diff --git a/src/eva2/server/go/operators/selection/SelectBest.java b/src/eva2/server/go/operators/selection/SelectBest.java
deleted file mode 100644
index cfc201c3..00000000
--- a/src/eva2/server/go/operators/selection/SelectBest.java
+++ /dev/null
@@ -1,134 +0,0 @@
-package eva2.server.go.operators.selection;
-
-import eva2.server.go.individuals.AbstractEAIndividual;
-import eva2.server.go.populations.Population;
-import wsi.ra.math.RNG;
-
-
-/** Select best individual multiple times if necessary.
- * In case of multiple fitness values the selection
- * critria is selected randomly for each selection event.
- * Created by IntelliJ IDEA.
- * User: streiche
- * Date: 18.03.2003
- * Time: 16:17:10
- * To change this template use Options | File Templates.
- */
-public class SelectBest implements InterfaceSelection, java.io.Serializable {
-
-    private boolean     m_ObeyDebsConstViolationPrinciple = true;
-
-    public SelectBest() {
-    }
-
-    public SelectBest(SelectBest a) {
-        this.m_ObeyDebsConstViolationPrinciple = a.m_ObeyDebsConstViolationPrinciple;
-    }
-
-    public Object clone() {
-        return (Object) new SelectBest(this);
-    }
-
-    /** This method allows an selection method to do some preliminary
-     * calculations on the population before selection is performed.
-     * For example: Homologeuos mate could compute all the distances
-     * before hand...
-     * @param population    The population that is to be processed.
-     */
-    public void prepareSelection(Population population) {
-        // nothing to prepare here
-    }
-
-    /** This method will select >size< indiviudals from the given
-     * Population.
-     * @param population    The source population where to select from
-     * @param size          The number of Individuals to select
-     * @return The selected population.
-     */
-    public Population selectFrom(Population population, int size) {
-        Population              result = new Population();
-        AbstractEAIndividual    tmpIndy = null;
-        int                     currentCriteria = 0, critSize;
-        double                  currentBestValue;
-
-        critSize = ((AbstractEAIndividual)population.get(0)).getFitness().length;
-        result.setPopulationSize(size);
-        if (this.m_ObeyDebsConstViolationPrinciple) {
-            for (int i = 0; i < size; i++) {
-                currentCriteria     = RNG.randomInt(0, critSize-1);
-                currentBestValue    = Double.POSITIVE_INFINITY;
-                tmpIndy = null;
-                for (int j = 0; j < population.size(); j++) {
-                    if ((!((AbstractEAIndividual)population.get(j)).violatesConstraint()) && (((AbstractEAIndividual)population.get(j)).getFitness(currentCriteria) < currentBestValue)) {
-                        currentBestValue = ((AbstractEAIndividual)population.get(j)).getFitness(currentCriteria);
-                        tmpIndy = (AbstractEAIndividual)population.get(j);
-                    }
-                }
-                if (tmpIndy == null) {
-                    // darn all individuals violate the constraints
-                    // so select the guy with the least worst constraint violation
-                    for (int j = 0; j < population.size(); j++) {
-                        if (((AbstractEAIndividual)population.get(j)).getConstraintViolation() < currentBestValue) {
-                            currentBestValue = ((AbstractEAIndividual)population.get(j)).getConstraintViolation();
-                            tmpIndy = (AbstractEAIndividual)population.get(j);
-                        }
-                    }
-                }
-                result.add(tmpIndy);
-            }
-        } else {
-            for (int i = 0; i < size; i++) {
-                currentCriteria     = RNG.randomInt(0, critSize-1);
-                currentBestValue    = Double.POSITIVE_INFINITY;
-                for (int j = 0; j < population.size(); j++) {
-                    if (((AbstractEAIndividual)population.get(j)).getFitness(currentCriteria) < currentBestValue) {
-                        currentBestValue = ((AbstractEAIndividual)population.get(j)).getFitness(currentCriteria);
-                        tmpIndy = (AbstractEAIndividual)population.get(j);
-                    }
-                }
-                result.add(tmpIndy);
-            }
-        }
-        return result;
-    }
-
-    /** This method allows you to select >size< partners for a given Individual
-     * @param dad               The already seleceted parent
-     * @param avaiablePartners  The mating pool.
-     * @param size              The number of partners needed.
-     * @return The selected partners.
-     */
-    public Population findPartnerFor(AbstractEAIndividual dad, Population avaiablePartners, int size) {
-        return this.selectFrom(avaiablePartners, size);
-    }
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /** This method returns a global info string
-     * @return description
-     */
-    public String globalInfo() {
-        return "This selection method will select THE Best individual (n-times if necessary)." +
-                "This is a single objective selecting method, it will select in respect to a random criteria.";
-    }
-    /** This method will return a naming String
-     * @return The name of the algorithm
-     */
-    public String getName() {
-        return "Totalitarian Selection";
-    }
-
-    /** Toggel the use of obeying the constraint violation principle
-     * of Deb
-     * @param b     The new state
-     */
-    public void setObeyDebsConstViolationPrinciple(boolean b) {
-        this.m_ObeyDebsConstViolationPrinciple = b;
-    }
-    public boolean getObeyDebsConstViolationPrinciple() {
-        return this.m_ObeyDebsConstViolationPrinciple;
-    }
-    public String obeyDebsConstViolationPrincipleToolTip() {
-        return "Toggle the use of Deb's coonstraint violation principle.";
-    }
-}