Merge branch '61-terminators' into 'master'

Tests for Terminators



See merge request !12

build.gradle

@@ -1,7 +1,8 @@
 apply plugin: 'java'
 apply plugin: 'maven'
+apply plugin: 'com.bmuschko.nexus'
 
-group = 'eva2'
+group = 'de.openea'
 version = '2.2.0'
 
 description = """EvA2"""
@@ -25,9 +26,62 @@ task fatJar(type: Jar) {
 repositories {
     mavenCentral()        
 }
+
 dependencies {
     compile group: 'javax.help', name: 'javahelp', version: '2.0.05'
     compile group: 'org.yaml', name: 'snakeyaml', version: '1.16'
     compile group: 'gov.nist.math', name: 'jama', version: '1.0.3'
     testCompile group: 'junit', name: 'junit', version: '4.12'
+    testCompile group: 'org.mockito', name: 'mockito-core', version: '1.+'
+}
+
+buildscript {
+    repositories {
+        jcenter()
+    }
+    dependencies {
+        classpath 'com.bmuschko:gradle-nexus-plugin:2.3.1'
+    }
+}
+
+modifyPom {
+    project {
+        name 'EvA2'
+        description 'Gradle plugin that provides tasks for configuring and uploading artifacts to Sonatype Nexus.'
+        url 'http://www.ra.cs.uni-tuebingen.de/software/eva2/'
+        inceptionYear '2008'
+
+        scm {
+            url 'https://gitlab.cs.uni-tuebingen.de/eva2/eva2/'
+            connection 'scm:https://gitlab.cs.uni-tuebingen.de/eva2/eva2/.git'
+            developerConnection 'scm:https://gitlab.cs.uni-tuebingen.de/eva2/eva2/.git'
+        }
+
+        licenses {
+            license {
+                name 'GNU Lesser General Public License, Version 3.0'
+                url 'http://www.gnu.org/licenses/lgpl-3.0.html'
+                distribution 'repo'
+            }
+        }
+
+        developers {
+            developer {
+                id 'halfdan'
+                name 'Fabian Becker'
+                email 'halfdan@xnorfz.de'
+            }
+        }
+    }
+}
+
+extraArchive {
+    sources = true
+    tests = true
+    javadoc = true
+}
+
+nexus {
+    repositoryUrl = 'https://oss.sonatype.org/service/local/staging/deploy/maven2'
+    snapshotRepositoryUrl = 'https://oss.sonatype.org/content/repositories/snapshots'
 }

src/main/java/eva2/optimization/individuals/EAIndividualComparator.java

@@ -19,7 +19,7 @@ import java.util.Comparator;
  * @see AbstractEAIndividual#isDominatingFitness(double[], double[])
  */
 @eva2.util.annotation.Description(value = "A comparator class for general EA individuals. Compares individuals based on their fitness in context of minimization.")
-public class EAIndividualComparator implements Comparator<Object>, Serializable {
+public class EAIndividualComparator implements Comparator<AbstractEAIndividual>, Serializable {
     // flag whether a data field should be used.
     private String indyDataKey = "";
     private int fitCriterion = -1;
@@ -122,7 +122,7 @@ public class EAIndividualComparator implements Comparator<Object>, Serializable
      * @return -1 if the first is dominant, 1 if the second is dominant, otherwise 0
      */
     @Override
-    public int compare(Object o1, Object o2) {
+    public int compare(AbstractEAIndividual o1, AbstractEAIndividual o2) {
         boolean o1domO2, o2domO1;
 
         if (preferFeasible) { // check constraint violation first?

src/main/java/eva2/optimization/individuals/ESIndividualPermutationData.java

@@ -237,10 +237,6 @@ public class ESIndividualPermutationData extends AbstractEAIndividual implements
         this.firstindex = firstindex;
     }
 
-    /************************************************************************************
-     * AbstractEAIndividual methods
-     */
-
     /**
      * This method will initialize the individual with a given value for the
      * phenotype.
@@ -305,7 +301,7 @@ public class ESIndividualPermutationData extends AbstractEAIndividual implements
     }
 
 
-    public double[] mapMatrixToVector(double[][] matrix) {
+    public static double[] mapMatrixToVector(double[][] matrix) {
         int sumentries = 0;
         for (int i = 0; i < matrix.length; i++) {
             sumentries += matrix[i].length;
@@ -321,7 +317,7 @@ public class ESIndividualPermutationData extends AbstractEAIndividual implements
         return res;
     }
 
-    public double[][] mapVectorToMatrix(double[] vector, int[] sizes) {
+    public static double[][] mapVectorToMatrix(double[] vector, int[] sizes) {
         double[][] matrix = new double[sizes.length][];
         int counter = 0;
         for (int i = 0; i < sizes.length; i++) {
@@ -353,8 +349,6 @@ public class ESIndividualPermutationData extends AbstractEAIndividual implements
                 }
             }
         }
-
-
     }
 
     /**

src/main/java/eva2/optimization/individuals/IndividualDistanceComparator.java

@@ -9,7 +9,7 @@ import java.util.Comparator;
  * Compare two AbstractEAIndividuals by their distance to a reference individual.
  * Usable to sort by a distance.
  */
-public class IndividualDistanceComparator implements Comparator<Object>, Serializable {
+public class IndividualDistanceComparator implements Comparator<AbstractEAIndividual>, Serializable {
 
     private AbstractEAIndividual refIndy = null;
     private InterfaceDistanceMetric distMetric = null;
@@ -30,9 +30,9 @@ public class IndividualDistanceComparator implements Comparator<Object>, Seriali
     }
 
     @Override
-    public int compare(Object o1, Object o2) {
+    public int compare(AbstractEAIndividual o1, AbstractEAIndividual o2) {
-        double d1 = distMetric.distance((AbstractEAIndividual) o1, refIndy);
+        double d1 = distMetric.distance(o1, refIndy);
-        double d2 = distMetric.distance((AbstractEAIndividual) o2, refIndy);
+        double d2 = distMetric.distance(o2, refIndy);
 
         if (d1 == d2) {
             return 0;

src/main/java/eva2/optimization/individuals/IndividualWeightedFitnessComparator.java

@@ -1,6 +1,7 @@
 package eva2.optimization.individuals;
 
 import eva2.tools.EVAERROR;
+import eva2.util.annotation.Parameter;
 
 import java.io.Serializable;
 import java.util.Comparator;
@@ -10,7 +11,7 @@ import java.util.Comparator;
  *
  * @author mkron
  */
-public class IndividualWeightedFitnessComparator implements Comparator<Object>, Serializable {
+public class IndividualWeightedFitnessComparator implements Comparator<AbstractEAIndividual>, Serializable {
     /**
      * Generated serial version identifier
      */
@@ -75,12 +76,12 @@ public class IndividualWeightedFitnessComparator implements Comparator<Object>,
      * @see java.util.Comparator#compare(java.lang.Object, java.lang.Object)
      */
     @Override
-    public int compare(Object o1, Object o2) {
+    public int compare(AbstractEAIndividual o1, AbstractEAIndividual o2) {
-        double[] f1 = ((AbstractEAIndividual) o1).getFitness();
+        double[] f1 = o1.getFitness();
-        double[] f2 = ((AbstractEAIndividual) o2).getFitness();
+        double[] f2 = o2.getFitness();
 
-        double score1 = calcScore(f1);
+        double score1 = calculateScore(f1);
-        double score2 = calcScore(f2);
+        double score2 = calculateScore(f2);
 
         if (score1 < score2) {
             return -1;
@@ -95,7 +96,7 @@ public class IndividualWeightedFitnessComparator implements Comparator<Object>,
      * @param f
      * @return
      */
-    private double calcScore(double[] f) {
+    private double calculateScore(double[] f) {
         if (f == null || fitWeights == null) {
             throw new RuntimeException("Error, missing information in " + this.getClass());
         }
@@ -117,9 +118,9 @@ public class IndividualWeightedFitnessComparator implements Comparator<Object>,
      * @param indy
      * @return
      */
-    public double calcScore(AbstractEAIndividual indy) {
+    public double calculateScore(AbstractEAIndividual indy) {
         double[] f = indy.getFitness();
-        return calcScore(f);
+        return calculateScore(f);
     }
 
     /**
@@ -133,6 +134,7 @@ public class IndividualWeightedFitnessComparator implements Comparator<Object>,
         }
     }
 
+    @Parameter(description = "Weights of the fitness values in the linear combination")
     public void setFitWeights(double[] fitWeights) {
         this.fitWeights = fitWeights;
     }
@@ -140,23 +142,4 @@ public class IndividualWeightedFitnessComparator implements Comparator<Object>,
     public double[] getFitWeights() {
         return fitWeights;
     }
-
-    public String fitWeightsTipText() {
-        return "Weights of the fitness values in the linear combination";
-    }
-
-//	public static void main(String[] args) {
-//		TF1Problem prob = new TF1Problem();
-//		Population pop = new Population(10);
-//		prob.initializePopulation(pop);
-//		prob.evaluate(pop);
-//		System.out.println(pop.getStringRepresentation());
-//		System.out.println("***");
-//		IndividualWeightedFitnessComparator wfComp = new IndividualWeightedFitnessComparator(new double[]{0.5,0.5});
-//		System.out.println("***"); System.out.println(pop.getSortedPop(wfComp).getStringRepresentation());
-//		wfComp.setFitWeights(new double[] {0.1, 0.9});
-//		System.out.println("***"); System.out.println(pop.getSortedPop(wfComp).getStringRepresentation());
-//		wfComp.setFitWeights(new double[] {0.9, 0.1});
-//		System.out.println("***"); System.out.println(pop.getSortedPop(wfComp).getStringRepresentation());
-//	}
 }

src/main/java/eva2/optimization/operator/archiving/ArchivingNSGAIISMeasure.java

@@ -52,7 +52,7 @@ public class ArchivingNSGAIISMeasure extends ArchivingNSGAII {
 
     public void calculateCrowdingDistance(Population front) {
 
-        Object[] frontArray = front.toArray();
+        AbstractEAIndividual[] frontArray = front.toArray(new AbstractEAIndividual[front.size()]);
         boolean[] assigned = new boolean[frontArray.length];
 
         double[] v = new double[frontArray.length];
@@ -68,8 +68,8 @@ public class ArchivingNSGAIISMeasure extends ArchivingNSGAII {
         Arrays.sort(frontArray, new EAIndividualComparator(0));
 
 
-        ((AbstractEAIndividual) frontArray[0]).putData("HyperCube", Double.MAX_VALUE); //die beiden aussen bekommen maximal wert als measure
+        frontArray[0].putData("HyperCube", Double.MAX_VALUE); //die beiden aussen bekommen maximal wert als measure
-        ((AbstractEAIndividual) frontArray[frontArray.length - 1]).putData("HyperCube", Double.MAX_VALUE);
+        frontArray[frontArray.length - 1].putData("HyperCube", Double.MAX_VALUE);
         v[0] = Double.MAX_VALUE;
         v[frontArray.length - 1] = Double.MAX_VALUE;
 

src/main/java/eva2/optimization/operator/distancemetric/DoubleIntegralMetric.java

@@ -4,6 +4,7 @@ import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.tools.EVAERROR;
 import eva2.tools.math.Mathematics;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Parameter;
 
 import java.io.Serializable;
 
@@ -19,8 +20,7 @@ import java.io.Serializable;
 public class DoubleIntegralMetric implements InterfaceDistanceMetric, Serializable {
     boolean oneNormed = true; // if true, the vectors are normed to unity sum before comparison.
 
-    public DoubleIntegralMetric() {
+    public DoubleIntegralMetric() {}
-    }
 
     public DoubleIntegralMetric(boolean normed) {
         oneNormed = normed;
@@ -35,10 +35,8 @@ public class DoubleIntegralMetric implements InterfaceDistanceMetric, Serializab
     public double distance(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
         double[] dIndy1 = null, dIndy2 = null;
 
-        if (dIndy1 == null || dIndy2 == null) {
+        dIndy1 = AbstractEAIndividual.getDoublePositionShallow(indy1);
-            dIndy1 = AbstractEAIndividual.getDoublePositionShallow(indy1);
+        dIndy2 = AbstractEAIndividual.getDoublePositionShallow(indy2);
-            dIndy2 = AbstractEAIndividual.getDoublePositionShallow(indy2);
-        }
 
         if (oneNormed) {
             double l1 = Mathematics.sum(dIndy1);
@@ -53,9 +51,8 @@ public class DoubleIntegralMetric implements InterfaceDistanceMetric, Serializab
         }
 
         double sum1 = 0, sum2 = 0;
-        double tmpDiff = 0, sqrDiffSum = 0;
+        double tmpDiff, sqrDiffSum = 0;
-//		System.out.println(Mathematics.sum(dIndy1));
+
-//		System.out.println(Mathematics.sum(dIndy2));
         for (int i = 0; (i < dIndy1.length) && (i < dIndy2.length); i++) {
             sum1 += dIndy1[i];
             sum2 += dIndy2[i];
@@ -73,12 +70,8 @@ public class DoubleIntegralMetric implements InterfaceDistanceMetric, Serializab
         return oneNormed;
     }
 
+    @Parameter(description = "If true, both vectors are normed to unity sum before comparison.")
     public void setOneNormed(boolean normedByLength) {
         this.oneNormed = normedByLength;
     }
-
-    public String oneNormedTipText() {
-        return "If true, both vectors are normed to unity sum before comparison.";
-    }
-
 }

src/main/java/eva2/optimization/operator/distancemetric/GenotypeMetricBitSet.java

@@ -13,15 +13,11 @@ import java.util.BitSet;
 @Description("This is a genotype based distance metric suited for binary data. The hamming distance is computed and normalized by chromosome length.")
 public class GenotypeMetricBitSet implements InterfaceDistanceMetric, java.io.Serializable {
 
-    public GenotypeMetricBitSet() {
+    public GenotypeMetricBitSet() {}
-    }
-
-    public GenotypeMetricBitSet(GenotypeMetricBitSet a) {
-    }
 
     @Override
     public Object clone() {
-        return new GenotypeMetricBitSet(this);
+        return new GenotypeMetricBitSet();
     }
 
     /**

src/main/java/eva2/optimization/operator/distancemetric/PhenotypeMetric.java

@@ -3,6 +3,7 @@ package eva2.optimization.operator.distancemetric;
 
 import eva2.gui.BeanInspector;
 import eva2.optimization.individuals.*;
+import eva2.tools.math.Mathematics;
 import eva2.util.annotation.Description;
 
 import java.util.BitSet;
@@ -15,15 +16,11 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
     private static PhenotypeMetric pMetric = null;
     private static GenotypeMetricBitSet bitMetric = null;
 
-    public PhenotypeMetric() {
+    public PhenotypeMetric() {}
-    }
-
-    public PhenotypeMetric(PhenotypeMetric a) {
-    }
 
     @Override
     public Object clone() {
-        return new PhenotypeMetric(this);
+        return new PhenotypeMetric();
     }
 
     private static int min(int a, int b, int c) {
@@ -109,7 +106,6 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
             r2 = ((InterfaceDataTypeInteger) indy2).getIntRange();
             for (int i = 0; (i < d1.length) && (i < d2.length); i++) {
                 tmpResult += Math.pow(((d1[i] - r1[i][0]) / ((double) (r1[i][1] - r1[i][0]))) - ((d2[i] - r2[i][0]) / ((double) (r2[i][1] - r2[i][0]))), 2);
-                //tmpResult += Math.abs(d1[i] - d2[i])/((double)(r1[i][1]-r1[i][0]));
             }
             result += Math.sqrt(tmpResult);
         }
@@ -130,7 +126,6 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
         if ((indy1 instanceof InterfaceDataTypePermutation) && (indy2 instanceof InterfaceDataTypePermutation)) {
             int[] dIndy1, dIndy2;
             String s1 = "", s2 = "";
-//            double  tmpResult = 0;
             for (int p = 0; p < ((InterfaceDataTypePermutation) indy1).getPermutationData().length; p++) {
                 dIndy1 = ((InterfaceDataTypePermutation) indy1).getPermutationData()[p];
                 dIndy2 = ((InterfaceDataTypePermutation) indy2).getPermutationData()[p];
@@ -192,7 +187,7 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
             return result / d1.length;
         }
         if (indy instanceof InterfaceDataTypeDouble) {
-            result = norm(((InterfaceDataTypeDouble) indy).getDoubleData());
+            result = Mathematics.norm(((InterfaceDataTypeDouble) indy).getDoubleData());
             return result;
         }
         if (indy instanceof InterfaceDataTypePermutation) {
@@ -208,20 +203,6 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
         return 0;
     }
 
-    /**
-     * Calculates the 2 norm of a given vector.
-     *
-     * @param v1
-     * @return
-     */
-    public static double norm(double[] v1) {
-        double result = 0;
-        for (int i = 0; i < v1.length; i++) {
-            result += Math.pow(v1[i], 2);
-        }
-        return Math.sqrt(result);
-    }
-
     /**
      * This method will return a naming String
      *

src/main/java/eva2/optimization/operator/distancemetric/SigmaSingleMetricGlobalMutation.java

@@ -15,15 +15,11 @@ import eva2.util.annotation.Description;
 @Description("This is an experimental method for individuals using global ES mutation.")
 public class SigmaSingleMetricGlobalMutation implements InterfaceDistanceMetric, java.io.Serializable {
 
-    public SigmaSingleMetricGlobalMutation() {
+    public SigmaSingleMetricGlobalMutation() {}
-    }
-
-    public SigmaSingleMetricGlobalMutation(SigmaSingleMetricGlobalMutation a) {
-    }
 
     @Override
     public Object clone() {
-        return new SigmaSingleMetricGlobalMutation(this);
+        return new SigmaSingleMetricGlobalMutation();
     }
 
     /**

src/main/java/eva2/optimization/operator/postprocess/PostProcess.java

@@ -248,9 +248,9 @@ public class PostProcess {
         Population result = new Population();
         for (int i = 0; i < pop.size(); i++) {
             indy = pop.getEAIndividual(i);
-            if (bSmallerEq && (PhenotypeMetric.norm(indy.getFitness()) <= fitNorm)) {
+            if (bSmallerEq && (Mathematics.norm(indy.getFitness()) <= fitNorm)) {
                 result.add(indy);
-            } else if (!bSmallerEq && (PhenotypeMetric.norm(indy.getFitness()) > fitNorm)) {
+            } else if (!bSmallerEq && (Mathematics.norm(indy.getFitness()) > fitNorm)) {
                 result.add(indy);
             }
         }
@@ -279,7 +279,7 @@ public class PostProcess {
             if ((crit >= 0) && (crit < indy.getFitness().length)) {
                 curFit = indy.getFitness(crit);
             } else {
-                curFit = PhenotypeMetric.norm(indy.getFitness());
+                curFit = Mathematics.norm(indy.getFitness());
             }
 
             if (bSmallerEq && (curFit <= fitThresh)) {
@@ -1058,7 +1058,7 @@ public class PostProcess {
                 plot = draw("After " + stepsDone + " steps (" + params.getPPMethod() + ")" + ((params.getPostProcessClusterSigma() > 0) ? " and second clustering" : ""), null, outputPop, null, problem);
             }
             // ##### some statistics
-            double upBnd = PhenotypeMetric.norm(outputPop.getWorstEAIndividual().getFitness()) * 1.1;
+            double upBnd = Mathematics.norm(outputPop.getWorstEAIndividual().getFitness()) * 1.1;
             upBnd = Math.pow(10, Math.floor(Math.log10(upBnd) + 1));
             double lowBnd = 0;
             int fitCrit = 0; // use first fitness criterion

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

@@ -3,6 +3,7 @@ package eva2.optimization.operator.selection;
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.population.Population;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Parameter;
 
 /**
  * Simple method to select all.
@@ -14,8 +15,7 @@ public class SelectAll implements InterfaceSelection, java.io.Serializable {
 
     private boolean obeyDebsConstViolationPrinciple = true;
 
-    public SelectAll() {
+    public SelectAll() {}
-    }
 
     public SelectAll(SelectAll a) {
         this.obeyDebsConstViolationPrinciple = a.obeyDebsConstViolationPrinciple;
@@ -41,7 +41,7 @@ public class SelectAll implements InterfaceSelection, java.io.Serializable {
 
     /**
      * This method will select one Individual from the given
-     * Population in respect to the selection propability of the
+     * Population in respect to the selection probability of the
      * individual.
      *
      * @param population The source population where to select from
@@ -105,6 +105,7 @@ public class SelectAll implements InterfaceSelection, java.io.Serializable {
      * @param b The new state
      */
     @Override
+    @Parameter(description = "Toggle the use of Deb's constraint violation principle.")
     public void setObeyDebsConstViolationPrinciple(boolean b) {
         this.obeyDebsConstViolationPrinciple = b;
     }
@@ -112,8 +113,4 @@ public class SelectAll implements InterfaceSelection, java.io.Serializable {
     public boolean getObeyDebsConstViolationPrinciple() {
         return this.obeyDebsConstViolationPrinciple;
     }
-
-    public String obeyDebsConstViolationPrincipleToolTip() {
-        return "Toggle the use of Deb's coonstraint violation principle.";
-    }
 }

src/main/java/eva2/optimization/operator/selection/SelectParticleWheel.java

@@ -2,8 +2,8 @@ package eva2.optimization.operator.selection;
 
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.operator.selection.probability.InterfaceSelectionProbability;
-import eva2.optimization.operator.selection.probability.SelProbStandard;
+import eva2.optimization.operator.selection.probability.SelectionProbabilityStandard;
-import eva2.optimization.operator.selection.probability.SelProbStandardScaling;
+import eva2.optimization.operator.selection.probability.SelectionProbabilityStandardScaling;
 import eva2.optimization.population.Population;
 import eva2.tools.math.RNG;
 import eva2.util.annotation.Description;
@@ -23,14 +23,14 @@ public class SelectParticleWheel implements InterfaceSelection, java.io.Serializ
      * Comment for {@code serialVersionUID}
      */
     private static final long serialVersionUID = 1L;
-    private InterfaceSelectionProbability selProbCalculator = new SelProbStandard();
+    private InterfaceSelectionProbability selProbCalculator = new SelectionProbabilityStandard();
     private boolean selectFixedSteps = false;
 
     public SelectParticleWheel() {
     }
 
     public SelectParticleWheel(double scalingProb) {
-        selProbCalculator = new SelProbStandardScaling(scalingProb);
+        selProbCalculator = new SelectionProbabilityStandardScaling(scalingProb);
     }
 
     public SelectParticleWheel(InterfaceSelectionProbability selProb) {

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

@@ -2,7 +2,7 @@ package eva2.optimization.operator.selection;
 
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.operator.selection.probability.InterfaceSelectionProbability;
-import eva2.optimization.operator.selection.probability.SelProbStandard;
+import eva2.optimization.operator.selection.probability.SelectionProbabilityStandard;
 import eva2.optimization.population.Population;
 import eva2.tools.math.RNG;
 import eva2.util.annotation.Description;
@@ -17,7 +17,7 @@ import eva2.util.annotation.Description;
 public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Serializable {
 
     private transient TreeElement[] treeRoot = null;
-    private InterfaceSelectionProbability selectionProbability = new SelProbStandard();
+    private InterfaceSelectionProbability selectionProbability = new SelectionProbabilityStandard();
     private boolean obeyDebsConstViolationPrinciple = true;
 
     public SelectXProbRouletteWheel() {

src/main/java/eva2/optimization/operator/selection/probability/AbstractSelectionProbability.java

@@ -11,7 +11,7 @@ import java.util.ArrayList;
  * This abstract implementation gives some general
  * methods for retrieving and cleaning fitness values.
  */
-public abstract class AbstractSelProb implements InterfaceSelectionProbability, java.io.Serializable {
+public abstract class AbstractSelectionProbability implements InterfaceSelectionProbability, java.io.Serializable {
 
     /**
      * This method allows you to make a deep clone of

src/main/java/eva2/optimization/operator/selection/probability/SelectionProbabilityBoltzman.java

@@ -9,24 +9,24 @@ import eva2.util.annotation.Description;
  * invariant to any linear transition function.
  */
 @Description("This is the Boltzman Normation.")
-public class SelProbBoltzman extends AbstractSelProb implements java.io.Serializable {
+public class SelectionProbabilityBoltzman extends AbstractSelectionProbability implements java.io.Serializable {
 
     private double q = 1.0;
 
-    public SelProbBoltzman() {
+    public SelectionProbabilityBoltzman() {
     }
 
-    public SelProbBoltzman(double q) {
+    public SelectionProbabilityBoltzman(double q) {
         this.q = q;
     }
 
-    public SelProbBoltzman(SelProbBoltzman a) {
+    public SelectionProbabilityBoltzman(SelectionProbabilityBoltzman a) {
         this.q = a.q;
     }
 
     @Override
     public Object clone() {
-        return new SelProbBoltzman(this);
+        return new SelectionProbabilityBoltzman(this);
     }
 
     /**

src/main/java/eva2/optimization/operator/selection/probability/SelectionProbabilityFitnessSharing.java

@@ -10,16 +10,16 @@ import eva2.util.annotation.Description;
  * Here we have the infamous fitness sharing method.
  */
 @Description("This is a fitness sharing based normation method.")
-public class SelProbFitnessSharing extends AbstractSelProb implements java.io.Serializable {
+public class SelectionProbabilityFitnessSharing extends AbstractSelectionProbability implements java.io.Serializable {
 
-    private InterfaceSelectionProbability basicNormationMethod = new SelProbStandard();
+    private InterfaceSelectionProbability basicNormationMethod = new SelectionProbabilityStandard();
     private InterfaceDistanceMetric distanceMetric = new PhenotypeMetric();
     private double sharingDistance = 0.1;
 
-    public SelProbFitnessSharing() {
+    public SelectionProbabilityFitnessSharing() {
     }
 
-    public SelProbFitnessSharing(SelProbFitnessSharing a) {
+    public SelectionProbabilityFitnessSharing(SelectionProbabilityFitnessSharing a) {
         if (a.basicNormationMethod != null) {
             this.basicNormationMethod = (InterfaceSelectionProbability) a.basicNormationMethod.clone();
         }
@@ -31,7 +31,7 @@ public class SelProbFitnessSharing extends AbstractSelProb implements java.io.Se
 
     @Override
     public Object clone() {
-        return new SelProbFitnessSharing(this);
+        return new SelectionProbabilityFitnessSharing(this);
     }
 
     /**

src/main/java/eva2/optimization/operator/selection/probability/SelectionProbabilityInvertByMax.java

@@ -10,24 +10,24 @@ import eva2.util.annotation.Description;
  *
  */
 @Description("This is a standard normation method inverted by maximum fitness.")
-public class SelProbInvertByMax extends AbstractSelProb {
+public class SelectionProbabilityInvertByMax extends AbstractSelectionProbability {
 
     private double maxFit = 1.;
 
-    public SelProbInvertByMax() {
+    public SelectionProbabilityInvertByMax() {
     }
 
-    public SelProbInvertByMax(double mF) {
+    public SelectionProbabilityInvertByMax(double mF) {
         maxFit = mF;
     }
 
-    public SelProbInvertByMax(SelProbInvertByMax a) {
+    public SelectionProbabilityInvertByMax(SelectionProbabilityInvertByMax a) {
         this.maxFit = a.maxFit;
     }
 
     @Override
     public Object clone() {
-        return new SelProbInvertByMax(this);
+        return new SelectionProbabilityInvertByMax(this);
     }
 
     /**

src/main/java/eva2/optimization/operator/selection/probability/SelectionProbabilityLinearRanking.java

@@ -7,22 +7,22 @@ import eva2.util.annotation.Description;
  * A linear ranking method with offsets.
  */
 @Description("This is linear ranking normation.")
-public class SelProbLinearRanking extends AbstractSelProb implements java.io.Serializable {
+public class SelectionProbabilityLinearRanking extends AbstractSelectionProbability implements java.io.Serializable {
 
     private double nappaPlus = 1.1;
     private double nappaMinus = 0.9;
 
-    public SelProbLinearRanking() {
+    public SelectionProbabilityLinearRanking() {
     }
 
-    public SelProbLinearRanking(SelProbLinearRanking a) {
+    public SelectionProbabilityLinearRanking(SelectionProbabilityLinearRanking a) {
         this.nappaPlus = a.nappaPlus;
         this.nappaMinus = a.nappaMinus;
     }
 
     @Override
     public Object clone() {
-        return new SelProbLinearRanking(this);
+        return new SelectionProbabilityLinearRanking(this);
     }
 
     /**

src/main/java/eva2/optimization/operator/selection/probability/SelectionProbabilityNonLinearRanking.java

@@ -8,24 +8,24 @@ import eva2.util.annotation.Description;
  * the given optimization problem i guess.
  */
 @Description("This is non-linear ranking normation.")
-public class SelProbNonLinearRanking extends AbstractSelProb implements java.io.Serializable {
+public class SelectionProbabilityNonLinearRanking extends AbstractSelectionProbability implements java.io.Serializable {
 
     private double c = 0.04;
 
-    public SelProbNonLinearRanking() {
+    public SelectionProbabilityNonLinearRanking() {
     }
 
-    public SelProbNonLinearRanking(double theC) {
+    public SelectionProbabilityNonLinearRanking(double theC) {
         this.c = theC;
     }
 
-    public SelProbNonLinearRanking(SelProbNonLinearRanking a) {
+    public SelectionProbabilityNonLinearRanking(SelectionProbabilityNonLinearRanking a) {
         this.c = a.c;
     }
 
     @Override
     public Object clone() {
-        return new SelProbNonLinearRanking(this);
+        return new SelectionProbabilityNonLinearRanking(this);
     }
 
     /**

src/main/java/eva2/optimization/operator/selection/probability/SelectionProbabilityRanking.java

@@ -8,17 +8,17 @@ import eva2.util.annotation.Description;
  * This truly scaling invariant.
  */
 @Description("This is ranking normation.")
-public class SelProbRanking extends AbstractSelProb implements java.io.Serializable {
+public class SelectionProbabilityRanking extends AbstractSelectionProbability implements java.io.Serializable {
 
-    public SelProbRanking() {
+    public SelectionProbabilityRanking() {
     }
 
-    public SelProbRanking(SelProbRanking a) {
+    public SelectionProbabilityRanking(SelectionProbabilityRanking a) {
     }
 
     @Override
     public Object clone() {
-        return new SelProbRanking(this);
+        return new SelectionProbabilityRanking(this);
     }
 
     /**

src/main/java/eva2/optimization/operator/selection/probability/SelectionProbabilityStandard.java

@@ -9,17 +9,17 @@ import eva2.util.annotation.Description;
  * p(i is selected) = exp(-fitness(i))/sum_j(exp(-fitness(j)))
  */
 @Description("This is a standard normation method using the exp function.")
-public class SelProbStandard extends AbstractSelProb implements java.io.Serializable {
+public class SelectionProbabilityStandard extends AbstractSelectionProbability implements java.io.Serializable {
 
-    public SelProbStandard() {
+    public SelectionProbabilityStandard() {
     }
 
-    public SelProbStandard(SelProbStandard a) {
+    public SelectionProbabilityStandard(SelectionProbabilityStandard a) {
     }
 
     @Override
     public Object clone() {
-        return new SelProbStandard(this);
+        return new SelectionProbabilityStandard(this);
     }
 
     /**

src/main/java/eva2/optimization/operator/selection/probability/SelectionProbabilityStandardScaling.java

@@ -7,24 +7,24 @@ import eva2.util.annotation.Description;
  * 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 {
+public class SelectionProbabilityStandardScaling extends AbstractSelectionProbability implements java.io.Serializable {
 
     private double Q = 0;
 
-    public SelProbStandardScaling() {
+    public SelectionProbabilityStandardScaling() {
     }
 
-    public SelProbStandardScaling(double q) {
+    public SelectionProbabilityStandardScaling(double q) {
         Q = q;
     }
 
-    public SelProbStandardScaling(SelProbStandardScaling a) {
+    public SelectionProbabilityStandardScaling(SelectionProbabilityStandardScaling a) {
         this.Q = a.Q;
     }
 
     @Override
     public Object clone() {
-        return new SelProbStandardScaling(this);
+        return new SelectionProbabilityStandardScaling(this);
     }
 
     /**

src/main/java/eva2/optimization/operator/terminators/DiversityTerminator.java

@@ -5,6 +5,7 @@ import eva2.optimization.operator.distancemetric.PhenotypeMetric;
 import eva2.optimization.population.Population;
 import eva2.optimization.population.PopulationInterface;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Parameter;
 
 import java.io.Serializable;
 
@@ -40,6 +41,7 @@ public class DiversityTerminator extends PopulationMeasureTerminator implements
     /**
      * @return the metric
      */
+    @Parameter(description = "Set the metric to be used to calculate individual distances.")
     public InterfaceDistanceMetric getMetric() {
         return metric;
     }
@@ -48,13 +50,10 @@ public class DiversityTerminator extends PopulationMeasureTerminator implements
         this.metric = metric;
     }
 
-    public String metricTipText() {
-        return "Set the metric to be used to calculate individual distances.";
-    }
-
     /**
      * @return the criterion
      */
+    @Parameter(description = "Define the distance criterion to check for in a population.")
     public DiversityCriterion getCriterion() {
         return criterion;
     }
@@ -63,17 +62,13 @@ public class DiversityTerminator extends PopulationMeasureTerminator implements
         this.criterion = criterion;
     }
 
-    public String criterionTipText() {
+    @Override
-        return "Define the distance criterion to check for in a population.";
+    protected double calculateInitialMeasure(PopulationInterface pop) {
+        return calculatePopulationMeasure(pop);
     }
 
     @Override
-    protected double calcInitialMeasure(PopulationInterface pop) {
+    protected double calculatePopulationMeasure(PopulationInterface pop) {
-        return calcPopulationMeasure(pop);
-    }
-
-    @Override
-    protected double calcPopulationMeasure(PopulationInterface pop) {
         double[] measures = ((Population) pop).getPopulationMeasures(metric);
         int measureIndex = criterion.ordinal();
         return measures[measureIndex];

src/main/java/eva2/optimization/operator/terminators/EvaluationTerminator.java

@@ -23,8 +23,7 @@ public class EvaluationTerminator implements InterfaceTerminator,
      */
     protected int maxFitnessCalls = 1000;
 
-    public EvaluationTerminator() {
+    public EvaluationTerminator() {}
-    }
 
     @Override
     public void initialize(InterfaceOptimizationProblem prob) {

src/main/java/eva2/optimization/operator/terminators/FitnessConvergenceTerminator.java

@@ -30,12 +30,12 @@ public class FitnessConvergenceTerminator extends PopulationMeasureTerminator
     }
 
     @Override
-    protected double calcInitialMeasure(PopulationInterface pop) {
+    protected double calculateInitialMeasure(PopulationInterface pop) {
         return Mathematics.norm(pop.getBestFitness());
     }
 
     @Override
-    protected double calcPopulationMeasure(PopulationInterface pop) {
+    protected double calculatePopulationMeasure(PopulationInterface pop) {
 //		if (oldFit==null) return Double.MAX_VALUE;
 //		return EuclideanMetric.euclideanDistance(oldFit, pop.getBestFitness());
         return Mathematics.norm(pop.getBestFitness());

src/main/java/eva2/optimization/operator/terminators/FitnessValueTerminator.java

@@ -44,8 +44,8 @@ public class FitnessValueTerminator implements InterfaceTerminator,
     }
 
     @Override
-    public boolean isTerminated(PopulationInterface Pop) {
+    public boolean isTerminated(PopulationInterface pop) {
-        double[] fit = Pop.getBestFitness();
+        double[] fit = pop.getBestFitness();
         for (int i = 0; i < fit.length; i++) {
             if (fitnessValue[i] < fit[i]) {
                 return false;

src/main/java/eva2/optimization/operator/terminators/GenerationTerminator.java

@@ -4,6 +4,7 @@ import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.PopulationInterface;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Parameter;
 
 import java.io.Serializable;
 
@@ -24,8 +25,7 @@ public class GenerationTerminator implements InterfaceTerminator, Serializable {
         msg = "Not terminated.";
     }
 
-    public GenerationTerminator() {
+    public GenerationTerminator() {}
-    }
 
     public GenerationTerminator(int gens) {
         maxGenerations = gens;
@@ -37,8 +37,8 @@ public class GenerationTerminator implements InterfaceTerminator, Serializable {
     }
 
     @Override
-    public boolean isTerminated(PopulationInterface Pop) {
+    public boolean isTerminated(PopulationInterface pop) {
-        if (maxGenerations < Pop.getGeneration()) {
+        if (maxGenerations < pop.getGeneration()) {
             msg = maxGenerations + " generations reached.";
             return true;
         }
@@ -52,10 +52,10 @@ public class GenerationTerminator implements InterfaceTerminator, Serializable {
 
     @Override
     public String toString() {
-        String ret = "Generations calls=" + maxGenerations;
+        return "Generations calls=" + maxGenerations;
-        return ret;
     }
 
+    @Parameter(description = "Number of generations to evaluate.")
     public void setGenerations(int x) {
         maxGenerations = x;
     }
@@ -63,13 +63,4 @@ public class GenerationTerminator implements InterfaceTerminator, Serializable {
     public int getGenerations() {
         return maxGenerations;
     }
-
-    /**
-     * Returns the tip text for this property
-     *
-     * @return tip text for this property
-     */
-    public String generationsTipText() {
-        return "number of generations to evaluate.";
-    }
 }

src/main/java/eva2/optimization/operator/terminators/KnownOptimaFoundTerminator.java

@@ -7,6 +7,7 @@ import eva2.problems.InterfaceMultimodalProblemKnown;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.tools.EVAERROR;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Parameter;
 
 import java.io.Serializable;
 import java.util.logging.Level;
@@ -25,16 +26,15 @@ public class KnownOptimaFoundTerminator implements InterfaceTerminator, Serializ
     private int reqOptima = 1;
     private String msg = "";
 
-    public KnownOptimaFoundTerminator() {
+    public KnownOptimaFoundTerminator() {}
-    }
 
     @Override
-    public void initialize(InterfaceOptimizationProblem prob) {
+    public void initialize(InterfaceOptimizationProblem prob) throws IllegalArgumentException {
         if (prob != null) {
             if (prob instanceof InterfaceMultimodalProblemKnown) {
                 mProblem = (InterfaceMultimodalProblemKnown) prob;
             } else {
-                LOGGER.log(Level.WARNING, "KnownOptimaFoundTerminator only works with InterfaceMultimodalProblemKnown instances!");
+                throw new IllegalArgumentException("KnownOptimaFoundTerminator only works with InterfaceMultimodalProblemKnown instances!");
             }
         } else {
             LOGGER.log(Level.WARNING, "KnownOptimaFoundTerminator wont work with null problem!");
@@ -71,21 +71,18 @@ public class KnownOptimaFoundTerminator implements InterfaceTerminator, Serializ
     /**
      * @return the reqOptima
      */
-    public int getReqOptima() {
+    public int getRequiredOptima() {
         return reqOptima;
     }
 
     /**
      * @param reqOptima the reqOptima to set
      */
-    public void setReqOptima(int reqOptima) {
+    @Parameter(description = "The number of optima that need to be found to terminate the optimization.")
+    public void setRequiredOptima(int reqOptima) {
         this.reqOptima = reqOptima;
     }
 
-    public String reqOptimaTipText() {
-        return "The number of optima that need to be found to terminate the optimization.";
-    }
-
     @Override
     public String toString() {
         return "KnownOptimaFoundTerminator requiring " + reqOptima + " optima.";

src/main/java/eva2/optimization/operator/terminators/ParetoMetricTerminator.java

@@ -9,6 +9,7 @@ import eva2.problems.AbstractMultiObjectiveOptimizationProblem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.tools.EVAERROR;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Parameter;
 
 import java.io.Serializable;
 
@@ -50,13 +51,12 @@ public class ParetoMetricTerminator extends PopulationMeasureTerminator implemen
         if (prob instanceof AbstractMultiObjectiveOptimizationProblem) {
             moProb = (AbstractMultiObjectiveOptimizationProblem) prob;
         } else {
-            moProb = null;
+            throw new IllegalArgumentException("Error, " + this.getClass() + " works only with problems inheriting from " + AbstractMultiObjectiveOptimizationProblem.class + "!");
-            EVAERROR.errorMsgOnce("Error, " + this.getClass() + " works only with problems inheriting from " + AbstractMultiObjectiveOptimizationProblem.class + "!");
         }
     }
 
     @Override
-    protected double calcInitialMeasure(PopulationInterface pop) {
+    protected double calculateInitialMeasure(PopulationInterface pop) {
         if (moProb == null) {
             return Double.MAX_VALUE;
         } else {
@@ -69,8 +69,8 @@ public class ParetoMetricTerminator extends PopulationMeasureTerminator implemen
     }
 
     @Override
-    protected double calcPopulationMeasure(PopulationInterface pop) {
+    protected double calculatePopulationMeasure(PopulationInterface pop) {
-        return calcInitialMeasure(pop);
+        return calculateInitialMeasure(pop);
     }
 
     @Override
@@ -89,6 +89,7 @@ public class ParetoMetricTerminator extends PopulationMeasureTerminator implemen
         }
     }
 
+    @Parameter(description = "The pareto metric to use")
     public void setParetoMetric(InterfaceParetoFrontMetric pMetric) {
         this.pMetric = pMetric;
     }
@@ -97,10 +98,7 @@ public class ParetoMetricTerminator extends PopulationMeasureTerminator implemen
         return pMetric;
     }
 
-    public String paretoMetricTipText() {
+    @Parameter(description = "If true, the current population is used, otherwise the pareto front of the multi-objective problem instance is used")
-        return "The pareto metric to use";
-    }
-
     public void setUseCurrentPop(boolean useCurrentPop) {
         this.useCurrentPop = useCurrentPop;
     }
@@ -108,8 +106,4 @@ public class ParetoMetricTerminator extends PopulationMeasureTerminator implemen
     public boolean isUseCurrentPop() {
         return useCurrentPop;
     }
-
-    public String useCurrentPopTipText() {
-        return "If true, the current population is used, otherwise the pareto front of the multi-objective problem instance is used";
-    }
 }

src/main/java/eva2/optimization/operator/terminators/PhenotypeConvergenceTerminator.java

@@ -34,13 +34,13 @@ public class PhenotypeConvergenceTerminator extends PopulationMeasureTerminator
     }
 
     @Override
-    protected double calcInitialMeasure(PopulationInterface pop) {
+    protected double calculateInitialMeasure(PopulationInterface pop) {
         oldIndy = (AbstractEAIndividual) ((AbstractEAIndividual) pop.getBestIndividual()).clone();
         return Double.MAX_VALUE;
     }
 
     @Override
-    protected double calcPopulationMeasure(PopulationInterface pop) {
+    protected double calculatePopulationMeasure(PopulationInterface pop) {
         return pMetric.distance(oldIndy, (AbstractEAIndividual) pop.getBestIndividual());
     }
 

src/main/java/eva2/optimization/operator/terminators/PopulationArchiveTerminator.java

@@ -4,6 +4,7 @@ import eva2.optimization.individuals.IndividualWeightedFitnessComparator;
 import eva2.optimization.population.Population;
 import eva2.optimization.population.PopulationInterface;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Parameter;
 
 /**
  * Terminate if a score based on the archive of the population converges.
@@ -14,22 +15,22 @@ public class PopulationArchiveTerminator extends PopulationMeasureTerminator {
     IndividualWeightedFitnessComparator wfComp = new IndividualWeightedFitnessComparator(new double[]{1.});
 
     @Override
-    protected double calcInitialMeasure(PopulationInterface pop) {
+    protected double calculateInitialMeasure(PopulationInterface pop) {
         Population archive = ((Population) pop).getArchive();
         if (archive == null || (archive.size() < 1)) {
             return Double.MAX_VALUE;
         } else {
-            return wfComp.calcScore(archive.getBestEAIndividual(wfComp));
+            return wfComp.calculateScore(archive.getBestEAIndividual(wfComp));
         }
     }
 
     @Override
-    protected double calcPopulationMeasure(PopulationInterface pop) {
+    protected double calculatePopulationMeasure(PopulationInterface pop) {
         Population archive = ((Population) pop).getArchive();
         if (archive == null || (archive.size() < 1)) {
             return Double.MAX_VALUE;
         } else {
-            return wfComp.calcScore(archive.getBestEAIndividual(wfComp));
+            return wfComp.calculateScore(archive.getBestEAIndividual(wfComp));
         }
     }
 
@@ -42,11 +43,8 @@ public class PopulationArchiveTerminator extends PopulationMeasureTerminator {
         return wfComp.getFitWeights();
     }
 
+    @Parameter(description = "Weights of the fitness values in the linear combination")
     public void setFitWeights(double[] fWeights) {
         wfComp.setFitWeights(fWeights);
     }
-
-    public String fitWeightsTipText() {
-        return wfComp.fitWeightsTipText();
-    }
 }

src/main/java/eva2/optimization/operator/terminators/PopulationMeasureTerminator.java

@@ -4,6 +4,7 @@ import eva2.optimization.population.PopulationInterface;
 import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Parameter;
 
 import java.io.Serializable;
 
@@ -14,11 +15,11 @@ import java.io.Serializable;
  * The class detects changes of a population measure over time and may signal convergence
  * if the measure m(P) behaved in a certain way for a given time. Convergence may
  * be signaled
- * - if the measure reached absolute values below convThresh (absolute value),
+ * - if the measure reached absolute values below convergenceThresh (absolute value),
- * - if the measure remained within m(P)+/-convThresh (absolute change),
+ * - if the measure remained within m(P)+/-convergenceThresh (absolute change),
- * - if the measure remained above m(P)-convThresh (absolute change and regard improvement only),
+ * - if the measure remained above m(P)-convergenceThresh (absolute change and regard improvement only),
- * - if the measure remained within m(P)*[1-convThresh, 1+convThresh] (relative change),
+ * - if the measure remained within m(P)*[1-convergenceThresh, 1+convergenceThresh] (relative change),
- * - if the measure remained above m(P)*(1-convThresh) (relative change and regard improvement only).
+ * - if the measure remained above m(P)*(1-convergenceThresh) (relative change and regard improvement only).
  */
 @Description("Stop if a convergence criterion has been met.")
 public abstract class PopulationMeasureTerminator implements InterfaceTerminator, Serializable {
@@ -28,9 +29,9 @@ public abstract class PopulationMeasureTerminator implements InterfaceTerminator
 
     public enum StagnationTypeEnum {fitnessCallBased, generationBased}
 
-    private double convThresh = 0.01; //, convThreshLower=0.02;
+    private double convergenceThresh = 0.01;
     private double oldMeasure = -1;
-    private int stagTime = 1000;
+    private int stagnationTime = 1000;
     private int oldPopFitCalls = 1000;
     private int oldPopGens = 1000;
     private boolean firstTime = true;
@@ -43,16 +44,16 @@ public abstract class PopulationMeasureTerminator implements InterfaceTerminator
     }
 
     public PopulationMeasureTerminator(double convergenceThreshold, int stagnationTime, StagnationTypeEnum stagType, ChangeTypeEnum changeType, DirectionTypeEnum dirType) {
-        this.convThresh = convergenceThreshold;
+        this.convergenceThresh = convergenceThreshold;
-        this.stagTime = stagnationTime;
+        this.stagnationTime = stagnationTime;
         this.stagnationMeasure = stagType;
         this.changeType = changeType;
         this.condDirection = dirType;
     }
 
     public PopulationMeasureTerminator(PopulationMeasureTerminator o) {
-        convThresh = o.convThresh;
+        convergenceThresh = o.convergenceThresh;
-        stagTime = o.stagTime;
+        stagnationTime = o.stagnationTime;
         oldPopFitCalls = o.oldPopFitCalls;
         oldPopGens = o.oldPopGens;
         firstTime = o.firstTime;
@@ -80,16 +81,16 @@ public abstract class PopulationMeasureTerminator implements InterfaceTerminator
         if (!firstTime && isStillConverged(pop)) {
 
             if (stagnationTimeHasPassed(pop)) {
-                // population hasnt changed much for max time, criterion is met
+                // population hasn't changed much for max time, criterion is met
                 msg = getTerminationMessage();
                 return true;
             } else {
-                // population hasnt changed much for i<max time, keep running
+                // population hasn't changed much for i<max time, keep running
                 return false;
             }
         } else {
             // first call at all - or population improved more than "allowed" to terminate
-            oldMeasure = calcInitialMeasure(pop);
+            oldMeasure = calculateInitialMeasure(pop);
             saveState(pop);
             return false;
         }
@@ -100,7 +101,7 @@ public abstract class PopulationMeasureTerminator implements InterfaceTerminator
      *
      * @return
      */
-    protected abstract double calcInitialMeasure(PopulationInterface pop);
+    protected abstract double calculateInitialMeasure(PopulationInterface pop);
 
     @Override
     public String lastTerminationMessage() {
@@ -114,7 +115,6 @@ public abstract class PopulationMeasureTerminator implements InterfaceTerminator
      */
     protected String getTerminationMessage() {
         StringBuilder sb = new StringBuilder(getMeasureName());
-//		if (convergenceCondition.isSelectedString("Relative")) sb.append(" converged relatively ");
         switch (changeType) {
             case absoluteChange:
                 sb.append(" changed absolutely ");
@@ -128,15 +128,13 @@ public abstract class PopulationMeasureTerminator implements InterfaceTerminator
         }
         if (doCheckImprovement()) {
             sb.append("less than ");
-            sb.append(convThresh);
+            sb.append(convergenceThresh);
         } else {
             sb.append("within +/-");
-//			sb.append(convThreshLower);
+            sb.append(convergenceThresh);
-//			sb.append("/");
-            sb.append(convThresh);
         }
         sb.append(" for ");
-        sb.append(stagTime);
+        sb.append(stagnationTime);
         if (stagnationMeasure == StagnationTypeEnum.generationBased) {
             sb.append(" generations.");
         } else {
@@ -159,8 +157,7 @@ public abstract class PopulationMeasureTerminator implements InterfaceTerminator
      * @param pop
      */
     protected void saveState(PopulationInterface pop) {
-//		oldFit = pop.getBestFitness().clone();
+        oldMeasure = calculatePopulationMeasure(pop);
-        oldMeasure = calcPopulationMeasure(pop);
         oldPopFitCalls = pop.getFunctionCalls();
         oldPopGens = pop.getGeneration();
         firstTime = false;
@@ -173,7 +170,7 @@ public abstract class PopulationMeasureTerminator implements InterfaceTerminator
      * @param pop
      * @return
      */
-    protected abstract double calcPopulationMeasure(PopulationInterface pop);
+    protected abstract double calculatePopulationMeasure(PopulationInterface pop);
 
     /**
      * Return true if the population measure did not exceed the
@@ -183,22 +180,21 @@ public abstract class PopulationMeasureTerminator implements InterfaceTerminator
      * @return
      */
     protected boolean isStillConverged(PopulationInterface pop) {
-        double measure = calcPopulationMeasure(pop);
+        double measure = calculatePopulationMeasure(pop);
         double allowedLower = Double.NEGATIVE_INFINITY, allowedUpper = Double.POSITIVE_INFINITY;
         boolean ret;
         switch (changeType) {
             case absoluteChange:
-                allowedLower = oldMeasure - convThresh;
+                allowedLower = oldMeasure - convergenceThresh;
                 if (!doCheckImprovement()) {
-                    allowedUpper = oldMeasure + convThresh;
+                    allowedUpper = oldMeasure + convergenceThresh;
                 }
                 break;
             case absoluteValue:
-                allowedUpper = convThresh;
+                allowedUpper = convergenceThresh;
-//			if (!doCheckImprovement()) allowedUpper = convThreshUpper;
                 break;
             case relativeChange:
-                double delta = oldMeasure * convThresh;
+                double delta = oldMeasure * convergenceThresh;
                 allowedLower = oldMeasure - delta;
                 if (!doCheckImprovement()) {
                     allowedUpper = oldMeasure + delta;
@@ -211,7 +207,6 @@ public abstract class PopulationMeasureTerminator implements InterfaceTerminator
 
     public boolean doCheckImprovement() {
         return (condDirection == DirectionTypeEnum.decrease);
-//		return condImprovementOrChange.isSelectedString("Improvement");
     }
 
     public boolean isRelativeConvergence() {
@@ -220,43 +215,38 @@ public abstract class PopulationMeasureTerminator implements InterfaceTerminator
 
     /**
      * Return true if the defined stagnation time (function calls or generations) has passed
-     * since the last noteable change.
+     * since the last notable change.
      *
      * @param pop
      * @return
      */
     private boolean stagnationTimeHasPassed(PopulationInterface pop) {
         if (stagnationMeasure == StagnationTypeEnum.fitnessCallBased) { // by fitness calls
-            return (pop.getFunctionCalls() - oldPopFitCalls) >= stagTime;
+            return (pop.getFunctionCalls() - oldPopFitCalls) >= stagnationTime;
         } else {// by generation
-            return (pop.getGeneration() - oldPopGens) >= stagTime;
+            return (pop.getGeneration() - oldPopGens) >= stagnationTime;
         }
     }
 
+    @Parameter(description = "Ratio of improvement/change or absolute value of improvement/change to determine convergence.")
     public void setConvergenceThreshold(double x) {
-        convThresh = x;
+        convergenceThresh = x;
     }
 
     public double getConvergenceThreshold() {
-        return convThresh;
+        return convergenceThresh;
-    }
-
-    public String convergenceThresholdTipText() {
-        return "Ratio of improvement/change or absolute value of improvement/change to determine convergence.";
     }
 
+    @Parameter(description = "Terminate if the population has not improved/changed for this time")
     public void setStagnationTime(int k) {
-        stagTime = k;
+        stagnationTime = k;
     }
 
     public int getStagnationTime() {
-        return stagTime;
+        return stagnationTime;
-    }
-
-    public String stagnationTimeTipText() {
-        return "Terminate if the population has not improved/changed for this time";
     }
 
+    @Parameter(description = "Stagnation time is measured in fitness calls or generations")
     public StagnationTypeEnum getStagnationMeasure() {
         return stagnationMeasure;
     }
@@ -265,31 +255,21 @@ public abstract class PopulationMeasureTerminator implements InterfaceTerminator
         this.stagnationMeasure = stagnationTimeIn;
     }
 
-    public String stagnationMeasureTipText() {
-        return "Stagnation time is measured in fitness calls or generations";
-    }
-
     public ChangeTypeEnum getConvergenceCondition() {
         return changeType;
     }
 
+    @Parameter(description = "Select absolute or relative convergence condition")
     public void setConvergenceCondition(ChangeTypeEnum convergenceCondition) {
         this.changeType = convergenceCondition;
     }
 
-    public String convergenceConditionTipText() {
-        return "Select absolute or relative convergence condition";
-    }
-
     public DirectionTypeEnum getCheckType() {
         return condDirection;
     }
 
+    @Parameter(description = "Detect improvement only (decreasing measure) or change in both directions (decrease and increase)")
     public void setCheckType(DirectionTypeEnum dt) {
         this.condDirection = dt;
     }
-
-    public String checkTypeTipText() {
-        return "Detect improvement only (decreasing measure) or change in both directions (decrease and increase)";
-    }
 }

src/main/java/eva2/optimization/population/Population.java

@@ -7,7 +7,7 @@ import eva2.optimization.operator.distancemetric.EuclideanMetric;
 import eva2.optimization.operator.distancemetric.InterfaceDistanceMetric;
 import eva2.optimization.operator.distancemetric.PhenotypeMetric;
 import eva2.optimization.operator.postprocess.PostProcess;
-import eva2.optimization.operator.selection.probability.AbstractSelProb;
+import eva2.optimization.operator.selection.probability.AbstractSelectionProbability;
 import eva2.tools.EVAERROR;
 import eva2.tools.Pair;
 import eva2.tools.Serializer;
@@ -722,9 +722,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
             this.historyList.add((AbstractEAIndividual) this.getBestEAIndividual().clone());
         }
         if (isAutoAging()) {
-            for (AbstractEAIndividual individual : this) {
+            this.forEach(AbstractEAIndividual::incrAge);
-                individual.incrAge();
-            }
         }
         this.generationCount++;
         firePropertyChangedEvent(NEXT_GENERATION_PERFORMED);
@@ -885,7 +883,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
     }
 
     /**
-     * Resets the fitnes to the maximum possible value for the given individual.
+     * Resets the fitness to the maximum possible value for the given individual.
      *
      * @param indy an individual whose fitness will be reset
      */
@@ -1040,7 +1038,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
      * @param comparator indicate whether constraints should be regarded
      * @return The index of the best (worst) individual.
      */
-    public int getIndexOfBestOrWorstIndividual(boolean bBest, Comparator<Object> comparator) {
+    public int getIndexOfBestOrWorstIndividual(boolean bBest, Comparator<AbstractEAIndividual> comparator) {
         ArrayList<?> sorted = getSorted(comparator);
         if (bBest) {
             return indexOf(sorted.get(0));
@@ -1053,7 +1051,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
         return getIndexOfBestOrWorstIndividual(true, comparator);
     }
 
-    public AbstractEAIndividual getBestEAIndividual(Comparator<Object> comparator) {
+    public AbstractEAIndividual getBestEAIndividual(Comparator<AbstractEAIndividual> comparator) {
         int index = getIndexOfBestOrWorstIndividual(true, comparator);
         return getEAIndividual(index);
     }
@@ -1201,7 +1199,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
      *         fitness first
      * @see #getSortedNIndividuals(int, boolean, Population, Comparator)
      */
-    public Population getSortedBestFirst(Comparator<Object> comp) {
+    public Population getSortedBestFirst(Comparator<AbstractEAIndividual> comp) {
         Population result = this.cloneWithoutInds();
         getSortedNIndividuals(size(), true, result, comp);
         result.synchSize();
@@ -1221,7 +1219,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
      * @param comp         the Comparator to use with individuals
      * @param res The m sorted best or worst individuals, where m &lt;= n (will be added to res)
      */
-    public void getSortedNIndividuals(int n, boolean bBestOrWorst, Population res, Comparator<Object> comp) {
+    public void getSortedNIndividuals(int n, boolean bBestOrWorst, Population res, Comparator<AbstractEAIndividual> comp) {
         if ((n < 0) || (n > super.size())) {
             // this may happen, treat it gracefully
             //System.err.println("invalid request to getSortedNIndividuals: n="+n + ", size is " + super.size());
@@ -1293,7 +1291,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
      *             on AbstractEAIndividual instances.
      * @return
      */
-    protected ArrayList<AbstractEAIndividual> sortBy(Comparator<Object> comp) {
+    protected ArrayList<AbstractEAIndividual> sortBy(Comparator<AbstractEAIndividual> comp) {
         if (super.isEmpty()) {
             return new ArrayList<>();
         }
@@ -1322,7 +1320,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
      * @param comp The comparator
      * @return
      */
-    public ArrayList<AbstractEAIndividual> getSorted(Comparator<Object> comp) {
+    public ArrayList<AbstractEAIndividual> getSorted(Comparator<AbstractEAIndividual> comp) {
         if (!comp.equals(lastSortingComparator) || (sortedArr == null) || (super.modCount != lastQModCount)) {
             ArrayList<AbstractEAIndividual> sArr = sortBy(comp);
             if (sortedArr == null) {
@@ -1342,7 +1340,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
      *
      * @see #getSorted(java.util.Comparator)
      */
-    public Population getSortedPop(Comparator<Object> comp) {
+    public Population getSortedPop(Comparator<AbstractEAIndividual> comp) {
         Population pop = this.cloneWithoutInds();
         ArrayList<AbstractEAIndividual> sortedIndies = getSorted(comp);
         pop.addAll(sortedIndies);
@@ -2093,7 +2091,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
      */
     public double[] getCenter() {
         if (size() == 0) {
-            EVAERROR.errorMsgOnce("Invalid pop size in DistractingPopulation:getCenter!");
+            return null;
         }
         double[] centerPos = AbstractEAIndividual.getDoublePosition(getEAIndividual(0));
         for (int i = 1; i < size(); i++) {
@@ -2125,7 +2123,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
      */
     public double[] getCenterWeighted(double[] weights) {
         if (size() == 0 || (weights.length > size()) || (weights.length == 0)) {
-            EVAERROR.errorMsgOnce("Invalid pop size in DistractingPopulation:getCenterWeighted!");
+            return null;
         }
         double[] centerPos = AbstractEAIndividual.getDoublePosition(getEAIndividual(0));
         Mathematics.svMult(weights[0], centerPos, centerPos);
@@ -2145,7 +2143,7 @@ public class Population extends ArrayList<AbstractEAIndividual> implements Popul
      * @param criterion
      * @return
      */
-    public double[] getCenterWeighted(AbstractSelProb selProb, int criterion, boolean obeyConst) {
+    public double[] getCenterWeighted(AbstractSelectionProbability selProb, int criterion, boolean obeyConst) {
         selProb.computeSelectionProbability(this, "Fitness", obeyConst);
         double[] mean = AbstractEAIndividual.getDoublePosition(getEAIndividual(0));
 

src/main/java/eva2/optimization/strategies/ParticleSwarmOptimization.java

@@ -44,7 +44,7 @@ import java.util.Vector;
 public class ParticleSwarmOptimization extends AbstractOptimizer implements java.io.Serializable, InterfaceAdditionalPopulationInformer {
 
     public enum PSOType { Inertness, Constriction }
-    Object[] sortedPop = null;
+    AbstractEAIndividual[] sortedPop = null;
     protected AbstractEAIndividual bestIndividual = null;
     protected boolean checkRange = true;
     protected boolean checkSpeedLimit = false;
@@ -1299,7 +1299,7 @@ public class ParticleSwarmOptimization extends AbstractOptimizer implements java
             }
         }
         if ((topology == PSOTopology.multiSwarm) || (topology == PSOTopology.tree)) {
-            sortedPop = pop.toArray();
+            sortedPop = pop.toArray(new AbstractEAIndividual[pop.size()]);
             if ((topology == PSOTopology.multiSwarm) || (treeStruct >= 2)) {
                 Arrays.sort(sortedPop, new EAIndividualComparator());
             } else {

src/main/java/eva2/tools/Primitives.java

@@ -3,9 +3,6 @@ package eva2.tools;
 import java.util.HashMap;
 import java.util.Map;
 
-/**
- * Created by halfdan on 17/12/15.
- */
 public class Primitives {
     public static Class<?> unwrap(Class<?> clazz) {
         return getWrapperTypes().get(clazz);

src/main/java/eva2/tools/SelectedTag.java

@@ -71,9 +71,6 @@ public class SelectedTag implements java.io.Serializable {
         }
     }
 
-    //~ Methods ////////////////////////////////////////////////////////////////
-
-
     /**
      * Set the selected tag by index.
      *
@@ -197,23 +194,5 @@ public class SelectedTag implements java.io.Serializable {
     @Override
     public String toString() {
         return tags[selectedId].getString();
-//		Character selSign = '*';
-//		Character separator = '|';
-//		StringBuffer sbuf;
-//		if (selectedId != 0) sbuf = new StringBuffer(tags[0].getString());
-//		else {
-//			sbuf = new StringBuffer(selSign.toString());
-//			sbuf.append(tags[0].getString());
-//			sbuf.append(selSign);
-//		}
-//		for (int i=1; i<tags.length; i++) {
-//			sbuf.append(separator);
-//			if (selectedId == i) {
-//				sbuf.append(selSign);
-//				sbuf.append(tags[i].getString());
-//				sbuf.append(selSign);
-//			} else sbuf.append(tags[i].getString());
-//		}
-//		return sbuf.toString();
     }
 }

src/test/java/eva2/optimization/individuals/AbstractEAIndividualTest.java

@@ -2,18 +2,43 @@ package eva2.optimization.individuals;
 
 import org.junit.Test;
 
+import static org.junit.Assert.assertFalse;
+import static org.junit.Assert.assertTrue;
+
 /**
  */
 public class AbstractEAIndividualTest {
 
     @Test
     public void testIsDominatingFitness() throws Exception {
+        // Single objective
+        double[] fit1 = new double[]{405.231322};
+        double[] fit2 = new double[]{123.128371293};
 
+        assertTrue(AbstractEAIndividual.isDominatingFitness(fit2, fit1));
+        assertFalse(AbstractEAIndividual.isDominatingFitness(fit1, fit2));
+
+        // Multi objective
+        fit1 = new double[]{12.0, 10.0, 8.0};
+        fit2 = new double[]{11.0, 10.0, 9.0};
+
+        assertFalse(AbstractEAIndividual.isDominatingFitness(fit1, fit2));
+        assertFalse(AbstractEAIndividual.isDominatingFitness(fit2, fit1));
+
+        fit2 = new double[]{11.0, 9.9, 0.9};
+        assertTrue(AbstractEAIndividual.isDominatingFitness(fit2, fit1));
+
+        // Is dominating itself
+        assertTrue(AbstractEAIndividual.isDominatingFitness(fit1, fit1));
     }
 
     @Test
     public void testIsDominatingFitnessNotEqual() throws Exception {
+        // Single objective
+        double[] fit1 = new double[]{415.231322};
 
+        // Is not dominating itself
+        assertFalse(AbstractEAIndividual.isDominatingFitnessNotEqual(fit1, fit1));
     }
 
     @Test

src/test/java/eva2/optimization/individuals/ESIndividualPermutationDataTest.java

@@ -0,0 +1,30 @@
+package eva2.optimization.individuals;
+
+import org.junit.Test;
+
+import static org.junit.Assert.*;
+
+public class ESIndividualPermutationDataTest {
+
+    @Test
+    public void testMapXToY() throws Exception {
+        double[][] matrix = new double[][]{
+                {1.2, 2.3, 3.4, 1.2},
+                {0.2, 0.3, 0.4}
+        };
+
+        // Properly constructs vector from matrix
+        assertArrayEquals(
+                new double[]{1.2, 2.3, 3.4, 1.2, 0.2, 0.3, 0.4},
+                ESIndividualPermutationData.mapMatrixToVector(matrix), 0.0
+        );
+
+        // Properly converts to vector and back to matrix
+        assertArrayEquals(
+                matrix,
+                ESIndividualPermutationData.mapVectorToMatrix(
+                        ESIndividualPermutationData.mapMatrixToVector(matrix), new int[]{4,3}
+                )
+        );
+    }
+}

src/test/java/eva2/optimization/operator/distancemetric/PhenotypeMetricTest.java

@@ -0,0 +1,79 @@
+package eva2.optimization.operator.distancemetric;
+
+import eva2.optimization.individuals.*;
+import org.junit.Before;
+import org.junit.Test;
+
+import static org.junit.Assert.*;
+import static org.mockito.Mockito.mock;
+import static org.mockito.Mockito.when;
+
+public class PhenotypeMetricTest {
+    private PhenotypeMetric metric;
+
+    @Before
+    public void setUp() throws Exception {
+        metric = new PhenotypeMetric();
+    }
+
+    @Test
+    public void testDistance() throws Exception {
+        // DOUBLE
+        ESIndividualDoubleData dindy1, dindy2;
+        dindy1 = mock(ESIndividualDoubleData.class);
+        when(dindy1.getDoubleData()).thenReturn(new double[]{1.0, 1.0, 1.0, 1.0, 1.0});
+        when(dindy1.getDoubleRange()).thenReturn(new double[][]{
+                {0.0, 10.0},{0.0, 10.0},{0.0, 10.0},{0.0, 10.0},{0.0, 10.0}
+        });
+        dindy2 = mock(ESIndividualDoubleData.class);
+        when(dindy2.getDoubleData()).thenReturn(new double[]{2.0, 2.0, 2.0, 2.0, 2.0});
+        when(dindy2.getDoubleRange()).thenReturn(new double[][]{
+                {0.0, 10.0},{0.0, 10.0},{0.0, 10.0},{0.0, 10.0},{0.0, 10.0}
+        });
+
+        // Should be zero for distance to itself
+        assertEquals(0.0, metric.distance(dindy1, dindy1), 0.0);
+
+        assertEquals(0.2236, metric.distance(dindy1, dindy2), 1E-4);
+
+        // INTEGER
+        ESIndividualIntegerData iindy1, iindy2;
+        iindy1 = mock(ESIndividualIntegerData.class);
+        when(iindy1.getIntegerData()).thenReturn(new int[]{1, 1, 1, 1, 1});
+        when(iindy1.getIntRange()).thenReturn(new int[][]{
+                {0, 10},{0, 10},{0, 10},{0, 10},{0, 10}
+        });
+        iindy2 = mock(ESIndividualIntegerData.class);
+        when(iindy2.getIntegerData()).thenReturn(new int[]{3, 3, 3, 3, 3});
+        when(iindy2.getIntRange()).thenReturn(new int[][]{
+                {0, 10},{0, 10},{0, 10},{0, 10},{0, 10}
+        });
+
+        // Should be zero for distance to itself
+        assertEquals(0.0, metric.distance(iindy1, iindy1), 0.0);
+
+        assertEquals(0.4472, metric.distance(iindy1, iindy2), 1E-4);
+
+        // PERMUTATION
+        ESIndividualPermutationData pindy1, pindy2;
+        pindy1 = mock(ESIndividualPermutationData.class);
+        when(pindy1.getPermutationData()).thenReturn(new int[][]{
+                {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {0, 1, 2}, {}
+        });
+        pindy2 = mock(ESIndividualPermutationData.class);
+        when(pindy2.getPermutationData()).thenReturn(new int[][]{
+                {0, 1, 3}, {0, 1, 1}, {0, 0, 2}, {1, 1, 3}, {}, {1, 2, 3}
+        });
+
+        // Should be zero for distance to itself
+        assertEquals(0.0, metric.distance(pindy1, pindy1), 0.0);
+
+        assertEquals(2.35, metric.distance(pindy1, pindy2), 1E-4);
+
+    }
+
+    @Test
+    public void testNorm() throws Exception {
+
+    }
+}

src/test/java/eva2/optimization/operator/terminators/CombinedTerminatorTest.java

@@ -0,0 +1,94 @@
+package eva2.optimization.operator.terminators;
+
+import eva2.optimization.population.PopulationInterface;
+import eva2.optimization.population.SolutionSet;
+import eva2.problems.InterfaceOptimizationProblem;
+import org.junit.Before;
+import org.junit.Test;
+
+import static org.junit.Assert.*;
+import static org.mockito.Mockito.*;
+
+public class CombinedTerminatorTest {
+    private CombinedTerminator terminator;
+    private InterfaceTerminator term1;
+    private InterfaceTerminator term2;
+    private InterfaceOptimizationProblem prob;
+
+    @Before
+    public void setUp() throws Exception {
+        terminator = new CombinedTerminator();
+        term1 = mock(InterfaceTerminator.class);
+        term2 = mock(InterfaceTerminator.class);
+        prob = mock(InterfaceOptimizationProblem.class);
+
+        terminator.setTerminatorOne(term1);
+        terminator.setTerminatorTwo(term2);
+    }
+
+    @Test
+    public void testInitialize() throws Exception {
+        terminator.initialize(prob);
+
+        verify(term1).initialize(prob);
+        verify(term2).initialize(prob);
+    }
+
+    @Test
+    public void testIsTerminatedPop() throws Exception {
+        PopulationInterface pop = mock(PopulationInterface.class);
+
+        terminator.isTerminated(pop);
+        verify(term1).isTerminated(pop);
+        verify(term2).isTerminated(pop);
+
+        reset(term1, term2);
+
+        terminator.setLogicalOperator(CombinedTerminator.LogicalOperator.OR);
+        // Should still call both terminators
+        terminator.isTerminated(pop);
+        verify(term1).isTerminated(pop);
+        verify(term2).isTerminated(pop);
+    }
+
+    @Test
+    public void testIsTerminatedSolSet() throws Exception {
+        SolutionSet solutionSet = mock(SolutionSet.class);
+
+        terminator.isTerminated(solutionSet);
+        verify(term1).isTerminated(solutionSet);
+        verify(term2).isTerminated(solutionSet);
+
+        reset(term1, term2);
+
+        terminator.setLogicalOperator(CombinedTerminator.LogicalOperator.OR);
+        // Should still call both terminators
+        terminator.isTerminated(solutionSet);
+        verify(term1).isTerminated(solutionSet);
+        verify(term2).isTerminated(solutionSet);
+    }
+
+    @Test
+    public void testLastTerminationMessage() throws Exception {
+
+    }
+
+    @Test
+    public void testGetLogicalOperator() throws Exception {
+        // Defaults to an AND
+        assertEquals(CombinedTerminator.LogicalOperator.AND, terminator.getLogicalOperator());
+
+        terminator.setLogicalOperator(CombinedTerminator.LogicalOperator.OR);
+        assertEquals(CombinedTerminator.LogicalOperator.OR, terminator.getLogicalOperator());
+    }
+
+    @Test
+    public void testGetTerminatorOne() throws Exception {
+        assertEquals(term1, terminator.getTerminatorOne());
+    }
+
+    @Test
+    public void testGetTerminatorTwo() throws Exception {
+        assertEquals(term2, terminator.getTerminatorTwo());
+    }
+}

src/test/java/eva2/optimization/operator/terminators/FitnessValueTerminatorTest.java

@@ -0,0 +1,46 @@
+package eva2.optimization.operator.terminators;
+
+import eva2.optimization.population.Population;
+import eva2.optimization.population.SolutionSet;
+import org.junit.Before;
+import org.junit.Test;
+
+import static org.junit.Assert.*;
+import static org.mockito.Mockito.mock;
+import static org.mockito.Mockito.when;
+
+public class FitnessValueTerminatorTest {
+    private FitnessValueTerminator terminator;
+
+    @Before
+    public void setUp() throws Exception {
+        terminator = new FitnessValueTerminator();
+    }
+
+    @Test
+    public void testIsTerminated() throws Exception {
+        Population pop = mock(Population.class);
+        when(pop.getBestFitness()).thenReturn(new double[]{10E-3});
+
+        assertFalse(terminator.isTerminated(pop));
+
+
+        when(pop.getBestFitness()).thenReturn(new double[]{10E-5});
+        assertTrue(terminator.isTerminated(pop));
+
+        SolutionSet solSet = mock(SolutionSet.class);
+        when(solSet.getCurrentPopulation()).thenReturn(pop);
+
+        assertTrue(terminator.isTerminated(pop));
+    }
+
+    @Test
+    public void testSetFitnessValue() throws Exception {
+        assertArrayEquals(new double[]{10E-4}, terminator.getFitnessValue(), 0.0);
+
+        double[] newFitness = {10E-4, 10E-4, 10E-4};
+        terminator.setFitnessValue(newFitness);
+        assertEquals(3, terminator.getFitnessValue().length);
+        assertArrayEquals(newFitness, terminator.getFitnessValue(), 0.0);
+    }
+}

src/test/java/eva2/optimization/operator/terminators/GenerationTerminatorTest.java

@@ -0,0 +1,58 @@
+package eva2.optimization.operator.terminators;
+
+import eva2.optimization.population.Population;
+import eva2.optimization.population.SolutionSet;
+import org.junit.Before;
+import org.junit.Test;
+
+import static org.junit.Assert.*;
+
+public class GenerationTerminatorTest {
+    GenerationTerminator terminator;
+
+    @Before
+    public void setUp() throws Exception {
+        terminator = new GenerationTerminator();
+    }
+
+    @Test
+    public void testIsTerminated() throws Exception {
+        Population pop = new Population();
+        pop.setGeneration(100);
+        assertFalse(terminator.isTerminated(pop));
+        pop.incrGeneration();
+        assertTrue(terminator.isTerminated(pop));
+
+        pop.setGeneration(100);
+        SolutionSet sset = new SolutionSet(pop);
+        assertFalse(terminator.isTerminated(sset));
+        pop.incrGeneration();
+        assertTrue(terminator.isTerminated(sset));
+    }
+
+    @Test
+    public void testLastTerminationMessage() throws Exception {
+        assertNotNull(terminator.toString());
+    }
+
+    @Test
+    public void testToString() throws Exception {
+        assertEquals("Generations calls=100", terminator.toString());
+    }
+
+    @Test
+    public void testSetGenerations() throws Exception {
+        // Defaults to 100 generations
+        assertEquals(100, terminator.getGenerations());
+
+        terminator.setGenerations(42);
+
+        assertEquals(42, terminator.getGenerations());
+    }
+
+    @Test
+    public void testGetGenerations() throws Exception {
+        // Defaults to 100 generations
+        assertEquals(100, terminator.getGenerations());
+    }
+}

src/test/java/eva2/optimization/operator/terminators/MaximumTimeTerminatorTest.java

@@ -0,0 +1,52 @@
+package eva2.optimization.operator.terminators;
+
+import eva2.optimization.population.Population;
+import org.junit.Before;
+import org.junit.Test;
+
+import static org.junit.Assert.*;
+
+public class MaximumTimeTerminatorTest {
+    MaximumTimeTerminator terminator;
+
+    @Before
+    public void setUp() throws Exception {
+        terminator = new MaximumTimeTerminator();
+        terminator.setMaximumTime(1);
+    }
+
+    @Test(timeout = 2500)
+    public void testIsTerminated() throws Exception {
+        terminator.initialize(null);
+
+        while(!terminator.isTerminated(new Population()));
+    }
+
+    @Test
+    public void testLastTerminationMessage() throws Exception {
+        assertNotNull(terminator.lastTerminationMessage());
+    }
+
+    @Test
+    public void testInitialize() throws Exception {
+        terminator.initialize(null);
+        // Should not be terminated after reinitialization
+        assertFalse(terminator.isTerminated(new Population()));
+    }
+
+    @Test
+    public void testToString() throws Exception {
+        assertNotNull(terminator.toString());
+    }
+
+    @Test
+    public void testGetMaximumTime() throws Exception {
+        assertEquals(1, terminator.getMaximumTime());
+    }
+
+    @Test
+    public void testSetMaximumTime() throws Exception {
+        terminator.setMaximumTime(10);
+        assertEquals(10, terminator.getMaximumTime());
+    }
+}

src/test/java/eva2/optimization/population/PopulationTest.java

@@ -1,9 +1,14 @@
 package eva2.optimization.population;
 
+import com.sun.org.apache.bcel.internal.generic.POP;
+import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.optimization.individuals.ESIndividualDoubleData;
 import org.junit.Before;
 import org.junit.Test;
 import static org.junit.Assert.*;
+import static org.mockito.Mockito.mock;
+import static org.mockito.Mockito.verify;
+import static org.mockito.Mockito.when;
 
 public class PopulationTest {
     Population emptyPopulation;
@@ -18,11 +23,6 @@ public class PopulationTest {
 
     }
 
-    @Test
-    public void testPutData() throws Exception {
-
-    }
-
     @Test
     public void testGetData() throws Exception {
         // Simple data
@@ -182,8 +182,13 @@ public class PopulationTest {
     @Test
     public void testIncrGeneration() throws Exception {
         int currentGeneration = emptyPopulation.getGeneration();
+        InterfacePopulationChangedEventListener listener = mock(InterfacePopulationChangedEventListener.class);
+
+        emptyPopulation.addPopulationChangedEventListener(listener);
         emptyPopulation.incrGeneration();
 
+        verify(listener).registerPopulationStateChanged(emptyPopulation, Population.NEXT_GENERATION_PERFORMED);
+
         assertEquals(currentGeneration + 1, emptyPopulation.getGeneration());
     }
 
@@ -204,11 +209,6 @@ public class PopulationTest {
 
     }
 
-    @Test
-    public void testAddPopulation1() throws Exception {
-
-    }
-
     @Test
     public void testResetFitness() throws Exception {
 
@@ -219,11 +219,6 @@ public class PopulationTest {
 
     }
 
-    @Test
-    public void testGetDominatingSet1() throws Exception {
-
-    }
-
     @Test
     public void testGetIndexOfBestIndividualPrefFeasible() throws Exception {
 
@@ -234,16 +229,6 @@ public class PopulationTest {
 
     }
 
-    @Test
-    public void testGetIndexOfBestIndividualPrefFeasible1() throws Exception {
-
-    }
-
-    @Test
-    public void testGetIndexOfWorstIndividualNoConstr1() throws Exception {
-
-    }
-
     @Test
     public void testMoveNInds() throws Exception {
 
@@ -319,11 +304,6 @@ public class PopulationTest {
 
     }
 
-    @Test
-    public void testGetCorrelations1() throws Exception {
-
-    }
-
     @Test
     public void testGetFitnessMeasures() throws Exception {
 
@@ -331,7 +311,17 @@ public class PopulationTest {
 
     @Test
     public void testGetCenter() throws Exception {
+        assertNull(emptyPopulation.getCenter());
 
+        for(int i = 0; i < 10; i++) {
+            ESIndividualDoubleData indy = mock(ESIndividualDoubleData.class);
+            when(indy.getDGenotype()).thenReturn(new double[]{i, i, i, i, i});
+            emptyPopulation.add(indy);
+        }
+
+        assertArrayEquals(new double[]{
+                4.5, 4.5, 4.5, 4.5, 4.5
+        }, emptyPopulation.getCenter(), 0.0);
     }
 
     @Test
@@ -351,7 +341,15 @@ public class PopulationTest {
 
     @Test
     public void testGetFitSum() throws Exception {
+        for(int i = 0; i < 10; i++) {
+            ESIndividualDoubleData indy = mock(ESIndividualDoubleData.class);
+            when(indy.getFitness(0)).thenReturn((double)i);
+            when(indy.getFitness(1)).thenReturn((double)i*2);
+            emptyPopulation.add(indy);
+        }
 
+        assertEquals(45.0, emptyPopulation.getFitSum(0), 0.0);
+        assertEquals(90.0, emptyPopulation.getFitSum(1), 0.0);
     }
 
     @Test
@@ -371,7 +369,12 @@ public class PopulationTest {
 
     @Test
     public void testGetFreeSlots() throws Exception {
+        assertEquals(10, emptyPopulation.getFreeSlots());
 
+        ESIndividualDoubleData indy = mock(ESIndividualDoubleData.class);
+        emptyPopulation.add(indy);
+
+        assertEquals(9, emptyPopulation.getFreeSlots());
     }
 
     @Test
@@ -384,6 +387,19 @@ public class PopulationTest {
 
     }
 
+    @Test
+    public void testPutDataAllIndies() throws Exception {
+        ESIndividualDoubleData indy1 = mock(ESIndividualDoubleData.class);
+        ESIndividualDoubleData indy2 = mock(ESIndividualDoubleData.class);
+
+        emptyPopulation.add(indy1);
+        emptyPopulation.add(indy2);
+        emptyPopulation.putDataAllIndies("test", 12);
+
+        verify(indy1).putData("test", 12);
+        verify(indy2).putData("test", 12);
+    }
+
     @Test
     public void testFilterByFitness() throws Exception {
         ESIndividualDoubleData indy1, indy2, indy3;
@@ -401,4 +417,29 @@ public class PopulationTest {
         // Get all individuals with fitness <= 10.0 (first fitness dimension)
         assertEquals(2, emptyPopulation.filterByFitness(10.0, 0).size());
     }
+
+    @Test
+    public void testGetBestEAIndividual() throws Exception {
+
+    }
+
+    @Test
+    public void testGetBestNIndividuals() throws Exception {
+
+    }
+
+    @Test
+    public void testGetWorstNIndividuals() throws Exception {
+
+    }
+
+    @Test
+    public void testGetWorstEAIndividual() throws Exception {
+
+    }
+
+    @Test
+    public void testRemoveNIndividuals() throws Exception {
+
+    }
 }