Rename SetFitness to setFitnessAt

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

@@ -546,7 +546,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
      * @param index   The index of the fitness value to set.
      * @param fitness The new fitness value.
      */
-    public void SetFitness(int index, double fitness) {
+    public void setFitnessAt(int index, double fitness) {
         if (this.fitness.length > index) {
             this.fitness[index] = fitness;
         } else {

src/main/java/eva2/optimization/operator/constraint/AbstractConstraint.java

@@ -81,7 +81,7 @@ public abstract class AbstractConstraint implements InterfaceDoubleConstraint, S
                 if (v > 0) {
                     indy.setMarkPenalized(true);
                     for (int i = 0; i < indy.getFitness().length; i++) {
-                        indy.SetFitness(i, indy.getFitness(i) + v + penaltyFactor);
+                        indy.setFitnessAt(i, indy.getFitness(i) + v + penaltyFactor);
                     }
                 }
                 break;
@@ -89,7 +89,7 @@ public abstract class AbstractConstraint implements InterfaceDoubleConstraint, S
                 if (v > 0) {
                     indy.setMarkPenalized(true);
                     for (int i = 0; i < indy.getFitness().length; i++) {
-                        indy.SetFitness(i, indy.getFitness(i) * (v + penaltyFactor));
+                        indy.setFitnessAt(i, indy.getFitness(i) * (v + penaltyFactor));
                     }
                 }
             case specificTag:

src/main/java/eva2/optimization/operator/fitnessmodifier/FitnessAdaptiveClustering.java

@@ -57,7 +57,7 @@ public class FitnessAdaptiveClustering implements java.io.Serializable, Interfac
             }
 
             for (int i = 0; i < population.size(); i++) {
-                population.get(i).SetFitness(x, result[i]);
+                population.get(i).setFitnessAt(x, result[i]);
             }
         }
     }

src/main/java/eva2/optimization/operator/fitnessmodifier/FitnessSharing.java

@@ -62,7 +62,7 @@ public class FitnessSharing implements java.io.Serializable, InterfaceFitnessMod
             }
 
             for (int i = 0; i < population.size(); i++) {
-                population.get(i).SetFitness(x, result[i]);
+                population.get(i).setFitnessAt(x, result[i]);
             }
         }
     }

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

@@ -86,7 +86,7 @@ public class SteadyStateGA extends AbstractOptimizer implements java.io.Serializ
         GAIndividualBinaryData tmpIndy;
         for (int i = 0; i < population.size(); i++) {
             tmpIndy = (GAIndividualBinaryData) population.get(i);
-            tmpIndy.SetFitness(0, tmpIndy.defaultEvaulateAsMiniBits());
+            tmpIndy.setFitnessAt(0, tmpIndy.defaultEvaulateAsMiniBits());
             population.incrFunctionCalls();
         }
         population.incrGeneration();

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

@@ -108,8 +108,8 @@ public class TribesExplorer extends AbstractEAIndividual implements InterfaceDat
      * by reducing the fitness (in the first dimension).
      */
     @Override
-    public void SetFitness(int index, double fitness) {
+    public void setFitnessAt(int index, double fitness) {
-        super.SetFitness(index, fitness);
+        super.setFitnessAt(index, fitness);
         if (index > position.fitness.length) {
             double[] newFit = new double[index + 1];
             System.arraycopy(position.fitness, 0, newFit, 0, position.fitness.length);
@@ -447,7 +447,7 @@ public class TribesExplorer extends AbstractEAIndividual implements InterfaceDat
 //                                             pb.fitnessSize, evaluate);
             } else { // Artificial fitness by using penalties
                 for (n = 0; n < position.fitness.length; n++) {
-                    SetFitness(n, swarm.tribes[fromTribe].memory[
+                    setFitnessAt(n, swarm.tribes[fromTribe].memory[
                             contact].
                             getPos().
                             fitness[n] +

src/main/java/eva2/problems/AbstractProblemInteger.java

@@ -62,7 +62,7 @@ public abstract class AbstractProblemInteger extends AbstractOptimizationProblem
         fitness = this.evaluate(x);
         for (int i = 0; i < fitness.length; i++) {
             // set the fitness of the individual
-            individual.SetFitness(i, fitness[i]);
+            individual.setFitnessAt(i, fitness[i]);
         }
         if ((this.bestIndividuum == null) || (this.bestIndividuum.getFitness(0) > individual.getFitness(0))) {
             this.bestIndividuum = (AbstractEAIndividual) individual.clone();

src/main/java/eva2/problems/BKnapsackProblem.java

@@ -254,7 +254,7 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S
             }
         }
         result[0] += 5100;
-        individual.SetFitness(0, result[0]);
+        individual.setFitnessAt(0, result[0]);
     }
 
     /**

src/main/java/eva2/problems/FLensProblem.java

@@ -13,7 +13,6 @@ import eva2.tools.math.RNG;
 
 import javax.swing.*;
 import java.awt.*;
-import java.awt.image.BufferStrategy;
 import java.awt.image.BufferedImage;
 
 class MyLensViewer extends JPanel implements InterfaceSolutionViewer {
@@ -317,7 +316,7 @@ public class FLensProblem extends AbstractOptimizationProblem
             fitness[i] += RNG.gaussianDouble(this.noise);
             fitness[i] += this.yOffset;
             // set the fitness of the individual
-            individual.SetFitness(i, fitness[i]);
+            individual.setFitnessAt(i, fitness[i]);
         }
         if ((this.overallBest == null) || (this.overallBest.getFitness(0) > individual.getFitness(0))) {
             this.overallBest = (AbstractEAIndividual) individual.clone();

src/main/java/eva2/problems/PSymbolicRegression.java

@@ -196,21 +196,21 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
                 AbstractEAIndividual tmpBestConst = (AbstractEAIndividual) ((GAPIndividualProgramData) tmpIndy).getNumbers();
                 AbstractEAIndividual tmpConst;
                 this.evaluate(tmpIndy);
-                tmpBestConst.SetFitness(0, tmpIndy.getFitness(0));
+                tmpBestConst.setFitnessAt(0, tmpIndy.getFitness(0));
                 population.incrFunctionCalls();
                 for (int j = 0; j < 10; j++) {
                     tmpConst = (AbstractEAIndividual) tmpBestConst.clone();
                     tmpConst.mutate();
                     ((GAPIndividualProgramData) tmpIndy).setNumbers((InterfaceDataTypeDouble) tmpConst);
                     this.evaluate(tmpIndy);
-                    tmpConst.SetFitness(0, tmpIndy.getFitness(0));
+                    tmpConst.setFitnessAt(0, tmpIndy.getFitness(0));
                     population.incrFunctionCalls();
                     if (tmpBestConst.getFitness(0) > tmpConst.getFitness(0)) {
                         tmpBestConst = (AbstractEAIndividual) tmpConst.clone();
                     }
                 }
                 ((GAPIndividualProgramData) tmpIndy).setNumbers((InterfaceDataTypeDouble) tmpBestConst);
-                tmpIndy.SetFitness(0, tmpBestConst.getFitness(0));
+                tmpIndy.setFitnessAt(0, tmpBestConst.getFitness(0));
             } else {
                 if (useLocalHillClimbing) {
                     EVAERROR.errorMsgOnce("Error: local hill climbing only works on GAPIndividualProgramData individuals!");
@@ -251,7 +251,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
         // add noise to the fitness
         fitness += RNG.gaussianDouble(this.noise);
         // set the fitness of the individual
-        individual.SetFitness(0, fitness);
+        individual.setFitnessAt(0, fitness);
         if ((this.plot != null) && (this.plot.getFunctionArea().getContainerSize() == 0)) {
             this.overallBestIndividuum = null;
         }

src/main/java/eva2/problems/TF1Problem.java

@@ -132,7 +132,7 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem
             fitness[i] += RNG.gaussianDouble(this.noise);
             fitness[i] += this.yOffset;
             // set the fitness of the individual
-            individual.SetFitness(i, fitness[i]);
+            individual.setFitnessAt(i, fitness[i]);
         }
         if (this.applyConstraints) {
             if (fitness[0] > 0.5) {