Refactor @Parameter annotation to be applied to setter methods.

fixes #29
- Change Parameter annotation to exist on methods
- Add default value to name field to make it optional
- Adjust classes that already use the annotation

src/eva2/optimization/modules/AbstractOptimizationParameters.java

@@ -25,13 +25,11 @@ import java.util.logging.Logger;
 public abstract class AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
     protected static final Logger LOGGER = Logger.getLogger(AbstractOptimizationParameters.class.getName());
 
-    @Parameter(name = "Random Seed", description = "Random number seed, set to zero to use current system time.")
     protected long randomSeed = (long) 0.0;
 
     /**
      * The optimizer to be executed.
      */
-    @Parameter(name = "Optimizer", description = "Choose an optimization strategy.")
     protected InterfaceOptimizer optimizer;
 
     /**
@@ -39,14 +37,12 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
      * When changed it is automatically applied to the
      * selected optimizer.
      */
-    @Parameter(name = "Problem", description = "Choose the problem that is to optimize and the EA individual parameters.")
     protected InterfaceOptimizationProblem problem;
 
     /**
      * The termination criteria that terminated an
      * optimization run.
      */
-    @Parameter(name = "Terminator", description = "Choose a termination criterion.")
     protected InterfaceTerminator terminator;
 
     /**
@@ -54,7 +50,6 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
      * This can be enabled in the UI and will perform additional
      * optimization e.g. with Hill Climbing.
      */
-    @Parameter(name = "Post Processing", description = "Parameters for the post processing step.")
     protected InterfacePostProcessParams postProcessParams = new PostProcessParams(false);
 
     transient protected InterfacePopulationChangedEventListener populationChangedEventListener;
@@ -183,6 +178,7 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
     }
 
     @Override
+    @Parameter(description = "The optimization strategy to use.")
     public void setOptimizer(InterfaceOptimizer optimizer) {
         this.optimizer = optimizer;
         this.optimizer.setProblem(this.problem);
@@ -219,6 +215,7 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
      * @param problem
      */
     @Override
+    @Parameter(description = "Choose the problem that is to optimize and the EA individual parameters.")
     public void setProblem(InterfaceOptimizationProblem problem) {
         this.problem = problem;
         this.optimizer.setProblem(this.problem);
@@ -236,6 +233,7 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
      * @param x Long seed.
      */
     @Override
+    @Parameter(name = "seed", description = "Random number seed, set to zero to use current system time.")
     public void setRandomSeed(long x) {
         randomSeed = x;
     }
@@ -257,6 +255,7 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
      * @param term The new terminator
      */
     @Override
+    @Parameter(description = "The termination criterion.")
     public void setTerminator(InterfaceTerminator term) {
         this.terminator = term;
     }
@@ -272,6 +271,7 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
     }
 
     @Override
+    @Parameter(description = "Parameters for the post processing step.")
     public void setPostProcessParams(InterfacePostProcessParams ppp) {
         postProcessParams = ppp;
     }

src/eva2/optimization/operator/terminators/EvaluationTerminator.java

@@ -21,7 +21,6 @@ public class EvaluationTerminator implements InterfaceTerminator,
     /**
      * Number of fitness calls on the problem which is optimized.
      */
-    @Parameter(name = "Max. Fitness Calls", description = "Number of calls to fitness function.")
     protected int maxFitnessCalls = 1000;
 
     public EvaluationTerminator() {
@@ -67,6 +66,7 @@ public class EvaluationTerminator implements InterfaceTerminator,
         return ret;
     }
 
+    @Parameter(name = "calls", description = "Number of calls to fitness function.")
     public void setFitnessCalls(int x) {
         maxFitnessCalls = x;
     }

src/eva2/optimization/operator/terminators/MaximumTimeTerminator.java

@@ -15,7 +15,6 @@ import eva2.util.annotation.Parameter;
  */
 @Description("Terminate if a maximum time (seconds) was reached.")
 public class MaximumTimeTerminator implements InterfaceTerminator {
-    @Parameter(name = "maxTime", description = "Maximum time in seconds")
     private int maximumTime = 5;
 
     private long startTime;
@@ -49,6 +48,7 @@ public class MaximumTimeTerminator implements InterfaceTerminator {
         return maximumTime;
     }
 
+    @Parameter(name = "time", description = "Maximum time in seconds")
     public void setMaximumTime(int maximumTime) {
         this.maximumTime = maximumTime;
     }

src/eva2/optimization/strategies/AbstractOptimizer.java

@@ -14,7 +14,6 @@ import java.util.ArrayList;
  */
 public abstract class AbstractOptimizer implements InterfaceOptimizer {
 
-    @Parameter(name = "Population", description = "Edit the properties of the population used.")
     protected Population population = new Population();
 
     protected InterfaceOptimizationProblem optimizationProblem = new F1Problem();
@@ -56,6 +55,7 @@ public abstract class AbstractOptimizer implements InterfaceOptimizer {
     }
 
     @Override
+    @Parameter(description = "Edit the properties of the population used.")
     public void setPopulation(Population pop) {
         this.population = pop;
     }

src/eva2/optimization/strategies/AdaptiveDifferentialEvolution.java

@@ -1,13 +1,12 @@
 package eva2.optimization.strategies;
 
-import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
+import eva2.optimization.population.InterfacePopulationChangedEventListener;
 import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
 import eva2.problems.AbstractOptimizationProblem;
 import eva2.problems.F1Problem;
 import eva2.util.annotation.Description;
-import eva2.util.annotation.Parameter;
 
 import java.util.ArrayList;
 
@@ -19,13 +18,8 @@ import java.util.ArrayList;
 public class AdaptiveDifferentialEvolution extends AbstractOptimizer {
     protected Population population;
 
-    @Parameter(name = "groups", description = "Number of sub-groups to use during optimization.")
     protected int nonOverlappingGroups = 5;
-
-    @Parameter(name = "F", description = "Differential Weight")
     protected double differentialWeight = 0.8;
-
-    @Parameter(name = "CR", description = "Crossover Rate")
     protected double crossoverRate = 0.6;
 
 

src/eva2/optimization/strategies/ArtificialBeeColony.java

@@ -23,7 +23,6 @@ public class ArtificialBeeColony extends AbstractOptimizer implements Serializab
     /**
      * A food source which could not be improved through "maxTrials" trials is abandoned by its employed bee.
      */
-    @Parameter(name = "trials", description = "Maximum number of trials until bee abandons the food source")
     protected int maxTrials = 100;
 
     protected AbstractEAIndividual bestIndividual;
@@ -243,6 +242,7 @@ public class ArtificialBeeColony extends AbstractOptimizer implements Serializab
         this.population = pop;
     }
 
+    @Parameter(name = "trials", description = "Maximum number of trials until bee abandons the food source")
     public void setMaxTrials(int maxTrials) {
         this.maxTrials = maxTrials;
     }

src/eva2/optimization/strategies/DifferentialEvolution.java

@@ -31,16 +31,12 @@ import java.util.Vector;
 public class DifferentialEvolution extends AbstractOptimizer implements java.io.Serializable {
     protected transient Population children = null;
 
-    @Parameter(name = "DEType", description = "Mutation type for DE")
     private eva2.optimization.enums.DEType DEType;
 
-    @Parameter(name = "F", description = "Differential Weight")
     private double differentialWeight = 0.8;
 
-    @Parameter(name = "CR", description = "Crossover Rate")
     private double crossoverRate = 0.6;
 
-    @Parameter(name = "Lambda", description = "Enhance greediness through amplification of the differential vector to the best individual for DE2.")
     private double lambda = 0.6;
 
     private double mt = 0.05;
@@ -697,6 +693,7 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
      *
      * @param f
      */
+    @Parameter(name = "F", description = "Differential Weight")
     public void setDifferentialWeight(double f) {
         this.differentialWeight = f;
     }
@@ -715,6 +712,7 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
      *
      * @param k
      */
+    @Parameter(name = "CR", description = "Crossover Rate")
     public void setCrossoverRate(double k) {
         if (k < 0) {
             k = 0;
@@ -739,6 +737,7 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
      *
      * @param l
      */
+    @Parameter(description = "Enhance greediness through amplification of the differential vector to the best individual for DE2.")
     public void setLambda(double l) {
         this.lambda = l;
     }
@@ -775,6 +774,7 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
      *
      * @param s The type.
      */
+    @Parameter(name = "type", description = "Mutation type for DE")
     public void setDEType(eva2.optimization.enums.DEType s) {
         this.DEType = s;
         // show mt for trig. DE only

src/eva2/optimization/strategies/EvolutionStrategies.java

@@ -24,14 +24,8 @@ import eva2.util.annotation.Parameter;
  */
 @Description(value = "This is an Evolution Strategy. Note that the population size depends on mu (number of parents) and lambda (number of offspring)")
 public class EvolutionStrategies extends AbstractOptimizer implements java.io.Serializable {
-
-    @Parameter(description = "Mu", name = "mu")
     protected int mu = 5;
-
-    @Parameter(description = "Lambda", name = "lambda")
     protected int lambda = 20;
-
-    @Parameter(description = "Determines whether the +-Strategy should be used.", name = "usePlus")
     protected boolean usePlusStrategy = false;
     private InterfaceSelection parentSelection = new SelectRandom();
     private InterfaceSelection partnerSelection = new SelectRandom();
@@ -369,6 +363,7 @@ public class EvolutionStrategies extends AbstractOptimizer implements java.io.Se
      *
      * @param elitism
      */
+    @Parameter(description = "Determines whether the +-Strategy should be used.", name = "usePlus")
     public void setPlusStrategy(boolean elitism) {
         this.usePlusStrategy = elitism;
     }
@@ -405,8 +400,9 @@ public class EvolutionStrategies extends AbstractOptimizer implements java.io.Se
     /**
      * This method allows you to set parent population size myu
      *
-     * @param myu The parent population size.
+     * @param mu The parent population size.
      */
+    @Parameter(description = "The parent population size.")
     public void setMu(int mu) {
         this.mu = mu;
     }
@@ -424,6 +420,7 @@ public class EvolutionStrategies extends AbstractOptimizer implements java.io.Se
      *
      * @param lambda The children population size.
      */
+    @Parameter(description = "The children population size.")
     public void setLambda(int lambda) {
         this.lambda = lambda;
     }

src/eva2/optimization/strategies/FloodAlgorithm.java

@@ -26,7 +26,6 @@ public class FloodAlgorithm extends AbstractOptimizer implements java.io.Seriali
     GAIndividualBinaryData bestIndividual, testIndividual;
     public double initialFloodPeak = 2000.0, currentFloodPeak;
 
-    @Parameter(name = "drainRate", description = "Set the drain rate that reduces the current flood level each generation.")
     public double drainRate = 1.0;
 
     public FloodAlgorithm() {
@@ -227,6 +226,7 @@ public class FloodAlgorithm extends AbstractOptimizer implements java.io.Seriali
         return this.drainRate;
     }
 
+    @Parameter(description = "Set the drain rate that reduces the current flood level each generation.")
     public void setDrainRate(double a) {
         this.drainRate = a;
         if (this.drainRate < 0) {

src/eva2/problems/AbstractOptimizationProblem.java

@@ -29,7 +29,6 @@ import eva2.util.annotation.Parameter;
 import javax.swing.*;
 import java.awt.*;
 import java.io.Serializable;
-import java.util.Vector;
 import java.util.concurrent.ExecutorService;
 import java.util.concurrent.Executors;
 import java.util.concurrent.Semaphore;
@@ -72,10 +71,8 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
 
     protected AbstractEAIndividual template = null;
 
-    @Parameter(name = "defaultAccuracy", description = "A default threshold to identify optima - e.g. the assumed minimal distance between any two optima.")
     private double defaultAccuracy = 0.001; // default accuracy for identifying optima.
 
-    @Parameter(name = "problemDimension", description = "Length of the x vector to be optimized.")
     protected int problemDimension = 10;
 
     /**
@@ -561,6 +558,7 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
         return defaultAccuracy;
     }
 
+    @Parameter(name = "accuracy", description = "A default threshold to identify optima - e.g. the assumed minimal distance between any two optima.")
     public void setDefaultAccuracy(double defAcc) {
         defaultAccuracy = defAcc;
     }

src/eva2/problems/AbstractParallelOptimizationProblem.java

@@ -6,9 +6,7 @@ import eva2.util.annotation.Parameter;
  * This class is under construction.
  */
 public abstract class AbstractParallelOptimizationProblem extends AbstractOptimizationProblem {
-    @Parameter(name = "localCPUs", description = "Set the number of local CPUS (only active in non-parallelized mode).")
     private int localCPUs = 4;
-    @Parameter(name = "paralellize", description = "Toggle between parallel and serial implementation.")
     private boolean parallelize = false;
 
     @Override
@@ -34,6 +32,7 @@ public abstract class AbstractParallelOptimizationProblem extends AbstractOptimi
         return this.parallelize;
     }
 
+    @Parameter(description = "Toggle between parallel and serial implementation.")
     public void setParallelize(boolean b) {
         this.parallelize = b;
     }
@@ -43,6 +42,7 @@ public abstract class AbstractParallelOptimizationProblem extends AbstractOptimi
      *
      * @param n Number of processors.
      */
+    @Parameter(name = "cpu", description = "Set the number of local CPUS (only active in non-parallelized mode).")
     public void setNumberLocalCPUs(int n) {
         this.localCPUs = n;
     }

src/eva2/util/annotation/Parameter.java

@@ -3,9 +3,8 @@ package eva2.util.annotation;
 import java.lang.annotation.*;
 
 @Retention(RetentionPolicy.RUNTIME)
-@Target(ElementType.FIELD)
+@Target(ElementType.METHOD)
 public @interface Parameter {
-    String name();
-
+    String name() default "";
     String description();
 }