Fix performance issue with CLI execution (SnakeYAML doesn't perform well

when serializing large objects)
Implement easier access for terminators.

src/eva2/cli/Main.java

@@ -96,7 +96,12 @@ final class YamlStatistics implements InterfaceStatistics {
     public void stopOptimizationPerformed(boolean normal, String stopMessage) {
         this.currentRun.put("stopMessage", stopMessage);
         this.currentRun.put("totalFunctionCalls", this.currentParameters.getOptimizer().getPopulation().getFunctionCalls());
-        this.currentRun.put("generations", currentGenerations);
+        // ToDo: Figure out a sane way to do this. Having multirun > 1 increases SnakeYAML memory consumption to beyond infinity
+        //this.currentRun.put("generations", currentGenerations);
+        Population pop = this.currentParameters.getOptimizer().getAllSolutions().getSolutions();
+        this.currentRun.put("solution", pop.getBestEAIndividual().getDoublePosition().clone());
+        this.currentRun.put("bestFitness", pop.getBestFitness().clone());
+        this.currentRun.put("meanFitness", pop.getMeanFitness().clone());
         this.runs.add(currentRun);
     }
 

src/eva2/cli/OptimizationBuilder.java

@@ -168,9 +168,10 @@ public final class OptimizationBuilder {
                     if (type.isPrimitive() && ((ArgumentTree)tree.get(name)).getValue() != null) {
                         obj = BeanInspector.stringToPrimitive((String) ((ArgumentTree) tree.get(name)).getValue(), type);
                     } else if (type.isArray() && ((ArgumentTree)tree.get(name)).getValue() != null) {
-
+                        // ToDo: Implement array parsing
                     } else if (type.isEnum() && ((ArgumentTree)tree.get(name)).getValue() != null) {
                         int enumIndex = Integer.parseInt((String)((ArgumentTree)tree.get(name)).getValue());
+
                         // ToDo: Properly check
                         obj = type.getEnumConstants()[enumIndex];
                     } else {
@@ -180,7 +181,11 @@ public final class OptimizationBuilder {
                         Class subType;
                         if (className != null) {
                             // Try to get the actual class from its name
-                            subType = getClassFromName(className, type);
+                            if (className.endsWith("Problem")) {
+                                subType = getClassFromName("eva2.problems", className, type);
+                            } else {
+                                subType = getClassFromName("eva2.optimization", className, type);
+                            }
                         } else {
                             subType = type;
                         }
@@ -207,8 +212,8 @@ public final class OptimizationBuilder {
         return instance;
     }
 
-    private static Class<?> getClassFromName(String name, Class type) {
+    private static Class<?> getClassFromName(String packageName, String name, Class type) {
-        Class<?>[] classes = ReflectPackage.getAssignableClassesInPackage("eva2", type, true, true);
+        Class<?>[] classes = ReflectPackage.getAssignableClassesInPackage(packageName, type, true, true);
         for (Class clazz : classes) {
             // We allow both the fully qualified name (eva2.optimization.strategies.GeneticAlgorithm
             // and the simple name (GeneticAlgorithm)

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

@@ -129,7 +129,7 @@ public class CombinedTerminator implements InterfaceTerminator, Serializable {
     /**
      * @param logicalOperator the logicalOperator to set
      */
-    @Parameter(description = "Set the boolean operator to be used to combine the two terminators.")
+    @Parameter(name = "operator", description = "Set the boolean operator to be used to combine the two terminators.")
     public void setLogicalOperator(LogicalOperator logicalOperator) {
         this.logicalOperator = logicalOperator;
     }

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

@@ -5,6 +5,7 @@ import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.PopulationInterface;
 import eva2.problems.InterfaceOptimizationProblem;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Parameter;
 
 import java.io.Serializable;
 import java.util.Arrays;
@@ -22,7 +23,7 @@ public class FitnessValueTerminator implements InterfaceTerminator,
      *
      */
     public FitnessValueTerminator() {
-        fitnessValue = new double[]{0.1};
+        fitnessValue = new double[]{10E-4};
     }
 
     @Override
@@ -70,6 +71,7 @@ public class FitnessValueTerminator implements InterfaceTerminator,
     /**
      *
      */
+    @Parameter(name = "fitness", description = "Set the fitness objective value.")
     public void setFitnessValue(double[] x) {
         fitnessValue = x;
     }
@@ -80,8 +82,4 @@ public class FitnessValueTerminator implements InterfaceTerminator,
     public double[] getFitnessValue() {
         return fitnessValue;
     }
-
-    public String fitnessValueTipText() {
-        return "Set the fitness objective value.";
-    }
 }

src/eva2/optimization/strategies/DifferentialEvolution.java

@@ -693,7 +693,7 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
      *
      * @param f
      */
-    @Parameter(name = "F", description = "Differential Weight")
+    @Parameter(name = "F", description = "F is a real and constant factor which controls the amplification of the differential variation.")
     public void setDifferentialWeight(double f) {
         this.differentialWeight = f;
     }
@@ -702,17 +702,13 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
         return this.differentialWeight;
     }
 
-    public String differentialWeightTipText() {
-        return "F is a real and constant factor which controls the amplification of the differential variation.";
-    }
-
     /**
      * Probability of alteration through DE (something like a discrete uniform
      * crossover is performed here)
      *
      * @param k
      */
-    @Parameter(name = "CR", description = "Crossover Rate")
+    @Parameter(name = "CR", description = "Probability of alteration through DE (a.k.a. CR, similar to discrete uniform crossover).")
     public void setCrossoverRate(double k) {
         if (k < 0) {
             k = 0;
@@ -727,10 +723,6 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
         return this.crossoverRate;
     }
 
-    public String crossoverrateTipText() {
-        return "Probability of alteration through DE (a.k.a. CR, similar to discrete uniform crossover).";
-    }
-
     /**
      * Enhance greediness through amplification of the differential vector to
      * the best individual for DE2
@@ -751,6 +743,7 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
      *
      * @param l
      */
+    @Parameter(description = "In case of trigonometric mutation DE, the TMO is applied with probability Mt.")
     public void setMt(double l) {
         this.mt = l;
         if (this.mt < 0) {
@@ -765,16 +758,12 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
         return this.mt;
     }
 
-    public String mtTipText() {
-        return "In case of trigonometric mutation DE, the TMO is applied with probability Mt.";
-    }
-
     /**
      * This method allows you to choose the type of Differential Evolution.
      *
      * @param s The type.
      */
-    @Parameter(name = "type", description = "Mutation type for DE")
+    @Parameter(name = "type", description = "Choose the type of Differential Evolution.")
     public void setDEType(eva2.optimization.enums.DEType s) {
         this.DEType = s;
         // show mt for trig. DE only
@@ -786,10 +775,6 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
         return this.DEType;
     }
 
-    public String dETypeTipText() {
-        return "Choose the type of Differential Evolution.";
-    }
-
     /**
      * @return the maximumAge
      */
@@ -800,14 +785,11 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
     /**
      * @param maximumAge the maximumAge to set
      */
+    @Parameter(description = "The maximum age of individuals, older ones are discarded. Set to -1 (or 0) to deactivate")
     public void setMaximumAge(int maximumAge) {
         this.maximumAge = maximumAge;
     }
 
-    public String maximumAgeTipText() {
-        return "The maximum age of individuals, older ones are discarded. Set to -1 (or 0) to deactivate";
-    }
-
     /**
      * Check whether the problem range will be enforced.
      *
@@ -820,62 +802,47 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
     /**
      * @param forceRange the forceRange to set
      */
+    @Parameter(description = "Set whether to enforce the problem range.")
     public void setCheckRange(boolean forceRange) {
         this.forceRange = forceRange;
     }
 
-    public String checkRangeTipText() {
-        return "Set whether to enforce the problem range.";
-    }
-
     public boolean isRandomizeFKLambda() {
         return randomizeFKLambda;
     }
 
+    @Parameter(description = "If true, values for k, f, lambda are randomly sampled around +/- 20% of the given values.")
     public void setRandomizeFKLambda(boolean randomizeFK) {
         this.randomizeFKLambda = randomizeFK;
     }
 
-    public String randomizeFKLambdaTipText() {
-        return "If true, values for k, f, lambda are randomly sampled around +/- 20% of the given values.";
-    }
-
     public boolean isCompareToParent() {
         return compareToParent;
     }
 
+    @Parameter(description = "Compare a challenge individual to its original parent instead of a random one.")
     public void setCompareToParent(boolean compareToParent) {
         this.compareToParent = compareToParent;
     }
 
-    public String compareToParentTipText() {
-        return "Compare a challenge individual to its original parent instead of a random one.";
-    }
-
     public boolean isGenerational() {
         return generational;
     }
 
+    @Parameter(description = "Switch to generational DE as opposed to standard steady-state DE")
     public void setGenerational(boolean generational) {
         this.generational = generational;
     }
 
-    public String generationalTipText() {
-        return "Switch to generational DE as opposed to standard steady-state DE";
-    }
-
     public boolean isCyclePop() {
         return cyclePop;
     }
 
+    @Parameter(description = "if true, individuals are used as parents in a cyclic sequence - otherwise randomly ")
     public void setCyclePop(boolean cycle) {
         this.cyclePop = cycle;
     }
 
-    public String cyclePopTipText() {
-        return "if true, individuals are used as parents in a cyclic sequence - otherwise randomly ";
-    }
-
     /**
      * @return the maximumAge
      */
@@ -885,11 +852,8 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
 
     /**
      */
+    @Parameter(description = "Re-evaluates individuals which are older than maximum age instead of discarding them")
     public void setReEvaluate(boolean reEvaluate) {
         this.reEvaluate = reEvaluate;
     }
-
-    public String reEvaluateTipText() {
-        return "Re-evaluates individuals which are older than maximum age instead of discarding them";
-    }
 }