SelProb -> SelectionProbability

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/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;
@@ -2143,7 +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));