Updates to problem classes

src/eva2/server/go/problems/AbstractMultiModalProblemKnown.java

@@ -14,7 +14,8 @@ import eva2.tools.EVAERROR;
 import eva2.tools.ToolBox;
 import eva2.tools.math.Mathematics;
 
-public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDouble implements Interface2DBorderProblem, InterfaceMultimodalProblemKnown {
+public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDouble 
+implements Interface2DBorderProblem, InterfaceMultimodalProblemKnown {
 	protected static InterfaceDistanceMetric   m_Metric = new PhenotypeMetric();
 	private double                    m_GlobalOpt = 0;
 	protected Population                  m_ListOfOptima;
@@ -126,6 +127,11 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 		return ToolBox.appendArrays(new String[]{"numOptsFound", "maxPeakRatio"}, super.getAdditionalDataHeader());
 	}
 
+	@Override 
+	public String[] getAdditionalDataInfo() {
+		return ToolBox.appendArrays(new String[]{"The number of optima identified with default accuracy", "The maximum peak ratio measure in [0,1], best at 1, if multiple local optima are known."}, super.getAdditionalDataInfo());
+	}
+	
 	@Override
 	public Object[] getAdditionalDataValue(PopulationInterface pop) {
 		Object[] result = new Object[2];
@@ -333,7 +339,9 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 	/**
 	 * Returns -1 if the full list is not available. Otherwise calculates the maximum peak ratio
 	 * based on the full list of known optima. Assumes that both realOpts and pop have fitness
-	 * values assigned as in a maximization problem. This is the standard formulation of MPR.
+	 * values assigned as in a minimization problem. Using a fitness value fitThreshold, they are inverted
+	 * and then treated as in maximization. An optimum which has not been found closely (by epsilon)
+	 * in the population is assumed to be covered with an individual of fitness fitThreshold.
 	 * 
 	 * @param mmProb
 	 * @param pop
@@ -348,13 +356,15 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 		for (int i=0; i<realOpts.size(); i++) {
 			// store the optimal fitness values and remember the smallest one
 			realFits[i]=realOpts.getEAIndividual(i).getFitness(fitCrit);
-			if (realFits[i]>fitThreshold) System.err.println("Warning: The fitness threshold to turn minimization fitness values into " +
+			if (realFits[i]>fitThreshold) EVAERROR.errorMsgOnce("Warning: The fitness threshold to turn minimization fitness values into " +
 					"maximization values should be larger than any optimal fitness! (AbstractMultiModalProblemKnown)");
 			if (i==0 || (minOpt>realFits[i])) minOpt = realFits[i];
 			// check if the opt. was found and store the corr. found fitness
 			if (optsFound[i]!=null) {
 				foundFits[i] = new Double(optsFound[i].getFitness(fitCrit));
-			} else foundFits[i]=fitThreshold; // note that it wasnt found -- will result in zero
+			} else {
+				foundFits[i]=fitThreshold; // note that it wasnt found -- will result in zero
+			}
 		}
 		// now we mirror all values with the threshold - provided they are below the threshold...
 		for (int i=0; i<realOpts.size(); i++) {

src/eva2/server/go/problems/AbstractOptimizationProblem.java

@@ -35,6 +35,7 @@ import eva2.server.go.operators.terminators.PopulationMeasureTerminator.Directio
 import eva2.server.go.operators.terminators.PopulationMeasureTerminator.StagnationTypeEnum;
 import eva2.server.go.populations.Population;
 import eva2.server.go.strategies.InterfaceOptimizer;
+import eva2.tools.ToolBox;
 
 /**
  * Created by IntelliJ IDEA.
@@ -87,7 +88,7 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
     protected 	AbstractEAIndividual      m_Template = null;
 //    private transient ArrayList<ParamChangeListener> changeListeners = null;
 
-	private double defaultAccuracy = 0.01; // default accuracy for identifying optima.
+	private double defaultAccuracy = 0.001; // default accuracy for identifying optima.
 
     /** This method returns a deep clone of the problem.
      * @return  the clone
@@ -252,8 +253,39 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
      * @return String
      */
     public String[] getAdditionalDataHeader() {
-    	if (this instanceof InterfaceInterestingHistogram) return new String[]{STAT_SOLUTION_HEADER,"histogram","score"};
+    	String[] header = null;
-    	else return new String[]{STAT_SOLUTION_HEADER};
+    	if (this instanceof InterfaceInterestingHistogram) header = new String[]{STAT_SOLUTION_HEADER,"histogram","score"};
+    	else header = new String[]{STAT_SOLUTION_HEADER};
+    	
+    	header = (String[])checkAndAppendAdd(0, getIndividualTemplate(), header, null);
+    	header = (String[])checkAndAppendAdd(0, getIndividualTemplate().getCrossoverOperator(), header, null);
+    	header = (String[])checkAndAppendAdd(0, getIndividualTemplate().getMutationOperator(), header, null);
+    	return header;
+    }
+    
+    /**
+     * Generic method to append additional information of another object.
+     * 
+     * @param type indicate header (0), info (1), or value (2)
+     * @param o	the object to retrieve data from
+     * @param dat 	the data array to which to append to
+     * @param pop	the current population 
+     * @return
+     */
+    private static Object[] checkAndAppendAdd(int type, Object o, Object[] dat, PopulationInterface pop) {
+    	if (o instanceof InterfaceAdditionalPopulationInformer) {
+    		switch(type) {
+    		case 0: // header
+        		return ToolBox.appendArrays((String[])dat, (String[])((InterfaceAdditionalPopulationInformer)o).getAdditionalDataHeader());
+    		case 1: // info
+        		return ToolBox.appendArrays((String[])dat, (String[])((InterfaceAdditionalPopulationInformer)o).getAdditionalDataInfo());
+    		case 2: // value
+        		return ToolBox.appendArrays(dat, ((InterfaceAdditionalPopulationInformer)o).getAdditionalDataValue(pop));
+        	default: 
+        		System.err.println("Error, invalid type in AbstractOptimizationProblem.appendAdd");
+        		return dat;
+    		}
+    	} else return dat;
     }
     
     /** This method returns the header for the additional data that is to be written into a file
@@ -261,12 +293,17 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
      * @return String
      */
     public String[] getAdditionalDataInfo() {
-    	if (this instanceof InterfaceInterestingHistogram) {
+    	String[] info=null;
-    		return new String[]{"Representation of the current best individual",
+    	if (this instanceof InterfaceInterestingHistogram) 
+    		info = new String[]{"Representation of the current best individual",
     			"Fitness histogram of the current population",
     			"Fitness threshold based score of the current population"};
-    	}
+    	else info = new String[]{"Representation of the current best individual"};
-    	return new String[]{"Representation of the current best individual"};
+    	
+    	info = (String[])checkAndAppendAdd(1, getIndividualTemplate(), info, null);
+    	info = (String[])checkAndAppendAdd(1, getIndividualTemplate().getCrossoverOperator(), info, null);
+    	info = (String[])checkAndAppendAdd(1, getIndividualTemplate().getMutationOperator(), info, null);
+    	return info;
     }
     
     /** This method returns the additional data that is to be written into a file
@@ -274,9 +311,8 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
      * @return String
      */
     public Object[] getAdditionalDataValue(PopulationInterface pop) {
-    	Object solObj;
+    	String solStr = AbstractEAIndividual.getDefaultDataString(pop.getBestIndividual()); 
-//    	solObj = AbstractEAIndividual.getDefaultDataString(pop.getBestIndividual());
+    	Object[] vals = null;
-    	solObj = AbstractEAIndividual.getDefaultDataObject(pop.getBestIndividual());
     	if (this instanceof InterfaceInterestingHistogram) {
     		int fitCrit=0;
     		SolutionHistogram hist = ((InterfaceInterestingHistogram)this).getHistogram();
@@ -288,8 +324,13 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
         		}
     			Population sols = PostProcess.clusterBestUpdateHistogram((Population)maybeFiltered, this, hist, fitCrit, getDefaultAccuracy());
     		}
-    		return new Object[]{solObj, hist, hist.getScore()};
+    		vals = new Object[]{solStr, hist, hist.getScore()};
-    	} else return new Object[]{solObj}; 
+    	} else vals = new Object[]{solStr}; 
+    	
+    	vals = checkAndAppendAdd(2, pop.getBestIndividual(), vals, pop);
+    	vals = checkAndAppendAdd(2, ((AbstractEAIndividual)pop.getBestIndividual()).getCrossoverOperator(), vals, pop);
+    	vals = checkAndAppendAdd(2, ((AbstractEAIndividual)pop.getBestIndividual()).getMutationOperator(), vals, pop);
+    	return vals;
     }
 
     /**

src/eva2/server/go/problems/AbstractProblemDouble.java

@@ -142,19 +142,18 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 		double[] x;
 		double[] fitness;
 
-		x = getEvalArray(individual);
+        x = getEvalArray(individual);
-		((InterfaceDataTypeDouble) individual).SetDoublePhenotype(x);
+        ((InterfaceDataTypeDouble)individual).SetDoublePhenotype(x);
-		// evaluate the vector
+        // evaluate the vector
-		fitness = this.eval(x);
+        fitness = this.eval(x);
-		// if indicated, add Gaussian noise
+        // if indicated, add Gaussian noise
-		if (m_Noise != 0)
+        if (m_Noise != 0) RNG.addNoise(fitness, m_Noise); 
-			RNG.addNoise(fitness, m_Noise);
+        // set the fitness
-		// set the fitness
+        setEvalFitness(individual, x, fitness);
-		setEvalFitness(individual, x, fitness);
+        if (isWithConstraints()) {
-		if (isWithConstraints()) {
+        	individual.putData(rawFitKey, individual.getFitness().clone());
-			individual.putData(rawFitKey, individual.getFitness().clone());
+        	addConstraints(individual, x);
-			addConstraints(individual, x);
+        }
-		}
 	}
 
 	protected double[] rotateMaybe(double[] x) {
@@ -573,10 +572,6 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 		return "(De-)Activate constraints for the problem.";
 	}
 
-	/*
-	 * (non-Javadoc)
-	 * @see eva2.server.go.problems.AbstractOptimizationProblem#getAdditionalDataHeader()
-	 */
 	@Override
 	public String[] getAdditionalDataHeader() {
 		String[] superHeader = super.getAdditionalDataHeader();
@@ -587,10 +582,6 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 			return superHeader;
 	}
 
-	/*
-	 * (non-Javadoc)
-	 * @see eva2.server.go.problems.AbstractOptimizationProblem#getAdditionalDataInfo()
-	 */
 	@Override
 	public String[] getAdditionalDataInfo() {
 		String[] superInfo = super.getAdditionalDataInfo();

src/eva2/server/go/problems/B1Problem.java

@@ -11,7 +11,8 @@ import eva2.server.go.operators.mutation.MutateGAUniform;
 import eva2.server.go.strategies.InterfaceOptimizer;
 
 /**
- * Created by IntelliJ IDEA.
+ * The minimize bits problem for binary optimization.
+ * 
  * User: streiche
  * Date: 21.03.2003
  * Time: 13:05:33