renaming doEvaluation got complicated...

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

@@ -35,19 +35,28 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 		}
 	}
 	
+	protected double[] getEvalArray(AbstractEAIndividual individual){
+		double[] x = new double[((InterfaceDataTypeDouble) individual).getDoubleData().length];
+        System.arraycopy(((InterfaceDataTypeDouble) individual).getDoubleData(), 0, x, 0, x.length);
+        return x;
+	}
+	
 	@Override
 	public void evaluate(AbstractEAIndividual individual) {
         double[]        x;
         double[]        fitness;
 
-        x = new double[((InterfaceDataTypeDouble) individual).getDoubleData().length];
+        x = getEvalArray(individual);
-        System.arraycopy(((InterfaceDataTypeDouble) individual).getDoubleData(), 0, x, 0, x.length);
         // evaluate the vector
         fitness = this.eval(x);
         // if indicated, add Gaussian noise
         if (m_Noise != 0) RandomNumberGenerator.addNoise(fitness, m_Noise); 
-        // set the fitness 
+        // set the fitness
-        individual.SetFitness(fitness);
+        setEvalFitness(individual, x, fitness);
+	}
+	
+	protected void setEvalFitness(AbstractEAIndividual individual, double[] x, double[] fit) {
+		individual.SetFitness(fit);
 	}
 	
 	/**
@@ -63,9 +72,8 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 	public void initPopulation(Population population) {
         AbstractEAIndividual tmpIndy;
         population.clear();
-        ((InterfaceDataTypeDouble)this.m_Template).setDoubleDataLength(this.getProblemDimension());
+        initTemplate();
-        ((InterfaceDataTypeDouble)this.m_Template).SetDoubleRange(makeRange());
+        
-
         for (int i = 0; i < population.getPopulationSize(); i++) {
             tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone();
             tmpIndy.init(this);

src/javaeva/server/go/problems/F10Problem.java

@@ -47,7 +47,7 @@ public class F10Problem extends F1Problem implements java.io.Serializable {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
         double[] result = new double[1];
         double c1 = this.calculateC(1);
         result[0]     = 0.;

src/javaeva/server/go/problems/F11Problem.java

@@ -48,7 +48,7 @@ public class F11Problem extends F1Problem implements java.io.Serializable {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
         double[] result = new double[1];
         double tmpProd = 1;
         result[0]     = 0;

src/javaeva/server/go/problems/F12Problem.java

@@ -70,7 +70,7 @@ public class F12Problem extends F1Problem implements java.io.Serializable {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
         double[] result = new double[1];
         double tmp = -5;
         result[0]     = 0;

src/javaeva/server/go/problems/F13Problem.java

@@ -48,7 +48,7 @@ public class F13Problem extends F1Problem {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
         double[] result = new double[1];
         result[0] = 0;
         

src/javaeva/server/go/problems/F14Problem.java

@@ -43,7 +43,7 @@ public class F14Problem extends F1Problem implements java.io.Serializable {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
         double[] result = new double[1];
         double x0 = x[0]-2;
         double x1 = x[1]-2;

src/javaeva/server/go/problems/F1Problem.java

@@ -1,11 +1,9 @@
 package javaeva.server.go.problems;
 
 import javaeva.server.go.individuals.AbstractEAIndividual;
-import javaeva.server.go.individuals.ESIndividualDoubleData;
 import javaeva.server.go.individuals.InterfaceDataTypeDouble;
 import javaeva.server.go.populations.Population;
 import javaeva.server.go.strategies.InterfaceOptimizer;
-import javaeva.server.go.tools.RandomNumberGenerator;
 
 /**
  * Created by IntelliJ IDEA.
@@ -27,9 +25,7 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde
     protected boolean                   m_UseTestConstraint = false;
 
     public F1Problem() {
-        this.m_Template         = new ESIndividualDoubleData();
+    	super();
-        ((ESIndividualDoubleData)this.m_Template).setDoubleDataLength(m_ProblemDimension);
-        ((ESIndividualDoubleData)this.m_Template).SetDoubleRange(makeRange());
     }
     
     public F1Problem(F1Problem b) {
@@ -64,62 +60,26 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde
      * @param population    The populations that is to be inited
      */
     public void initPopulation(Population population) {
-        AbstractEAIndividual tmpIndy;
+        super.initPopulation(population);
 
-        this.m_OverallBest = null;
-
-        population.clear();
-
-        ((InterfaceDataTypeDouble)this.m_Template).setDoubleDataLength(this.m_ProblemDimension);
-        ((InterfaceDataTypeDouble)this.m_Template).SetDoubleRange(makeRange());
-
-        for (int i = 0; i < population.getPopulationSize(); i++) {
-            tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone();
-            tmpIndy.init(this);
-            population.add(tmpIndy);
-        }
-        // population init must be last
-        // it set's fitcalls and generation to zero
-        population.init();
-    }
-    
-    protected double[][] getDoubleRange() {
-    	return ((InterfaceDataTypeDouble)this.m_Template).getDoubleRange();                             
     }
 
-    /** This method evaluate a single individual and sets the fitness values
+	protected double[] getEvalArray(AbstractEAIndividual individual){
-     * @param individual    The individual that is to be evalutated
+		double[] x = super.getEvalArray(individual);
-     */
-    public void evaluate(AbstractEAIndividual individual) {
-        double[]        x;
-        double[]        fitness;
-
-        // retrieve the individual data
-        x = new double[((InterfaceDataTypeDouble) individual).getDoubleData().length];
-        System.arraycopy(((InterfaceDataTypeDouble) individual).getDoubleData(), 0, x, 0, x.length);
-
         // add an offset in solution space
         for (int i = 0; i < x.length; i++) x[i] = x[i] - this.m_XOffSet;
-        
+        return x;
-        // evaluate the vector
+	}
-        fitness = this.eval(x);
+     
-        
+	protected void setEvalFitness(AbstractEAIndividual individual, double[] x, double[] fit) {
-        // add noise to the fitness
+		super.setEvalFitness(individual, x, fit);
-        if (m_Noise != 0) RandomNumberGenerator.addNoise(fitness, m_Noise);
-        
-        // add an offset in fitness space
-        for (int i = 0; i < fitness.length; i++) fitness[i] += this.m_YOffSet;
-        
-        // finally set the fitness
-        individual.SetFitness(fitness);
-        
         if (this.m_UseTestConstraint) {
             if (x[0] < 1) individual.addConstraintViolation(1-x[0]);
         }
         if ((this.m_OverallBest == null) || (this.m_OverallBest.getFitness(0) > individual.getFitness(0))) {
             this.m_OverallBest = (AbstractEAIndividual)individual.clone();
-        }
+        }	
-    }
+	}
 
     /** Ths method allows you to evaluate a simple bit string to determine the fitness
      * @param x     The n-dimensional input vector
@@ -228,6 +188,6 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde
 	}
 	
 	public double[][] get2DBorder() {
-		return getDoubleRange();
+    	return ((InterfaceDataTypeDouble)this.m_Template).getDoubleRange();                             
 	}
 }

src/javaeva/server/go/problems/F2Problem.java

@@ -41,7 +41,7 @@ public class F2Problem extends F1Problem implements java.io.Serializable {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
         double[] result = new double[1];
         result[0]     = 0;
         for (int i = 0; i < x.length-1; i++) {

src/javaeva/server/go/problems/F3Problem.java

@@ -40,7 +40,7 @@ public class F3Problem extends F1Problem implements java.io.Serializable {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
         double[] result = new double[1];
         result[0]     = 6*x.length;
         for (int i = 0; i < x.length-1; i++) {

src/javaeva/server/go/problems/F4Problem.java

@@ -71,7 +71,7 @@ public class F4Problem extends F1Problem implements java.io.Serializable {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
         double[] result = new double[1];
         result[0]     = 0;
         for (int i = 0; i < x.length-1; i++) {

src/javaeva/server/go/problems/F5Problem.java

@@ -71,7 +71,7 @@ public class F5Problem extends F1Problem implements java.io.Serializable {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
         double[]    result = new double[1];
         double      tmp;
         result[0]     = 0;

src/javaeva/server/go/problems/F6Problem.java

@@ -59,7 +59,7 @@ public class F6Problem extends F1Problem implements java.io.Serializable {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
     	
     	if (doRotation) {
 	    	Matrix resVec = rotation.times(new Matrix(x, x.length));

src/javaeva/server/go/problems/F7Problem.java

@@ -83,7 +83,7 @@ public class F7Problem extends F1Problem implements java.io.Serializable {
         System.arraycopy(((InterfaceDataTypeDouble) individual).getDoubleData(), 0, x, 0, x.length);
 
         for (int i = 0; i < x.length; i++) x[i] = x[i] - this.m_XOffSet;
-        fitness = this.doEvaluation(x);
+        fitness = this.eval(x);
         for (int i = 0; i < fitness.length; i++) {
             // add noise to the fitness
             fitness[i] += RandomNumberGenerator.gaussianDouble(this.m_Noise);
@@ -103,7 +103,7 @@ public class F7Problem extends F1Problem implements java.io.Serializable {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
         double[] result = new double[1];
         result[0]     = 0;
         if ((Math.floor(this.m_CurrentTimeStamp / this.m_t)%2) == 0) {

src/javaeva/server/go/problems/F8Problem.java

@@ -75,7 +75,7 @@ public class F8Problem extends F1Problem implements java.io.Serializable {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
         double[]        result = new double[1];
         double          sum1 = 0, sum2 = 0, exp1, exp2;
 

src/javaeva/server/go/problems/F9Problem.java

@@ -40,7 +40,7 @@ public class F9Problem extends F1Problem implements java.io.Serializable {
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
      */
-    public double[] doEvaluation(double[] x) {
+    public double[] eval(double[] x) {
         double[] result = new double[1];
         result[0]     = 0;
         for (int i = 0; i < x.length; i++) {