Rename more init() methods to initialize()

src/eva2/optimization/operator/crossover/TestESCrossover.java

@@ -113,7 +113,7 @@ public class TestESCrossover implements java.io.Serializable {
                 tmpIndyEA.initialize(optimizationProblem);
                 partners.add(tmpIndyEA);
             }
-            partners.init();
+            partners.initialize();
             daddy = (AbstractEAIndividual) ((AbstractEAIndividual) tmpIndyD).clone();
             daddy.initialize(optimizationProblem);
             plot.clearAll();

src/eva2/optimization/population/PBILPopulation.java

@@ -40,7 +40,7 @@ public class PBILPopulation extends Population implements Cloneable, java.io.Ser
      * have been inited by a problem.
      */
     @Override
-    public void init() {
+    public void initialize() {
         this.generationCount = 0;
         this.functionCallCount = 0;
         if (!(this.get(0) instanceof InterfaceGAIndividual)) {

src/eva2/optimization/population/Population.java

@@ -363,19 +363,17 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     }
 
     /**
-     * This method inits the state of the population AFTER the individuals have
-     * been inited by a problem
+     * This method initializes the state of the population AFTER the individuals have
+     * been initialized by a problem
      */
-    public void init() {
+    public void initialize() {
         this.historyList = new LinkedList<>();
         this.generationCount = 0;
         this.functionCallCount = 0;
         double[] popSeed = null;
-//    	evaluationTimeHashes = null;
-//    	evaluationTimeModCount = -1;
         if (this.populationArchive != null) {
             this.populationArchive.clear();
-            this.populationArchive.init();
+            this.populationArchive.initialize();
         }
         switch (initMethod) {
             case individualDefault:
@@ -433,14 +431,12 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         return seedPos;
     }
 
+    @Parameter(description = "Position around which the population will be (randomly) initialized. Be aware that the vector length must match (or exceed) problem dimension!")
     public void setInitPos(double[] si) {
         seedPos = si;
     }
 
-    public String initPosTipText() {
-        return "Position around which the population will be (randomly) initialized. Be aware that the vector length must match (or exceed) problem dimension!";
-    }
-
+    @Parameter(description = "Length of hypercube within which individuals are initialized around the initial position.")
     public void setInitAround(double d) {
         aroundDist = d;
     }
@@ -449,10 +445,6 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         return aroundDist;
     }
 
-    public String initAroundTipText() {
-        return "Lenght of hypercube within which individuals are initialized around the initial position.";
-    }
-
     /**
      * This method inits the population. Function and generation counters are
      * reset and size default Individuals are created and initialized by the

src/eva2/optimization/strategies/AdaptiveDifferentialEvolution.java

@@ -65,7 +65,7 @@ public class AdaptiveDifferentialEvolution extends AbstractOptimizer {
     public void initializeByPopulation(Population pop, boolean reset) {
         this.population = (Population) pop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.evaluatePopulation(this.population);
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/ArtificialBeeColony.java

@@ -64,7 +64,7 @@ public class ArtificialBeeColony extends AbstractOptimizer implements Serializab
     public void initializeByPopulation(Population pop, boolean reset) {
         this.population = (Population) pop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.evaluatePopulation(this.population);
             this.bestIndividual = this.population.getBestEAIndividual();
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);

src/eva2/optimization/strategies/CHCAdaptiveSearchAlgorithm.java

@@ -78,7 +78,7 @@ public class CHCAdaptiveSearchAlgorithm extends AbstractOptimizer implements jav
     public void initializeByPopulation(Population pop, boolean reset) {
         this.population = (Population) pop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
         }
         AbstractEAIndividual tmpIndy = ((AbstractEAIndividual) (this.population.get(0)));
         if (tmpIndy instanceof InterfaceGAIndividual) {

src/eva2/optimization/strategies/ClusterBasedNichingEA.java

@@ -216,7 +216,7 @@ public class ClusterBasedNichingEA extends AbstractOptimizer implements Interfac
     public void initializeByPopulation(Population pop, boolean reset) {
         this.undifferentiatedPopulation = (Population) pop.clone();
         if (reset) {
-            this.undifferentiatedPopulation.init();
+            this.undifferentiatedPopulation.initialize();
         }
         initDefaults(reset);
     }

src/eva2/optimization/strategies/ClusteringHillClimbing.java

@@ -114,7 +114,7 @@ public class ClusteringHillClimbing extends AbstractOptimizer implements Interfa
         this.population = (Population) pop.clone();
         population.addPopulationChangedEventListener(null);
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.optimizationProblem.evaluate(this.population);
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/DifferentialEvolution.java

@@ -117,7 +117,7 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
     public void initializeByPopulation(Population pop, boolean reset) {
         this.population = (Population) pop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.evaluatePopulation(this.population);
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/EsDpiNiching.java

@@ -908,7 +908,7 @@ public class EsDpiNiching extends AbstractOptimizer implements Serializable, Int
     public void initializeByPopulation(Population pop, boolean reset) {
         this.population = (Population) pop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.optimizationProblem.evaluate(population);
             population.incrGeneration();
         }

src/eva2/optimization/strategies/EvolutionStrategies.java

@@ -101,7 +101,7 @@ public class EvolutionStrategies extends AbstractOptimizer implements java.io.Se
         origPopSize = pop.getTargetSize();
         this.population = (Population) pop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.evaluatePopulation(this.population);
         }
     }

src/eva2/optimization/strategies/EvolutionaryProgramming.java

@@ -59,7 +59,7 @@ public class EvolutionaryProgramming extends AbstractOptimizer implements java.i
     public void initializeByPopulation(Population pop, boolean reset) {
         this.population = (Population) pop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.evaluatePopulation(this.population);
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/FloodAlgorithm.java

@@ -65,7 +65,7 @@ public class FloodAlgorithm extends AbstractOptimizer implements java.io.Seriali
     public void initializeByPopulation(Population pop, boolean reset) {
         this.population = (Population) pop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.optimizationProblem.evaluate(this.population);
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/GeneticAlgorithm.java

@@ -61,7 +61,7 @@ public class GeneticAlgorithm extends AbstractOptimizer implements java.io.Seria
         this.population = (Population) pop.clone();
         if (reset) {
             this.optimizationProblem.initializePopulation(population);
-            this.population.init();
+            this.population.initialize();
             this.evaluatePopulation(this.population);
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/GradientDescentAlgorithm.java

@@ -52,7 +52,7 @@ public class GradientDescentAlgorithm extends AbstractOptimizer implements java.
     public void initializeByPopulation(Population pop, boolean reset) {
         this.setPopulation((Population) pop.clone());
         if (reset) {
-            this.getPopulation().init();
+            this.getPopulation().initialize();
             this.optimizationProblem.evaluate(this.getPopulation());
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/HillClimbing.java

@@ -50,7 +50,7 @@ public class HillClimbing extends AbstractOptimizer implements java.io.Serializa
     public void initializeByPopulation(Population pop, boolean reset) {
         this.population = (Population) pop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.optimizationProblem.evaluate(this.population);
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/IslandModelEA.java

@@ -83,7 +83,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
 
 //        this.population = new Population();
         this.population.clear();
-        this.population.init();
+        this.population.initialize();
         this.optimizer.initialize();
         this.optimizer.setProblem(this.optimizationProblem);
         this.optimizer.setPopulation((Population) population.clone());
@@ -151,7 +151,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
 
         this.population = (Population) tpop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.population.incrGeneration();
         }
         this.optimizer.initialize();

src/eva2/optimization/strategies/MemeticAlgorithm.java

@@ -64,7 +64,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
     public void initializeByPopulation(Population pop, boolean reset) {
         this.setPopulation((Population) pop.clone());
         if (reset) {
-            this.getPopulation().init();
+            this.getPopulation().initialize();
             this.optimizationProblem.evaluate(this.getPopulation());
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/MonteCarloSearch.java

@@ -57,7 +57,7 @@ public class MonteCarloSearch extends AbstractOptimizer implements java.io.Seria
     public void initializeByPopulation(Population pop, boolean reset) {
         this.population = (Population) pop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.optimizationProblem.evaluate(this.population);
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/ParticleFilterOptimization.java

@@ -97,7 +97,7 @@ public class ParticleFilterOptimization extends AbstractOptimizer implements jav
     public void initializeByPopulation(Population pop, boolean reset) {
         this.population = (Population) pop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.evaluatePopulation(this.population);
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/ParticleSubSwarmOptimization.java

@@ -484,7 +484,7 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
             tmpIndy.initialize(prob);
             tmp.add(tmpIndy);
         }
-        tmp.init();
+        tmp.initialize();
         ///////////
 
         ParticleSubSwarmOptimization tmpopt = new ParticleSubSwarmOptimization();

src/eva2/optimization/strategies/ParticleSwarmOptimization.java

@@ -424,7 +424,7 @@ public class ParticleSwarmOptimization extends AbstractOptimizer implements java
             externalInitialPop = true;
         }
         if (reset) {
-            this.population.init();
+            this.population.initialize();
         }
 
         AbstractEAIndividual indy;

src/eva2/optimization/strategies/PopulationBasedIncrementalLearning.java

@@ -91,7 +91,7 @@ public class PopulationBasedIncrementalLearning extends AbstractOptimizer implem
         this.population = new PBILPopulation();
         this.population.addPopulation((Population) pop.clone());
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.evaluatePopulation(this.population);
         }
         ((PBILPopulation) this.population).buildProbabilityVector();

src/eva2/optimization/strategies/ScatterSearch.java

@@ -844,7 +844,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
         }
         Population pop = new Population();
         pop.setTargetSize(refSetSize);
-        pop.init();
+        pop.initialize();
         problem.initializePopulation(pop);
         ss.initializeByPopulation(pop, true);
 

src/eva2/optimization/strategies/SimulatedAnnealing.java

@@ -64,7 +64,7 @@ public class SimulatedAnnealing extends AbstractOptimizer implements java.io.Ser
         this.population = (Population) pop.clone();
         this.currentTemperature = this.initialTemperature;
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.optimizationProblem.evaluate(this.population);
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/SteadyStateGA.java

@@ -60,7 +60,7 @@ public class SteadyStateGA extends AbstractOptimizer implements java.io.Serializ
     public void initializeByPopulation(Population pop, boolean reset) {
         this.population = (Population) pop.clone();
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.evaluatePopulation(this.population);
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/ThresholdAlgorithm.java

@@ -61,7 +61,7 @@ public class ThresholdAlgorithm extends AbstractOptimizer implements java.io.Ser
         this.population = (Population) pop.clone();
         this.currentT = this.initialT;
         if (reset) {
-            this.population.init();
+            this.population.initialize();
             this.optimizationProblem.evaluate(this.population);
             this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
         }

src/eva2/optimization/strategies/Tribes.java

@@ -229,7 +229,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
 
         population.clear();
         population.addAll(swarm.toPopulation());
-        population.init();    // necessary to allow for multi-runs
+        population.initialize();    // necessary to allow for multi-runs
 
         if (show) {
             show();

src/eva2/problems/AbstractMultiModalProblemKnown.java

@@ -62,7 +62,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
         }
         // population initialize must be last
         // it set's fitcalls and generation to zero
-        population.init();
+        population.initialize();
         if (listOfOptima == null) {
             this.globalOptimum = Double.NEGATIVE_INFINITY;
             listOfOptima = new Population();

src/eva2/problems/AbstractOptimizationProblem.java

@@ -194,7 +194,7 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
         }
         // population initialize must be last
         // it set's fitcalls and generation to zero
-        population.init();
+        population.initialize();
     }
 
     /**

src/eva2/problems/AbstractProblemDouble.java

@@ -62,14 +62,14 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
     public static String rawFitKey = "UnconstrainedFitnessValue";
 
     public AbstractProblemDouble() {
-        initTemplate();
+        initializeTemplate();
     }
 
     public AbstractProblemDouble(AbstractProblemDouble o) {
         cloneObjects(o);
     }
 
-    protected void initTemplate() {
+    protected void initializeTemplate() {
         if (template == null) {
             template = new ESIndividualDoubleData();
         }
@@ -174,9 +174,9 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
      * @param indyPos    may contain the decoded individual position
      */
     protected void addConstraints(AbstractEAIndividual individual, double[] indyPos) {
-        AbstractConstraint[] contraints = getConstraints();
-        for (AbstractConstraint contraint : contraints) {
-            contraint.addViolation(individual, indyPos);
+        AbstractConstraint[] constraints = getConstraints();
+        for (AbstractConstraint constraint : constraints) {
+            constraint.addViolation(individual, indyPos);
         }
     }
 
@@ -205,7 +205,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 
     @Override
     public void initializePopulation(Population population) {
-        initTemplate();
+        initializeTemplate();
         AbstractOptimizationProblem.defaultInitializePopulation(population, template, this);
     }
 
@@ -257,7 +257,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 
     @Override
     public void initializeProblem() {
-        initTemplate();
+        initializeTemplate();
         if (isDoRotation()) {
             rotation = initializeDefaultRotationMatrix(rotAngle, getProblemDimension());
         } else {
@@ -339,7 +339,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
      */
     public void setDefaultRange(double defaultRange) {
         this.defaultRange = defaultRange;
-        initTemplate();
+        initializeTemplate();
     }
 
     public String defaultRangeTipText() {