More refactoring.

src/eva2/optimization/individuals/AbstractEAIndividual.java

@@ -1262,10 +1262,10 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
      */
     public static boolean setDoublePosition(AbstractEAIndividual indy, double[] pos) {
         if (indy instanceof InterfaceESIndividual) {
-            ((InterfaceESIndividual) indy).SetDGenotype(pos);
+            ((InterfaceESIndividual) indy).setDGenotype(pos);
             return true;
         } else if (indy instanceof InterfaceDataTypeDouble) {
-            ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(pos);
+            ((InterfaceDataTypeDouble) indy).setDoubleGenotype(pos);
             return true;
         } else if (indy instanceof InterfaceDataTypeInteger) {
             EVAERROR.errorMsgOnce("Warning, double position truncated to integer! (AbstractEAIndividual.setDoublePosition)");

src/eva2/optimization/individuals/ESIndividualBinaryData.java

@@ -279,7 +279,7 @@ public class ESIndividualBinaryData extends AbstractEAIndividual implements Inte
      * @param b The new genotype of the Individual
      */
     @Override
-    public void SetDGenotype(double[] b) {
+    public void setDGenotype(double[] b) {
         this.m_Genotype = b;
         for (int i = 0; i < this.m_Genotype.length; i++) {
             if (this.m_Genotype[i] < this.m_Range[i][0]) {
@@ -296,7 +296,7 @@ public class ESIndividualBinaryData extends AbstractEAIndividual implements Inte
 //     * ranges.
 //     * @param range     The new range for the double data.
 //     */
-//    public void SetDoubleRange(double[][] range) {
+//    public void setDoubleRange(double[][] range) {
 //        this.m_Range = range;
 //    }
 

src/eva2/optimization/individuals/ESIndividualDoubleData.java

@@ -168,10 +168,10 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
      * @param range The new range for the double data.
      */
     @Override
-    public void SetDoubleRange(double[][] range) {
+    public void setDoubleRange(double[][] range) {
         if (range.length != this.m_Range.length) {
             System.out.println("Warning: Trying to set a range of length " + range.length + " to a vector of length "
-                    + this.m_Range.length + "!\n Use method setDoubleDataLength first! (ESIndividualDoubleData:SetDoubleRange)");
+                    + this.m_Range.length + "!\n Use method setDoubleDataLength first! (ESIndividualDoubleData:setDoubleRange)");
         }
         for (int i = 0; ((i < this.m_Range.length) && (i < range.length)); i++) {
             this.m_Range[i][0] = range[i][0];
@@ -230,7 +230,7 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
      * @param doubleData The new double data.
      */
     @Override
-    public void SetDoublePhenotype(double[] doubleData) {
+    public void setDoublePhenotype(double[] doubleData) {
         this.m_Phenotype = doubleData;
     }
 
@@ -241,9 +241,9 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
      * @param doubleData The new double data.
      */
     @Override
-    public void SetDoubleGenotype(double[] doubleData) {
+    public void setDoubleGenotype(double[] doubleData) {
-//        this.SetDoublePhenotype(doubleData);
+//        this.setDoublePhenotype(doubleData);
-        this.SetDoublePhenotype(null); // tag it as invalid
+        this.setDoublePhenotype(null); // tag it as invalid
         this.m_Genotype = new double[doubleData.length];
         System.arraycopy(doubleData, 0, this.m_Genotype, 0, doubleData.length);
     }
@@ -279,7 +279,7 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
             if (bs.length != this.m_Genotype.length) {
                 System.out.println("Init value and requested length doesn't match!");
             }
-            this.SetDoubleGenotype(bs);
+            this.setDoubleGenotype(bs);
         } else {
             this.defaultInit(opt);
             System.out.println("Initial value for ESIndividualDoubleData is not double[]!");
@@ -336,7 +336,7 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
      * @param b The new genotype of the Individual
      */
     @Override
-    public void SetDGenotype(double[] b) {
+    public void setDGenotype(double[] b) {
         this.m_Genotype = b;
         this.m_Phenotype = null; // mark it as invalid
         for (int i = 0; i < this.m_Genotype.length; i++) {
@@ -354,7 +354,7 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
      *
      * @param b The new genotype of the Individual
      */
-    public void SetDGenotypeNocheck(double[] b) {
+    public void setDGenotypeNocheck(double[] b) {
         this.m_Phenotype = null; // mark it as invalid
         this.m_Genotype = b;
     }
@@ -432,12 +432,4 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
     public static String globalInfo() {
         return "This is an ES individual suited to optimize double values.";
     }
-
-//    public String toString() {
-//    	String str = "Ind " + m_Genotype[0];
-//    	for (int i=1; i<this.m_Genotype.length; i++) str += "/" + m_Genotype[i];
-//    	str += "~" + fitness[0];
-//    	for (int i=1; i<this.fitness.length; i++) str += "/" + fitness[i];
-//    	return str;
-//    }
 }

src/eva2/optimization/individuals/ESIndividualIntegerData.java

@@ -297,7 +297,7 @@ public class ESIndividualIntegerData extends AbstractEAIndividual implements Int
      * @param b The new genotype of the Individual
      */
     @Override
-    public void SetDGenotype(double[] b) {
+    public void setDGenotype(double[] b) {
         this.m_Genotype = b;
         for (int i = 0; i < this.m_Genotype.length; i++) {
             if (this.m_Genotype[i] < this.m_Range[i][0]) {

src/eva2/optimization/individuals/ESIndividualPermutationData.java

@@ -348,7 +348,7 @@ public class ESIndividualPermutationData extends AbstractEAIndividual implements
      * @param b The new genotype of the Individual
      */
     @Override
-    public void SetDGenotype(double[] b) {
+    public void setDGenotype(double[] b) {
         this.m_Genotype = mapVectorToMatrix(b, this.sizePermutation());
         for (int i = 0; i < this.m_Genotype.length; i++) {
             for (int j = 0; j < this.m_Genotype[i].length; j++) {

src/eva2/optimization/individuals/GAESIndividualBinaryDoubleData.java

@@ -246,8 +246,8 @@ public class GAESIndividualBinaryDoubleData extends AbstractEAIndividual impleme
      * @param range The new range for the double data.
      */
     @Override
-    public void SetDoubleRange(double[][] range) {
+    public void setDoubleRange(double[][] range) {
-        this.m_Numbers.SetDoubleRange(range);
+        this.m_Numbers.setDoubleRange(range);
     }
 
     /**
@@ -288,8 +288,8 @@ public class GAESIndividualBinaryDoubleData extends AbstractEAIndividual impleme
      * @see InterfaceDataTypeDouble.SetDoubleData()
      */
     @Override
-    public void SetDoublePhenotype(double[] doubleData) {
+    public void setDoublePhenotype(double[] doubleData) {
-        this.m_Numbers.SetDoublePhenotype(doubleData);
+        this.m_Numbers.setDoublePhenotype(doubleData);
     }
 
     /**
@@ -300,8 +300,8 @@ public class GAESIndividualBinaryDoubleData extends AbstractEAIndividual impleme
      * @see InterfaceDataTypeDouble.SetDoubleDataLamarckian()
      */
     @Override
-    public void SetDoubleGenotype(double[] doubleData) {
+    public void setDoubleGenotype(double[] doubleData) {
-        this.m_Numbers.SetDoubleGenotype(doubleData);
+        this.m_Numbers.setDoubleGenotype(doubleData);
     }
 
     /**

src/eva2/optimization/individuals/GAIndividualDoubleData.java

@@ -167,7 +167,7 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
      * @param range The new range for the double data.
      */
     @Override
-    public void SetDoubleRange(double[][] range) {
+    public void setDoubleRange(double[][] range) {
         if (range.length != this.m_Range.length) {
             System.out.println("Warning: Trying to set a range of length " + range.length + " to a vector of length "
                     + this.m_Range.length + "!\n Use method setDoubleDataLength first!");
@@ -223,7 +223,7 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
      * @param doubleData The new double data.
      */
     @Override
-    public void SetDoublePhenotype(double[] doubleData) {
+    public void setDoublePhenotype(double[] doubleData) {
         this.m_Phenotype = doubleData;
     }
 
@@ -234,8 +234,8 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
      * @param doubleData The new double data.
      */
     @Override
-    public void SetDoubleGenotype(double[] doubleData) {
+    public void setDoubleGenotype(double[] doubleData) {
-        this.SetDoublePhenotype(doubleData);
+        this.setDoublePhenotype(doubleData);
         int[] locus = new int[2];
         for (int i = 0; i < doubleData.length; i++) {
             locus[0] = i * this.m_Precision;
@@ -262,7 +262,7 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
             if (bs.length != this.m_Range.length) {
                 System.out.println("Init value and requested length doesn't match!");
             }
-            this.SetDoubleGenotype(bs);
+            this.setDoubleGenotype(bs);
         } else {
             this.defaultInit(opt);
             System.out.println("Initial value for GAIndividualDoubleData is not double[]!");

src/eva2/optimization/individuals/GAPIndividualProgramData.java

@@ -229,8 +229,8 @@ public class GAPIndividualProgramData extends AbstractEAIndividual implements In
      * @param range The new range for the double data.
      */
     @Override
-    public void SetDoubleRange(double[][] range) {
+    public void setDoubleRange(double[][] range) {
-        this.m_Numbers.SetDoubleRange(range);
+        this.m_Numbers.setDoubleRange(range);
     }
 
     /**
@@ -271,8 +271,8 @@ public class GAPIndividualProgramData extends AbstractEAIndividual implements In
      * @param doubleData The new double data.
      */
     @Override
-    public void SetDoublePhenotype(double[] doubleData) {
+    public void setDoublePhenotype(double[] doubleData) {
-        this.m_Numbers.SetDoublePhenotype(doubleData);
+        this.m_Numbers.setDoublePhenotype(doubleData);
     }
 
     /**
@@ -282,8 +282,8 @@ public class GAPIndividualProgramData extends AbstractEAIndividual implements In
      * @param doubleData The new double data.
      */
     @Override
-    public void SetDoubleGenotype(double[] doubleData) {
+    public void setDoubleGenotype(double[] doubleData) {
-        this.m_Numbers.SetDoubleGenotype(doubleData);
+        this.m_Numbers.setDoubleGenotype(doubleData);
     }
 
 /************************************************************************************

src/eva2/optimization/individuals/InterfaceDataTypeDouble.java

@@ -33,7 +33,7 @@ public interface InterfaceDataTypeDouble {
      *
      * @param range The new range for the double data.
      */
-    public void SetDoubleRange(double[][] range);
+    public void setDoubleRange(double[][] range);
 
     /**
      * This method will return the range for all double attributes.
@@ -63,7 +63,7 @@ public interface InterfaceDataTypeDouble {
      *
      * @param doubleData The new double data.
      */
-    public void SetDoublePhenotype(double[] doubleData);
+    public void setDoublePhenotype(double[] doubleData);
 
     /**
      * This method allows you to set the double data, this can be used for
@@ -71,5 +71,5 @@ public interface InterfaceDataTypeDouble {
      *
      * @param doubleData The new double data.
      */
-    public void SetDoubleGenotype(double[] doubleData);
+    public void setDoubleGenotype(double[] doubleData);
 }

src/eva2/optimization/individuals/InterfaceESIndividual.java

@@ -24,14 +24,14 @@ public interface InterfaceESIndividual {
      *
      * @param b The new genotype of the Individual
      */
-    public void SetDGenotype(double[] b);
+    public void setDGenotype(double[] b);
 //
 //    /** This method will set the range of the double attributes.
 //     * Note: range[d][0] gives the lower bound and range[d] gives the upper bound
 //     * for dimension d.
 //     * @param range     The new range for the double data.
 //     */
-//    public void SetDoubleRange(double[][] range);
+//    public void setDoubleRange(double[][] range);
 
     /**
      * This method will return the range for all double attributes.

src/eva2/optimization/operator/cluster/ClusteringKMeans.java

@@ -316,7 +316,7 @@ public class ClusteringKMeans implements InterfaceClustering, java.io.Serializab
      */
     private double distance(AbstractEAIndividual indy, double[] p) {
         if (m_UseSearchSpace) {
-            ((InterfaceDataTypeDouble) tmpIndy).SetDoubleGenotype(p);
+            ((InterfaceDataTypeDouble) tmpIndy).setDoubleGenotype(p);
         } else {
             tmpIndy.setFitness(p);
         }

src/eva2/optimization/operator/cluster/ClusteringXMeans.java

@@ -356,7 +356,7 @@ public class ClusteringXMeans implements InterfaceClustering, java.io.Serializab
                     x[0] = 10;
                     x[1] = 10;
                 }
-                ((InterfaceDataTypeDouble) pop.get(i)).SetDoubleGenotype(x);
+                ((InterfaceDataTypeDouble) pop.get(i)).setDoubleGenotype(x);
             }
         } else {
             f1.initializePopulation(pop);

src/eva2/optimization/operator/constraint/InterfaceDoubleConstraint.java

@@ -15,7 +15,7 @@ public interface InterfaceDoubleConstraint {
     /**
      * Returns the boolean information whether the constraint is satisfied.
      *
-     * @param indy
+     * @param indyX
      * @return
      */
     public boolean isSatisfied(double[] indyX);
@@ -24,7 +24,6 @@ public interface InterfaceDoubleConstraint {
      * Return the absolute (positive) degree of violation or zero if the constraint
      * is fulfilled.
      *
-     * @param indy  The individual to check.
      * @param indyX possibly the decoded individual position
      * @return true if valid false else.
      */

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

@@ -89,7 +89,7 @@ public class CrossoverESArithmetical implements InterfaceCrossover, java.io.Seri
             }
             // write the result back
             for (int i = 0; i < result.length; i++) {
-                ((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
+                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
             }
         }
         //in case the crossover was successful lets give the mutation operators a chance to mate the strategy parameters

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

@@ -84,7 +84,7 @@ public class CrossoverESBLXAlpha implements InterfaceCrossover, java.io.Serializ
             }
             // write the result back
             for (int i = 0; i < result.length; i++) {
-                ((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
+                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
             }
         }
         //in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters

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

@@ -84,7 +84,7 @@ public class CrossoverESDefault implements InterfaceCrossover, java.io.Serializa
             }
             // write the result back
             for (int i = 0; i < result.length; i++) {
-                ((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
+                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
             }
         }
         //in case the crossover was successful lets give the mutation operators a chance to mate the strategy parameters

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

@@ -88,7 +88,7 @@ public class CrossoverESFlat implements InterfaceCrossover, java.io.Serializable
             }
             // write the result back
             for (int i = 0; i < result.length; i++) {
-                ((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
+                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
             }
         }
         //in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters

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

@@ -75,7 +75,7 @@ public class CrossoverESIntermediate implements InterfaceCrossover, java.io.Seri
             }
             // write the result back
             for (int i = 0; i < result.length; i++) {
-                ((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
+                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
             }
         }
         //in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters

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

@@ -84,7 +84,7 @@ public class CrossoverESNPointDiscrete implements InterfaceCrossover, java.io.Se
 
             // write the result back
             for (int i = 0; i < result.length; i++) {
-                ((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
+                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
             }
         }
         //in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters

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

@@ -99,7 +99,7 @@ public class CrossoverESNPointDiscreteDislocation implements InterfaceCrossover,
 
             // write the result back
             for (int i = 0; i < result.length; i++) {
-                ((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
+                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
             }
         }
         //in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters

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

@@ -118,7 +118,7 @@ public class CrossoverESPCX implements InterfaceCrossover, java.io.Serializable
 
             // write the result back
             for (int i = 0; i < result.length; i++) {
-                ((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
+                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
             }
         }
         //in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters
@@ -238,7 +238,7 @@ public class CrossoverESPCX implements InterfaceCrossover, java.io.Serializable
             range[i][1] = 2;
         }
         indy1.setDoubleDataLength(n);
-        indy1.SetDoubleRange(range);
+        indy1.setDoubleRange(range);
         // init values
         indy2 = (ESIndividualDoubleData) indy1.clone();
         indy3 = (ESIndividualDoubleData) indy1.clone();

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

@@ -86,7 +86,7 @@ public class CrossoverESSBX implements InterfaceCrossover, java.io.Serializable
             }
             // write the result back
             for (int i = 0; i < result.length; i++) {
-                ((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
+                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
             }
         }
         //in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters
@@ -144,7 +144,7 @@ public class CrossoverESSBX implements InterfaceCrossover, java.io.Serializable
         range[1][0] = -2.0;
         range[1][1] = 2.0;
         indy1.setDoubleDataLength(2);
-        indy1.SetDoubleRange(range);
+        indy1.setDoubleRange(range);
         // init values
         indy2 = (ESIndividualDoubleData) indy1.clone();
         indy3 = (ESIndividualDoubleData) indy1.clone();

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

@@ -97,7 +97,7 @@ public class CrossoverESSPX implements InterfaceCrossover, java.io.Serializable
 
             // write the result back
             for (int i = 0; i < result.length; i++) {
-                ((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
+                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
             }
         }
         //in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters
@@ -156,7 +156,7 @@ public class CrossoverESSPX implements InterfaceCrossover, java.io.Serializable
             range[i][1] = 2.0;
         }
         indy1.setDoubleDataLength(n);
-        indy1.SetDoubleRange(range);
+        indy1.setDoubleRange(range);
         // init values
         indy2 = (ESIndividualDoubleData) indy1.clone();
         indy3 = (ESIndividualDoubleData) indy1.clone();

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

@@ -122,7 +122,7 @@ public class CrossoverESUNDX implements InterfaceCrossover, java.io.Serializable
 
             // write the result back
             for (int i = 0; i < result.length; i++) {
-                ((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
+                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
             }
         }
         //in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters
@@ -240,7 +240,7 @@ public class CrossoverESUNDX implements InterfaceCrossover, java.io.Serializable
             range[i][1] = 2.0;
         }
         indy1.setDoubleDataLength(n);
-        indy1.SetDoubleRange(range);
+        indy1.setDoubleRange(range);
         // init values
         indy2 = (ESIndividualDoubleData) indy1.clone();
         indy3 = (ESIndividualDoubleData) indy1.clone();

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

@@ -76,7 +76,7 @@ public class CrossoverESUniformDiscrete implements InterfaceCrossover, java.io.S
 
             // write the result back
             for (int i = 0; i < result.length; i++) {
-                ((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
+                ((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
             }
         }
         //in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters

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

@@ -107,7 +107,7 @@ public class TestESCrossover implements java.io.Serializable {
                 newRange[i][1] = 2;
             }
             tmpIndyD.setDoubleDataLength(m_Dimension);
-            tmpIndyD.SetDoubleRange(newRange);
+            tmpIndyD.setDoubleRange(newRange);
             for (int i = 0; i < m_Partners.getTargetSize(); i++) {
                 tmpIndyEA = (AbstractEAIndividual) ((AbstractEAIndividual) tmpIndyD).clone();
                 tmpIndyEA.init(m_Problem);
@@ -146,7 +146,7 @@ public class TestESCrossover implements java.io.Serializable {
                 newRange[i][1] = 2;
             }
             tmpIndyD.setDoubleDataLength(m_Dimension);
-            tmpIndyD.SetDoubleRange(newRange);
+            tmpIndyD.setDoubleRange(newRange);
 
             double[] tmpD = new double[2];
             tmpD[0] = 1;
@@ -190,7 +190,7 @@ public class TestESCrossover implements java.io.Serializable {
                 newRange[i][1] = 2;
             }
             tmpIndyD.setDoubleDataLength(m_Dimension);
-            tmpIndyD.SetDoubleRange(newRange);
+            tmpIndyD.setDoubleRange(newRange);
 
             double[] tmpD = new double[2];
             tmpD[0] = 0.5;

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

@@ -178,7 +178,7 @@ public class MutateESCorrVector implements InterfaceMutation, java.io.Serializab
             }
 
             // write genotype back 
-            ((InterfaceESIndividual) individual).SetDGenotype(genes);
+            ((InterfaceESIndividual) individual).setDGenotype(genes);
 
         }
 //        if (TRACE) System.out.println("After Mutate:  " + AbstractEAIndividual.getDefaultDataString(individual));

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

@@ -181,7 +181,7 @@ public class MutateESCorrolated implements InterfaceMutation, java.io.Serializab
 //            	if (range[i][1] < x[i]) x[i] = range[i][1];
 //            }
 // 
-//            ((InterfaceESIndividual)individual).SetDGenotype(x);
+//            ((InterfaceESIndividual)individual).setDGenotype(x);
 //            
 //      //turn mutationvector with alphas
 ////      for (int i = 0; i < x.length-1; i++) {
@@ -250,7 +250,7 @@ public class MutateESCorrolated implements InterfaceMutation, java.io.Serializab
                 }
             }
 
-            ((InterfaceESIndividual) individual).SetDGenotype(x);
+            ((InterfaceESIndividual) individual).setDGenotype(x);
         }
     }
 

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

@@ -202,7 +202,7 @@ public class MutateESCovarianceMatrixAdaption implements InterfaceMutation, java
                     x[i] = ranges[i][1];
                 }
             }
-            ((InterfaceESIndividual) individual).SetDGenotype(x);
+            ((InterfaceESIndividual) individual).setDGenotype(x);
         }
         //System.out.println("After Mutate:  " +((GAIndividual)individual).getSolutionRepresentationFor());
     }

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

@@ -88,7 +88,7 @@ public class MutateESFixedStepSize implements InterfaceMutation, java.io.Seriali
                     x[i] = range[i][1];
                 }
             }
-            ((InterfaceESIndividual) individual).SetDGenotype(x);
+            ((InterfaceESIndividual) individual).setDGenotype(x);
 
         }
         //System.out.println("After Mutate:  " +((GAIndividual)individual).getSolutionRepresentationFor());

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

@@ -125,7 +125,7 @@ public class MutateESGlobal implements InterfaceMutation, java.io.Serializable,
                     x[i] = range[i][1];
                 }
             }
-            ((InterfaceESIndividual) individual).SetDGenotype(x);
+            ((InterfaceESIndividual) individual).setDGenotype(x);
 
         }
         //System.out.println("After Mutate:  " +((GAIndividual)individual).getSolutionRepresentationFor());

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

@@ -144,7 +144,7 @@ public class MutateESLocal implements InterfaceMutation, InterfaceAdditionalPopu
                     x[i] = range[i][1];
                 }
             }
-            ((InterfaceESIndividual) individual).SetDGenotype(x);
+            ((InterfaceESIndividual) individual).setDGenotype(x);
         }
         //System.out.println("After Mutate:  " +((GAIndividual)individual).getSolutionRepresentationFor());
     }

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

@@ -157,7 +157,7 @@ public class MutateESMainVectorAdaption implements InterfaceMutation, java.io.Se
             Z1 = RNG.gaussianDouble(1.0);
             evaluateNewObjectX(x, ranges);
 
-            ((InterfaceESIndividual) individual).SetDGenotype(x);
+            ((InterfaceESIndividual) individual).setDGenotype(x);
         }
         //System.out.println("After Mutate:  " +((GAIndividual)individual).getSolutionRepresentationFor());
     }

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

@@ -126,7 +126,7 @@ public class MutateESMutativeStepSizeControl implements InterfaceMutation, java.
                     x[i] = range[i][1];
                 }
             }
-            ((InterfaceESIndividual) individual).SetDGenotype(x);
+            ((InterfaceESIndividual) individual).setDGenotype(x);
             // System.out.println("new step size: " + mutationStepSize);
         }
         //System.out.println("After Mutate:  " +((GAIndividual)individual).getSolutionRepresentationFor());

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

@@ -150,7 +150,7 @@ public class MutateESPathLengthAdaption implements InterfaceMutation, java.io.Se
 
             this.mutateX(x, ranges, true); // this performs new mutation
 
-            ((InterfaceESIndividual) individual).SetDGenotype(x);
+            ((InterfaceESIndividual) individual).setDGenotype(x);
         }
         //System.out.println("After Mutate:  " +((GAIndividual)individual).getSolutionRepresentationFor());
     }

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

@@ -97,7 +97,7 @@ public class MutateESPolynomial implements InterfaceMutation, java.io.Serializab
                     x[i] = range[i][1];
                 }
             }
-            ((InterfaceESIndividual) individual).SetDGenotype(x);
+            ((InterfaceESIndividual) individual).setDGenotype(x);
 
         }
         //System.out.println("After Mutate:  " +((GAIndividual)individual).getSolutionRepresentationFor());

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

@@ -612,7 +612,7 @@ public class MutateESRankMuCMA implements InterfaceAdaptOperatorGenerational, In
             // this is a critical point: where do the CMA parameters for this individual's mutation come from?
             // for GA and ES we can expect that selection occured directly before the mutation cycle,
             // so we take the parameter set from the last adpation step.
-            ((InterfaceDataTypeDouble) individual).SetDoubleGenotype(mutate(lastParams, x, range, 0));
+            ((InterfaceDataTypeDouble) individual).setDoubleGenotype(mutate(lastParams, x, range, 0));
 
 //			if (TRACE) System.out.println("WCMA mutate, aft: " + BeanInspector.toString(x));
         } else {

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

@@ -66,7 +66,7 @@ package eva2.optimization.operator.mutation;
 //                if (range[i][0] > x[i]) x[i] = range[i][0];
 //                if (range[i][1] < x[i]) x[i] = range[i][1];
 //            }
-//            ((InterfaceESIndividual)individual).SetDGenotype(x);
+//            ((InterfaceESIndividual)individual).setDGenotype(x);
 //
 //        }
 //        //System.out.println("After Mutate:  " +((GAIndividual)individual).getSolutionRepresentationFor());

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

@@ -797,9 +797,9 @@ public class PostProcess {
     public static void setDoubleData(AbstractEAIndividual indy, double[] data) {
         if (indy instanceof InterfaceDataTypeDouble || (indy instanceof InterfaceESIndividual)) {
             if (indy instanceof InterfaceESIndividual) {
-                ((InterfaceESIndividual) indy).SetDGenotype(data);
+                ((InterfaceESIndividual) indy).setDGenotype(data);
             } else {
-                ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(data);
+                ((InterfaceDataTypeDouble) indy).setDoubleGenotype(data);
             }
         } else {
             throw new RuntimeException("Error, unable to set double data to individual instance " + indy.getClass() + " in PostProcess.setDoubleData");

src/eva2/optimization/population/Population.java

@@ -515,7 +515,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
             Matrix rlhM = StatisticUtils.rlh(pop.size(), range, true);
             for (int i = 0; i < pop.size(); i++) {
                 AbstractEAIndividual tmpIndy = pop.getEAIndividual(i);
-                ((InterfaceDataTypeDouble) tmpIndy).SetDoubleGenotype(rlhM.getRowShallow(i));
+                ((InterfaceDataTypeDouble) tmpIndy).setDoubleGenotype(rlhM.getRowShallow(i));
             }
         } else {
             System.err.println("Error: data type double required for Population.createUniformSampling");
@@ -2383,11 +2383,11 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      */
     public void updateRange(double[][] range, boolean forceRange) {
         for (int i = 0; i < size(); i++) {
-            ((InterfaceDataTypeDouble) getEAIndividual(i)).SetDoubleRange(range);
+            ((InterfaceDataTypeDouble) getEAIndividual(i)).setDoubleRange(range);
             double[] pos = ((InterfaceDataTypeDouble) getEAIndividual(i)).getDoubleData();
             if (!Mathematics.isInRange(pos, range)) {
                 Mathematics.projectToRange(pos, range);
-                ((InterfaceDataTypeDouble) getEAIndividual(i)).SetDoubleGenotype(pos);
+                ((InterfaceDataTypeDouble) getEAIndividual(i)).setDoubleGenotype(pos);
             }
         }
     }

src/eva2/optimization/problems/AbstractDynTransProblem.java

@@ -103,7 +103,7 @@ public abstract class AbstractDynTransProblem extends AbstractSynchronousOptimiz
             /* individuum moves towords untranslated problem */
             indyData[i] -= getTranslation(i, time);
         }
-        ((InterfaceDataTypeDouble) individual).SetDoubleGenotype(indyData);
+        ((InterfaceDataTypeDouble) individual).setDoubleGenotype(indyData);
     }
 
     /*

src/eva2/optimization/problems/AbstractMultiModalProblemKnown.java

@@ -55,7 +55,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
         population.clear();
 
         ((InterfaceDataTypeDouble) this.template).setDoubleDataLength(this.problemDimension);
-        ((InterfaceDataTypeDouble) this.template).SetDoubleRange(makeRange());
+        ((InterfaceDataTypeDouble) this.template).setDoubleRange(makeRange());
         for (int i = 0; i < population.getTargetSize(); i++) {
             tmpIndy = (AbstractEAIndividual) ((AbstractEAIndividual) this.template).clone();
             tmpIndy.init(this);
@@ -148,7 +148,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
     protected void addOptimum(double[] point) {
         InterfaceDataTypeDouble tmpIndy;
         tmpIndy = (InterfaceDataTypeDouble) ((AbstractEAIndividual) this.template).clone();
-        tmpIndy.SetDoubleGenotype(point);
+        tmpIndy.setDoubleGenotype(point);
         ((AbstractEAIndividual) tmpIndy).setFitness(evalUnnormalized(point));
         if (((AbstractEAIndividual) tmpIndy).getFitness(0) >= globalOptimum) {
             globalOptimum = ((AbstractEAIndividual) tmpIndy).getFitness(0);
@@ -166,7 +166,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
         }
         if (isDoRotation()) {
             point = inverseRotateMaybe(point); // theres an inverse rotation required
-            tmpIndy.SetDoubleGenotype(point);
+            tmpIndy.setDoubleGenotype(point);
         }
         this.listOfOptima.add(tmpIndy);
     }

src/eva2/optimization/problems/AbstractProblemDouble.java

@@ -80,7 +80,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
             ((InterfaceDataTypeDouble) this.template)
                     .setDoubleDataLength(getProblemDimension());
             ((InterfaceDataTypeDouble) this.template)
-                    .SetDoubleRange(makeRange());
+                    .setDoubleRange(makeRange());
         }
     }
 
@@ -136,7 +136,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
         double[] fitness;
 
         x = getEvalArray(individual);
-        ((InterfaceDataTypeDouble) individual).SetDoublePhenotype(x);
+        ((InterfaceDataTypeDouble) individual).setDoublePhenotype(x);
         // evaluate the vector
         fitness = this.eval(x);
         // if indicated, add Gaussian noise
@@ -427,11 +427,11 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
         InterfaceDataTypeDouble tmpIndy;
         tmpIndy = (InterfaceDataTypeDouble) prob.getIndividualTemplate()
                 .clone();
-        tmpIndy.SetDoubleGenotype(pos);
+        tmpIndy.setDoubleGenotype(pos);
         if (prob.isDoRotation()) {
             pos = prob.inverseRotateMaybe(pos); // theres an inverse rotation
             // required
-            tmpIndy.SetDoubleGenotype(pos);
+            tmpIndy.setDoubleGenotype(pos);
         }
         ((AbstractEAIndividual) tmpIndy).setFitness(prob.eval(pos));
         if (!Mathematics.isInRange(pos, prob.makeRange())) {
@@ -453,7 +453,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
         InterfaceDataTypeDouble tmpIndy;
         tmpIndy = (InterfaceDataTypeDouble) prob.getIndividualTemplate()
                 .clone();
-        tmpIndy.SetDoubleGenotype(pos);
+        tmpIndy.setDoubleGenotype(pos);
         ((AbstractEAIndividual) tmpIndy).setFitness(prob.eval(pos));
         pop.add(tmpIndy);
         FitnessConvergenceTerminator convTerm = new FitnessConvergenceTerminator(

src/eva2/optimization/problems/ExternalRuntimeProblem.java

@@ -77,7 +77,7 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem
     public ExternalRuntimeProblem() {
         this.template = new ESIndividualDoubleData();
         ((ESIndividualDoubleData) this.template).setDoubleDataLength(m_ProblemDimension);
-        ((ESIndividualDoubleData) this.template).SetDoubleRange(makeRange());
+        ((ESIndividualDoubleData) this.template).setDoubleRange(makeRange());
     }
 
     public ExternalRuntimeProblem(ExternalRuntimeProblem b) {
@@ -153,7 +153,7 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem
         this.m_OverallBest = null;
 
         ((InterfaceDataTypeDouble) this.template).setDoubleDataLength(this.m_ProblemDimension);
-        ((InterfaceDataTypeDouble) this.template).SetDoubleRange(makeRange());
+        ((InterfaceDataTypeDouble) this.template).setDoubleRange(makeRange());
 
         AbstractOptimizationProblem.defaultInitPopulation(population, template, this);
     }

src/eva2/optimization/problems/F8Problem.java

@@ -315,7 +315,7 @@ public class F8Problem extends AbstractProblemDoubleOffset
 //		Population population = new Population();
 //		InterfaceDataTypeDouble tmpIndy;
 //		tmpIndy = (InterfaceDataTypeDouble)((AbstractEAIndividual)this.template).clone();
-//		tmpIndy.SetDoubleGenotype(pos);
+//		tmpIndy.setDoubleGenotype(pos);
 //		((AbstractEAIndividual)tmpIndy).SetFitness(eval(pos));
 //		population.add(tmpIndy);
 //		FitnessConvergenceTerminator convTerm = new FitnessConvergenceTerminator(1e-15, 10, false, true);

src/eva2/optimization/problems/FLensProblem.java

@@ -329,7 +329,7 @@ public class FLensProblem extends AbstractOptimizationProblem
             range[i][0] = 0.1;
             range[i][1] = 5.0;
         }
-        ((InterfaceDataTypeDouble) this.template).SetDoubleRange(range);
+        ((InterfaceDataTypeDouble) this.template).setDoubleRange(range);
 
         AbstractOptimizationProblem.defaultInitPopulation(population, template, this);
         if (this.m_Show) {

src/eva2/optimization/problems/FM0Problem.java

@@ -17,11 +17,11 @@ public class FM0Problem extends AbstractMultiModalProblemKnown implements Interf
         this.problemDimension = 2;
         this.template = new ESIndividualDoubleData();
 //        this.m_Extrema          = new double[2];
-//        this.m_Range            = new double [this.problemDimension][2];
+//        this.range            = new double [this.problemDimension][2];
-//        this.m_Range[0][0]      = -2.0;
+//        this.range[0][0]      = -2.0;
-//        this.m_Range[0][1]      =  2.0;
+//        this.range[0][1]      =  2.0;
-//        this.m_Range[1][0]      = -2.8;
+//        this.range[1][0]      = -2.8;
-//        this.m_Range[1][1]      =  2.8;
+//        this.range[1][1]      =  2.8;
 //        this.m_Extrema[0]       = -2;
 //        this.m_Extrema[1]       = 6;
     }

src/eva2/optimization/problems/MatlabProblem.java

@@ -142,7 +142,7 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
                 }
                 if (getProblemDimension() > 0) { // avoid evil case setting dim to 0 during object init
                     ((InterfaceDataTypeDouble) this.template).setDoubleDataLength(getProblemDimension());
-                    ((InterfaceDataTypeDouble) this.template).SetDoubleRange(range);
+                    ((InterfaceDataTypeDouble) this.template).setDoubleRange(range);
                 }
                 break;
             case typeBinary:
@@ -330,7 +330,7 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
     private void setIndyGenotype(AbstractEAIndividual indy, double[] ds) {
         switch (dataType) {
             case typeDouble:
-                ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(ds);
+                ((InterfaceDataTypeDouble) indy).setDoubleGenotype(ds);
                 break;
             case typeBinary:
                 ((InterfaceDataTypeBinary) indy).SetBinaryGenotype(toBinary(ds));

src/eva2/optimization/problems/SimpleProblemWrapper.java

@@ -129,7 +129,7 @@ public class SimpleProblemWrapper extends AbstractOptimizationProblem {
         }
         if (template instanceof InterfaceDataTypeDouble) {
             ((InterfaceDataTypeDouble) this.template).setDoubleDataLength(simProb.getProblemDimension());
-            ((InterfaceDataTypeDouble) this.template).SetDoubleRange(makeRange());
+            ((InterfaceDataTypeDouble) this.template).setDoubleRange(makeRange());
         } else if (template instanceof InterfaceDataTypeBinary) {
             ((InterfaceDataTypeBinary) this.template).setBinaryDataLength(simProb.getProblemDimension());
         } else {

src/eva2/optimization/problems/TF1Problem.java

@@ -99,7 +99,7 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem
         double[][] newRange = makeRange();
 
         ((InterfaceDataTypeDouble) this.template).setDoubleDataLength(this.m_ProblemDimension);
-        ((InterfaceDataTypeDouble) this.template).SetDoubleRange(newRange);
+        ((InterfaceDataTypeDouble) this.template).setDoubleRange(newRange);
 
         AbstractOptimizationProblem.defaultInitPopulation(population, template, this);
     }

src/eva2/optimization/strategies/DifferentialEvolution.java

@@ -448,7 +448,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
         if (forceRange) {
             Mathematics.projectToRange(nX, esIndy.getDoubleRange());
         } // why did this never happen before?
-        esIndy.SetDoubleGenotype(nX);
+        esIndy.setDoubleGenotype(nX);
         indy.SetAge(0);
         indy.resetConstraintViolation();
         double[] fit = new double[1];

src/eva2/optimization/strategies/DynamicParticleSwarmOptimization.java

@@ -158,9 +158,9 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
         }
 
         if (indy instanceof InterfaceDataTypeDouble) {
-            ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(newPos);
+            ((InterfaceDataTypeDouble) indy).setDoubleGenotype(newPos);
         } else {
-            endy.SetDoubleGenotype(newPos);
+            endy.setDoubleGenotype(newPos);
         }
 
         resetFitness(indy);

src/eva2/optimization/strategies/GradientDescentAlgorithm.java

@@ -229,7 +229,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
 
                 } // end loop iterations
 
-                ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(params);
+                ((InterfaceDataTypeDouble) indy).setDoubleGenotype(params);
 
             } // end if ((this.problem instanceof InterfaceFirstOrderDerivableProblem) && (indy instanceof InterfaceDataTypeDouble)) {
             else {
@@ -256,7 +256,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
                     if (TRACE) {
                         System.out.println("Gradient Descent: Fitness critical:" + indy.getFitness()[0]);
                     }
-                    ((InterfaceDataTypeDouble) indy).SetDoublePhenotype((double[]) indy.getData(oldParamsKey));
+                    ((InterfaceDataTypeDouble) indy).setDoublePhenotype((double[]) indy.getData(oldParamsKey));
                     double[] changes = (double[]) indy.getData(changesKey);
                     int[] lock = (int[]) indy.getData(lockKey);
 

src/eva2/optimization/strategies/NelderMeadSimplex.java

@@ -163,7 +163,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
             }
         }
 //		AbstractEAIndividual reflectedInd = (AbstractEAIndividual)((AbstractEAIndividual)bestpop.getIndividual(1)).clone(); 
-//		((InterfaceDataTypeDouble)reflectedInd).SetDoubleGenotype(r);
+//		((InterfaceDataTypeDouble)reflectedInd).setDoubleGenotype(r);
 //
 //		problem.evaluate(reflectedInd);
         AbstractEAIndividual reflectedInd = createEvalIndy(bestpop, r);
@@ -199,7 +199,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
             }
 
 //			AbstractEAIndividual c_ind = (AbstractEAIndividual)((AbstractEAIndividual)bestpop.getIndividual(1)).clone(); 
-//			((InterfaceDataTypeDouble)c_ind).SetDoubleGenotype(c);
+//			((InterfaceDataTypeDouble)c_ind).setDoubleGenotype(c);
 //			problem.evaluate(c_ind);
             AbstractEAIndividual c_ind = createEvalIndy(bestpop, c);
             this.m_Population.incrFunctionCalls();
@@ -212,7 +212,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 
     private AbstractEAIndividual createEvalIndy(Population pop, double[] newGenotype) {
         AbstractEAIndividual e_ind = (AbstractEAIndividual) ((AbstractEAIndividual) pop.getIndividual(1)).clone();
-        ((InterfaceDataTypeDouble) e_ind).SetDoubleGenotype(newGenotype);
+        ((InterfaceDataTypeDouble) e_ind).setDoubleGenotype(newGenotype);
         e_ind.resetConstraintViolation();
         m_Problem.evaluate(e_ind);
         if (e_ind.getFitness(0) < 6000) {
@@ -294,7 +294,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
                 if (!Mathematics.isInRange(x, range)) {
                     System.err.println("WARNING: nelder mead step produced indy out of range!");
 //					Mathematics.projectToRange(x, range);
-//					((InterfaceDataTypeDouble)ind).SetDoubleGenotype(x);
+//					((InterfaceDataTypeDouble)ind).setDoubleGenotype(x);
 //					problem.evaluate(ind);
 //					this.m_Population.incrFunctionCalls();
                 }
@@ -308,7 +308,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
                     for (int i = 0; i < c.length; i++) {
                         c[i] = 0.5 * c[i] + 0.5 * u_1[i];
                     }
-                    ((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).SetDoubleGenotype(c);
+                    ((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).setDoubleGenotype(c);
 //					m_Population.getEAIndividual(j).resetConstraintViolation(); // not a good idea because during evaluation, a stats update may be performed which mustnt see indies which are evaluated, but possible constraints have been reset.
                 }
                 m_Problem.evaluate(m_Population);
@@ -470,7 +470,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
             } else {
                 dat[i] = Math.min(dat[i] + curPerturb, range[i][1]);
             }
-            ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(dat);
+            ((InterfaceDataTypeDouble) indy).setDoubleGenotype(dat);
             indy.resetConstraintViolation();
             initialPop.add((AbstractEAIndividual) indy.clone());
         }

src/eva2/optimization/strategies/PDDifferentialEvolution.java

@@ -414,7 +414,7 @@ public class PDDifferentialEvolution implements InterfaceOptimizer, java.io.Seri
         if (forceRange) {
             Mathematics.projectToRange(nX, esIndy.getDoubleRange());
         } // why did this never happen before?
-        esIndy.SetDoubleGenotype(nX);
+        esIndy.setDoubleGenotype(nX);
         indy.SetAge(0);
         indy.resetConstraintViolation();
         double[] fit = new double[1];

src/eva2/optimization/strategies/ParticleSubSwarmOptimization.java

@@ -373,8 +373,8 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
             minValInDim[i] = range[i][0]; // get lower boarder for dimension i
             maxValInDim[i] = range[i][1]; // get upper boarder for dimension i
         }
-//		min.SetDoubleGenotype(minValInDim); // set all dimensions to min
+//		min.setDoubleGenotype(minValInDim); // set all dimensions to min
-//		max.SetDoubleGenotype(maxValInDim); // set all dimensions to max
+//		max.setDoubleGenotype(maxValInDim); // set all dimensions to max
         this.maxPosDist = Mathematics.euclidianDist(minValInDim, maxValInDim);
     }
 

src/eva2/optimization/strategies/ParticleSwarmOptimization.java

@@ -1253,16 +1253,16 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 
         // finally set the new position and the current velocity
         if (indy instanceof InterfaceDataTypeDouble) {
-            ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(newPosition);
+            ((InterfaceDataTypeDouble) indy).setDoubleGenotype(newPosition);
         } else {
-            ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(newPosition); // WARNING, this does a checkBounds in any case!
+            ((InterfaceDataTypeDouble) indy).setDoubleGenotype(newPosition); // WARNING, this does a checkBounds in any case!
             if (!m_CheckRange) {
                 System.err.println("warning, checkbounds will be forced by InterfaceESIndividual!");
             }
         }
 
         indy.putData(partVelKey, curVelocity);
-//		((InterfaceESIndividual) indy).SetDGenotype(newPosition);
+//		((InterfaceESIndividual) indy).setDGenotype(newPosition);
     }
 
     /**
@@ -2228,7 +2228,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
                 System.err.println("partInfo: " + i + " - " + getParticleInfo(population.getEAIndividual(i)));
                 throw new RuntimeException("Mismatching best fitness!! " + personalBestfit[0] + " vs. " + ((InterfaceProblemDouble) m_Problem).eval(personalBestPos)[0]);
             }
-            ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(personalBestPos);
+            ((InterfaceDataTypeDouble) indy).setDoubleGenotype(personalBestPos);
             indy.setFitness(personalBestfit);
             bests.add((AbstractEAIndividual) indy.clone());
         }

src/eva2/optimization/strategies/ScatterSearch.java

@@ -187,7 +187,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
 
     //	public double eval(double[] x) {
 //		AbstractEAIndividual indy = (AbstractEAIndividual)template.clone();
-//		((InterfaceDataTypeDouble)indy).SetDoubleGenotype(x);
+//		((InterfaceDataTypeDouble)indy).setDoubleGenotype(x);
 //		problem.evaluate(indy);
 //		return indy.getFitness(0);
 //	}
@@ -554,7 +554,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
         if (checkRange) {
             Mathematics.projectToRange(combi, range);
         }
-        ((InterfaceDataTypeDouble) resIndy).SetDoubleGenotype(combi);
+        ((InterfaceDataTypeDouble) resIndy).setDoubleGenotype(combi);
         problem.evaluate(resIndy);
         refSet.incrFunctionCalls();
         return resIndy;
@@ -673,7 +673,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
             freq[i][interv]++;
         }
 
-        dblIndy.SetDoubleGenotype(genes);
+        dblIndy.setDoubleGenotype(genes);
         return indy;
     }
 
@@ -717,7 +717,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
         for (int i = 0; i < probDim; i++) {
             genes[i] = randInRangeInterval(i, interval);
         }
-        dblIndy.SetDoubleGenotype(genes);
+        dblIndy.setDoubleGenotype(genes);
         return indy;
     }
 

src/eva2/optimization/strategies/Tribes.java

@@ -253,7 +253,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
         if (bestMemPos.firstIsBetter(bestMemPos.getFitness(), bestExp.getFitness())) {
             AbstractEAIndividual indy = (AbstractEAIndividual) bestExp.clone();
             indy.setFitness(bestMemPos.getFitness());
-            ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(bestMemPos.getPos());
+            ((InterfaceDataTypeDouble) indy).setDoubleGenotype(bestMemPos.getPos());
             return indy;
         } else {
             return bestExp;
@@ -609,7 +609,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
         }
         TribesExplorer indy = tmp.clone();
         indy.clearPosVel();
-        indy.SetDoubleGenotype(pos.getPos());
+        indy.setDoubleGenotype(pos.getPos());
         indy.setFitness(pos.getFitness());
         return indy;
     }

src/eva2/optimization/strategies/tribes/TribesExplorer.java

@@ -41,7 +41,7 @@ public class TribesExplorer extends AbstractEAIndividual implements InterfaceDat
 
     public TribesExplorer(double[][] range, double objFirstDim) {
         init(range.length, Tribes.maxTribeNb);
-        SetDoubleRange(range);
+        setDoubleRange(range);
         objectiveValueFirstDim = objFirstDim;
     }
 
@@ -981,7 +981,7 @@ v[d] = cmin * v[d];
             if (x.length != position.x.length) {
                 System.err.println("Init value and requested length doesn't match!");
             }
-            this.SetDoubleGenotype(x);
+            this.setDoubleGenotype(x);
         } else {
             this.init(opt);
             System.err.println("Initial value for ESIndividualDoubleData is not double[]!");
@@ -1000,17 +1000,17 @@ v[d] = cmin * v[d];
     }
 
     @Override
-    public void SetDoublePhenotype(double[] doubleData) {
+    public void setDoublePhenotype(double[] doubleData) {
         position.setDoubleArray(doubleData);
     }
 
     @Override
-    public void SetDoubleGenotype(double[] doubleData) {
+    public void setDoubleGenotype(double[] doubleData) {
         position.setDoubleArray(doubleData);
     }
 
     @Override
-    public void SetDoubleRange(double[][] range) {
+    public void setDoubleRange(double[][] range) {
         if (position.x.length != range.length) {    // we will need to fully reinit the particle
             initPositions(range.length);
         }
@@ -1048,7 +1048,7 @@ v[d] = cmin * v[d];
         return position.x.length;
     }
 
-//	public void SetDGenotype(double[] b) {
+//	public void setDGenotype(double[] b) {
 //		position.setDoubleArray(b);
 //	}
 //

src/eva2/optimization/strategies/tribes/TribesSwarm.java

@@ -599,7 +599,7 @@ public class TribesSwarm implements java.io.Serializable {
     	 */
 
         TribesExplorer expl = new TribesExplorer(range, masterTribe.getObjectiveFirstDim());
-        expl.SetDoubleRange(range);
+        expl.setDoubleRange(range);
 
 //    	System.out.println("generating expl, option " + option + ", init " + initType + ", from tribe " + fromTribe);