Added simple initialization range interface (req. change of IndividualInterface); added MutateGAUniform;

resources/MatlabInterface/@JEInterface/JEInterface.m

@@ -1,8 +1,9 @@
 function int = JEInterface(fhandle, range, varargin)
 % EvA2 Interface for Matlab
 %       JEInterface(fhandle, range)
-%       JEInterface(fhandle, range, defaultargs)
+%       JEInterface(fhandle, range, initRange)
-%       JEInterface(fhandle, range, defaultargs, options...)
+%       JEInterface(fhandle, range, initRange, defaultargs)
+%       JEInterface(fhandle, range, initRange, defaultargs, options...)
 %
 % Arguments: 
 %   fhandle: a function handle defining the optimization target.
@@ -43,6 +44,7 @@ int.resultArr = [];
 int.f = '';
 int.dim = 0;
 int.range = [];
+int.initialRange=[];
 int.mp = [];
 int.msg = '';
 int.funCalls = 0;
@@ -78,21 +80,42 @@ int = class(int,'JEInterface');
 int.opts = makeOptions(int);
 
 if (nargin>2)
-    int.args = varargin{1};
+    int.initialRange=varargin{1};
-    disp('Fitness function argument: '); disp(int.args);
+    
-    if (nargin > 3)
+    if (isa(varargin{1}, 'double') && (size(varargin{1},1) == 2))
-        if (rem(nargin,2)==0)
+        if (int.dim ~= size(varargin{1},2))
-            error('Invalid number of arguments!');
+            error('Invalid initial range: wrong dimensions');
         end
-        disp('Reading options:');
+        int.initialRange=transpose(varargin{1});
-        for i=2:2:nargin-2
+        s = ['Double valued initial search space: ' mat2str(int.initialRange)];
-            int=setOpt(int, varargin{i}, varargin{i+1});
+        disp(s);
+    end
+    
+    if (nargin>3)
+        int.args = varargin{2};
+        disp('Fitness function argument: '); disp(int.args);
+        if (nargin > 4)
+            if (rem(nargin,2)==1)
+                error('Invalid number of arguments!');
+            end
+            disp('Reading options:');
+            for i=3:2:nargin-2
+                int=setOpt(int, varargin{i}, varargin{i+1});
+            end
         end
     end
 end  
 display(getOptions(int));
 % finally create the java object
-int.mp = eva2.server.go.problems.MatlabProblem(int.dim, int.range);
+if (isempty(int.initialRange))
+    int.mp = eva2.server.go.problems.MatlabProblem(int.dim, int.range);
+else
+    if (eq(size(int.range), size(int.initialRange)))
+        int.mp = eva2.server.go.problems.MatlabProblem(int.dim, int.range, int.initialRange);
+    else 
+        error('Mismatching dimensions of range and initial range!');
+    end
+end
 disp('Java object created');
 
 testEvalFunc(int);

src/eva2/server/go/IndividualInterface.java

@@ -1,4 +1,7 @@
 package eva2.server.go;
+
+import eva2.server.go.problems.InterfaceOptimizationProblem;
+
 /*
  * Title:        EvA2
  * Description:
@@ -62,7 +65,9 @@ public interface IndividualInterface {
 	public void defaultMutate();
 
 	/** 
-	 * Initialize the genotype randomly, usually in a uniform distribution.
+	 * Initialize the genotype randomly, usually in a uniform distribution. Make sure,
+	 * if the problem has an initial range (it implements InterfaceHasInitialRange), that this
+	 * initial range is used.
 	 */
-	public void defaultInit();
+	public void defaultInit(InterfaceOptimizationProblem prob);
 }

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

@@ -269,7 +269,11 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
     /** This method will allow a default initialisation of the individual
      * @param opt   The optimization problem that is to be solved.
      */
-    public abstract void init(InterfaceOptimizationProblem opt);
+    public void init(InterfaceOptimizationProblem opt) {
+        this.defaultInit(opt);
+        this.m_MutationOperator.init(this, opt);
+        this.m_CrossoverOperator.init(this, opt);
+    }
 
     /** This method will init the individual with a given value for the
      * phenotype.

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

@@ -5,6 +5,7 @@ import java.util.BitSet;
 import eva2.server.go.operators.crossover.CrossoverESDefault;
 import eva2.server.go.operators.mutation.InterfaceMutation;
 import eva2.server.go.operators.mutation.MutateESGlobal;
+import eva2.server.go.problems.InterfaceHasInitRange;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.RNG;
 
@@ -170,15 +171,6 @@ public class ESIndividualBinaryData extends AbstractEAIndividual implements Inte
 /************************************************************************************
  * AbstractEAIndividual methods
  */
-    /** This method will allow a default initialisation of the individual
-     * @param opt   The optimization problem that is to be solved.
-     */
-    public void init(InterfaceOptimizationProblem opt) {
-        this.defaultInit();
-        this.m_MutationOperator.init(this, opt);
-        this.m_CrossoverOperator.init(this, opt);
-    }
-
     /** This method will init the individual with a given value for the
      * phenotype.
      * @param obj   The initial value for the phenotype
@@ -189,7 +181,7 @@ public class ESIndividualBinaryData extends AbstractEAIndividual implements Inte
             BitSet  bs = (BitSet) obj;
             this.SetBinaryGenotype(bs);
         } else {
-            this.defaultInit();
+            this.defaultInit(opt);
             System.out.println("Initial value for ESIndividualBinaryData is no BitSet!");
         }
         this.m_MutationOperator.init(this, opt);
@@ -260,10 +252,9 @@ public class ESIndividualBinaryData extends AbstractEAIndividual implements Inte
     	ESIndividualDoubleData.defaultMutate(m_Genotype, m_Range);
     }
 
-    /** This method initializes the double vector
+    public void defaultInit(InterfaceOptimizationProblem prob) {
-     */
+    	if (prob instanceof InterfaceHasInitRange && (((InterfaceHasInitRange)prob).getInitRange()!=null)) ESIndividualDoubleData.defaultInit(m_Genotype, (double[][])((InterfaceHasInitRange)prob).getInitRange());
-    public void defaultInit() {
+    	else ESIndividualDoubleData.defaultInit(m_Genotype, m_Range);
-    	ESIndividualDoubleData.defaultInit(m_Genotype, m_Range);
     }
 /**********************************************************************************************************************
  * These are for GUI

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

@@ -4,6 +4,7 @@ package eva2.server.go.individuals;
 import eva2.server.go.operators.crossover.CrossoverESDefault;
 import eva2.server.go.operators.mutation.InterfaceMutation;
 import eva2.server.go.operators.mutation.MutateESGlobal;
+import eva2.server.go.problems.InterfaceHasInitRange;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.tools.EVAERROR;
 import eva2.tools.math.Mathematics;
@@ -200,9 +201,7 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
      * @param opt   The optimization problem that is to be solved.
      */
     public void init(InterfaceOptimizationProblem opt) {
-        this.defaultInit();
+        super.init(opt);
-        this.m_MutationOperator.init(this, opt);
-        this.m_CrossoverOperator.init(this, opt);
         // evil operators may not respect the range, so at least give some hint
         if (!Mathematics.isInRange(m_Genotype, m_Range)) EVAERROR.errorMsgOnce("Warning: Individual out of range after initialization (and potential initial crossover/mutation)!");
     }
@@ -218,7 +217,7 @@ 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);
         } else {
-            this.defaultInit();
+            this.defaultInit(opt);
             System.out.println("Initial value for ESIndividualDoubleData is not double[]!");
         }
         this.m_MutationOperator.init(this, opt);
@@ -300,10 +299,9 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
         if (genotype[mutationIndex] > range[mutationIndex][1]) genotype[mutationIndex] = range[mutationIndex][1];
     }
     
-    /** This method initializes the double vector
+    public void defaultInit(InterfaceOptimizationProblem prob) {
-     */
+    	if (prob instanceof InterfaceHasInitRange && (((InterfaceHasInitRange)prob).getInitRange()!=null)) ESIndividualDoubleData.defaultInit(m_Genotype, (double[][])((InterfaceHasInitRange)prob).getInitRange());
-    public void defaultInit() {
+    	else ESIndividualDoubleData.defaultInit(m_Genotype, m_Range);
-        ESIndividualDoubleData.defaultInit(m_Genotype, m_Range);
     }
     
     /**

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

@@ -1,12 +1,9 @@
 package eva2.server.go.individuals;
 
-
-import java.util.Arrays;
-
-import eva2.server.go.IndividualInterface;
 import eva2.server.go.operators.crossover.CrossoverESDefault;
 import eva2.server.go.operators.mutation.InterfaceMutation;
 import eva2.server.go.operators.mutation.MutateESGlobal;
+import eva2.server.go.problems.InterfaceHasInitRange;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.RNG;
 
@@ -187,14 +184,6 @@ public class ESIndividualIntegerData extends AbstractEAIndividual implements Int
 /************************************************************************************
  * AbstractEAIndividual methods
  */
-    /** This method will allow a default initialisation of the individual
-     * @param opt   The optimization problem that is to be solved.
-     */
-    public void init(InterfaceOptimizationProblem opt) {
-        this.defaultInit();
-        this.m_MutationOperator.init(this, opt);
-        this.m_CrossoverOperator.init(this, opt);
-    }
 
     /** This method will init the individual with a given value for the
      * phenotype.
@@ -207,7 +196,7 @@ public class ESIndividualIntegerData extends AbstractEAIndividual implements Int
             if (bs.length != this.m_Genotype.length) System.out.println("Init value and requested length doesn't match!");
             this.SetIntGenotype(bs);
         } else {
-            this.defaultInit();
+            this.defaultInit(opt);
             System.out.println("Initial value for ESIndividualIntegerData is not int[]!");
         }
         this.m_MutationOperator.init(this, opt);
@@ -276,11 +265,11 @@ public class ESIndividualIntegerData extends AbstractEAIndividual implements Int
         return result;
     }
 
-    /** This method initializes the double vector
+    public void defaultInit(InterfaceOptimizationProblem prob) {
-     */
+    	int[][] range = m_Range;
-    public void defaultInit() {
+        if (prob instanceof InterfaceHasInitRange && (((InterfaceHasInitRange)prob).getInitRange()!=null)) range = (int[][])((InterfaceHasInitRange)prob).getInitRange();
-        for (int i = 0; i < this.m_Genotype.length; i++) {
+    	for (int i = 0; i < this.m_Genotype.length; i++) {
-            this.m_Genotype[i] = RNG.randomInt(this.m_Range[i][0], this.m_Range[i][1]);
+            this.m_Genotype[i] = RNG.randomInt(range[i][0], range[i][1]);
         }
     }
 /**********************************************************************************************************************

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

@@ -1,9 +1,9 @@
 package eva2.server.go.individuals;
 
-import eva2.server.go.IndividualInterface;
 import eva2.server.go.operators.crossover.CrossoverESDefault;
 import eva2.server.go.operators.mutation.InterfaceMutation;
 import eva2.server.go.operators.mutation.MutateESGlobal;
+import eva2.server.go.problems.InterfaceHasInitRange;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.RNG;
 
@@ -214,14 +214,6 @@ public class ESIndividualPermutationData extends AbstractEAIndividual implements
 	/************************************************************************************
 	 * AbstractEAIndividual methods
 	 */
-	/** This method will allow a default initialisation of the individual
-	 * @param opt   The optimization problem that is to be solved.
-	 */
-	public void init(InterfaceOptimizationProblem opt) {
-		this.defaultInit();
-		this.m_MutationOperator.init(this, opt);
-		this.m_CrossoverOperator.init(this, opt);
-	}
 
 	/** This method will init the individual with a given value for the
 	 * phenotype.
@@ -234,7 +226,7 @@ public class ESIndividualPermutationData extends AbstractEAIndividual implements
 			if (bs.length != this.m_Genotype.length) System.out.println("Init value and requested length doesn't match!");
 			this.SetPermutationGenotype(bs);
 		} else {
-			this.defaultInit();
+			this.defaultInit(opt);
 			System.out.println("Initial value for ESIndividualPermutationData is not int[]!");
 		}
 		this.m_MutationOperator.init(this, opt);
@@ -325,12 +317,12 @@ public class ESIndividualPermutationData extends AbstractEAIndividual implements
 		 }
 	 }
 
-	 /** 
+	 public void defaultInit(InterfaceOptimizationProblem prob) {
-	  * This method initializes the double vector
+		 double[][][] range = m_Range;
-	  */
+		 if (prob instanceof InterfaceHasInitRange && (((InterfaceHasInitRange)prob).getInitRange()!=null)) range = (double[][][])((InterfaceHasInitRange)prob).getInitRange();
-	 public void defaultInit() {
+	    	
 		 for (int i = 0; i < this.m_Genotype.length; i++) {
-			 ESIndividualDoubleData.defaultInit(m_Genotype[i], m_Range[i]);
+			 ESIndividualDoubleData.defaultInit(m_Genotype[i], range[i]);
 		 }
 	 }
 

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

@@ -75,9 +75,9 @@ public class GAESIndividualBinaryDoubleData extends AbstractEAIndividual impleme
         ((AbstractEAIndividual)this.m_BitSet).init(opt);
     }
 
-    public void defaultInit() {
+    public void defaultInit(InterfaceOptimizationProblem prob) {
-        ((AbstractEAIndividual)this.m_Numbers).defaultInit();
+        ((AbstractEAIndividual)this.m_Numbers).defaultInit(prob);
-        ((AbstractEAIndividual)this.m_BitSet).defaultInit();   	
+        ((AbstractEAIndividual)this.m_BitSet).defaultInit(prob);   	
     }
     
     /** This method will init the individual with a given value for the

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

@@ -91,15 +91,6 @@ public class GAIndividualBinaryData extends AbstractEAIndividual implements Inte
 /************************************************************************************
  * AbstractEAIndividual methods
  */
-    /** This method will allow a default initialisation of the individual
-     * @param opt   The optimization problem that is to be solved.
-     */
-    public void init(InterfaceOptimizationProblem opt) {
-        this.defaultInit();
-        this.m_MutationOperator.init(this, opt);
-        this.m_CrossoverOperator.init(this, opt);
-    }
-
     /** This method will init the individual with a given value for the
      * phenotype.
      * @param obj   The initial value for the phenotype
@@ -110,7 +101,7 @@ public class GAIndividualBinaryData extends AbstractEAIndividual implements Inte
             BitSet  bs = (BitSet) obj;
             this.SetBinaryGenotype(bs);
         } else {
-            this.defaultInit();
+            this.defaultInit(opt);
             System.out.println("Initial value for GAIndividualBinaryData is no BitSet!");
         }
         this.m_MutationOperator.init(this, opt);
@@ -175,9 +166,7 @@ public class GAIndividualBinaryData extends AbstractEAIndividual implements Inte
         return this.m_GenotypeLength;
     }
 
-    /** This method inits the genotpye of the individual
+    public void defaultInit(InterfaceOptimizationProblem prob) {
-     */
-    public void defaultInit() {
         for (int i = 0; i < this.m_GenotypeLength; i++) {
             if (RNG.flipCoin(0.5)) this.m_Genotype.set(i);
             else this.m_Genotype.clear(i);

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

@@ -208,14 +208,6 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
 /************************************************************************************
  * AbstractEAIndividual methods
  */
-    /** This method will allow a default initialisation of the individual
-     * @param opt   The optimization problem that is to be solved.
-     */
-    public void init(InterfaceOptimizationProblem opt) {
-        this.defaultInit();
-        this.m_MutationOperator.init(this, opt);
-        this.m_CrossoverOperator.init(this, opt);
-    }
 
     /** This method will init the individual with a given value for the
      * phenotype.
@@ -228,7 +220,7 @@ 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);
         } else {
-            this.defaultInit();
+            this.defaultInit(opt);
             System.out.println("Initial value for GAIndividualDoubleData is not double[]!");
         }
         this.m_MutationOperator.init(this, opt);
@@ -290,9 +282,7 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
         return this.m_GenotypeLength;
     }
 
-    /** This method inits the genotpye of the individual
+    public void defaultInit(InterfaceOptimizationProblem prob) {
-     */
-    public void defaultInit() {
         for (int i = 0; i < this.m_GenotypeLength; i++) {
             if (RNG.flipCoin(0.5)) this.m_Genotype.set(i);
             else this.m_Genotype.clear(i);

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

@@ -237,15 +237,6 @@ public class GAIndividualIntegerData extends AbstractEAIndividual implements Int
 /************************************************************************************
  * AbstractEAIndividual methods
  */
-    /** This method will allow a default initialisation of the individual
-     * @param opt   The optimization problem that is to be solved.
-     */
-    public void init(InterfaceOptimizationProblem opt) {
-        this.defaultInit();
-        this.m_MutationOperator.init(this, opt);
-        this.m_CrossoverOperator.init(this, opt);
-    }
-
     /** This method will init the individual with a given value for the
      * phenotype.
      * @param obj   The initial value for the phenotype
@@ -257,7 +248,7 @@ public class GAIndividualIntegerData extends AbstractEAIndividual implements Int
             if (bs.length != this.m_Range.length) System.out.println("Init value and requested length doesn't match!");
             this.SetIntGenotype(bs);
         } else {
-            this.defaultInit();
+            this.defaultInit(opt);
             System.out.println("Initial value for GAIndividualDoubleData is not double[]!");
         }
         this.m_MutationOperator.init(this, opt);
@@ -333,9 +324,7 @@ public class GAIndividualIntegerData extends AbstractEAIndividual implements Int
         return overallLength;
     }
 
-    /** This method inits the genotpye of the individual
+    public void defaultInit(InterfaceOptimizationProblem prob) {
-     */
-    public void defaultInit() {
         int overallLength = 0;
         for (int i = 0; i < this.m_CodingLenghts.length; i++) overallLength += this.m_CodingLenghts[i];
         for (int i = 0; i < overallLength; i++) {
@@ -365,7 +354,7 @@ public class GAIndividualIntegerData extends AbstractEAIndividual implements Int
         }
         indy.setIntegerDataLength(dimension);
         indy.SetIntRange(range);
-        indy.defaultInit();
+        indy.defaultInit(null);
         System.out.println(""+indy.getStringRepresentation());
         System.out.println("System.exit(0)");
         int[] data = indy.getIntegerData();

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

@@ -74,9 +74,9 @@ public class GAPIndividualProgramData extends AbstractEAIndividual implements In
         ((AbstractEAIndividual)this.m_Program).init(opt);
     }
     
-    public void defaultInit() {
+    public void defaultInit(InterfaceOptimizationProblem prob) {
-        ((AbstractEAIndividual)this.m_Numbers).defaultInit();
+        ((AbstractEAIndividual)this.m_Numbers).defaultInit(prob);
-        ((AbstractEAIndividual)this.m_Program).defaultInit();   	
+        ((AbstractEAIndividual)this.m_Program).defaultInit(prob);   	
     }
     
     /** This method will init the individual with a given value for the

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

@@ -501,15 +501,6 @@ public class GEIndividualProgramData extends AbstractEAIndividual implements Int
 /************************************************************************************
  * InterfaceEAIndividual methods
  */
-    /** This method will allow a default initialisation of the individual
-     * @param opt   The optimization problem that is to be solved.
-     */
-    public void init(InterfaceOptimizationProblem opt) {
-        this.defaultInit();
-        this.m_MutationOperator.init(this, opt);
-        this.m_CrossoverOperator.init(this, opt);
-    }
-
     /** This method will init the individual with a given value for the
      * phenotype.
      * @param obj   The initial value for the phenotype
@@ -519,7 +510,7 @@ public class GEIndividualProgramData extends AbstractEAIndividual implements Int
         if (obj instanceof InterfaceProgram) {
             this.SetProgramGenotype((InterfaceProgram[])obj);
         } else {
-            this.defaultInit();
+            this.defaultInit(opt);
             System.out.println("Initial value for GPIndividualDoubleData is no InterfaceProgram[]!");
         }
         this.m_MutationOperator.init(this, opt);
@@ -574,9 +565,7 @@ public class GEIndividualProgramData extends AbstractEAIndividual implements Int
             return this.m_GenotypeLengthPerProgram*this.m_Area.length;
         }
 
-        /** This method inits the genotpye of the individual
+        public void defaultInit(InterfaceOptimizationProblem prob) {
-         */
-        public void defaultInit() {
             for (int i = 0; i < this.m_GenotypeLengthPerProgram*this.m_Area.length; i++) {
                 if (RNG.flipCoin(0.5)) this.m_Genotype.set(i);
                 else this.m_Genotype.clear(i);

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

@@ -3,6 +3,7 @@ package eva2.server.go.individuals;
 import eva2.server.go.operators.crossover.CrossoverGIDefault;
 import eva2.server.go.operators.mutation.InterfaceMutation;
 import eva2.server.go.operators.mutation.MutateDefault;
+import eva2.server.go.problems.InterfaceHasInitRange;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.RNG;
 
@@ -189,15 +190,6 @@ public class GIIndividualIntegerData extends AbstractEAIndividual implements Int
 /************************************************************************************
  * AbstractEAIndividual methods
  */
-    /** This method will allow a default initialisation of the individual
-     * @param opt   The optimization problem that is to be solved.
-     */
-    public void init(InterfaceOptimizationProblem opt) {
-        this.defaultInit();
-        this.m_MutationOperator.init(this, opt);
-        this.m_CrossoverOperator.init(this, opt);
-    }
-
     /** This method will init the individual with a given value for the
      * phenotype.
      * @param obj   The initial value for the phenotype
@@ -209,7 +201,7 @@ public class GIIndividualIntegerData extends AbstractEAIndividual implements Int
             if (bs.length != this.m_Range.length) System.out.println("Init value and requested length doesn't match!");
             this.SetIntGenotype(bs);
         } else {
-            this.defaultInit();
+            this.defaultInit(opt);
             System.out.println("Initial value for GAIndividualDoubleData is not double[]!");
         }
         this.m_MutationOperator.init(this, opt);
@@ -279,11 +271,12 @@ public class GIIndividualIntegerData extends AbstractEAIndividual implements Int
         this.m_Genotype[mutationIndex] = RNG.randomInt(this.m_Range[mutationIndex][0], this.m_Range[mutationIndex][1]);
     }
 
-    /** This method initializes the GA genotype randomly
+    public void defaultInit(InterfaceOptimizationProblem prob) {
-     */
+    	int[][] range = m_Range;
-    public void defaultInit() {
+        if (prob instanceof InterfaceHasInitRange && (((InterfaceHasInitRange)prob).getInitRange()!=null)) range = (int[][])((InterfaceHasInitRange)prob).getInitRange();
+    	
         for (int i = 0; i < this.m_Genotype.length; i++) {
-            this.m_Genotype[i] = RNG.randomInt(this.m_Range[i][0], this.m_Range[i][1]);
+            this.m_Genotype[i] = RNG.randomInt(range[i][0], range[i][1]);
         }
     }
 

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

@@ -75,9 +75,9 @@ public class GIOBGAIndividualIntegerPermutationData extends AbstractEAIndividual
         ((AbstractEAIndividual)this.m_Permutation).init(opt);
     }
     
-    public void defaultInit() {
+    public void defaultInit(InterfaceOptimizationProblem prob) {
-        ((AbstractEAIndividual)this.m_Integer).defaultInit();
+        ((AbstractEAIndividual)this.m_Integer).defaultInit(prob);
-        ((AbstractEAIndividual)this.m_Permutation).defaultInit();
+        ((AbstractEAIndividual)this.m_Permutation).defaultInit(prob);
     }
 
     /** This method will init the individual with a given value for the

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

@@ -193,15 +193,6 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
 /************************************************************************************
  * InterfaceEAIndividual methods
  */
-    /** This method will allow a default initialisation of the individual
-     * @param opt   The optimization problem that is to be solved.
-     */
-    public void init(InterfaceOptimizationProblem opt) {
-        this.defaultInit();
-        this.m_MutationOperator.init(this, opt);
-        this.m_CrossoverOperator.init(this, opt);
-    }
-
     /** This method will init the individual with a given value for the
      * phenotype.
      * @param obj   The initial value for the phenotype
@@ -211,7 +202,7 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
         if (obj instanceof InterfaceProgram[]) {
             this.SetProgramGenotype((InterfaceProgram[])obj);
         } else {
-            this.defaultInit();
+            this.defaultInit(opt);
             System.out.println("Initial value for GPIndividualDoubleData is no InterfaceProgram[]!");
         }
         this.m_MutationOperator.init(this, opt);
@@ -271,7 +262,7 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
             this.m_Genotype[i].addNodesTo(allNodes);
             AbstractGPNode nodeToMutate = (AbstractGPNode) allNodes.get(RNG.randomInt(0, allNodes.size()-1));
             if (nodeToMutate.getParent() == null) {
-                this.defaultInit();
+                this.defaultInit(null);
             } else {
                 AbstractGPNode parent = nodeToMutate.getParent();
                 AbstractGPNode newNode = (AbstractGPNode)(((AbstractGPNode)this.m_Area[i].getRandomNode().clone()));
@@ -282,9 +273,7 @@ public class GPIndividualProgramData extends AbstractEAIndividual implements Int
         }
     }
 
-    /** This method initializes the program
+    public void defaultInit(InterfaceOptimizationProblem prob) {
-     */
-    public void defaultInit() {
         for (int i = 0; i < this.m_Area.length; i++) {
             if (this.m_Area[i] == null) {
             	EVAERROR.errorMsgOnce("Error in GPIndividualProgramData.defaultInit(): Area["+i+"] == null !!");

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

@@ -91,12 +91,6 @@ public class OBGAIndividualPermutationData extends AbstractEAIndividual implemen
    * AbstractEAIndividual methods
    */
 
-  public void init(InterfaceOptimizationProblem opt) {
-    this.defaultInit();
-    this.m_MutationOperator.init(this, opt);
-    this.m_CrossoverOperator.init(this, opt);
- }
-
  /** This method will init the individual with a given value for the
   * phenotype.
   * @param obj   The initial value for the phenotype
@@ -106,7 +100,7 @@ public class OBGAIndividualPermutationData extends AbstractEAIndividual implemen
    if (obj instanceof int[]) {
     this.SetPermutationGenotype((int[][]) obj);
    } else {
-     this.defaultInit();
+     this.defaultInit(opt);
      System.out.println("Initial value for OBGAIndividualBinaryData is no Permutation!");
    }
    this.m_MutationOperator.init(this, opt);
@@ -180,8 +174,7 @@ public class OBGAIndividualPermutationData extends AbstractEAIndividual implemen
     this.SetPermutationGenotype(permmatrix);
   }
 
-/*generates a random permutation */
+  public void defaultInit(InterfaceOptimizationProblem prob){
-  public void defaultInit(){
     //System.out.println("Default Init!");
     int[][] perm = new int[this.m_Genotype.length][];
     for (int p = 0; p < perm.length; p++) {

src/eva2/server/go/operators/crossover/CrossoverESPCX.java

@@ -216,10 +216,10 @@ public class CrossoverESPCX implements InterfaceCrossover, java.io.Serializable
         indy4 = (ESIndividualDoubleData)indy1.clone();
         if (false) {
             // random init
-            indy1.defaultInit();
+            indy1.defaultInit(prob);
-            indy2.defaultInit();
+            indy2.defaultInit(prob);
-            indy3.defaultInit();
+            indy3.defaultInit(prob);
-            indy4.defaultInit();
+            indy4.defaultInit(prob);
         } else {
             // value init
             tmpD[0] = 0;

src/eva2/server/go/operators/crossover/CrossoverESSBX.java

@@ -130,9 +130,9 @@ public class CrossoverESSBX implements InterfaceCrossover, java.io.Serializable
         indy3 = (ESIndividualDoubleData)indy1.clone();
         if (false) {
             // random init
-            indy1.defaultInit();
+            indy1.defaultInit(prob);
-            indy2.defaultInit();
+            indy2.defaultInit(prob);
-            indy3.defaultInit();
+            indy3.defaultInit(prob);
         } else {
             // value init
             tmpD[0] = 0.5;

src/eva2/server/go/operators/crossover/CrossoverESSPX.java

@@ -145,10 +145,10 @@ public class CrossoverESSPX implements InterfaceCrossover, java.io.Serializable
         indy4 = (ESIndividualDoubleData)indy1.clone();
         if (false) {
             // random init
-            indy1.defaultInit();
+            indy1.defaultInit(prob);
-            indy2.defaultInit();
+            indy2.defaultInit(prob);
-            indy3.defaultInit();
+            indy3.defaultInit(prob);
-            indy4.defaultInit();
+            indy4.defaultInit(prob);
         } else {
             // value init
             tmpD[0] = -1;

src/eva2/server/go/operators/crossover/CrossoverESUNDX.java

@@ -205,10 +205,10 @@ public class CrossoverESUNDX implements InterfaceCrossover, java.io.Serializable
         indy4 = (ESIndividualDoubleData)indy1.clone();
         if (false) {
             // random init
-            indy1.defaultInit();
+            indy1.defaultInit(prob);
-            indy2.defaultInit();
+            indy2.defaultInit(prob);
-            indy3.defaultInit();
+            indy3.defaultInit(prob);
-            indy4.defaultInit();
+            indy4.defaultInit(prob);
         } else {
             // value init
             tmpD[0] =  -1.9;

src/eva2/server/go/operators/mutation/MutateGAUniform.java

@@ -0,0 +1,97 @@
+package eva2.server.go.operators.mutation;
+
+import java.io.Serializable;
+
+import eva2.gui.GenericObjectEditor;
+import eva2.server.go.individuals.AbstractEAIndividual;
+import eva2.server.go.individuals.InterfaceGAIndividual;
+import eva2.server.go.populations.Population;
+import eva2.server.go.problems.InterfaceOptimizationProblem;
+import eva2.tools.EVAERROR;
+import eva2.tools.math.RNG;
+
+/**
+ * Uniform mutation mutates every GA bit with a fixed probability.
+ * 
+ * @author mkron
+ *
+ */
+public class MutateGAUniform implements InterfaceMutation, Serializable {
+	double bitwiseProb = 0.1;
+	private boolean useInvertedLength = true;
+	
+	public MutateGAUniform() {}
+	
+	public MutateGAUniform(MutateGAUniform o) {
+		setBitwiseProb(o.getBitwiseProb());
+	}
+
+	public void crossoverOnStrategyParameters(AbstractEAIndividual indy1,
+			Population partners) {
+		if (indy1.getMutationOperator() instanceof MutateGAUniform) {
+			MutateGAUniform mute = (MutateGAUniform)indy1.getMutationOperator();
+			double smallerProb, largerProb;
+			smallerProb = Math.min(getBitwiseProb(), mute.getBitwiseProb());
+			largerProb = Math.max(getBitwiseProb(), mute.getBitwiseProb());
+			setBitwiseProb(RNG.randomDouble(smallerProb, largerProb));
+		}
+	}
+
+	public Object clone() {
+		return new MutateGAUniform(this);
+	}
+	
+	public void hideHideable() {
+		setUseInvertedLength(isUseInvertedLength());
+	}
+	
+	public String getStringRepresentation() {
+		return "Uniform GA mutation (" + getBitwiseProb() + ")";
+	}
+
+	public void init(AbstractEAIndividual individual,
+			InterfaceOptimizationProblem opt) {
+		if (useInvertedLength && (individual instanceof InterfaceGAIndividual)) setBitwiseProb(((InterfaceGAIndividual)individual).getGenotypeLength());
+	}
+
+	/**
+	 * Flip every bit with a certain probability.
+	 */
+	public void mutate(AbstractEAIndividual individual) {
+		if (individual instanceof InterfaceGAIndividual) {
+			InterfaceGAIndividual indy = (InterfaceGAIndividual)individual;
+			for (int i=0; i<indy.getGenotypeLength(); i++) {
+				if (RNG.flipCoin(bitwiseProb)) indy.getBGenotype().flip(i);
+			}
+		} else EVAERROR.errorMsgOnce("Error: Skipped mutation since " + this.getClass() + " is applicable for InterfaceGAIndividual individuals only! (pot. multiple occ.)");
+	}
+
+	public double getBitwiseProb() {
+		return bitwiseProb;
+	}
+	public void setBitwiseProb(double bitwiseProb) {
+		this.bitwiseProb = bitwiseProb;
+	}
+	public String bitwiseProbTipText() {
+		return "The probability of every bit to be flipped.";
+	}
+
+	public void setUseInvertedLength(boolean useInvertedLength) {
+		this.useInvertedLength = useInvertedLength;
+		GenericObjectEditor.setHideProperty(this.getClass(), "bitwiseProb", useInvertedLength);
+	}
+	public boolean isUseInvertedLength() {
+		return useInvertedLength;
+	}
+	public String useInvertedLengthTipText() {
+		return "Switch for using 1/l as mutation rate.";
+	}
+	
+	public String getName() {
+		return "Uniform-GA-Mutation";
+	}
+	
+	public String globalInfo() {
+		return "Uniform mutation mutates every GA bit with a fixed probability.";
+	}
+}

src/eva2/server/go/operators/postprocess/PostProcess.java

@@ -1054,7 +1054,7 @@ public class PostProcess {
 			for (int i=0; i<curPopSize; i++) {
 				// fill pop randomly
 				indy = prob.getIndividualTemplate().getClone();
-				indy.defaultInit();
+				indy.defaultInit(prob);
 				pop.add(indy);
 			}
 			pop.synchSize();

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

@@ -240,21 +240,21 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         firePropertyChangedEvent(Population.populationInitialized);
     }
 
-    /** This method inits the population. Function and generation counters
+//    /** This method inits the population. Function and generation counters
-     * are reset and m_Size default Individuals are created and initialized by
+//     * are reset and m_Size default Individuals are created and initialized by
-     * the GAIndividual default init() method.
+//     * the GAIndividual default init() method.
-      */
+//      */
-    public void defaultInit(AbstractEAIndividual template) {
+//    public void defaultInit(AbstractEAIndividual template) { Were missing the optimization problem - thus no initial range can be specified. And since no one calls this method, I removed it for now
-        this.m_Generation       = 0;
+//        this.m_Generation       = 0;
-        this.m_FunctionCalls    = 0;
+//        this.m_FunctionCalls    = 0;
-        this.m_Archive          = null;
+//        this.m_Archive          = null;
-        this.clear();
+//        this.clear();
-        for (int i = 0; i < this.m_TargetSize; i++) {
+//        for (int i = 0; i < this.m_TargetSize; i++) {
-            AbstractEAIndividual tmpIndy = (AbstractEAIndividual)template.clone();
+//            AbstractEAIndividual tmpIndy = (AbstractEAIndividual)template.clone();
-            tmpIndy.defaultInit();
+//            tmpIndy.defaultInit(null);
-            super.add(tmpIndy);
+//            super.add(tmpIndy);
-        }
+//        }
-    }
+//    }
 
     /**
      * Create a population instance which distributes the individuals according to 

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

@@ -184,7 +184,13 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
      */
     public abstract void evaluate(AbstractEAIndividual individual);
 
-    
+    /**
+     * The default initialization method. Clone the given template individual, calls its init method 
+     * and add it to the population until the target size is reached. Earlier individuals are removed. 
+     * @param population
+     * @param template
+     * @param prob
+     */
 	public static void defaultInitPopulation(Population population, AbstractEAIndividual template, InterfaceOptimizationProblem prob) {
         AbstractEAIndividual tmpIndy;
         population.clear();

src/eva2/server/go/problems/F1Problem.java

@@ -1,6 +1,7 @@
 package eva2.server.go.problems;
 
 import eva2.server.go.strategies.InterfaceOptimizer;
+import eva2.tools.math.Mathematics;
 
 /**
  * Created by IntelliJ IDEA.
@@ -9,7 +10,9 @@ import eva2.server.go.strategies.InterfaceOptimizer;
  * Time: 17:58:55
  * To change this template use Options | File Templates.
  */
-public class F1Problem extends AbstractProblemDoubleOffset implements Interface2DBorderProblem, java.io.Serializable, InterfaceFirstOrderDerivableProblem {
+public class F1Problem extends AbstractProblemDoubleOffset implements Interface2DBorderProblem, InterfaceHasInitRange, java.io.Serializable, InterfaceFirstOrderDerivableProblem {
+	private double initialRangeRatio=1.; // reduce to initialize in a smaller subrange of the original range (in the corner box)
+	
     public F1Problem() {
     	super();
     	setDefaultRange(10);
@@ -93,4 +96,23 @@ public class F1Problem extends AbstractProblemDoubleOffset implements Interface2
 		}
 		return grads;
 	}
+
+	/**
+	 * If initialRangeRatio<1, produce a reduced initial range in the negative corner of the range.
+	 */
+	public Object getInitRange() {
+		if (initialRangeRatio<1.) {
+			double[][] gR=makeRange();
+			double[][] initR = makeRange();
+			//		double[] rng = Mathematics.shiftRange(initR);
+			Mathematics.scaleRange(initialRangeRatio, initR);
+			for (int i=0; i<getProblemDimension(); i++) {
+				double d=gR[i][0]-initR[i][0];
+				initR[i][0]+=d; // shift back by original offsets
+				initR[i][1]+=d;
+			}
+//			System.out.println(BeanInspector.toString(initR));
+			return initR;
+		} else return makeRange();
+	}
 }

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

@@ -2,13 +2,20 @@ package eva2.server.go.problems;
 
 /**
  * An interface for optimization problems having an extra initial range
- * opposed to the all-over problem range. 
+ * opposed to the global search range. This makes sense mainly for double
- * 
+ * and integer data types such as ESIndividuals and GIIndividuals. For binary types, 
- * TODO generalize this!
+ * the search range is defined by the bit length of the individual, respectively the 
+ * dimension of the problem.
  * 
  * @author mkron
  *
  */
 public interface InterfaceHasInitRange {
-	public double[][] getInitRange();
+	/**
+	 * The method should return the type expected by the individual type, e.g. double[][] or int[][].
+	 * It may return null, in that case the global search range is used as initial range.
+	 * 
+	 * @return An initial search range or null in case it is equal to the global search range.
+	 */
+	public Object getInitRange();
 }

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

@@ -4,6 +4,7 @@ import java.io.FileNotFoundException;
 import java.io.FileOutputStream;
 import java.io.PrintStream;
 import java.io.Serializable;
+import java.util.Arrays;
 
 import eva2.OptimizerFactory;
 import eva2.OptimizerRunnable;
@@ -28,7 +29,7 @@ import eva2.server.stat.InterfaceTextListener;
  * @author mkron
  *
  */
-public class MatlabProblem extends AbstractOptimizationProblem implements InterfaceTextListener, Serializable {
+public class MatlabProblem extends AbstractOptimizationProblem implements InterfaceHasInitRange, InterfaceTextListener, Serializable {
 	private static final long serialVersionUID = 4913310869887420815L;
 	public static boolean 				TRACE = false; 
 	transient OptimizerRunnable			runnable = null;
@@ -42,6 +43,7 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
 	int 								verbosityLevel	= 0;
 	private MatlabEvalMediator 			handler = null;
 	private boolean isDouble = true;
+	private double[][] initialRange = null; // the initial range for double-valued problems
 
 	public static boolean hideFromGOE = true; 
 
@@ -59,6 +61,7 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
 //		if (o.res != null) if (o.res.get() != null) res.set(o.res.get());
 		this.range = o.range;
 		this.isDouble = o.isDouble;
+		this.initialRange = o.initialRange;
 //		this.mtCmd = o.mtCmd;
 //		currArray = null;
 	}
@@ -72,7 +75,11 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
 	}
 
 	public MatlabProblem(int dim, double[][] range) {
-		init(dim, range, defTestOut);
+		init(dim, range, null, defTestOut);
+	}
+	
+	public MatlabProblem(int dim, double[][] range, double[][] initRange) {
+		init(dim, range, initRange, defTestOut);
 	}
 
 	public MatlabProblem(int dim, double lower, double upper) {
@@ -112,13 +119,36 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
 	}
 
 	public void initProblem() {
-		init(this.problemDimension, range, defTestOut);
+		init(this.problemDimension, range, initialRange, defTestOut);
 	}
 
-	private void init(int dim, double[][] rng, String outFile) {
+	/**
-		problemDimension = dim;
+	 * Make deep clones for the ranges, or there may be deadlocks in communicating with Matlab!
+	 *  
+	 * @param dim
+	 * @param globalRange
+	 * @param initRange
+	 * @param outFile
+	 */
+	private void init(int dim, double[][] globalRange, double[][] initRange, String outFile) {
+		this.problemDimension = dim;
 //		if ((rng != null) && (dim != rng.length)) throw new ArrayIndexOutOfBoundsException("Mismatching dimension and range!");
-		range = rng;
+		if (globalRange!=null) { // these may be Matlab objects, so I do it by foot, just to be sure not to clone them within Matlab instead of here 
+			this.range = new double[globalRange.length][globalRange[0].length];
+			for (int i=0; i<this.range.length; i++) {
+				for (int j=0; j<this.range[0].length; j++) this.range[i][j]=globalRange[i][j]; 
+			}
+		} else this.range=null;
+		
+		if (initialRange!=null) { // these may be Matlab objects, so I do it by foot, just to be sure not to clone them within Matlab instead of here
+			this.initialRange = new double[initRange.length][initRange[0].length];
+			for (int i=0; i<this.initialRange.length; i++) {
+				for (int j=0; j<this.initialRange[0].length; j++) this.initialRange[i][j]=initRange[i][j]; 
+			}
+		} else this.initialRange=null;
+		
+		if (Arrays.deepEquals(initialRange, range)) initialRange=null;
+		
 		if (range==null) isDouble = false;
 		else isDouble = true;
 
@@ -128,6 +158,7 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
 //		res = new ResultArr();
 		
 		setDebugOut(TRACE, defTestOut);
+		log("Initial range is " + BeanInspector.toString(initialRange) + "\n");
 
 //		log("range is " + BeanInspector.toString(range)+ "\n");
 //		log("template len: " + ((ESIndividualDoubleData)m_Template).getDGenotype().length + "\n");
@@ -448,4 +479,9 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
         //sb.append("\n");
         return sb.toString();
 	}
+
+	public Object getInitRange() {
+		log("retrieving initial range..., first entry: " + ((initialRange==null) ? "null" : BeanInspector.toString(initialRange[0])));
+		return initialRange;
+	}
 }

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

@@ -132,7 +132,7 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
     public void defaultInit() {
         this.m_FitnessCallsNeeded = 0;
         this.m_Best = new GAIndividualBinaryData();
-        this.m_Best.defaultInit();
+        this.m_Best.defaultInit(m_Problem);
     }
 
     /** This method will optimize

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

@@ -76,13 +76,17 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
     }
 
     /** 
-     * This method will optimize without specific operators, by just calling the problem method
+     * This method will optimize without specific operators, by just calling the individual method
-     * for population initialization.
+     * for initialization.
      */
     public void optimize() {
         Population original = (Population)this.m_Population.clone();
 
-        this.m_Problem.initPopulation(this.m_Population);
+//        this.m_Problem.initPopulation(this.m_Population);
+        for (int i=0; i<m_Population.size(); i++) {
+        	m_Population.getEAIndividual(i).defaultInit(null);
+        }
+        
         this.m_Population.SetFunctionCalls(original.getFunctionCalls());
         this.m_Problem.evaluate(this.m_Population);
         for (int i = 0; i < this.m_Population.size(); i++) {
@@ -111,7 +115,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
     public void defaultInit() {
         this.m_FitnessCallsNeeded = 0;
         this.m_Best = new GAIndividualBinaryData();
-        this.m_Best.defaultInit();
+        this.m_Best.defaultInit(m_Problem);
     }
 
     /** This method will optimize
@@ -119,7 +123,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
     public void defaultOptimize() {
         for (int i = 0; i < m_FitnessCalls; i++) {
             this.m_Test = new GAIndividualBinaryData();
-            this.m_Test.defaultInit();
+            this.m_Test.defaultInit(m_Problem);
             if (this.m_Test.defaultEvaulateAsMiniBits() < this.m_Best.defaultEvaulateAsMiniBits()) this.m_Best = this.m_Test;
             this.m_FitnessCallsNeeded = i;
             if (this.m_Best.defaultEvaulateAsMiniBits() == 0) i = this.m_FitnessCalls +1;

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

@@ -102,7 +102,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	transient final static String				partTypeKey = "ParticleType";
 	public transient final static String		partBestPosKey = "BestPosition";
 	transient final static String				partBestFitKey = "BestFitness";
-	transient final static String				partVelKey = "Velocity";
+	public transient final static String				partVelKey = "Velocity";
 	transient final static String 				multiSwTypeKey="MultiSwarmType";
 	transient final static String 				multiSwSizeKey="MultiSwarmSize";
 	transient final static String				indexKey="particleIndex";
@@ -569,7 +569,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	public static void resetIndividual(AbstractEAIndividual indy, double initialV) {
 		if (indy instanceof InterfaceDataTypeDouble) {
 			indy.setParents(null);
-			indy.defaultInit();
+			indy.defaultInit(null);
 			indy.putData(partTypeKey, defaultType); // turn into default type
 			initIndividualDefaults(indy, initialV);
 			initIndividualMemory(indy);

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

@@ -138,7 +138,7 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
     public void defaultInit() {
         this.m_FitnessCallsNeeded = 0;
         this.m_Best = new GAIndividualBinaryData();
-        this.m_Best.defaultInit();
+        this.m_Best.defaultInit(m_Problem);
     }
 
     /** This method will optimize

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

@@ -130,7 +130,7 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
     public void defaultInit() {
         this.m_FitnessCallsNeeded = 0;
         this.m_Best = new GAIndividualBinaryData();
-        this.m_Best.defaultInit();
+        this.m_Best.defaultInit(m_Problem);
     }
 
     /** This method will optimize

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

@@ -207,7 +207,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
 		m_problem = (AbstractOptimizationProblem)problem;
 		range = null;
 		if (problem instanceof InterfaceHasInitRange) {
-			initRange = ((InterfaceHasInitRange)problem).getInitRange();
+			initRange = (double[][])((InterfaceHasInitRange)problem).getInitRange();
 		}
 		Population pop = new Population(1);
 		problem.initPopulation(pop);

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

@@ -956,7 +956,7 @@ v[d] = cmin * v[d];
         }
 	}
 
-	public void defaultInit() {
+	public void defaultInit(InterfaceOptimizationProblem prob) {
 		// shouldnt be called as we are beyond the EvA framework in this class
         for (int i = 0; i < this.position.x.length; i++) {
             this.position.x[0] = 0.;