Refactored "SetFitness" function to "setFitness". Coding Standards for the win.

src/eva2/server/go/IndividualInterface.java

@@ -36,7 +36,7 @@ public interface IndividualInterface {
 	 * 
 	 * @param fit new fitness of the individual
 	 */
-	void SetFitness (double[] fit);
+	void setFitness (double[] fit);
 
 	/**
 	 * Check whether the instance is dominating the given other individual and return

src/eva2/server/go/individuals/AbstractEAIndividual.java

@@ -618,7 +618,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
      * @param fitness The new fitness array
      * @deprecated
      */
-    public void SetFitness(double[] fitness) {
+    public void setFitness(double[] fitness) {
         this.m_Fitness = fitness;
     }
 

src/eva2/server/go/operators/archiving/ArchivingMaxiMin.java

@@ -62,7 +62,7 @@ public class ArchivingMaxiMin implements InterfaceArchiving, java.io.Serializabl
         // now unconvert from SO to MO
         for (int i = 0; i < archive.size(); i++) {
             tmpD = (double[])((AbstractEAIndividual)archive.get(i)).getData("MOFitness");
-            ((AbstractEAIndividual)archive.get(i)).SetFitness(tmpD);
+            ((AbstractEAIndividual)archive.get(i)).setFitness(tmpD);
         }
 
         pop.SetArchive(archive);

src/eva2/server/go/operators/cluster/ClusteringKMeans.java

@@ -280,7 +280,7 @@ public class ClusteringKMeans implements InterfaceClustering, java.io.Serializab
      */
     private double distance(AbstractEAIndividual indy, double[] p) {
         if (m_UseSearchSpace) ((InterfaceDataTypeDouble)tmpIndy).SetDoubleGenotype(p);
-        else tmpIndy.SetFitness(p);
+        else tmpIndy.setFitness(p);
         
         return metric.distance(indy, tmpIndy);
     }

src/eva2/server/go/operators/moso/MOSODynamicallyWeightedFitness.java

@@ -59,7 +59,7 @@ public class MOSODynamicallyWeightedFitness implements InterfaceMOSOConverter, j
 
         for (int i = 0; (i <2) && (i < tmpFit.length); i++)
             resultFit[0] += tmpFit[i]*weights[i];
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     /** This method allows the problem to set the current output size of

src/eva2/server/go/operators/moso/MOSOEpsilonConstraint.java

@@ -59,7 +59,7 @@ public class MOSOEpsilonConstraint implements InterfaceMOSOConverter, java.io.Se
                 indy.addConstraintViolation(Math.max(0, tmpFit[i] - this.m_EpsilonConstraint.m_TargetValue[i]));
             }
         }
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     /** This method allows the problem to set the current output size of

src/eva2/server/go/operators/moso/MOSOEpsilonThreshold.java

@@ -78,7 +78,7 @@ public class MOSOEpsilonThreshold implements InterfaceMOSOConverter, java.io.Ser
             if (new Double(tmpFit[i]).isNaN()) System.out.println("-Fitness is NaN");
             if (new Double(tmpFit[i]).isInfinite()) System.out.println("-Fitness is Infinite");
         }
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     /** This method allows the problem to set the current output size of

src/eva2/server/go/operators/moso/MOSOGoalProgramming.java

@@ -54,7 +54,7 @@ public class MOSOGoalProgramming implements InterfaceMOSOConverter, java.io.Seri
         resultFit[0] = 0;
         for (int i = 0; (i < this.m_Goals.getNumRows()) && (i < tmpFit.length) ; i++)
             resultFit[0] += tmpFit[i]-this.m_Goals.getValue(i, 0);
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     /** This method allows the problem to set the current output size of

src/eva2/server/go/operators/moso/MOSOLpMetric.java

@@ -68,7 +68,7 @@ public class MOSOLpMetric implements InterfaceMOSOConverter, java.io.Serializabl
             }
         }
 
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     /** This method allows the problem to set the current output size of

src/eva2/server/go/operators/moso/MOSOMOGARankBased.java

@@ -57,7 +57,7 @@ public class MOSOMOGARankBased implements InterfaceMOSOConverter, java.io.Serial
         tmpFit = indy.getFitness();
         indy.putData("MOFitness", tmpFit);
         resultFit[0] = ((Integer)indy.getData("MOGARank")).doubleValue();
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     /** This method allows the problem to set the current output size of

src/eva2/server/go/operators/moso/MOSOMaxiMin.java

@@ -65,7 +65,7 @@ public class MOSOMaxiMin implements InterfaceMOSOConverter, java.io.Serializable
             tmpIndy.putData("MOFitness", tmpFit);
             resultFit   = new double[1];
             resultFit[0] = result[i];
-            tmpIndy.SetFitness(resultFit);
+            tmpIndy.setFitness(resultFit);
         }
         ////////////////////////////////////////////////////////////////////////////////////
 //        if (false) {
@@ -106,7 +106,7 @@ public class MOSOMaxiMin implements InterfaceMOSOConverter, java.io.Serializable
         indy.putData("MOFitness", tmpFit);
         System.err.println("The MaxiMin MOSO can not be applied to single individuals! I default to random criterion.");
         resultFit[0]    = tmpFit[RNG.randomInt(0, tmpFit.length)];
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     /** This method allows the problem to set the current output size of

src/eva2/server/go/operators/moso/MOSORandomChoice.java

@@ -47,7 +47,7 @@ public class MOSORandomChoice  implements InterfaceMOSOConverter, java.io.Serial
         tmpFit          = indy.getFitness();
         indy.putData("MOFitness", tmpFit);
         resultFit[0]    = tmpFit[RNG.randomInt(0, tmpFit.length-1)];
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     /** This method allows the problem to set the current output size of

src/eva2/server/go/operators/moso/MOSORandomWeight.java

@@ -57,7 +57,7 @@ public class MOSORandomWeight implements InterfaceMOSOConverter, java.io.Seriali
         }
         for (int i = 0; (i < tmpWeight.length) && (i < tmpFit.length) ; i++)
             resultFit[0] += tmpFit[i]*tmpWeight[i];
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     /** This method allows the problem to set the current output size of

src/eva2/server/go/operators/moso/MOSORankbased.java

@@ -46,7 +46,7 @@ public class MOSORankbased implements InterfaceMOSOConverter, java.io.Serializab
         tmpFit = indy.getFitness();
         indy.putData("MOFitness", tmpFit);
         resultFit[0] = ((Integer)indy.getData("ParetoLevel")).doubleValue();
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     /** This method allows the problem to set the current output size of

src/eva2/server/go/operators/moso/MOSOUtilityFunction.java

@@ -59,7 +59,7 @@ public class MOSOUtilityFunction implements InterfaceMOSOConverter, java.io.Seri
         /**********************************************************************************************
          * and don't forget to set the reduced fitness to the individual
          */
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     /** This method allows the problem to set the current output size of

src/eva2/server/go/operators/moso/MOSOWeightedFitness.java

@@ -61,7 +61,7 @@ public class MOSOWeightedFitness implements InterfaceMOSOConverter, java.io.Seri
         indy.putData("MOFitness", tmpFit);
         for (int i = 0; (i < this.m_Weights.getNumRows()) && (i < tmpFit.length) ; i++)
             resultFit[0] += tmpFit[i]*this.m_Weights.getValue(i,0);
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     private void checkingWeights() {

src/eva2/server/go/operators/moso/MOSOWeightedLPTchebycheff.java

@@ -65,7 +65,7 @@ public class MOSOWeightedLPTchebycheff implements InterfaceMOSOConverter, java.i
             }
         }
         if (this.m_WLPT.m_P > 0) resultFit[0] = Math.pow(resultFit[0], 1/((double)this.m_WLPT.m_P));
-        indy.SetFitness(resultFit);
+        indy.setFitness(resultFit);
     }
 
     /** This method allows the problem to set the current output size of

src/eva2/server/go/operators/paretofrontmetrics/MetricS.java

@@ -115,7 +115,7 @@ public class MetricS implements InterfaceParetoFrontMetric, java.io.Serializable
                 redF = new double[tmpF.length -1];
                 for (int j = 0; j < redF.length; j++) redF[j] = tmpF[j];
                 tmpIndy = new ESIndividualDoubleData();
-                tmpIndy.SetFitness(redF);
+                tmpIndy.setFitness(redF);
                 smPop.add(i, tmpIndy);
             }
         }

src/eva2/server/go/operators/paretofrontmetrics/MetricSWithReference.java

@@ -109,7 +109,7 @@ public class MetricSWithReference implements InterfaceParetoFrontMetric, java.io
             tmpPop.clear();
             for (int i = 0; i < this.m_Reference.length; i++) {
                 tmpIndy = new ESIndividualDoubleData();
-                tmpIndy.SetFitness(this.m_Reference[i]);
+                tmpIndy.setFitness(this.m_Reference[i]);
                 tmpPop.addIndividual(tmpIndy);
             }
             this.m_ReferenceSMetric = this.calculateSMetric(tmpPop, this.m_ObjectiveSpaceRange, this.m_ObjectiveSpaceRange.length);
@@ -149,7 +149,7 @@ public class MetricSWithReference implements InterfaceParetoFrontMetric, java.io
                 redF = new double[tmpF.length -1];
                 for (int j = 0; j < redF.length; j++) redF[j] = tmpF[j];
                 tmpIndy = new ESIndividualDoubleData();
-                tmpIndy.SetFitness(redF);
+                tmpIndy.setFitness(redF);
                 smPop.add(i, tmpIndy);
             }
         }

src/eva2/server/go/operators/selection/SelectMOMAIIDominanceCounter.java

@@ -77,11 +77,11 @@ public class SelectMOMAIIDominanceCounter implements InterfaceSelection, java.io
                 malta.m_SizeDominantSolutions = domCount;
                 double[] fitness = new double[1];
                 fitness[0] = 1/((double)(domCount+1));
-                tmpIndy1.SetFitness(fitness);
+                tmpIndy1.setFitness(fitness);
             } else {
                 double[] fitness = new double[1];
                 fitness[0] = 2;
-                tmpIndy1.SetFitness(fitness);
+                tmpIndy1.setFitness(fitness);
             }
         }
     }

src/eva2/server/go/operators/selection/SelectMOMaxiMin.java

@@ -61,7 +61,7 @@ public class SelectMOMaxiMin implements InterfaceSelection, java.io.Serializable
         // now unconvert from SO to MO
         for (int i = 0; i < result.size(); i++) {
             tmpD = (double[])((AbstractEAIndividual)result.get(i)).getData("MOFitness");
-            ((AbstractEAIndividual)result.get(i)).SetFitness(tmpD);
+            ((AbstractEAIndividual)result.get(i)).setFitness(tmpD);
         }
         return result;
     }

src/eva2/server/go/operators/selection/SelectMOSPEAII.java

@@ -64,7 +64,7 @@ public class SelectMOSPEAII implements InterfaceSelection, java.io.Serializable
             orgFit[i]   = ((AbstractEAIndividual)population.get(i)).getFitness();
             newFit      = new double[1];
             newFit[0]   = this.m_SPEAFitness[i];
-            ((AbstractEAIndividual)population.get(i)).SetFitness(newFit);
+            ((AbstractEAIndividual)population.get(i)).setFitness(newFit);
         }
 
         // then select
@@ -72,7 +72,7 @@ public class SelectMOSPEAII implements InterfaceSelection, java.io.Serializable
 
         // finally replace the fitness with the original
         for (int i = 0; i < population.size(); i++) {
-            ((AbstractEAIndividual)population.get(i)).SetFitness(orgFit[i]);
+            ((AbstractEAIndividual)population.get(i)).setFitness(orgFit[i]);
         }
 
         if (false) {

src/eva2/server/go/populations/Population.java

@@ -680,7 +680,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         AbstractEAIndividual indy;
         for (int i = 0; i < size(); i++) {
             indy = getEAIndividual(i);
-            indy.SetFitness(f.clone());
+            indy.setFitness(f.clone());
         }
     }
 
@@ -882,7 +882,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     public void resetFitness(IndividualInterface indy) {
         double[] tmpFit = indy.getFitness();
         java.util.Arrays.fill(tmpFit, Double.MAX_VALUE);
-        indy.SetFitness(tmpFit);
+        indy.setFitness(tmpFit);
     }
 
     /**
@@ -2150,7 +2150,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         AbstractEAIndividual indy = (AbstractEAIndividual) getEAIndividual(0).clone();
         double[] center = getCenter();
         AbstractEAIndividual.setDoublePosition(indy, center);
-        indy.SetFitness(null);
+        indy.setFitness(null);
         return indy;
     }
 

src/eva2/server/go/problems/AbstractDynTransProblem.java

@@ -48,7 +48,7 @@ public abstract class AbstractDynTransProblem extends AbstractSynchronousOptimiz
 		AbstractEAIndividual tussy = (AbstractEAIndividual)individual.clone();
 		transform(tussy, time);
         getProblem().evaluate(tussy);
-        individual.SetFitness(tussy.getFitness());       
+        individual.setFitness(tussy.getFitness());       
 	}
 
 	/**

src/eva2/server/go/problems/AbstractMultiModalProblemKnown.java

@@ -180,7 +180,7 @@ implements Interface2DBorderProblem, InterfaceMultimodalProblemKnown {
 		InterfaceDataTypeDouble tmpIndy;
 		tmpIndy = (InterfaceDataTypeDouble)((AbstractEAIndividual)this.m_Template).clone();
 		tmpIndy.SetDoubleGenotype(point);
-		((AbstractEAIndividual)tmpIndy).SetFitness(evalUnnormalized(point));
+		((AbstractEAIndividual)tmpIndy).setFitness(evalUnnormalized(point));
 		if (((AbstractEAIndividual)tmpIndy).getFitness(0)>=m_GlobalOpt) {
 			m_GlobalOpt = ((AbstractEAIndividual)tmpIndy).getFitness(0);
 			if (makeGlobalOptUnreachable) {

src/eva2/server/go/problems/AbstractProblemBinary.java

@@ -41,7 +41,7 @@ public abstract class AbstractProblemBinary extends AbstractOptimizationProblem
         // evaluate the fitness
         result = eval(tmpBitSet);
         // set the fitness
-        individual.SetFitness(result);
+        individual.setFitness(result);
 	}
 	
 	/**

src/eva2/server/go/problems/AbstractProblemDouble.java

@@ -204,7 +204,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 	 */
 	protected void setEvalFitness(AbstractEAIndividual individual, double[] x,
 			double[] fit) {
-		individual.SetFitness(fit);
+		individual.setFitness(fit);
 	}
 
 	/**
@@ -441,7 +441,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 												// required
 			tmpIndy.SetDoubleGenotype(pos);
 		}
-		((AbstractEAIndividual) tmpIndy).SetFitness(prob.eval(pos));
+		((AbstractEAIndividual) tmpIndy).setFitness(prob.eval(pos));
 		if (!Mathematics.isInRange(pos, prob.makeRange())) {
 			System.err.println("Warning, add optimum which is out of range!");
 		}
@@ -462,7 +462,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 		tmpIndy = (InterfaceDataTypeDouble) prob.getIndividualTemplate()
 				.clone();
 		tmpIndy.SetDoubleGenotype(pos);
-		((AbstractEAIndividual) tmpIndy).SetFitness(prob.eval(pos));
+		((AbstractEAIndividual) tmpIndy).setFitness(prob.eval(pos));
 		pop.add(tmpIndy);
 		FitnessConvergenceTerminator convTerm = new FitnessConvergenceTerminator(
 				1e-25, 10, StagnationTypeEnum.generationBased,

src/eva2/server/go/problems/ExternalRuntimeProblem.java

@@ -180,7 +180,7 @@ implements Interface2DBorderProblem, InterfaceProblemDouble, InterfaceHasInitRan
         x = getXVector(individual);
 
         double[] fit = eval(x);
-        individual.SetFitness(fit);
+        individual.setFitness(fit);
 		        
 //        if (this.m_UseTestConstraint) {
 //            if (x[0] < 1) individual.addConstraintViolation(1-x[0]);

src/eva2/server/go/problems/FLensProblem.java

@@ -56,7 +56,7 @@ class MyLensViewer extends JPanel implements InterfaceSolutionViewer {
 //    	this.m_BestFitness = Double.POSITIVE_INFINITY;
 //    	this.m_BestVariables = new double[10];
     	ESIndividualDoubleData dummy = new ESIndividualDoubleData();
-    	dummy.SetFitness(new double[]{Double.POSITIVE_INFINITY});
+    	dummy.setFitness(new double[]{Double.POSITIVE_INFINITY});
     	indiesToPaint = new Population();
     }
     

src/eva2/server/go/problems/MatlabProblem.java

@@ -329,7 +329,7 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
 				for (int i=0; i<seedData.length; i++) {
 					AbstractEAIndividual indy = (AbstractEAIndividual)m_Template.clone();
 					setIndyGenotype(indy, seedData[i]);
-					indy.SetFitness(seedDataFit[i]);
+					indy.setFitness(seedDataFit[i]);
 					seedPopulation.add(indy);
 				}
 			}
@@ -620,7 +620,7 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
 		double[] res = handler.requestEval(this, AbstractEAIndividual.getIndyData(indy));
 		log("evaluated to " + BeanInspector.toString(res) + "\n");
 		log("Free mem is " + Runtime.getRuntime().freeMemory() + ", time is " + System.currentTimeMillis() + "\n");
-		indy.SetFitness(res);
+		indy.setFitness(res);
 	}
 
 	@Override

src/eva2/server/go/problems/SimpleProblemWrapper.java

@@ -60,7 +60,7 @@ public class SimpleProblemWrapper extends AbstractOptimizationProblem {
 	        // if indicated, add Gaussian noise
 	        if (m_Noise != 0) RNG.addNoise(fitness, m_Noise); 
 	        // set the fitness 
-	        individual.SetFitness(fitness);
+	        individual.setFitness(fitness);
 		} else if (simProb instanceof SimpleProblemBinary) {
 	        BitSet          tmpBitSet;
 	        double[]        result;
@@ -69,7 +69,7 @@ public class SimpleProblemWrapper extends AbstractOptimizationProblem {
 	        // evaluate the fitness
 	        result = ((SimpleProblemBinary)simProb).eval(tmpBitSet);
 	        // set the fitness
-	        individual.SetFitness(result);
+	        individual.setFitness(result);
 		} else {
 			System.err.println("Error in SimpleProblemWrapper: " + simProb.getClass().getName() + " is unknown type!");
 		}

src/eva2/server/go/strategies/DifferentialEvolution.java

@@ -405,7 +405,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
         indy.resetConstraintViolation();
         double[] fit = new double[1];
         fit[0] = 0;
-        indy.SetFitness(fit);
+        indy.setFitness(fit);
         if (parents != null) indy.setParents(parents);
         return indy;
     }

src/eva2/server/go/strategies/MemeticAlgorithm.java

@@ -172,7 +172,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 					try {
 						AbstractEAIndividual newindy = (AbstractEAIndividual) antilamarckismcache
 						    .get(indy);
-						indy.SetFitness(newindy.getFitness());
+						indy.setFitness(newindy.getFitness());
 					} catch (Exception ex) {
 						System.err.println("individual not found in antilamarckismcache");
 					}

src/eva2/server/go/strategies/ParticleSwarmOptimization.java

@@ -2228,7 +2228,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
                 throw new RuntimeException("Mismatching best fitness!! " + personalBestfit[0] + " vs. " + ((InterfaceProblemDouble) m_Problem).eval(personalBestPos)[0]);
             }
             ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(personalBestPos);
-            indy.SetFitness(personalBestfit);
+            indy.setFitness(personalBestfit);
             bests.add((AbstractEAIndividual) indy.clone());
         }
         return bests;

src/eva2/server/go/strategies/Tribes.java

@@ -253,7 +253,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
 		AbstractEAIndividual bestExp = population.getBestEAIndividual();
 		if (bestMemPos.firstIsBetter(bestMemPos.getFitness(), bestExp.getFitness())) {
 			AbstractEAIndividual indy = (AbstractEAIndividual)bestExp.clone();
-			indy.SetFitness(bestMemPos.getFitness());
+			indy.setFitness(bestMemPos.getFitness());
 			((InterfaceDataTypeDouble)indy).SetDoubleGenotype(bestMemPos.getPos());
 			return indy;
 		} else return bestExp;
@@ -688,7 +688,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
     	TribesExplorer indy = tmp.clone();
     	indy.clearPosVel();
     	indy.SetDoubleGenotype(pos.getPos());
-    	indy.SetFitness(pos.getFitness());
+    	indy.setFitness(pos.getFitness());
     	return indy;
     }
 	

src/eva2/server/go/strategies/tribes/TribesExplorer.java

@@ -94,9 +94,9 @@ public class TribesExplorer extends AbstractEAIndividual implements InterfaceDat
      * Be aware that for a TribesExplorer, an objective value might be taken into account
      * by reducing the fitness (in the first dimension).
      */
-    public void SetFitness(double[] fitness) {
+    public void setFitness(double[] fitness) {
     	position.fitness = fitness;
-    	super.SetFitness(fitness);
+    	super.setFitness(fitness);
     	fitness[0] -= objectiveValueFirstDim;
     	position.setTotalError();
     }

src/eva2/server/go/tools/DeNovofilter.java

@@ -63,7 +63,7 @@ public class DeNovofilter {
                     tmpD[i-2] = new Double(tmpS[i]).doubleValue();
                 }
                 indy = new ESIndividualDoubleData();
-                indy.SetFitness(tmpD);
+                indy.setFitness(tmpD);
                 pop.add(indy);
             }
             reader.close();

src/eva2/server/go/tools/ImpactOfDimensionOnMOEAs.java

@@ -124,7 +124,7 @@ public class ImpactOfDimensionOnMOEAs {
                 if (j < x.length) fitness[j] =1/((double)fitness[j]) + x[j];
                 else  fitness[j] =1/((double)fitness[j]) + x[j%objectives] + x[(j+1)%objectives];
             }
-            ((AbstractEAIndividual)pop.get(i)).SetFitness(fitness);
+            ((AbstractEAIndividual)pop.get(i)).setFitness(fitness);
         }
     }