diff --git a/src/eva2/server/go/problems/AbstractProblemBinary.java b/src/eva2/server/go/problems/AbstractProblemBinary.java
index b9bc40bc..26dcf7e6 100644
--- a/src/eva2/server/go/problems/AbstractProblemBinary.java
+++ b/src/eva2/server/go/problems/AbstractProblemBinary.java
@@ -9,7 +9,12 @@ import eva2.server.go.individuals.InterfaceGAIndividual;
 import eva2.server.go.populations.Population;
 import eva2.server.go.strategies.InterfaceOptimizer;
 
-
+/**
+ * An abstract problem based on binary data.
+ * 
+ * @author mkron
+ *
+ */
 public abstract class AbstractProblemBinary extends AbstractOptimizationProblem {
 	
 	public AbstractProblemBinary() {
diff --git a/src/eva2/server/go/problems/AbstractProblemInteger.java b/src/eva2/server/go/problems/AbstractProblemInteger.java
new file mode 100644
index 00000000..f4462df2
--- /dev/null
+++ b/src/eva2/server/go/problems/AbstractProblemInteger.java
@@ -0,0 +1,122 @@
+package eva2.server.go.problems;
+
+import eva2.server.go.individuals.AbstractEAIndividual;
+import eva2.server.go.individuals.GAIndividualIntegerData;
+import eva2.server.go.individuals.InterfaceDataTypeInteger;
+import eva2.server.go.populations.Population;
+import eva2.server.go.strategies.InterfaceOptimizer;
+
+/**
+ * An abstract problem with integer data type.
+ * 
+ */
+public abstract class AbstractProblemInteger extends AbstractOptimizationProblem implements java.io.Serializable {
+
+    protected AbstractEAIndividual      m_OverallBest       = null;
+    protected int                       m_ProblemDimension  = 10;
+
+    public AbstractProblemInteger() {
+    	initTemplate();
+    }
+
+    protected void initTemplate() {
+    	this.m_Template         = new GAIndividualIntegerData();
+		if (((InterfaceDataTypeInteger)this.m_Template).size()!=this.getProblemDimension()) {
+			((InterfaceDataTypeInteger)this.m_Template).setIntegerDataLength(this.getProblemDimension());
+		}
+    }
+
+	public void cloneObjects(AbstractProblemInteger o) {
+		if (o.m_Template != null) m_Template = (AbstractEAIndividual)o.m_Template.clone();
+        if (o.m_OverallBest != null) m_OverallBest      = (AbstractEAIndividual)((AbstractEAIndividual)o.m_OverallBest).clone();
+        this.m_ProblemDimension = o.m_ProblemDimension;
+	}
+
+	@Override
+    public void initProblem() {
+		initTemplate();
+        this.m_OverallBest = null;
+    }
+
+	@Override
+    public void initPopulation(Population population) {
+        this.m_OverallBest = null;
+        ((InterfaceDataTypeInteger)this.m_Template).setIntegerDataLength(this.m_ProblemDimension);
+        AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this);
+    }
+
+    @Override
+    public void evaluate(AbstractEAIndividual individual) {
+        int[]           x;
+        double[]        fitness;
+
+
+        x = new int[((InterfaceDataTypeInteger) individual).getIntegerData().length];
+        System.arraycopy(((InterfaceDataTypeInteger) individual).getIntegerData(), 0, x, 0, x.length);
+
+        fitness = this.eval(x);
+        for (int i = 0; i < fitness.length; i++) {
+            // set the fitness of the individual
+            individual.SetFitness(i, fitness[i]);
+        }
+        if ((this.m_OverallBest == null) || (this.m_OverallBest.getFitness(0) > individual.getFitness(0))) {
+            this.m_OverallBest = (AbstractEAIndividual)individual.clone();
+        }
+    }
+
+    /** 
+     * Evaluate a simple integer array to determine the fitness.
+     * @param x     The n-dimensional input vector
+     * @return  The m-dimensional output vector.
+     */
+    public abstract double[] eval(int[] x);
+
+/**********************************************************************************************************************
+ * These are for GUI
+ */
+
+    public String getName() {
+    	return "AbstractProblemInteger";
+    }
+
+    /** This method returns a global info string
+     * @return description
+     */
+    public static String globalInfo() {
+    	return "The programmer did not give further details.";
+    }
+
+    /** This method returns a string describing the optimization problem.
+     * @param opt       The Optimizer that is used or had been used.
+     * @return The description.
+     */
+    public String getStringRepresentationForProblem(InterfaceOptimizer opt) {
+        StringBuffer sb = new StringBuffer(200);
+        sb.append("An integer valued problem:\n");
+        sb.append(globalInfo());
+        sb.append("Dimension   : "); 
+        sb.append(this.getProblemDimension());
+        return sb.toString();
+    }
+
+
+    /** This method allows you to set the problem dimension.
+     * @param n     The problem dimension
+     */
+    public void setProblemDimension(int n) {
+        this.m_ProblemDimension = n;
+    }
+    public int getProblemDimension() {
+        return this.m_ProblemDimension;
+    }
+    public String problemDimensionTipText() {
+        return "Length of the integer vector to be optimized";
+    }
+
+    /** This method allows you to choose the EA individual
+     * @param indy The EAIndividual type
+     */
+    public void setIndividualTemplate(AbstractEAIndividual indy) {
+        this.m_Template = (AbstractEAIndividual)indy;
+    }
+}
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