Fix GUI hidden properties (setProblem) in Optimizers

src/eva2/optimization/strategies/AdaptiveDifferentialEvolution.java

@@ -20,7 +20,7 @@ import java.util.ArrayList;
  * Yu, Shen, Chen, et.al. in 2014 (IEEE Transaction on Cybernetics, DOI: 10.1109/TCYB.2013.2279211).
  */
 @Description("Differential Evolution with Two-Level Parameter Adaption (Yu, Shen, Chen, et.al., 2014")
-public class AdaptiveDifferentialEvolution implements InterfaceOptimizer {
+public class AdaptiveDifferentialEvolution extends AbstractOptimizer {
     protected Population population;
 
     @Parameter(name = "groups", description = "Number of sub-groups to use during optimization.")
@@ -153,36 +153,11 @@ public class AdaptiveDifferentialEvolution implements InterfaceOptimizer {
         }
     }
 
-    @Override
-    public Population getPopulation() {
-        return population;
-    }
-
-    @Override
-    public void setPopulation(Population pop) {
-        this.population = pop;
-    }
-
     @Override
     public InterfaceSolutionSet getAllSolutions() {
         return null;
     }
 
-    /**
-     * Sets the optimization problem. Will cast to AbstractOptimizationProblem.
-     *
-     * @param problem The optimization problem.
-     */
-    @Override
-    public void setProblem(InterfaceOptimizationProblem problem) {
-        this.optimizationProblem = (AbstractOptimizationProblem) problem;
-    }
-
-    @Override
-    public InterfaceOptimizationProblem getProblem() {
-        return this.optimizationProblem;
-    }
-
     @Override
     public String getStringRepresentation() {
         return null;

src/eva2/optimization/strategies/CBNPSO.java

@@ -81,8 +81,6 @@ public class CBNPSO extends ClusterBasedNichingEA implements Serializable {
 
     /**
      * Return the period of the sinusoidal sigma adaption or -1 if not applicable.
-     *
-     * @param p
      */
     public int getSigmaAdaptionPeriod() {
         ParamAdaption[] prmAd = getParameterControl();
@@ -177,10 +175,6 @@ public class CBNPSO extends ClusterBasedNichingEA implements Serializable {
      * is in [0,1] if any solutions have been identified, or -1 if the archive
      * is empty.
      *
-     * @param cbpso
-     * @param pop
-     * @param iteration
-     * @param maxIteration
      * @return
      */
     public double getInterestingSolutionRatio() {
@@ -196,9 +190,6 @@ public class CBNPSO extends ClusterBasedNichingEA implements Serializable {
         Population archived = solSet.getSolutions();
         Population interesting = archived.filterByFitness(fitThres, 0);
 
-//		Population archived = getArchivedSolutions();
-//		Population interesting = archived.filterByFitness(fitThres, 0);
-
         if (archived.size() > 0) {
             return ((double) interesting.size()) / ((double) archived.size());
         } else {

src/eva2/optimization/strategies/ClusterBasedNichingEA.java

@@ -27,6 +27,7 @@ import eva2.tools.EVAERROR;
 import eva2.tools.chart2d.*;
 import eva2.tools.math.Mathematics;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Hidden;
 
 import java.util.*;
 
@@ -786,6 +787,7 @@ public class ClusterBasedNichingEA extends AbstractOptimizer implements Interfac
      * @param problem
      */
     @Override
+    @Hidden
     public void setProblem(InterfaceOptimizationProblem problem) {
         this.optimizationProblem = problem;
         this.optimizer.setProblem(this.optimizationProblem);

src/eva2/optimization/strategies/ClusteringHillClimbing.java

@@ -10,7 +10,6 @@ import eva2.optimization.population.Population;
 import eva2.optimization.population.PopulationInterface;
 import eva2.optimization.population.SolutionSet;
 import eva2.problems.AbstractOptimizationProblem;
-import eva2.problems.F1Problem;
 import eva2.problems.InterfaceAdditionalPopulationInformer;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.tools.Pair;
@@ -36,17 +35,11 @@ import java.io.Serializable;
  */
 @Description("Similar to multi-start HC, but clusters the population during optimization to remove redundant individuals for efficiency."
         + "If the local search step does not achieve a minimum improvement, the population may be reinitialized.")
-public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener,
+public class ClusteringHillClimbing extends AbstractOptimizer implements InterfacePopulationChangedEventListener, Serializable, InterfaceAdditionalPopulationInformer {
-        InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
 
-    transient private InterfacePopulationChangedEventListener populationChangedEventListener;
-    transient private String identifier = "";
-    private Population population = new Population();
     private transient Population archive = new Population();
-    private InterfaceOptimizationProblem optimizationProblem = new F1Problem();
     private int hcEvalCycle = 1000;
     private int initialPopSize = 100;
-    private int loopCnt = 0;
     private int notifyGuiEvery = 50;
     private double sigmaClust = 0.01;
     private double minImprovement = 0.000001;
@@ -82,7 +75,6 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
         initialStepSize = other.initialStepSize;
         reduceFactor = other.reduceFactor;
         mutator = (MutateESFixedStepSize) other.mutator.clone();
-        loopCnt = 0;
     }
 
     @Override
@@ -99,40 +91,8 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
         setLocalSearchMethod(getLocalSearchMethod());
     }
 
-    /**
-     * This method will set the problem that is to be optimized
-     *
-     * @param problem
-     */
-    @Override
-    public void setProblem(InterfaceOptimizationProblem problem) {
-        this.optimizationProblem = problem;
-    }
-
-    @Override
-    public InterfaceOptimizationProblem getProblem() {
-        return this.optimizationProblem;
-    }
-
-    @Override
-    public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
-        this.populationChangedEventListener = ea;
-    }
-
-    @Override
-    public boolean removePopulationChangedEventListener(
-            InterfacePopulationChangedEventListener ea) {
-        if (populationChangedEventListener == ea) {
-            populationChangedEventListener = null;
-            return true;
-        } else {
-            return false;
-        }
-    }
-
     @Override
     public void initialize() {
-        loopCnt = 0;
         mutator = new MutateESFixedStepSize(initialStepSize);
         archive = new Population();
         hideHideable();
@@ -151,7 +111,6 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
      */
     @Override
     public void initializeByPopulation(Population pop, boolean reset) {
-        loopCnt = 0;
         this.population = (Population) pop.clone();
         population.addPopulationChangedEventListener(null);
         if (reset) {
@@ -161,20 +120,10 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
         }
     }
 
-    /**
-     * Something has changed
-     */
-    protected void firePropertyChangedEvent(String name) {
-        if (this.populationChangedEventListener != null) {
-            this.populationChangedEventListener.registerPopulationStateChanged(this, name);
-        }
-    }
-
     @Override
     public void optimize() {
         double improvement;
 
-        loopCnt++;
         population.addPopulationChangedEventListener(this);
         population.setNotifyEvalInterval(notifyGuiEvery);
         Pair<Population, Double> popD;
@@ -244,23 +193,6 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
         }
     }
 
-    /**
-     * Assuming that all optimizer will store thier data in a population we will
-     * allow acess to this population to query to current state of the
-     * optimizer.
-     *
-     * @return The population of current solutions to a given problem.
-     */
-    @Override
-    public Population getPopulation() {
-        return this.population;
-    }
-
-    @Override
-    public void setPopulation(Population pop) {
-        this.population = pop;
-    }
-
     @Override
     public InterfaceSolutionSet getAllSolutions() {
         Population tmp = new Population();

src/eva2/optimization/strategies/DynamicParticleSwarmOptimization.java

@@ -11,6 +11,7 @@ import eva2.tools.SelectedTag;
 import eva2.tools.math.Mathematics;
 import eva2.tools.math.RNG;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Hidden;
 
 /**
  * This extends our particle swarm implementation to dynamic optimization problems.
@@ -130,7 +131,7 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
      *
      * @param index The individual to update.
      * @param pop   The current population.
-     * @param best  The best individual found so far.
+     * @param indy  The best individual found so far.
      */
     private void updateQuantumIndividual(int index, AbstractEAIndividual indy, Population pop) {
         InterfaceDataTypeDouble endy = (InterfaceDataTypeDouble) indy;
@@ -473,6 +474,7 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
 
 
     @Override
+    @Hidden
     public void setProblem(InterfaceOptimizationProblem problem) {
         super.setProblem(problem);
         if (problem instanceof AbstractOptimizationProblem) {

src/eva2/optimization/strategies/GeneticAlgorithm.java

@@ -186,16 +186,6 @@ public class GeneticAlgorithm extends AbstractOptimizer implements java.io.Seria
         }
     }
 
-    /**
-     * This method will set the problem that is to be optimized
-     *
-     * @param problem
-     */
-    @Override
-    public void setProblem(InterfaceOptimizationProblem problem) {
-        this.optimizationProblem = problem;
-    }
-
     @Override
     public InterfaceOptimizationProblem getProblem() {
         return this.optimizationProblem;

src/eva2/optimization/strategies/IslandModelEA.java

@@ -12,6 +12,7 @@ import eva2.problems.F8Problem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.problems.TF1Problem;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Hidden;
 
 /**
  * The one and only island model for parallelization. Since parallelization
@@ -297,6 +298,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
      * @param problem
      */
     @Override
+    @Hidden
     public void setProblem(InterfaceOptimizationProblem problem) {
         this.optimizationProblem = problem;
         this.optimizer.setProblem(problem);

src/eva2/optimization/strategies/MemeticAlgorithm.java

@@ -11,6 +11,7 @@ import eva2.problems.F1Problem;
 import eva2.problems.InterfaceLocalSearchable;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Hidden;
 
 import java.util.Hashtable;
 
@@ -196,6 +197,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
      * @param problem
      */
     @Override
+    @Hidden
     public void setProblem(InterfaceOptimizationProblem problem) {
         this.optimizationProblem = problem;
         this.globalOptimizer.setProblem(this.optimizationProblem);

src/eva2/optimization/strategies/MultiObjectiveEA.java

@@ -15,6 +15,7 @@ import eva2.problems.AbstractOptimizationProblem;
 import eva2.problems.FM0Problem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Hidden;
 
 /**
  * A generic framework for multi-objecitve optimization, you need to specify an
@@ -183,6 +184,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
      * @param problem
      */
     @Override
+    @Hidden
     public void setProblem(InterfaceOptimizationProblem problem) {
         this.optimizationProblem = problem;
         this.optimizer.setProblem(problem);

src/eva2/optimization/strategies/NelderMeadSimplex.java

@@ -52,11 +52,6 @@ public class NelderMeadSimplex extends AbstractOptimizer implements Serializable
         return new NelderMeadSimplex(this);
     }
 
-    @Override
-    public void setProblem(InterfaceOptimizationProblem problem) {
-        optimizationProblem = (AbstractOptimizationProblem) problem;
-    }
-
     public boolean setProblemAndPopSize(InterfaceOptimizationProblem problem) {
         setProblem(problem);
         if (optimizationProblem instanceof AbstractProblemDouble) {
@@ -77,8 +72,6 @@ public class NelderMeadSimplex extends AbstractOptimizer implements Serializable
         for (int i = 0; i < r.length; i++) {
             r[i] = 2 * centroid[i] - refX[i];
         }
-//		double alpha = 1.3;
-//		for (int i=0; i<r.length; i++) r[i] = centroid[i] + alpha*(centroid[i] - refX[i]);
         return r;
     }
 

src/eva2/optimization/strategies/NichePSO.java

@@ -30,6 +30,7 @@ import eva2.problems.*;
 import eva2.tools.SelectedTag;
 import eva2.tools.chart2d.*;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Hidden;
 
 import java.io.*;
 import java.text.SimpleDateFormat;
@@ -1194,6 +1195,7 @@ public class NichePSO extends AbstractOptimizer implements InterfaceAdditionalPo
      * @tested ps This method will set the problem that is to be optimized
      */
     @Override
+    @Hidden
     public void setProblem(InterfaceOptimizationProblem problem) {
         // set member
         this.optimizationProblem = problem;

src/eva2/optimization/strategies/ParticleFilterOptimization.java

@@ -16,6 +16,7 @@ import eva2.problems.AbstractOptimizationProblem;
 import eva2.problems.F1Problem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Hidden;
 
 /**
  * This is a Particle Filter implemented by Frank Senke, only some documentation
@@ -236,6 +237,7 @@ public class ParticleFilterOptimization extends AbstractOptimizer implements jav
      * @param problem
      */
     @Override
+    @Hidden
     public void setProblem(InterfaceOptimizationProblem problem) {
         this.optimizationProblem = problem;
         if (problem instanceof AbstractOptimizationProblem) {

src/eva2/optimization/strategies/PopulationBasedIncrementalLearning.java

@@ -13,6 +13,9 @@ import eva2.problems.AbstractOptimizationProblem;
 import eva2.problems.B1Problem;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Hidden;
+
+import java.util.logging.Logger;
 
 /**
  * Population based incremental learning in the PSM by Monmarche version with
@@ -27,6 +30,7 @@ import eva2.util.annotation.Description;
 @Description("The Population based incremental learning is based on a statistical distribution of bit positions. Please note: This optimizer requires a binary genotype!")
 public class PopulationBasedIncrementalLearning extends AbstractOptimizer implements java.io.Serializable {
 
+    private final static Logger LOGGER = Logger.getLogger(PopulationBasedIncrementalLearning.class.getName());
     // These variables are necessary for the simple testcase
     private InterfaceOptimizationProblem optimizationProblem = new B1Problem();
     private boolean useElitism = true;
@@ -144,11 +148,12 @@ public class PopulationBasedIncrementalLearning extends AbstractOptimizer implem
      * @param problem
      */
     @Override
+    @Hidden
     public void setProblem(InterfaceOptimizationProblem problem) {
         this.optimizationProblem = problem;
         if (optimizationProblem instanceof AbstractOptimizationProblem) {
             if (!(((AbstractOptimizationProblem) optimizationProblem).getIndividualTemplate() instanceof InterfaceGAIndividual)) {
-                System.err.println("Error: PBIL only works with GAIndividuals!");
+                LOGGER.warning("PBIL only works with GAIndividuals!");
             }
         }
     }

src/eva2/optimization/strategies/ScatterSearch.java

@@ -23,6 +23,7 @@ import eva2.tools.SelectedTag;
 import eva2.tools.math.Mathematics;
 import eva2.tools.math.RNG;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Hidden;
 
 import java.util.ArrayList;
 
@@ -102,6 +103,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
     }
 
     @Override
+    @Hidden
     public void setProblem(InterfaceOptimizationProblem problem) {
         this.optimizationProblem = (AbstractOptimizationProblem) problem;
     }

src/eva2/optimization/strategies/ThresholdAlgorithm.java

@@ -115,21 +115,6 @@ public class ThresholdAlgorithm extends AbstractOptimizer implements java.io.Ser
         return result;
     }
 
-    /**
-     * This method will set the problem that is to be optimized
-     *
-     * @param problem
-     */
-    @Override
-    public void setProblem(InterfaceOptimizationProblem problem) {
-        this.optimizationProblem = problem;
-    }
-
-    @Override
-    public InterfaceOptimizationProblem getProblem() {
-        return this.optimizationProblem;
-    }
-
     /**
      * This method will initialize the HillClimber
      */

src/eva2/optimization/strategies/WingedMultiObjectiveEA.java

@@ -20,16 +20,13 @@ import eva2.util.annotation.Description;
  * performance of this algorithm strongly depends on the optimization problem.
  */
 @Description("This is Evolutionary Multi-Criteria Optimization Algorithm hybridized with Local Searchers to span the Pareto-Front.")
-public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable {
+public class WingedMultiObjectiveEA extends AbstractOptimizer implements java.io.Serializable {
 
     private InterfaceOptimizer multiObjectiveEA = new MultiObjectiveEA();
     private InterfaceOptimizer singleObjectiveEA = new GeneticAlgorithm();
     private InterfaceOptimizer[] singleObjectiveOptimizers;
-    private Population population = new Population();
     private int migrationRate = 5;
     private int outputDimension = 2;
-    private InterfaceOptimizationProblem optimizationProblem = new FM0Problem();
-    transient private InterfacePopulationChangedEventListener populationChangedEventListener;
 
     public WingedMultiObjectiveEA() {
     }
@@ -207,51 +204,6 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
         }
     }
 
-    /**
-     * This method allows you to add the LectureGUI as listener to the Optimizer
-     *
-     * @param ea
-     */
-    @Override
-    public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
-        this.populationChangedEventListener = ea;
-    }
-
-    @Override
-    public boolean removePopulationChangedEventListener(
-            InterfacePopulationChangedEventListener ea) {
-        if (populationChangedEventListener == ea) {
-            populationChangedEventListener = null;
-            return true;
-        } else {
-            return false;
-        }
-    }
-
-    /**
-     * Something has changed
-     */
-    protected void firePropertyChangedEvent(String name) {
-        if (this.populationChangedEventListener != null) {
-            this.populationChangedEventListener.registerPopulationStateChanged(this, name);
-        }
-    }
-
-    /**
-     * This method will set the problem that is to be optimized
-     *
-     * @param problem
-     */
-    @Override
-    public void setProblem(InterfaceOptimizationProblem problem) {
-        this.optimizationProblem = problem;
-    }
-
-    @Override
-    public InterfaceOptimizationProblem getProblem() {
-        return this.optimizationProblem;
-    }
-
     /**
      * This method will return a string describing all properties of the
      * optimizer and the applied methods.