diff --git a/.classpath b/.classpath
index b5440fb..b492a72 100644
--- a/.classpath
+++ b/.classpath
@@ -7,6 +7,6 @@
 			<attribute name="javadoc_location" value="file:/opt/robocode/javadoc/"/>
 		</attributes>
 	</classpathentry>
-	<classpathentry kind="lib" path="/home/delmadord/dev/Java/lib/encog-core-3.2.0/lib/encog-core-3.2.0.jar"/>
+	<classpathentry kind="lib" path="/home/delmadord/dev/Java/lib/encog-core-3.3.0/lib/encog-core-3.3.0.jar"/>
 	<classpathentry kind="output" path="bin"/>
 </classpath>
diff --git a/src/neural/BattlefieldParameterEvaluator.java b/src/neural/BattlefieldParameterEvaluator.java
index 8eaeea0..e4bd11e 100644
--- a/src/neural/BattlefieldParameterEvaluator.java
+++ b/src/neural/BattlefieldParameterEvaluator.java
@@ -6,13 +6,16 @@ import java.awt.image.BufferedImage;
 import java.io.File;
 import java.io.IOException;
 import java.util.Random;
+
 import javax.imageio.ImageIO;
+
 import org.encog.engine.network.activation.ActivationSigmoid;
 import org.encog.ml.data.basic.BasicMLData;
 import org.encog.neural.data.basic.BasicNeuralDataSet;
 import org.encog.neural.networks.BasicNetwork;
 import org.encog.neural.networks.layers.BasicLayer;
 import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
+
 import robocode.BattleResults;
 import robocode.control.*;
 import robocode.control.events.*;
@@ -36,7 +39,7 @@ public class BattlefieldParameterEvaluator {
 	static int NdxBattle;
 	static double[] FinalScore1;
 	static double[] FinalScore2;
-
+	
 	public static void main(String[] args) {
 		double[] BattlefieldSize = new double[NUMSAMPLES];
 		double[] GunCoolingRate = new double[NUMSAMPLES];
@@ -63,8 +66,10 @@ public class BattlefieldParameterEvaluator {
 		RobotSetup[] robotSetups = new RobotSetup[2];
 		for (NdxBattle = 0; NdxBattle < NUMSAMPLES; NdxBattle++) {
 			// Choose the battlefield size and gun cooling rate  
-			BattlefieldSize[NdxBattle] = MAXBATTLEFIELDSIZE * (0.1 + 0.9 * rng.nextDouble());
-			GunCoolingRate[NdxBattle] = MAXGUNCOOLINGRATE * (0.1 + 0.9 * rng.nextDouble());
+//			BattlefieldSize[NdxBattle] = MAXBATTLEFIELDSIZE * (0.1 + 0.9 * rng.nextDouble());
+			BattlefieldSize[NdxBattle] = rangedRand(rng, MINBATTLEFIELDSIZE, MAXBATTLEFIELDSIZE);
+//			GunCoolingRate[NdxBattle] = MAXGUNCOOLINGRATE * (0.1 + 0.9 * rng.nextDouble());
+			GunCoolingRate[NdxBattle] = rangedRand(rng, MINGUNCOOLINGRATE, MAXGUNCOOLINGRATE);
 			// Create the battlefield
 			BattlefieldSpecification battlefield = new BattlefieldSpecification((int) BattlefieldSize[NdxBattle],
 					(int) BattlefieldSize[NdxBattle]);
@@ -79,7 +84,7 @@ public class BattlefieldParameterEvaluator {
 		}
 		// Cleanup our RobocodeEngine
 		engine.close();
-		System.out.println("END");
+		System.out.println("Fight ended:");
 		System.out.println(Arrays.toString(BattlefieldSize));
 		System.out.println(Arrays.toString(GunCoolingRate));
 		System.out.println(Arrays.toString(FinalScore1));
@@ -116,7 +121,7 @@ public class BattlefieldParameterEvaluator {
 		// Simulate the neural network with the test samples and fill a matrix
 		for (int NdxBattleSize = 0; NdxBattleSize < NUMBATTLEFIELDSIZES; NdxBattleSize++) {
 			for (int NdxCooling = 0; NdxCooling < NUMCOOLINGRATES; NdxCooling++) {
-				// INSERT SOMETHING HERE
+				// TODO INSERT SOMETHING HERE
 				double MyResult = 0.0; // MyTestData[NdxCooling+NdxBattleSize*NUMCOOLINGRATES];
 				MyValue = ClipColor(MyResult);
 				MyColor = new Color((float) MyValue, (float) MyValue, (float) MyValue);
@@ -184,7 +189,7 @@ public class BattlefieldParameterEvaluator {
 
 		// Called when the game sends out an information message during the battle
 		public void onBattleMessage(BattleMessageEvent e) {
-			// System.out.println("Msg> " + e.getMessage());
+//			 System.out.println("Msg> " + e.getMessage());
 		}
 
 		// Called when the game sends out an error message during the battle
@@ -192,4 +197,33 @@ public class BattlefieldParameterEvaluator {
 			System.out.println("Err> " + e.getError());
 		}
 	}
+	
+
+	
+	/**
+	 * Returns a pseudo-random number between min and max, inclusive. The difference between min and max can be at most
+	 *
+	 * @param min
+	 *            Minimum value
+	 * @param max
+	 *            Maximum value. Must be greater than min.
+	 * @return Double between min and max, inclusive.
+	 * @see java.util.Random#nextDouble(int)
+	 */
+	private static double rangedRand(Random rng, double min, double max) {
+
+		// Make sure the randomizer was initialised
+		if (rng == null)
+			throw new RuntimeException("PRNG not initialised prior to call");
+		
+		// Check for the limit
+		if (Double.valueOf(max - min).isInfinite())
+			throw new RuntimeException("Value od max - min is infinite. Change the range.");
+
+		// nextInt is normally exclusive of the top value, so add 1 to make it inclusive
+		double randomNum = min + (max - min) * rng.nextDouble();
+//		int randomNum = rng.nextDouble((max - min) + 1) + min;
+
+		return randomNum;
+	}	
 }