getInstance("OptimizationParameters.ser", true);
+        return getInstance("OptimizationParameters.set", true);
     }
 
     /**
@@ -80,13 +89,4 @@ public class OptimizationParameters extends AbstractOptimizationParameters imple
     public Object clone() {
         return new OptimizationParameters(this);
     }
-
-    /**
-     * This method returns a global info string.
-     *
-     * @return description
-     */
-    public static String globalInfo() {
-        return "Select the optimization parameters.";
-    }
 }

src/eva2/optimization/operator/crossover/CM2.java

@@ -16,14 +16,14 @@ import java.util.BitSet;
  */
 public class CM2 implements InterfaceCrossover, java.io.Serializable {
 
-    private InterfaceOptimizationProblem m_OptimizationProblem;
+    private InterfaceOptimizationProblem optimizationProblem;
 
     public CM2() {
 
     }
 
     public CM2(CM2 c) {
-        this.m_OptimizationProblem = c.m_OptimizationProblem;
+        this.optimizationProblem = c.optimizationProblem;
     }
 
     @Override
@@ -58,7 +58,7 @@ public class CM2 implements InterfaceCrossover, java.io.Serializable {
     @Override
     public void init(AbstractEAIndividual individual,
                      InterfaceOptimizationProblem opt) {
-        this.m_OptimizationProblem = opt;
+        this.optimizationProblem = opt;
     }
 
     @Override

src/eva2/optimization/operator/crossover/CrossoverEAMixer.java

@@ -10,11 +10,7 @@ import java.util.ArrayList;
 
 
 /**
- * Created by IntelliJ IDEA.
+ *
- * User: Dante Alighieri
- * Date: 21.05.2005
- * Time: 11:36:38
- * To change this template use File | Settings | File Templates.
  */
 public class CrossoverEAMixer implements InterfaceCrossover, InterfaceEvaluatingCrossoverOperator, java.io.Serializable {
     public static final String CROSSOVER_EA_MIXER_OPERATOR_KEY = "CrossoverEAMixerOperatorKey";
@@ -60,7 +56,7 @@ public class CrossoverEAMixer implements InterfaceCrossover, InterfaceEvaluating
         this.m_Crossers.setSelectedCrossers(tmpList);
         this.m_Crossers.normalizeWeights();
         this.m_Crossers.setDescriptiveString("Combining alternative mutation operators, please norm the weights!");
-        this.m_Crossers.setWeightsLabel("Weigths");
+        this.m_Crossers.setWeightsLabel("Weights");
     }
 
     public CrossoverEAMixer(CrossoverEAMixer mutator) {

src/eva2/optimization/operator/crossover/TestESCrossover.java

@@ -86,7 +86,8 @@ public class TestESCrossover implements java.io.Serializable {
         double[] tmpD = new double[2];
         tmpD[0] = 0;
         tmpD[1] = 0;
-        this.m_Plot = new Plot("ES Crossover Testert", "x", "y", tmpD, tmpD);
+        // ToDo: Fix plot (it's internal and not showing)
+        this.m_Plot = new Plot("ES Crossover Tester", "x", "y", tmpD, tmpD);
         // validate and show
         this.m_Frame.validate();
         this.m_Frame.setVisible(true);

src/eva2/optimization/operator/selection/SelectAll.java

@@ -6,7 +6,7 @@ import eva2.optimization.population.Population;
 /**
  * Simple method to selecet all.
  * In case of multiple fitness values the selection
- * critria is selected randomly for each selection event. pff
+ * criteria is selected randomly for each selection event. pff
  * Created by IntelliJ IDEA.
  * User: streiche
  * Date: 31.03.2004

src/eva2/optimization/operator/selection/SelectBestIndividuals.java

@@ -211,6 +211,6 @@ public class SelectBestIndividuals implements InterfaceSelection, java.io.Serial
     }
 
     public String obeyDebsConstViolationPrincipleToolTip() {
-        return "Toggle the use of Deb's coonstraint violation principle.";
+        return "Toggle the use of Deb's constraint violation principle.";
     }
 }

src/eva2/optimization/operator/selection/SelectTournament.java

@@ -17,8 +17,8 @@ import eva2.tools.math.RNG;
  */
 public class SelectTournament implements InterfaceSelection, java.io.Serializable {
 
-    private int m_TournamentSize = 4;
+    private int tournamentSize = 4;
-    private boolean m_ObeyDebsConstViolationPrinciple = true;
+    private boolean obeyDebsConstViolationPrinciple = true;
 
     public SelectTournament() {
     }
@@ -29,8 +29,8 @@ public class SelectTournament implements InterfaceSelection, java.io.Serializabl
     }
 
     public SelectTournament(SelectTournament a) {
-        this.m_TournamentSize = a.m_TournamentSize;
+        this.tournamentSize = a.tournamentSize;
-        this.m_ObeyDebsConstViolationPrinciple = a.m_ObeyDebsConstViolationPrinciple;
+        this.obeyDebsConstViolationPrinciple = a.obeyDebsConstViolationPrinciple;
     }
 
     @Override
@@ -82,9 +82,9 @@ public class SelectTournament implements InterfaceSelection, java.io.Serializabl
         try {
             critSize = ((AbstractEAIndividual) population.get(0)).getFitness().length;
             currentCriteria = RNG.randomInt(0, critSize - 1);
-            if (this.m_ObeyDebsConstViolationPrinciple) {
+            if (this.obeyDebsConstViolationPrinciple) {
                 Population tournamentGroup = new Population();
-                for (int i = 0; i < this.m_TournamentSize; i++) {
+                for (int i = 0; i < this.tournamentSize; i++) {
                     tournamentGroup.add(population.get(RNG.randomInt(0, population.size() - 1)));
                 }
                 SelectBestIndividuals best = new SelectBestIndividuals();
@@ -92,7 +92,7 @@ public class SelectTournament implements InterfaceSelection, java.io.Serializabl
                 result = (AbstractEAIndividual) best.selectFrom(tournamentGroup, 1).get(0);
             } else {
                 result = (AbstractEAIndividual) population.get(RNG.randomInt(0, population.size() - 1));
-                for (int i = 1; i < this.m_TournamentSize; i++) {
+                for (int i = 1; i < this.tournamentSize; i++) {
                     tmpIndy = (AbstractEAIndividual) population.get(RNG.randomInt(0, population.size() - 1));
                     if (tmpIndy.getFitness(currentCriteria) < result.getFitness(currentCriteria)) {
                         result = tmpIndy;
@@ -152,11 +152,11 @@ public class SelectTournament implements InterfaceSelection, java.io.Serializabl
     }
 
     public int getTournamentSize() {
-        return m_TournamentSize;
+        return tournamentSize;
     }
 
     public void setTournamentSize(int g) {
-        m_TournamentSize = g;
+        tournamentSize = g;
     }
 
     /**
@@ -167,11 +167,11 @@ public class SelectTournament implements InterfaceSelection, java.io.Serializabl
      */
     @Override
     public void setObeyDebsConstViolationPrinciple(boolean b) {
-        this.m_ObeyDebsConstViolationPrinciple = b;
+        this.obeyDebsConstViolationPrinciple = b;
     }
 
     public boolean getObeyDebsConstViolationPrinciple() {
-        return this.m_ObeyDebsConstViolationPrinciple;
+        return this.obeyDebsConstViolationPrinciple;
     }
 
     public String obeyDebsConstViolationPrincipleToolTip() {

src/eva2/optimization/operator/selection/SelectXProbRouletteWheel.java

@@ -6,81 +6,28 @@ import eva2.optimization.operator.selection.probability.SelProbStandard;
 import eva2.optimization.population.Population;
 import eva2.tools.math.RNG;
 
-
-class treeElement implements java.io.Serializable {
-    public double separator = 0;
-    public int m_Index = -1;
-    public treeElement m_Left = null, m_Right = null;
-
-    public treeElement(double[][] d, int list, int low, int high) {
-        //System.out.println("Calling Low/high: "+low+"/"+high);
-        if (low == high) {
-            // end reached
-            //System.out.println("This: "+low);
-            this.m_Index = low;
-        } else {
-            if (low == high - 1) {
-                //System.out.println("This: "+high);
-                this.m_Index = high;
-            } else {
-                int midPoint = (int) ((high + low) / 2);
-                this.separator = d[midPoint - 1][list];
-                //System.out.println("Branching: "+midPoint + " : " + this.separator);
-                this.m_Left = new treeElement(d, list, low, midPoint);
-                this.m_Right = new treeElement(d, list, midPoint, high);
-            }
-        }
-    }
-
-    public int getIndexFor(double d) {
-        if (this.m_Index >= 0) {
-            return this.m_Index - 1;
-        } else {
-            if (d < this.separator) {
-                return this.m_Left.getIndexFor(d);
-            } else {
-                return this.m_Right.getIndexFor(d);
-            }
-        }
-    }
-
-    @Override
-    public String toString() {
-        if (this.m_Index >= 0) {
-            return "Ind:" + this.m_Index;
-        } else {
-            return "{" + this.m_Left.toString() + "} X<" + this.separator + " {" + this.m_Right.toString() + "}";
-        }
-    }
-}
-
 /**
- * The RoulettWheel selection requires a selection probability calculator.
+ * The RouletteWheel selection requires a selection probability calculator.
  * In case of multiple fitness values the selection
- * critria is selected randomly for each selection event.
+ * criteria is selected randomly for each selection event.
- * Created by IntelliJ IDEA.
- * User: streiche
- * Date: 18.03.2003
- * Time: 16:36:11
- * To change this template use Options | File Templates.
  */
 public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Serializable {
 
-    private transient treeElement[] m_TreeRoot = null;
+    private transient TreeElement[] treeRoot = null;
-    private InterfaceSelectionProbability m_SelProbCalculator = new SelProbStandard();
+    private InterfaceSelectionProbability selectionProbability = new SelProbStandard();
-    private boolean m_ObeyDebsConstViolationPrinciple = true;
+    private boolean obeyDebsConstViolationPrinciple = true;
 
     public SelectXProbRouletteWheel() {
     }
 
     public SelectXProbRouletteWheel(SelectXProbRouletteWheel a) {
-        this.m_ObeyDebsConstViolationPrinciple = a.m_ObeyDebsConstViolationPrinciple;
+        this.obeyDebsConstViolationPrinciple = a.obeyDebsConstViolationPrinciple;
-        this.m_SelProbCalculator = (InterfaceSelectionProbability) a.m_SelProbCalculator.clone();
+        this.selectionProbability = (InterfaceSelectionProbability) a.selectionProbability.clone();
     }
 
     @Override
     public Object clone() {
-        return (Object) new SelectXProbRouletteWheel(this);
+        return new SelectXProbRouletteWheel(this);
     }
 
     /**
@@ -93,8 +40,8 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
      */
     @Override
     public void prepareSelection(Population population) {
-        this.m_SelProbCalculator.computeSelectionProbability(population, "Fitness", this.m_ObeyDebsConstViolationPrinciple);
+        this.selectionProbability.computeSelectionProbability(population, "Fitness", this.obeyDebsConstViolationPrinciple);
-        this.m_TreeRoot = this.buildSelectionTree(population);
+        this.treeRoot = this.buildSelectionTree(population);
     }
 
     /**
@@ -123,8 +70,8 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
      *
      * @param p The population
      */
-    private treeElement[] buildSelectionTree(Population p) {
+    private TreeElement[] buildSelectionTree(Population p) {
-        treeElement result[];
+        TreeElement result[];
         double[][] tmpList = new double[p.size()][];
 
         for (int i = 0; i < p.size(); i++) {
@@ -137,12 +84,12 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
             }
         }
 
-        result = new treeElement[tmpList[0].length];
+        result = new TreeElement[tmpList[0].length];
         for (int i = 0; i < tmpList[0].length; i++) {
             //String s = "Input: {";
             //for (int j = 0; j < tmpList.length; j++) s += tmpList[j][i] +"; ";
             //System.out.println(s+"}");
-            result[i] = new treeElement(tmpList, i, 0, tmpList.length);
+            result[i] = new TreeElement(tmpList, i, 0, tmpList.length);
             //System.out.println("Resulting Tree: " + result[i].toString());
         }
 
@@ -160,7 +107,7 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
         critSize = ((AbstractEAIndividual) population.get(0)).getSelectionProbability().length;
         currentCriteria = RNG.randomInt(0, critSize - 1);
         double d = RNG.randomDouble();
-        int index = this.m_TreeRoot[currentCriteria].getIndexFor(d);
+        int index = this.treeRoot[currentCriteria].getIndexFor(d);
         //System.out.println("Looking for: " + d + " found " +index);
         return ((AbstractEAIndividual) (population.get(index)));
     }
@@ -234,11 +181,11 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
      * @param normation
      */
     public void setSelProbCalculator(InterfaceSelectionProbability normation) {
-        this.m_SelProbCalculator = normation;
+        this.selectionProbability = normation;
     }
 
     public InterfaceSelectionProbability getSelProbCalculator() {
-        return this.m_SelProbCalculator;
+        return this.selectionProbability;
     }
 
     public String selProbCalculatorTipText() {
@@ -253,14 +200,62 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
      */
     @Override
     public void setObeyDebsConstViolationPrinciple(boolean b) {
-        this.m_ObeyDebsConstViolationPrinciple = b;
+        this.obeyDebsConstViolationPrinciple = b;
     }
 
     public boolean getObeyDebsConstViolationPrinciple() {
-        return this.m_ObeyDebsConstViolationPrinciple;
+        return this.obeyDebsConstViolationPrinciple;
     }
 
     public String obeyDebsConstViolationPrincipleToolTip() {
         return "Toggle the use of Deb's coonstraint violation principle.";
     }
 }
+
+
+class TreeElement implements java.io.Serializable {
+    public double separator = 0;
+    public int m_Index = -1;
+    public TreeElement m_Left = null, m_Right = null;
+
+    public TreeElement(double[][] d, int list, int low, int high) {
+        //System.out.println("Calling Low/high: "+low+"/"+high);
+        if (low == high) {
+            // end reached
+            //System.out.println("This: "+low);
+            this.m_Index = low;
+        } else {
+            if (low == high - 1) {
+                //System.out.println("This: "+high);
+                this.m_Index = high;
+            } else {
+                int midPoint = (int) ((high + low) / 2);
+                this.separator = d[midPoint - 1][list];
+                //System.out.println("Branching: "+midPoint + " : " + this.separator);
+                this.m_Left = new TreeElement(d, list, low, midPoint);
+                this.m_Right = new TreeElement(d, list, midPoint, high);
+            }
+        }
+    }
+
+    public int getIndexFor(double d) {
+        if (this.m_Index >= 0) {
+            return this.m_Index - 1;
+        } else {
+            if (d < this.separator) {
+                return this.m_Left.getIndexFor(d);
+            } else {
+                return this.m_Right.getIndexFor(d);
+            }
+        }
+    }
+
+    @Override
+    public String toString() {
+        if (this.m_Index >= 0) {
+            return "Ind:" + this.m_Index;
+        } else {
+            return "{" + this.m_Left.toString() + "} X<" + this.separator + " {" + this.m_Right.toString() + "}";
+        }
+    }
+}

src/eva2/optimization/strategies/EvolutionStrategies.java

@@ -12,6 +12,7 @@ import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.B1Problem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Parameter;
 
 /**
  * Evolution strategies by Rechenberg and Schwefel, but please remember that
@@ -26,8 +27,13 @@ import eva2.util.annotation.Description;
 @Description(value = "This is an Evolution Strategy. Note that the population size depends on mu (number of parents) and lambda (number of offspring)")
 public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializable {
 
+    @Parameter(description = "Mu", name = "mu")
     protected int mu = 5;
+
+    @Parameter(description = "Lambda", name = "lambda")
     protected int lambda = 20;
+
+    @Parameter(description = "Determines whether the +-Strategy should be used.", name = "usePlus")
     protected boolean usePlusStrategy = false;
     protected Population population = new Population();
     protected InterfaceOptimizationProblem optimizationProblem = new B1Problem();