Making PBIL available from the Matlab interface.

src/eva2/OptimizerFactory.java

@@ -36,8 +36,10 @@ import eva2.server.go.operators.selection.InterfaceSelection;
 import eva2.server.go.operators.selection.SelectBestIndividuals;
 import eva2.server.go.operators.terminators.CombinedTerminator;
 import eva2.server.go.operators.terminators.EvaluationTerminator;
+import eva2.server.go.populations.PBILPopulation;
 import eva2.server.go.populations.Population;
 import eva2.server.go.problems.AbstractOptimizationProblem;
+import eva2.server.go.problems.B1Problem;
 import eva2.server.go.strategies.ClusterBasedNichingEA;
 import eva2.server.go.strategies.ClusteringHillClimbing;
 import eva2.server.go.strategies.DifferentialEvolution;
@@ -50,6 +52,7 @@ import eva2.server.go.strategies.InterfaceOptimizer;
 import eva2.server.go.strategies.MonteCarloSearch;
 import eva2.server.go.strategies.MultiObjectiveEA;
 import eva2.server.go.strategies.ParticleSwarmOptimization;
+import eva2.server.go.strategies.PopulationBasedIncrementalLearning;
 import eva2.server.go.strategies.SimulatedAnnealing;
 import eva2.server.go.strategies.Tribes;
 import eva2.server.modules.GOParameters;
@@ -104,6 +107,8 @@ public class OptimizerFactory {
 	public final static int CMA_ES_IPOP = 11;
 	
 	public final static int CBN_GA = 12;
+	
+	public final static int PBIL = 13;
 
 	public final static int defaultFitCalls = 10000;
 
@@ -496,6 +501,41 @@ public class OptimizerFactory {
 		return sa;
 	}
 
+	/**
+	 * Calling init here makes problems when using the Matlab interface.
+	 * 
+	 * @param learningRate
+	 * @param mutateSigma
+	 * @param mutationRate
+	 * @param positiveSamples
+	 * @param selection
+	 * @param popsize
+	 * @param problem
+	 * @param listener
+	 * @return
+	 */
+	public static final PopulationBasedIncrementalLearning createPBIL(
+			double learningRate, double mutateSigma, double mutationRate, 
+			int positiveSamples, InterfaceSelection selection, int popsize,
+			AbstractOptimizationProblem problem, InterfacePopulationChangedEventListener listener) {
+		problem.initProblem();
+		PopulationBasedIncrementalLearning pbil = new PopulationBasedIncrementalLearning();
+
+		pbil.setLearningRate(learningRate);
+		pbil.setMutateSigma(mutateSigma);
+		pbil.setMutationRate(mutationRate);
+		pbil.setPopulation(new PBILPopulation(popsize));
+		pbil.setSelectionMethod(selection);
+		pbil.setPositiveSamples(positiveSamples);
+		
+		pbil.addPopulationChangedEventListener(listener);
+		pbil.SetProblem(problem);
+
+		if (listener != null) listener.registerPopulationStateChanged(pbil.getPopulation(), "");
+
+		return pbil;
+	}
+	
 	// /////////////////////////// Termination criteria
 	public static InterfaceTerminator makeDefaultTerminator() {
 		return new EvaluationTerminator(defaultFitCalls);
@@ -547,6 +587,8 @@ public class OptimizerFactory {
 			return cmaESIPOP(problem);
 		case CBN_GA:
 			return cbnGA(problem);
+		case PBIL:
+			return standardPBIL(problem);
 		default:
 			System.err.println("Error: optimizer type " + optType
 					+ " is unknown!");
@@ -562,7 +604,8 @@ public class OptimizerFactory {
 	 */
 	public static String showOptimizers() {
 		return "1: Standard ES \n2: CMA-ES \n3: GA \n4: PSO \n5: DE \n6: Tribes \n7: Random (Monte Carlo) "
-				+ "\n8: Hill-Climbing \n9: Cluster-based niching ES \n10: Clustering Hill-Climbing \n11: IPOP-CMA-ES.";
+				+ "\n8: Hill-Climbing \n9: Cluster-based niching ES \n10: Clustering Hill-Climbing \n11: IPOP-CMA-ES "
+				+ "\n12: Cluster-based niching GA \n13: PBIL";
 	}
 
 	/**
@@ -1110,6 +1153,13 @@ public class OptimizerFactory {
 		return makeParams(cbn, 100, problem, randSeed, makeDefaultTerminator());
 	}
 	
+	public static final GOParameters standardPBIL(AbstractOptimizationProblem problem) {
+		PopulationBasedIncrementalLearning pbil = createPBIL(0.04, 0.01, 0.5, 1, 
+				new SelectBestIndividuals(), 50, problem, null);
+
+		return makeParams(pbil, pbil.getPopulation(), problem, randSeed, makeDefaultTerminator());
+	}
+	
 	/**
 	 * Create a standard clustering hill climbing employing simple ES mutation with adaptive
 	 * step size, starting in parallel 100 local searches and clustering intermediate populations

src/eva2/server/go/populations/PBILPopulation.java

@@ -19,11 +19,15 @@ import wsi.ra.math.RNG;
 
 public class PBILPopulation extends Population implements Cloneable, java.io.Serializable {
 
-    private double[]    m_ProbabilityVector = new double[0];
+    private double[]    m_ProbabilityVector = new double[1];
 
     public PBILPopulation() {
     }
 
+    public PBILPopulation(int popSize) {
+    	super(popSize);
+    }
+    
     public PBILPopulation(PBILPopulation population) {
         this.m_Generation       = population.m_Generation;
         this.m_FunctionCalls    = population.m_FunctionCalls;
@@ -132,7 +136,7 @@ public class PBILPopulation extends Population implements Cloneable, java.io.Ser
     public void SetProbabilityVector(double[] pv) {
         this.m_ProbabilityVector = pv;
     }
-    public double[] getProbabilityVector(double[] pv) {
+    public double[] getProbabilityVector() {
         return this.m_ProbabilityVector;
     }
 

src/eva2/server/go/strategies/PopulationBasedIncrementalLearning.java

@@ -38,6 +38,7 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
     private double                                  m_MutationRate      = 0.5;
     private double                                  m_MutateSigma       = 0.01;
     private int                                     m_NumberOfPositiveSamples = 1;
+    private double[]								m_initialProbabilities = ((PBILPopulation)m_Population).getProbabilityVector();
 
     public PopulationBasedIncrementalLearning() {
     }
@@ -59,6 +60,11 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
 
     public void init() {
         this.m_Problem.initPopulation(this.m_Population);
+        if ((m_initialProbabilities!=null) && (m_initialProbabilities.length==((PBILPopulation)m_Population).getProbabilityVector().length)) {
+        	((PBILPopulation)m_Population).SetProbabilityVector(m_initialProbabilities);
+        } else {
+        	if (m_initialProbabilities!=null) System.err.println("Warning: initial probability vector doesnt match in length!");
+        }
         this.evaluatePopulation(this.m_Population);
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
@@ -308,4 +314,12 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
     public String positiveSamplesTipText() {
         return "The number of positive samples that update the PBIL vector.";
     }
+
+	public double[] getInitialProbabilities() {
+		return m_initialProbabilities;
+	}
+
+	public void setInitialProbabilities(double[] probabilities) {
+		m_initialProbabilities = probabilities;
+	}
 }