diff --git a/src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java b/src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java
index 8f1608a0..86925b54 100644
--- a/src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java
+++ b/src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java
@@ -16,11 +16,14 @@ import eva2.server.go.problems.B1Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.RNG;
 
-/** This is an implementation of the CHC Adaptive Search Algorithm by Eselman. It is
- * limited to binary data and is based on massively distruptive crossover. I'm not
- * shure whether i've implemented this correctly, but i definetly wasn't able to make
+/** This is an implementation of the CHC Adaptive Search Algorithm by Eshelman. It is
+ * limited to binary data and is based on massively disruptive crossover. I'm not
+ * sure whether i've implemented this correctly, but i definitely wasn't able to make
  * it competitive to a standard GA.. *sigh*
- * This is a implementation of the CHC Apative Search Algorithm.
+ * This is a implementation of the CHC Adaptive Search Algorithm (Cross generational
+ * elitist selection, Heterogeneous recombination and Cataclysmic mutation). 
+ * Citation: 
+ * 
  * Copyright:       Copyright (c) 2003
  * Company:         University of Tuebingen, Computer Architecture
  * @author          Felix Streichert
diff --git a/src/eva2/server/go/strategies/EvolutionaryProgramming.java b/src/eva2/server/go/strategies/EvolutionaryProgramming.java
index c069a0a2..9ae9ac2b 100644
--- a/src/eva2/server/go/strategies/EvolutionaryProgramming.java
+++ b/src/eva2/server/go/strategies/EvolutionaryProgramming.java
@@ -2,7 +2,6 @@ package eva2.server.go.strategies;
 
 import eva2.server.go.InterfacePopulationChangedEventListener;
 import eva2.server.go.individuals.AbstractEAIndividual;
-import eva2.server.go.individuals.GAIndividualBinaryData;
 import eva2.server.go.operators.selection.InterfaceSelection;
 import eva2.server.go.operators.selection.SelectEPTournaments;
 import eva2.server.go.populations.InterfaceSolutionSet;
@@ -14,6 +13,8 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
 /** Evolutionary programming by Fogel. Works fine but is actually a quite greedy local search
  * strategy solely based on mutation. To prevent any confusion, the mutation rate is temporaily
  * set to 1.0.
+ * Potential citation: the PhD thesis of David B. Fogel (1992).
+ * 
  * Copyright:       Copyright (c) 2003
  * Company:         University of Tuebingen, Computer Architecture
  * @author          Felix Streichert
@@ -74,20 +75,6 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
             population.incrGeneration();
         }
 
-        /** This method will assign fitness values to all individual in the
-         * current population.
-         * @param population The population that is to be evaluated
-         */
-        private void defaultEvaluatePopulation(Population population) {
-            GAIndividualBinaryData tmpIndy;
-            for (int i = 0; i < population.size(); i++) {
-                tmpIndy = (GAIndividualBinaryData) population.get(i);
-                tmpIndy.SetFitness(0, tmpIndy.defaultEvaulateAsMiniBits());
-                population.incrFunctionCalls();
-            }
-            population.incrGeneration();
-        }
-
         /** This method will generate the offspring population from the
          * given population of evaluated individuals.
          */
diff --git a/src/eva2/server/go/strategies/PopulationBasedIncrementalLearning.java b/src/eva2/server/go/strategies/PopulationBasedIncrementalLearning.java
index f1ddda94..16fba301 100644
--- a/src/eva2/server/go/strategies/PopulationBasedIncrementalLearning.java
+++ b/src/eva2/server/go/strategies/PopulationBasedIncrementalLearning.java
@@ -13,10 +13,15 @@ import eva2.server.go.problems.AbstractOptimizationProblem;
 import eva2.server.go.problems.B1Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
-/** Population based increamental learning in the PSM by Mommarche
+/** Population based incremental learning in the PSM by Monmarche
  * version with also allows to simulate ant systems due to the flexible
- * update rule of V. But both are limited to binary gentypes.
+ * update rule of V. But both are limited to binary genotypes.
  * This is a simple implementation of Population Based Incremental Learning.
+ * 
+ * Nicolas Monmarché ,  Eric Ramat ,  Guillaume Dromel ,  Mohamed Slimane ,  Gilles Venturini:
+ * On the similarities between AS, BSC and PBIL: toward the birth of a new meta-heuristic. 
+ * TecReport 215. Univ. de Tours, 1999.
+ * 
  * Copyright:       Copyright (c) 2003
  * Company:         University of Tuebingen, Computer Architecture
  * @author          Felix Streichert