Minor fixes, part 2 - some GP adaptions.

src/eva2/EvAInfo.java

@@ -10,7 +10,7 @@ import eva2.tools.BasicResourceLoader;
  * 
  * --- Changelog
  * 2.043: Added proper Population equality predicate.
- * 2.042: Some bugfixes. Removing dependecy on sun.beans.editors, replaced non-free jpeg-codec. There should
+ * 2.042: Some bugfixes. Removing dependency on sun.beans.editors, replaced non-free jpeg-codec. There should
  * 			be no more problems on OpenJDK. Added a simple initialization range, especially for use from Matlab.
  * 			Some restructurings (RNG and Mathematics is now in eva2.tools.math). Some cleanup.
  * 2.040: Several updates: The clustering interface has been changed for easier implementation of adaptive clustering

src/eva2/gui/PropertySheetPanel.java

@@ -367,18 +367,19 @@ public class PropertySheetPanel extends JPanel implements PropertyChangeListener
      * @return
      */
     private PropertyDescriptor[] reorderProperties(Method meth) {
-    	PropertyDescriptor[] oldProps = m_Properties.clone();
-        PropertyDescriptor[] newProps = new PropertyDescriptor[oldProps.length];
 //        Mathematics.revertArray(oldProps, newProps);
        	Object[] args      = { };
        	Object retV=null;
+       	PropertyDescriptor[] newProps = null;
         try {
-        	retV = meth.invoke(m_Target, args);
+        	retV = meth.invoke(m_Target, args); // should return String[] to be interpreted as a list of ordered properties
         } catch(Exception ex) {}
         if (retV!=null) {
             try {
-            	if (retV.getClass().isArray()) {
+            	if (retV.getClass().isArray()) { // reorder the properties
             		String[] swProps=(String[])retV;
+                	PropertyDescriptor[] oldProps = m_Properties.clone();
+                    newProps = new PropertyDescriptor[oldProps.length];
             		//int findFirst=findFirstProp(props[0], oldProps);
             		int firstNonNull=0;
             		for (int i=0; i<oldProps.length; i++) {

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

@@ -27,8 +27,8 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
     protected GPArea[]                    m_Area;
     protected double                      m_InitFullGrowRatio   = 0.5;
     protected int                         m_InitDepth           = 4;
-    protected int                         m_TargetDepth         = 8;
+    protected int                         m_maxDepth         = 8;
-    protected boolean                     m_CheckTargetDepth    = true;
+    protected boolean                     m_CheckMaxDepth    = true;
 
     public GPIndividualProgramData() {
         this.m_Area                 = new GPArea[1];
@@ -58,8 +58,8 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
         }
         this.m_InitFullGrowRatio        = individual.m_InitFullGrowRatio;
         this.m_InitDepth                = individual.m_InitDepth;
-        this.m_TargetDepth              = individual.m_TargetDepth;
+        this.m_maxDepth              = individual.m_maxDepth;
-        this.m_CheckTargetDepth         = individual.m_CheckTargetDepth;
+        this.m_CheckMaxDepth         = individual.m_CheckMaxDepth;
 
         // cloning the members of AbstractEAIndividual
         this.m_Age                      = individual.m_Age;
@@ -90,7 +90,7 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
         if (individual instanceof GPIndividualProgramData) {
             GPIndividualProgramData indy = (GPIndividualProgramData) individual;
             //@todo Eigendlich k<EFBFBD>nnte ich noch die Areas vergleichen
-            if (this.m_TargetDepth != indy.m_TargetDepth)
+            if (this.m_maxDepth != indy.m_maxDepth)
                 return false;
             if ((this.m_Genotype == null) || (indy.m_Genotype == null))
                 return false;
@@ -134,9 +134,9 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
         this.m_Phenotype = new AbstractGPNode[this.m_Genotype.length];
         for (int i = 0; i < this.m_Genotype.length; i++) {
             this.m_Phenotype[i] = (AbstractGPNode)this.m_Genotype[i].clone();
-            if ((this.m_CheckTargetDepth) && (this.m_Phenotype[i].isMaxDepthViolated(this.m_TargetDepth))) {
+            if ((this.m_CheckMaxDepth) && (this.m_Phenotype[i].isMaxDepthViolated(this.m_maxDepth))) {
                 //System.out.println("Trying to meet the Target Depth!");
-                this.m_Phenotype[i].repairMaxDepth(this.m_Area[i], this.m_TargetDepth);
+                this.m_Phenotype[i].repairMaxDepth(this.m_Area[i], this.m_maxDepth);
                 //System.out.println("TragetDepth: " + this.m_TargetDepth + " : " + this.m_Program.getMaxDepth());
             }
         }
@@ -267,7 +267,7 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
                 AbstractGPNode parent = nodeToMutate.getParent();
                 AbstractGPNode newNode = (AbstractGPNode)(((AbstractGPNode)this.m_Area[i].getRandomNode().clone()));
                 newNode.setDepth(nodeToMutate.getDepth());
-                newNode.initGrow(this.m_Area[i], this.m_TargetDepth);
+                newNode.initGrow(this.m_Area[i], this.m_maxDepth);
                 parent.setNode(newNode, nodeToMutate);
             }
         }
@@ -310,14 +310,14 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
     /** This method will toggle between checking for max depth or not.
      * @param b     the Switch.
      */
-    public void setCheckTargetDepth(boolean b) {
+    public void setCheckMaxDepth(boolean b) {
-        this.m_CheckTargetDepth = b;
+        this.m_CheckMaxDepth = b;
     }
-    public boolean getCheckTargetDepth() {
+    public boolean getCheckMaxDepth() {
-        return this.m_CheckTargetDepth;
+        return this.m_CheckMaxDepth;
     }
-    public String checkTargetDepthTipText() {
+    public String checkMaxDepthTipText() {
-        return "If activated the program will be checked for MaxDepth.";
+        return "If activated the maximum depth of the program tree will be enforced.";
     }
 
     /** This method set/get the init Full Grow Ratio.
@@ -339,9 +339,9 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
      * @param b     The new init Depth of the GP Tree.
      */
     public void setInitDepth(int b) {
-        if (b > this.m_TargetDepth) {
+        if (b > this.m_maxDepth) {
             System.out.println("Waring Init Depth will be set to Target Depth!");
-            b = this.m_TargetDepth;
+            b = this.m_maxDepth;
         }
         this.m_InitDepth = b;
     }
@@ -355,13 +355,17 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
     /** This method set/get the target depth.
      * @param b     The new target Depth of the GP Tree.
      */
-    public void setTargetDepth(int b) {
+    public void setMaxDepth(int b) {
-        this.m_TargetDepth = b;
+        this.m_maxDepth = b;
     }
-    public int getTargetDepth() {
+    public int getMaxDepth() {
-        return this.m_TargetDepth;
+        return this.m_maxDepth;
     }
-    public String targetDepthTipText() {
+    public String maxDepthTipText() {
-        return "The target depth of the GP Tree.";
+        return "The maximum depth of the GP tree.";
+    }
+    
+    public String[] customPropertyOrder() {
+    	return new String[] {"initDepth", "checkMaxDepth", "maxDepth"};
     }
 }

src/eva2/server/go/individuals/codings/gp/AbstractGPNode.java

@@ -468,8 +468,8 @@ public abstract class AbstractGPNode implements InterfaceProgram, java.io.Serial
      */
     public void repairMaxDepth(GPArea area, int depth) {
         if (this.m_Depth == depth-1) {
-            // in this case i need to check wether or not my
+            // in this case i need to check whether or not my
-            // followup nodes are terminals
+            // follow-up nodes are terminals
             for (int i = 0; i < this.m_Nodes.length; i++) {
                 if (this.m_Nodes[i].getArity() != 0) {
                     // replace this node with a new node
@@ -490,12 +490,6 @@ public abstract class AbstractGPNode implements InterfaceProgram, java.io.Serial
      * @param obj   The other subtree.
      * @return boolean if equal true else false.
      */
-//    public abstract boolean equals(Object obj);
-    /** This method allows you to determine wehter or not two subtrees
-     * are actually the same.
-     * @param obj   The other subtree.
-     * @return boolean if equal true else false.
-     */
     public boolean equals(Object obj) {
         if (obj.getClass().equals(this.getClass())) {
         	AbstractGPNode node = (AbstractGPNode)obj;
@@ -509,5 +503,4 @@ public abstract class AbstractGPNode implements InterfaceProgram, java.io.Serial
             return false;
         }
     }
-    
 }

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

@@ -1,18 +1,25 @@
 package eva2.server.go.problems;
 
+import java.io.Serializable;
+
 import eva2.gui.Plot;
+import eva2.server.go.PopulationInterface;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.GAPIndividualProgramData;
 import eva2.server.go.individuals.GPIndividualProgramData;
 import eva2.server.go.individuals.InterfaceDataTypeDouble;
 import eva2.server.go.individuals.InterfaceDataTypeProgram;
+import eva2.server.go.individuals.codings.gp.AbstractGPNode;
 import eva2.server.go.individuals.codings.gp.GPArea;
+import eva2.server.go.individuals.codings.gp.GPNodeAbs;
 import eva2.server.go.individuals.codings.gp.GPNodeAdd;
 import eva2.server.go.individuals.codings.gp.GPNodeCos;
 import eva2.server.go.individuals.codings.gp.GPNodeDiv;
 import eva2.server.go.individuals.codings.gp.GPNodeExp;
 import eva2.server.go.individuals.codings.gp.GPNodeInput;
 import eva2.server.go.individuals.codings.gp.GPNodeMult;
+import eva2.server.go.individuals.codings.gp.GPNodeOne;
+import eva2.server.go.individuals.codings.gp.GPNodePi;
 import eva2.server.go.individuals.codings.gp.GPNodePow2;
 import eva2.server.go.individuals.codings.gp.GPNodePow3;
 import eva2.server.go.individuals.codings.gp.GPNodeSin;
@@ -21,9 +28,10 @@ import eva2.server.go.individuals.codings.gp.GPNodeSub;
 import eva2.server.go.individuals.codings.gp.InterfaceProgram;
 import eva2.server.go.populations.Population;
 import eva2.server.go.problems.regression.InterfaceRegressionFunction;
-import eva2.server.go.problems.regression.RFKoza_GPI_10_2;
+import eva2.server.go.problems.regression.RFKoza_GPI_7_3;
 import eva2.server.go.strategies.InterfaceOptimizer;
 import eva2.tools.EVAERROR;
+import eva2.tools.ToolBox;
 import eva2.tools.math.RNG;
 
 /**
@@ -33,14 +41,14 @@ import eva2.tools.math.RNG;
  * Time: 16:44:25
  * To change this template use Options | File Templates.
  */
-public class PSymbolicRegression extends AbstractOptimizationProblem implements InterfaceProgramProblem, java.io.Serializable  {
+public class PSymbolicRegression extends AbstractOptimizationProblem implements InterfaceProgramProblem, InterfaceAdditionalPopulationInformer, Serializable  {
 
     private double[]                                m_X                     = new double[1];
-    private int                                     m_NumberOfConstants     = 10;
+    private int                                     m_NumberOfConstants     = 3;
-    private double                                  m_LowerBound            = 0;
+    private double                                  m_LowerBound            = -1;
-    private double                                  m_UpperBound            = 10;
+	private double                                  m_UpperBound            = 1;
-    private int                                     m_NumberOfCheckPoints   = 10;
+    private int                                     m_NumberOfCheckPoints   = 20;
-    transient private InterfaceRegressionFunction   m_TargetFunction        = new RFKoza_GPI_10_2();
+    transient private InterfaceRegressionFunction   m_TargetFunction        = new RFKoza_GPI_7_3();
     private double[]                                m_C                     = new double[m_NumberOfConstants];
     private boolean                                 m_UseInnerConst         = false;
     private boolean                                 m_UseLocalHillClimbing  = false;
@@ -97,7 +105,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
     /** This method inits the Problem to log multiruns
      */
     public void initProblem() {
-        if (m_TargetFunction == null) m_TargetFunction = new RFKoza_GPI_10_2();
+        if (m_TargetFunction == null) m_TargetFunction = new RFKoza_GPI_7_3();
         this.m_OverallBest  = null;
         this.m_C            = new double[this.m_NumberOfConstants];
         for (int i = 0; i < this.m_C.length; i++) this.m_C[i] = RNG.randomDouble(-10, 10);
@@ -113,18 +121,21 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
     	m_GPArea = new GPArea();
     	
     	if (m_GPArea.isEmpty()) {
+	        this.m_GPArea.add2CompleteList(new GPNodeOne());
+	        this.m_GPArea.add2CompleteList(new GPNodePi(), false);
 	        this.m_GPArea.add2CompleteList(new GPNodeAdd());
 	        this.m_GPArea.add2CompleteList(new GPNodeSub());
 	        this.m_GPArea.add2CompleteList(new GPNodeDiv());
 	        this.m_GPArea.add2CompleteList(new GPNodeMult());
+	        this.m_GPArea.add2CompleteList(new GPNodeAbs(), false);
 	        this.m_GPArea.add2CompleteList(new GPNodeSin(), false);
 	        this.m_GPArea.add2CompleteList(new GPNodeCos(), false);
 	        this.m_GPArea.add2CompleteList(new GPNodeExp(), false);
+	        this.m_GPArea.add2CompleteList(new GPNodeSqrt(), false);
 	        this.m_GPArea.add2CompleteList(new GPNodePow2(), false);
 	        this.m_GPArea.add2CompleteList(new GPNodePow3(), false);
-	        this.m_GPArea.add2CompleteList(new GPNodeSqrt(), false);
 	        for (int i = 0; i < this.m_X.length; i++) this.m_GPArea.add2CompleteList(new GPNodeInput("X"+i));
-	        for (int i = 0; i < this.m_C.length; i++) this.m_GPArea.add2CompleteList(new GPNodeInput("C"+i));
+	        for (int i = 0; i < this.m_C.length; i++) this.m_GPArea.add2CompleteList(new GPNodeInput("C"+i), false);
     	}
     	if ((oldArea!=null) && (oldArea.getBlackList()!=null) && (oldArea.getBlackList().size()==m_GPArea.getBlackList().size())) {
     		m_GPArea.SetBlackList(oldArea.getBlackList());
@@ -200,7 +211,9 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
     }
 
     /** 
-     * This method evaluates a single individual and sets the fitness values
+     * This method evaluates a single individual and sets the fitness values. In this case,
+     * the averaged square error between represented function and target function per check point is calculated.
+     * 
      * @param individual    The individual that is to be evaluated
      */
     public void evaluate(AbstractEAIndividual individual) {
@@ -214,9 +227,8 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
             this.m_C = ((GAPIndividualProgramData)tmpIndy).getDoubleData();
         fitness     = 0;
 
-        for (int j = 0; j < this.m_NumberOfCheckPoints; j++)
+        for (int j = 0; j < this.m_NumberOfCheckPoints; j++) {
-        	for (int i = 0; i < this.m_X.length; i++) {
+    		setCheckPoint(m_X, j);
-                this.m_X[i] = this.m_LowerBound +(j*(this.m_UpperBound-this.m_LowerBound)/this.m_NumberOfCheckPoints);
             tmpValue = ((Double)program.evaluate(this)).doubleValue();
             fitness += Math.pow((this.m_TargetFunction.evaluateFunction(this.m_X) - ((Double)program.evaluate(this)).doubleValue()), 2);
         }
@@ -233,8 +245,8 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
             	if (m_Plot==null) this.initEnvironmentPanel();
                 this.m_Plot.clearAll();
                 program     = ((InterfaceDataTypeProgram)this.m_OverallBest).getProgramData()[0];
-                for (double i = 0; i < this.m_NumberOfCheckPoints; i++) {
+                for (int i = 0; i < this.m_NumberOfCheckPoints; i++) {
-                    for (int j = 0; j < this.m_X.length; j++) this.m_X[j] = i ;
+                    setCheckPoint(m_X, i);
                     tmpValue = ((Double)program.evaluate(this)).doubleValue();
                     this.m_Plot.setConnectedPoint(this.m_X[0], tmpValue, 0);
                     tmpValue = this.m_TargetFunction.evaluateFunction(this.m_X);
@@ -245,6 +257,17 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
         }
     }
 
+    /**
+     * Select a test point - TODO this btw only makes much sense in 1D.
+     * @param x
+     * @param j
+     */
+	private void setCheckPoint(double[] x, int j) {
+    	for (int i = 0; i < x.length; i++) {
+            x[i] = this.m_LowerBound +(j*(this.m_UpperBound-this.m_LowerBound)/(this.m_NumberOfCheckPoints-1));
+    	}
+	}
+
 	/** This method returns a string describing the optimization problem.
      * @param opt       The Optimizer that is used or had been used.
      * @return The description.
@@ -458,4 +481,122 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
     public InterfaceDataTypeProgram getGPIndividual() {
         return (InterfaceDataTypeProgram)this.m_Template;
     }
+    public String GPIndividualTipText() {
+    	return "Modify the properties of the template GP individual such as maximum tree depth etc.";
+    }
+    
+    public double getLowerBound() {
+		return m_LowerBound;
+	}
+	public void setLowerBound(double mLowerBound) {
+		m_LowerBound = mLowerBound;
+	}
+	public String lowerBoundTipText() {
+		return "The lower bound of the 1D double interval where the target function is sampled.";
+	}
+
+	public double getUpperBound() {
+		return m_UpperBound;
+	}
+	public void setUpperBound(double mUpperBound) {
+		m_UpperBound = mUpperBound;
+	}
+	public String upperBoundTipText() {
+		return "The upper bound of the 1D double interval where the target function is sampled.";
+	}
+
+	public String[] customPropertyOrder() {
+		return new String[] {"lowerBound", "upperBound"};
+	}
+
+	@Override
+	public String[] getAdditionalFileStringHeader(PopulationInterface pop) {
+		String[] superHd = super.getAdditionalFileStringHeader(pop);
+		return ToolBox.appendArrays(new String[]{"bestIndySize","avgIndySize","avgIndyDepth"}, superHd);
+	}
+
+	@Override
+	public Object[] getAdditionalFileStringValue(PopulationInterface pop) {
+		Object[] superDat = super.getAdditionalFileStringValue(pop);
+		return ToolBox.appendArrays(new Object[]{getBestIndySize(pop), getAvgIndySize(pop), getAvgIndyDepth(pop)}, superDat);
+	}
+
+	/**
+	 * Get the average tree depth of the given population (for program individuals).
+	 * 
+	 * @see #getIndyDepth(AbstractEAIndividual)
+	 * @param pop
+	 * @return
+	 */
+	public static double getAvgIndyDepth(PopulationInterface pop) {
+		Population p = (Population)pop;
+		double sum=0;
+		for (int i=0; i<p.size(); i++) {
+			sum+=getIndyDepth(p.getEAIndividual(i));
+		}
+		return sum/p.size();
+	}
+
+	/**
+	 * Return the average number of nodes of program individuals.
+	 * 
+	 * @see #getIndySize(AbstractEAIndividual)
+	 * @param pop
+	 * @return
+	 */
+	public static double getAvgIndySize(PopulationInterface pop) {
+		Population p = (Population)pop;
+		double sum=0;
+		for (int i=0; i<p.size(); i++) {
+			sum+=getIndySize(p.getEAIndividual(i));
+		}
+		return sum/p.size();
+	}
+
+	/**
+	 * Return the number of nodes of the best current individual if it represents a program,
+	 * otherwise zero.
+	 * 
+	 * @param pop
+	 * @return
+	 */
+	public static int getBestIndySize(PopulationInterface pop) {
+		Population p = (Population)pop;
+		AbstractEAIndividual indy = p.getBestEAIndividual();
+		return getIndySize(indy);
+	}
+	
+	/**
+	 * Return the number of nodes in an individual representing a program or null
+	 * if it is of an incompatible type.
+	 * 
+	 * @param indy
+	 * @return
+	 */
+	public static int getIndySize(AbstractEAIndividual indy) {
+		if (indy instanceof InterfaceDataTypeProgram) {
+			InterfaceProgram prog = ((InterfaceDataTypeProgram)indy).getProgramData()[0];
+			if (prog instanceof AbstractGPNode) {
+				AbstractGPNode gpNode = (AbstractGPNode)prog;
+				return gpNode.getNumberOfNodes();
+			} else return 0;
+		} else return 0;
+	}
+	
+	/**
+	 * Return the depth of an individual representing a program or null
+	 * if it is of an incompatible type.
+	 * 
+	 * @param indy
+	 * @return
+	 */
+	public static int getIndyDepth(AbstractEAIndividual indy) {
+		if (indy instanceof InterfaceDataTypeProgram) {
+			InterfaceProgram prog = ((InterfaceDataTypeProgram)indy).getProgramData()[0];
+			if (prog instanceof AbstractGPNode) {
+				AbstractGPNode gpNode = (AbstractGPNode)prog;
+				return gpNode.getMaxDepth();
+			} else return 0;
+		} else return 0;
+	}
 }

src/eva2/server/go/problems/regression/RFKoza_GPI_7_3.java

@@ -27,7 +27,9 @@ public class RFKoza_GPI_7_3 implements InterfaceRegressionFunction, java.io.Seri
      */
     public double evaluateFunction(double[] x) {
         double result = 0;
-        for (int i = 0; i < x.length; i++) result += Math.pow(x[i], 4) + Math.pow(x[i], 3) + Math.pow(x[i], 2) + Math.pow(x[i], 1);
+        for (int i = 0; i < x.length; i++) {
+        	result += Math.pow(x[i], 4) + Math.pow(x[i], 3) + Math.pow(x[i], 2) + x[i];
+        }
         return result;
     }
 
@@ -46,6 +48,6 @@ public class RFKoza_GPI_7_3 implements InterfaceRegressionFunction, java.io.Seri
      * @return description
      */
     public static String globalInfo() {
-        return "This target function is given in Koza GP I chapter 7.3.";
+        return "This target function f(x)=x^4+x^3+x^2+x as given in Koza GP I chapter 7.3.";
     }
 }

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

@@ -1,16 +1,15 @@
 package eva2.server.go.strategies;
 
-import eva2.gui.BeanInspector;
 import eva2.server.go.InterfacePopulationChangedEventListener;
 import eva2.server.go.individuals.AbstractEAIndividual;
-import eva2.server.go.individuals.GAIndividualBinaryData;
 import eva2.server.go.operators.mutation.InterfaceMutationGenerational;
 import eva2.server.go.operators.selection.InterfaceSelection;
-import eva2.server.go.operators.selection.SelectTournament;
+import eva2.server.go.operators.selection.SelectRandom;
+import eva2.server.go.operators.selection.SelectXProbRouletteWheel;
 import eva2.server.go.populations.InterfaceSolutionSet;
 import eva2.server.go.populations.Population;
 import eva2.server.go.populations.SolutionSet;
-import eva2.server.go.problems.B1Problem;
+import eva2.server.go.problems.F1Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
 /** The traditional genetic algorithms as devised by Holland. To only special here
@@ -29,9 +28,9 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
 public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializable {
 
         private Population                      m_Population        = new Population();
-        private InterfaceOptimizationProblem    m_Problem           = new B1Problem();
+        private InterfaceOptimizationProblem    m_Problem           = new F1Problem();
-        private InterfaceSelection              m_ParentSelection   = new SelectTournament();
+        private InterfaceSelection              m_ParentSelection   = new SelectXProbRouletteWheel();
-        private InterfaceSelection              m_PartnerSelection  = new SelectTournament();
+        private InterfaceSelection              m_PartnerSelection  = new SelectRandom();
         private boolean                         m_UseElitism        = true;
         private int                             m_Plague            = 0;
         private int                             m_NumberOfPartners  = 1;