Cosmetics again. MK rev. 187.

src/eva2/gui/Plot.java

@@ -457,6 +457,17 @@ public class Plot implements PlotInterface, Serializable {
 		m_Frame = null;
 	}
 
+	/**
+	 * Add the corners of the given range as unconnected points.
+	 *  
+	 * @param range
+	 * @param graphLabel
+	 */
+	public void setCornerPoints(double[][] range, int graphLabel) {
+		setUnconnectedPoint(range[0][0], range[1][0], graphLabel);
+		setUnconnectedPoint(range[0][1], range[1][1], graphLabel);
+	}
+
 //	/**
 //	 * Just for testing the Plot class.
 //	 */

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

@@ -870,9 +870,10 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
     
 	/**
 	 * For any AbstractEAIndividual try to convert its position to double[] and return it.
+	 * Returns null if there is no conversion available.
 	 * 
 	 * @param indy
-	 * @return double valued position of an individual
+	 * @return double valued position of an individual or null
 	 */
 	public static double[] getDoublePosition(AbstractEAIndividual indy) {
 		if (indy instanceof InterfaceDataTypeDouble) {

src/eva2/server/go/operators/selection/probability/SelProbInvertByMax.java

@@ -47,7 +47,7 @@ public class SelProbInvertByMax extends AbstractSelProb {
 			// first check if anyone holds the constraints
 			int k=0;
 			while ((k < population.size()) && !isFeasible) {
-				if (!((AbstractEAIndividual)population.get(k)).violatesConstraint()) isFeasible = true;
+				if (!(population.getEAIndividual(k)).violatesConstraint()) isFeasible = true;
 				k++;
 			}
 		}
@@ -58,7 +58,8 @@ public class SelProbInvertByMax extends AbstractSelProb {
 				sum = 0;
 				// invert fitness
 				for (int i = 0; i < data.length; i++) {
-					result[i] = maxFit - data[i][x];
+					if (population.getEAIndividual(i).violatesConstraint()) result[i]=0;
+					else result[i] = maxFit - data[i][x];
 					sum += result[i];
 				}
 

src/eva2/server/go/operators/selection/probability/SelProbNonLinearRanking.java

@@ -18,6 +18,10 @@ public class SelProbNonLinearRanking extends AbstractSelProb implements java.io.
     public SelProbNonLinearRanking() {
     }
 
+    public SelProbNonLinearRanking(double theC) {
+        this.m_C    = theC;
+    }
+    
     public SelProbNonLinearRanking(SelProbNonLinearRanking a) {
         this.m_C    = a.m_C;
     }
@@ -178,7 +182,7 @@ public class SelProbNonLinearRanking extends AbstractSelProb implements java.io.
         return "Non-Linear Ranking";
     }
 
-    /** This methods allow you to set and get the nappa.
+    /** This methods allow you to set and get the scaling.
      * @param x     Long seed.
      */
     public void setC(double x) {
@@ -190,6 +194,6 @@ public class SelProbNonLinearRanking extends AbstractSelProb implements java.io.
         return m_C;
     }
     public String cTipText() {
-        return "The c should be << 1.";
+        return "The exponential base c is taken to the power of the individual's rank and should be << 1.";
     }
 }

src/eva2/server/go/operators/selection/probability/SelProbStandard.java

@@ -40,7 +40,10 @@ public class SelProbStandard extends AbstractSelProb implements java.io.Serializ
             // first check if anyone holds the constraints
             boolean isFeasible = false;
             for (int i = 0; i < population.size(); i++) {
-                if (!((AbstractEAIndividual)population.get(i)).violatesConstraint()) isFeasible = true;
+                if (!((AbstractEAIndividual)population.get(i)).violatesConstraint()) {
+                	isFeasible = true;
+                	break;
+                }
             }
             if (isFeasible) {
                 // at least one is feasible
@@ -53,9 +56,8 @@ public class SelProbStandard extends AbstractSelProb implements java.io.Serializ
                             result[i] = Math.exp(-data[i][x]);
                         else
                             result[i] = 0;
-                    }
-                    for (int i = 0; i < data.length; i++)
                         sum += result[i];
+                    }
                     for (int i = 0; i < population.size(); i++)
                         ((AbstractEAIndividual)population.get(i)).SetSelectionProbability(x, result[i]/sum);
                 }