Renamed eval function to evaluate.

Implemented more of CLI core.

src/eva2/OptimizerFactory.java

@@ -788,7 +788,6 @@ public class OptimizerFactory {
             return null;
         }
         runnable.run();
-        new Thread(runnable).start();
         lastRunnable = runnable;
         return runnable;
     }
@@ -872,8 +871,7 @@ public class OptimizerFactory {
     }
 
     public static double[] optimizeToDouble(OptimizationParameters params) {
-        OptimizerRunnable runnable = optimize(new OptimizerRunnable(params,
+        OptimizerRunnable runnable = optimize(new OptimizerRunnable(params, false));
-                false));
         return runnable.getDoubleSolution();
     }
 
@@ -883,15 +881,13 @@ public class OptimizerFactory {
         return runnable.getDoubleSolution();
     }
 
-    public static double[] optimizeToDouble(final int optType,
+    public static double[] optimizeToDouble(final int optType, AbstractOptimizationProblem problem) {
-                                            AbstractOptimizationProblem problem) {
         return optimizeToDouble(optType, problem, null);
     }
 
     public static double[] optimizeToDouble(final int optType,
                                             AbstractOptimizationProblem problem, String outputFilePrefix) {
-        OptimizerRunnable runnable = optimize(optType, problem,
+        OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix);
-                outputFilePrefix);
         return (runnable != null) ? runnable.getDoubleSolution() : null;
     }
 
@@ -901,13 +897,11 @@ public class OptimizerFactory {
     }
 
     public static IndividualInterface optimizeToInd(OptimizationParameters params) {
-        OptimizerRunnable runnable = optimize(new OptimizerRunnable(params,
+        OptimizerRunnable runnable = optimize(new OptimizerRunnable(params, false));
-                false));
         return runnable.getResult();
     }
 
-    public static IndividualInterface optimizeToInd(OptimizationParameters params,
+    public static IndividualInterface optimizeToInd(OptimizationParameters params, String outputFilePrefix) {
-                                                    String outputFilePrefix) {
         OptimizerRunnable runnable = optimize(new OptimizerRunnable(params,
                 outputFilePrefix));
         return runnable.getResult();

src/eva2/OptimizerRunnable.java

@@ -254,7 +254,6 @@ public class OptimizerRunnable implements Runnable {
      * Set the verbosity level in the statistics module to the given value.
      *
      * @param vLev
-     * @see StatsParameter
      */
     public void setVerbosityLevel(int vLev) {
         if (vLev >= 0 && vLev < proc.getStatistics().getStatisticsParameter().getOutputVerbosity().getTags().length) {
@@ -268,7 +267,6 @@ public class OptimizerRunnable implements Runnable {
      * Set the output direction in the statistics module.
      *
      * @param outp
-     * @see StatsParameter
      */
     public void setOutputTo(int outp) {
         ((StatisticsParameter) proc.getStatistics().getStatisticsParameter()).setOutputTo(outp);
@@ -287,7 +285,6 @@ public class OptimizerRunnable implements Runnable {
      * Indicate whether full stats should be printed as text (or only selected entries).
      *
      * @param addInfo
-     * @see StatsParameter
      */
     public void setOutputFullStatsToText(boolean addInfo) {
         ((AbstractStatistics) proc.getStatistics()).getStatisticsParameter().setOutputAllFieldsAsText(addInfo);

src/eva2/cli/Main.java

@@ -10,6 +10,8 @@ import eva2.optimization.operator.terminators.EvaluationTerminator;
 import eva2.optimization.operator.terminators.FitnessValueTerminator;
 import eva2.optimization.population.Population;
 import eva2.optimization.problems.AbstractOptimizationProblem;
+import eva2.optimization.problems.AbstractProblemDouble;
+import eva2.optimization.problems.AbstractProblemDoubleOffset;
 import eva2.optimization.strategies.DifferentialEvolution;
 import eva2.optimization.strategies.InterfaceOptimizer;
 import com.google.gson.*;
@@ -181,6 +183,7 @@ public class Main implements OptimizationStateListener, InterfacePopulationChang
 
         this.jsonObject = new JsonObject();
         this.optimizationRuns = new JsonArray();
+        this.generationsArray = new JsonArray();
 
         /**
          * Default command line options only require help or optimizer.
@@ -253,7 +256,6 @@ public class Main implements OptimizationStateListener, InterfacePopulationChang
         problemObject.addProperty("name", this.problem.getName());
         problemObject.addProperty("dimension", 30);
         this.jsonObject.add("problem", problemObject);
-
     }
 
     /**
@@ -312,6 +314,14 @@ public class Main implements OptimizationStateListener, InterfacePopulationChang
                 System.out.println("Genetic Algorithm");
                 break;
             case "ParticleSwarmOptimization":
+                opt.addOption("initialVelocity", true, "Initial Velocity");
+                opt.addOption("speedLimit", true, "Speed Limit");
+                opt.addOption("topology", true, "Particle Swarm Topology (0-7)");
+                opt.addOption("swarmRadius", true, "Radius of the Swarm");
+                opt.addOption("phi1", true, "Phi 1");
+                opt.addOption("phi2", true, "Phi 2");
+                opt.addOption("inertnessOrChi", true, "Inertness or Chi");
+                opt.addOption("algType", true, "Type of PSO");
 
                 break;
             default:
@@ -322,7 +332,7 @@ public class Main implements OptimizationStateListener, InterfacePopulationChang
     private void setProblemFromName(String problemName) {
         Map<String, Class<? extends AbstractOptimizationProblem>> problemList = createProblemList();
 
-        Class<? extends AbstractOptimizationProblem> problem = problemList.get(problemName);
+        Class<? extends AbstractProblemDoubleOffset> problem = problemList.get(problemName);
         try {
             this.problem = problem.newInstance();
         } catch (InstantiationException e) {
@@ -334,32 +344,31 @@ public class Main implements OptimizationStateListener, InterfacePopulationChang
 
     private void runOptimization() {
         for(int i = 0; i < this.numberOfRuns; i++) {
-
-            this.generationsArray = new JsonArray();
             // Terminate after 10000 function evaluations OR after reaching a fitness < 0.1
             OptimizerFactory.setEvaluationTerminator(50000);
-            OptimizerFactory.addTerminator(new FitnessValueTerminator(new double[]{0.01}), CombinedTerminator.OR);
+            OptimizerFactory.addTerminator(new FitnessValueTerminator(new double[]{0.00001}), CombinedTerminator.OR);
 
             LOGGER.log(Level.INFO, "Running {0}", "Differential Evolution");
 
             OptimizationParameters params = OptimizerFactory.makeParams(optimizer, this.populationSize, this.problem);
             double[] result = OptimizerFactory.optimizeToDouble(params);
 
-            // This is stupid - why isn't there a way to wait for the optimization to finish?
-            while(OptimizerFactory.terminatedBecause().equals("Not yet terminated")) {
-                // wait
-            }
-
             JsonObject optimizationDetails = new JsonObject();
             optimizationDetails.addProperty("total_time", 1.0);
             optimizationDetails.addProperty("total_function_calls", optimizer.getPopulation().getFunctionCalls());
             optimizationDetails.addProperty("termination_criteria", OptimizerFactory.terminatedBecause());
             optimizationDetails.add("generations", this.generationsArray);
 
+            JsonArray solutionArray = new JsonArray();
+            for(double val : result) {
+                solutionArray.add(new JsonPrimitive(val));
+            }
+            optimizationDetails.add("solution", solutionArray);
+
             this.optimizationRuns.add(optimizationDetails);
 
-            System.out.println(OptimizerFactory.terminatedBecause());
+            // Needs to be re-created here.
-            System.out.println(optimizer.getPopulation().getFunctionCalls());
+            this.generationsArray = new JsonArray();
         }
 
         this.jsonObject.add("runs", this.optimizationRuns);
@@ -407,8 +416,8 @@ public class Main implements OptimizationStateListener, InterfacePopulationChang
                 meanFitness.add(new JsonPrimitive(val));
             }
             newGeneration.add("mean_fitness", meanFitness);
-            System.out.println(newGeneration.toString());
+            //System.out.println(newGeneration.toString());
-            //this.generationsArray.add(newGeneration);
+            this.generationsArray.add(newGeneration);
         }
 
     }

src/eva2/optimization/individuals/codings/gp/AbstractGPNode.java

@@ -374,7 +374,7 @@ public abstract class AbstractGPNode implements InterfaceProgram, java.io.Serial
     public static void test(String constr, double[] pos) {
         AbstractGPNode node = AbstractGPNode.parseFromString(constr);
         GPFunctionProblem func = new GPFunctionProblem(node, null, pos.length, 0., 0.);
-        double[] ret = func.eval(pos);
+        double[] ret = func.evaluate(pos);
         System.out.println("testing " + constr + " evaluated to " + BeanInspector.toString(ret));
     }
 

src/eva2/optimization/operator/constraint/GenericConstraint.java

@@ -93,7 +93,7 @@ public class GenericConstraint extends AbstractConstraint implements InterfaceDo
                     if (func == null) {
                         func = new GPFunctionProblem(constraintProgram, null, indyX.length, 0., 0.);
                     }
-                    return func.eval(indyX)[0];
+                    return func.evaluate(indyX)[0];
                 } else {
                     return 0.;
                 }

src/eva2/optimization/problems/AbstractMultiModalProblemKnown.java

@@ -89,7 +89,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         result[0] = this.globalOptimum - evalUnnormalized(x)[0];

src/eva2/optimization/problems/AbstractProblemDouble.java

@@ -26,7 +26,7 @@ import eva2.tools.math.RNG;
 /**
  * For a double valued problem, there are two main methods to implement:
  * {@link #getProblemDimension()} must return the problem dimension, while
- * {@link #eval(double[])} is to evaluate a single double vector into the result
+ * {@link #evaluate(double[])} is to evaluate a single double vector into the result
  * fitness vector.
  * <p/>
  * To define the problem range, you may use the default range parameter
@@ -77,10 +77,8 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
         }
         if (getProblemDimension() > 0) { // avoid evil case setting dim to 0
             // during object init
-            ((InterfaceDataTypeDouble) this.template)
+            ((InterfaceDataTypeDouble) this.template).setDoubleDataLength(getProblemDimension());
-                    .setDoubleDataLength(getProblemDimension());
+            ((InterfaceDataTypeDouble) this.template).setDoubleRange(makeRange());
-            ((InterfaceDataTypeDouble) this.template)
-                    .setDoubleRange(makeRange());
         }
     }
 
@@ -126,9 +124,8 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
     /**
      * When implementing a double problem, inheriting classes should not
      * override this method (or only extend it) and do the fitness calculations
-     * in the method eval(double[]).
+     * in the method evaluate(double[]).
      *
-     * @see eval(double[] x)
      */
     @Override
     public void evaluate(AbstractEAIndividual individual) {
@@ -138,7 +135,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
         x = getEvalArray(individual);
         ((InterfaceDataTypeDouble) individual).setDoublePhenotype(x);
         // evaluate the vector
-        fitness = this.eval(x);
+        fitness = this.evaluate(x);
         // if indicated, add Gaussian noise
         if (noise != 0) {
             RNG.addNoise(fitness, noise);
@@ -206,7 +203,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
      * @return the target function value
      */
     @Override
-    public abstract double[] eval(double[] x);
+    public abstract double[] evaluate(double[] x);
 
     /**
      * Get the problem dimension.
@@ -410,7 +407,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
     @Override
     public double functionValue(double[] point) {
         double[] x = project2DPoint(point);
-        double v = eval(x)[0];
+        double v = evaluate(x)[0];
         return v;
     }
 
@@ -433,7 +430,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
             // required
             tmpIndy.setDoubleGenotype(pos);
         }
-        ((AbstractEAIndividual) tmpIndy).setFitness(prob.eval(pos));
+        ((AbstractEAIndividual) tmpIndy).setFitness(prob.evaluate(pos));
         if (!Mathematics.isInRange(pos, prob.makeRange())) {
             System.err.println("Warning, add optimum which is out of range!");
         }
@@ -454,7 +451,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
         tmpIndy = (InterfaceDataTypeDouble) prob.getIndividualTemplate()
                 .clone();
         tmpIndy.setDoubleGenotype(pos);
-        ((AbstractEAIndividual) tmpIndy).setFitness(prob.eval(pos));
+        ((AbstractEAIndividual) tmpIndy).setFitness(prob.evaluate(pos));
         pop.add(tmpIndy);
         FitnessConvergenceTerminator convTerm = new FitnessConvergenceTerminator(
                 1e-25, 10, StagnationTypeEnum.generationBased,

src/eva2/optimization/problems/AbstractProblemDoubleOffset.java

@@ -10,7 +10,7 @@ package eva2.optimization.problems;
 public abstract class AbstractProblemDoubleOffset extends AbstractProblemDouble implements Interface2DBorderProblem {
 
     protected int problemDimension = 10;
-    protected double xOffset = 0.0; // TODO make them private, implement eval() and create abstract evalWithoutOffsets
+    protected double xOffset = 0.0; // TODO make them private, implement evaluate() and create abstract evalWithoutOffsets
     protected double yOffset = 0.0;
 
     public AbstractProblemDoubleOffset() {
@@ -36,9 +36,7 @@ public abstract class AbstractProblemDoubleOffset extends AbstractProblemDouble
         setDefaultRange(defRange);
     }
 
-/**********************************************************************************************************************
+
- * These are for GUI
- */
     /**
      * This method allows the CommonJavaObjectEditorPanel to read the
      * name to the current object.

src/eva2/optimization/problems/AbstractSynchronousOptimizationProblem.java

@@ -110,7 +110,7 @@ public abstract class AbstractSynchronousOptimizationProblem extends
             // bogus. this must be reset right before population evaluation
             setConstantProblemEvals(9999999);
         } else {
-            // in absolute evals, so 1 means once per individual eval, .5 every second
+            // in absolute evals, so 1 means once per individual evaluate, .5 every second
             setConstantProblemEvals(1 / frequency);
         }
     }

src/eva2/optimization/problems/ConstrHimmelblauProblem.java

@@ -50,7 +50,7 @@ public class ConstrHimmelblauProblem extends AbstractProblemDouble implements Se
     }
 
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double v = 5.3578547 * x[2] * x[2] + 0.8356891 * x[0] * x[4] + 37.293239 * x[0] - 40792.141;
         if (useYOffset) {

src/eva2/optimization/problems/ConstrPressureVessel.java

@@ -62,7 +62,7 @@ public class ConstrPressureVessel extends AbstractProblemDouble {
     }
 
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         double v, thickS = x[0], thickH = x[1], R = x[2], L = x[3];
 
         v = 0.6224 * thickS * R * L + 1.7781 * thickH * R * R + 3.1661 * thickS * thickS * L + 19.84 * thickS * thickS * R;

src/eva2/optimization/problems/ExternalRuntimeProblem.java

@@ -209,7 +209,7 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem
 
         x = getXVector(individual);
 
-        double[] fit = eval(x);
+        double[] fit = evaluate(x);
         individual.setFitness(fit);
 
 //        if (this.m_UseTestConstraint) {
@@ -283,9 +283,9 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem
     }
 
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         if (x == null) {
-            throw new RuntimeException("Error, x=null value received in ExternalRuntimeProblem.eval");
+            throw new RuntimeException("Error, x=null value received in ExternalRuntimeProblem.evaluate");
         }
         ArrayList<Double> fitList = new ArrayList<Double>();
 
@@ -467,7 +467,7 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem
 
     @Override
     public double functionValue(double[] point) {
-        return eval(project2DPoint(point))[0];
+        return evaluate(project2DPoint(point))[0];
     }
 
     @Override

src/eva2/optimization/problems/F10Problem.java

@@ -43,7 +43,7 @@ public class F10Problem extends AbstractProblemDoubleOffset implements Interface
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         double c1 = this.calculateC(1);

src/eva2/optimization/problems/F11Problem.java

@@ -41,7 +41,7 @@ public class F11Problem extends AbstractProblemDoubleOffset implements Interface
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         double tmpProd = 1;

src/eva2/optimization/problems/F12Problem.java

@@ -37,7 +37,7 @@ public class F12Problem extends AbstractProblemDoubleOffset implements Serializa
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         double tmp = 0;//-5;

src/eva2/optimization/problems/F13Problem.java

@@ -57,7 +57,7 @@ public class F13Problem extends AbstractProblemDoubleOffset implements Interface
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         result[0] = yOffset;

src/eva2/optimization/problems/F14Problem.java

@@ -34,7 +34,7 @@ public class F14Problem extends AbstractProblemDoubleOffset implements Interface
     }
 
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         double[] result = new double[1];
         double x0 = x[0] - rotationDX - xOffset;
         double x1 = x[1] - rotationDX - xOffset;

src/eva2/optimization/problems/F15Problem.java

@@ -30,7 +30,7 @@ public class F15Problem extends AbstractProblemDouble implements Serializable, I
     }
 
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double t = 0, s = 0, sum = 0;
 

src/eva2/optimization/problems/F16Problem.java

@@ -24,7 +24,7 @@ public class F16Problem extends AbstractProblemDouble implements InterfaceMultim
     }
 
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] res = new double[1];
         double sum = 0;

src/eva2/optimization/problems/F17Problem.java

@@ -28,7 +28,7 @@ public class F17Problem extends AbstractProblemDouble implements
     }
 
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] res = new double[1];
         double sum = 0;

src/eva2/optimization/problems/F18Problem.java

@@ -14,7 +14,7 @@ public class F18Problem extends AbstractProblemDouble implements
     }
 
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] res = new double[1];
         double sum = 0;

src/eva2/optimization/problems/F19Problem.java

@@ -84,7 +84,7 @@ public class F19Problem extends AbstractProblemDouble implements
     }
 
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] res = new double[1];
         double[] Bs = transform(x);

src/eva2/optimization/problems/F1Problem.java

@@ -2,10 +2,12 @@ package eva2.optimization.problems;
 
 import eva2.optimization.strategies.InterfaceOptimizer;
 import eva2.tools.math.Mathematics;
+import eva2.util.annotation.Description;
 
 /**
  * F1 Sphere Problem
  */
+@Description(text="Sphere Problem")
 public class F1Problem extends AbstractProblemDoubleOffset implements Interface2DBorderProblem, InterfaceHasInitRange, java.io.Serializable, InterfaceFirstOrderDerivableProblem {
     private double initialRangeRatio = 1.; // reduce to initialize in a smaller subrange of the original range (in the corner box)
 
@@ -45,7 +47,7 @@ public class F1Problem extends AbstractProblemDoubleOffset implements Interface2
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         result[0] = yOffset;

src/eva2/optimization/problems/F20Problem.java

@@ -32,7 +32,7 @@ public class F20Problem extends AbstractProblemDouble implements Serializable, I
     }
 
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double sum = getYOffset();
 

src/eva2/optimization/problems/F21Problem.java

@@ -61,7 +61,7 @@ public class F21Problem extends AbstractProblemDouble implements InterfaceMultim
     }
 
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         double res[] = new double[1];
         double tmp, innerSum, sum = 0;
         x = rotateMaybe(x);

src/eva2/optimization/problems/F2Problem.java

@@ -45,7 +45,7 @@ public class F2Problem extends AbstractProblemDoubleOffset implements InterfaceL
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         result[0] = yOffset;

src/eva2/optimization/problems/F3Problem.java

@@ -36,7 +36,7 @@ public class F3Problem extends AbstractProblemDoubleOffset implements java.io.Se
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         result[0] = yOffset + 6 * x.length;

src/eva2/optimization/problems/F4Problem.java

@@ -37,7 +37,7 @@ public class F4Problem extends AbstractProblemDoubleOffset implements Serializab
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         result[0] = yOffset;

src/eva2/optimization/problems/F5Problem.java

@@ -37,7 +37,7 @@ public class F5Problem extends AbstractProblemDoubleOffset implements Serializab
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         double tmp;

src/eva2/optimization/problems/F6Problem.java

@@ -47,7 +47,7 @@ public class F6Problem extends AbstractProblemDoubleOffset
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         result[0] = x.length * this.m_A + yOffset;

src/eva2/optimization/problems/F7Problem.java

@@ -79,7 +79,7 @@ public class F7Problem extends AbstractProblemDoubleOffset implements Serializab
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         result[0] = yOffset;

src/eva2/optimization/problems/F8Problem.java

@@ -64,7 +64,7 @@ public class F8Problem extends AbstractProblemDoubleOffset
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         double sum1 = 0, sum2 = 0, exp1, exp2;
@@ -252,14 +252,14 @@ public class F8Problem extends AbstractProblemDoubleOffset
         double[] tmpP = pos.clone();
         double[] normedVect = Mathematics.normVect(vect);
         double dx = initStep;
-        double tmpFit, oldFit = prob.eval(pos)[fitCrit];
+        double tmpFit, oldFit = prob.evaluate(pos)[fitCrit];
 
         int dir = 1;
         while (dx > thresh) {
             // add a step to tmpP
             Mathematics.svvAddScaled(dx * dir, normedVect, pos, tmpP);
             // evaluate tmpP
-            tmpFit = prob.eval(tmpP)[fitCrit];
+            tmpFit = prob.evaluate(tmpP)[fitCrit];
             if (tmpFit < oldFit) {
                 // if tmpP is better than pos continue at new pos
                 double[] tmp = pos;
@@ -283,7 +283,7 @@ public class F8Problem extends AbstractProblemDoubleOffset
             return true;
         } else { // the number of optima is corret - now check different offset or rotation by comparing one fitness value
             AbstractEAIndividual indy = m_ListOfOptima.getEAIndividual(1);
-            double[] curFit = eval(indy.getDoublePosition());
+            double[] curFit = evaluate(indy.getDoublePosition());
             if (Math.abs(Mathematics.dist(curFit, indy.getFitness(), 2)) > 1e-10) {
                 return true;
             } else {
@@ -316,7 +316,7 @@ public class F8Problem extends AbstractProblemDoubleOffset
 //		InterfaceDataTypeDouble tmpIndy;
 //		tmpIndy = (InterfaceDataTypeDouble)((AbstractEAIndividual)this.template).clone();
 //		tmpIndy.setDoubleGenotype(pos);
-//		((AbstractEAIndividual)tmpIndy).SetFitness(eval(pos));
+//		((AbstractEAIndividual)tmpIndy).SetFitness(evaluate(pos));
 //		population.add(tmpIndy);
 //		FitnessConvergenceTerminator convTerm = new FitnessConvergenceTerminator(1e-15, 10, false, true);
 //		int calls = PostProcess.processWithGDA(population, this, convTerm, 0, 0.0000000000000001, 0.01);

src/eva2/optimization/problems/F9Problem.java

@@ -29,7 +29,7 @@ public class F9Problem extends AbstractProblemDoubleOffset implements java.io.Se
      * @return The m-dimensional output vector.
      */
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         x = rotateMaybe(x);
         double[] result = new double[1];
         result[0] = yOffset;

src/eva2/optimization/problems/GPFunctionProblem.java

@@ -82,7 +82,7 @@ public class GPFunctionProblem extends AbstractProblemDouble implements Interfac
     }
 
     @Override
-    public double[] eval(double[] x) {
+    public double[] evaluate(double[] x) {
         if (x.length != dim) {
             EVAERROR.errorMsgOnce("mismatching dimension of GPFunctionProblem! Setting to " + x.length);
             setProblemDimension(x.length);

src/eva2/optimization/problems/InterfaceProblemDouble.java

@@ -14,7 +14,7 @@ public interface InterfaceProblemDouble {
      * @param x the vector to evaluate
      * @return the target function value
      */
-    public double[] eval(double[] x);
+    public double[] evaluate(double[] x);
 
     /**
      * Get the problem dimension.

src/eva2/optimization/problems/MatlabEvalMediator.java

@@ -96,7 +96,7 @@ public class MatlabEvalMediator {
                 logMP(msg);
             }
             requesting = true;
-//			logMPAndSysOut("-- Requesting eval for " + BeanInspector.toString(x) + ", req state is " + requesting + "\n"); 
+//			logMPAndSysOut("-- Requesting evaluate for " + BeanInspector.toString(x) + ", req state is " + requesting + "\n");
         }
 
 

src/eva2/optimization/stat/StatisticsStandalone.java

@@ -1,19 +1,5 @@
 package eva2.optimization.stat;
 
-/*
- * Title:        EvA2
- * Description:
- * Copyright:    Copyright (c) 2003
- * Company:      University of Tuebingen, Computer Architecture
- * @author Holger Ulmer, Felix Streichert, Hannes Planatscher
- * @version:  $Revision: 306 $
- *            $Date: 2007-12-04 14:22:52 +0100 (Tue, 04 Dec 2007) $
- *            $Author: mkron $
- */
-/*==========================================================================*
- * IMPORTS
- *==========================================================================*/
-
 import eva2.optimization.population.PopulationInterface;
 import eva2.optimization.problems.InterfaceAdditionalPopulationInformer;
 
@@ -34,8 +20,6 @@ import java.util.List;
 public class StatisticsStandalone extends AbstractStatistics implements InterfaceStatistics, Serializable {
     private static final long serialVersionUID = -8451652609212653368L;
 
-    private static String m_MyHostName = "unknown";
-
     //	private String m_InfoString;
     private ArrayList<ArrayList<Object[]>> m_ResultData = null;
     private ArrayList<String> m_ResultHeaderStrings = null;
@@ -45,11 +29,6 @@ public class StatisticsStandalone extends AbstractStatistics implements Interfac
     public StatisticsStandalone(InterfaceStatisticsParameter statParams) {
         super();
         statisticsParameter = statParams;
-        try {
-            m_MyHostName = InetAddress.getLocalHost().getHostName();
-        } catch (Exception e) {
-            System.err.println("ERROR getting HostName " + e.getMessage());
-        }
     }
 
     public StatisticsStandalone(String resultFileName) {

src/eva2/optimization/strategies/DynamicParticleSwarmOptimization.java

@@ -265,9 +265,9 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
 			*/
             for (int i = 0; i < lastVelocity.length; i++) {
                 // the component from the old velocity
-                curVelocity[i] = this.m_InertnessOrChi * lastVelocity[i];
+                curVelocity[i] = this.inertnessOrChi * lastVelocity[i];
                 if (algType.getSelectedTag().getID() == 1) {
-                    chi = m_InertnessOrChi;
+                    chi = inertnessOrChi;
                 } else {
                     chi = 1.;
                 }

src/eva2/optimization/strategies/ParticleSwarmOptimization.java

@@ -77,8 +77,8 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
      * InertnessOrChi may contain the inertness or chi parameter depending on
      * algoType
      */
-    protected double m_InertnessOrChi = 0.73;
+    protected double inertnessOrChi = 0.73;
-    protected double m_ReduceSpeed = 0.8;
+    protected double reduceSpeed = 0.8;
     private double reduceSpeedOnConstViolation = 0.5;
     public static final int defaultType = 0;
     public static final int resetType = 99;
@@ -133,7 +133,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
         this.m_SpeedLimit = a.m_SpeedLimit;
         this.m_Phi1 = a.m_Phi1;
         this.m_Phi2 = a.m_Phi2;
-        this.m_InertnessOrChi = a.m_InertnessOrChi;
+        this.inertnessOrChi = a.inertnessOrChi;
         this.m_TopologyRange = a.m_TopologyRange;
         this.paramControl = (ParameterControlManager) a.paramControl.clone();
         //this.setCheckSpeedLimit(a.isCheckSpeedLimit());
@@ -787,7 +787,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 //		System.out.println("-- len aft " + vecLen(socCogn));
 //		rotateAllAxes(neiDiff, .5, false);
 //		// TODO!!!
-//		if (algType.getSelectedTag().getID()==1) chi=m_InertnessOrChi;
+//		if (algType.getSelectedTag().getID()==1) chi=inertnessOrChi;
 //		else chi = 1.;
 //		
 //		double scaleCog = this.m_Phi1*chi*RNG.randomDouble(0,1);
@@ -795,15 +795,15 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 //		
 //		
 //		for (int i=0; i<lastVelocity.length; i++) {
-//			curVelocity[i]  = this.m_InertnessOrChi * lastVelocity[i];
+//			curVelocity[i]  = this.inertnessOrChi * lastVelocity[i];
 //			curVelocity[i]  += scaleCog*socCogn[i];
 //			curVelocity[i]  += scaleNei*neiDiff[i];
 //		}
 
 //		for (int i = 0; i < lastVelocity.length; i++) {
 //			// the component from the old velocity
-//			curVelocity[i]  = this.m_InertnessOrChi * lastVelocity[i];
+//			curVelocity[i]  = this.inertnessOrChi * lastVelocity[i];
-//			if (algType.getSelectedTag().getID()==1) chi=m_InertnessOrChi;
+//			if (algType.getSelectedTag().getID()==1) chi=inertnessOrChi;
 //			else chi = 1.;
 //			// the component from the cognition model
 //			//curVelocity[i]  += this.m_Phi1*chi*RNG.randomDouble(0,1)*(personalBestPos[i]-curPosition[i]);
@@ -828,7 +828,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
         //System.out.println("accel is " + getVecNorm(accel));
 
         for (int i = 0; i < lastVelocity.length; i++) {
-            curVelocity[i] = this.m_InertnessOrChi * lastVelocity[i];
+            curVelocity[i] = this.inertnessOrChi * lastVelocity[i];
             curVelocity[i] += accel[i];
         }
         return curVelocity;
@@ -844,7 +844,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
             // the component from the old velocity
             accel[i] = 0;
             if (algType.getSelectedTag().getID() == 1) {
-                chi = m_InertnessOrChi;
+                chi = inertnessOrChi;
             } else {
                 chi = 1.;
             }
@@ -891,7 +891,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 //		double[] accel    = new double[curPosition.length];
 //		double chi;
 //
-//		if (algType.getSelectedTag().getID()==1) chi=m_InertnessOrChi;
+//		if (algType.getSelectedTag().getID()==1) chi=inertnessOrChi;
 //		else chi = 1.;
 //
 //		boolean rotatedVect=false;
@@ -1292,7 +1292,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
     protected void enforceSpeedLimit(double[] curVelocity, double[][] range, double speedLim) {
         while (Mathematics.getRelativeLength(curVelocity, range) > speedLim) {
             for (int i = 0; i < curVelocity.length; i++) {
-                curVelocity[i] *= this.m_ReduceSpeed;
+                curVelocity[i] *= this.reduceSpeed;
             }
         }
     }
@@ -1876,11 +1876,11 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
      * @param k
      */
     public void setInertnessOrChi(double k) {
-        this.m_InertnessOrChi = k;
+        this.inertnessOrChi = k;
     }
 
     public double getInertnessOrChi() {
-        return this.m_InertnessOrChi;
+        return this.inertnessOrChi;
     }
 
     public String inertnessOrChiTipText() {
@@ -2212,17 +2212,17 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 
             double relDiff;
             if (personalBestfit[0] != 0) {
-                relDiff = (personalBestfit[0] - ((InterfaceProblemDouble) m_Problem).eval(personalBestPos)[0]) / personalBestfit[0];
+                relDiff = (personalBestfit[0] - ((InterfaceProblemDouble) m_Problem).evaluate(personalBestPos)[0]) / personalBestfit[0];
             } else {
-                relDiff = (personalBestfit[0] - ((InterfaceProblemDouble) m_Problem).eval(personalBestPos)[0]); // absolute diff in this case
+                relDiff = (personalBestfit[0] - ((InterfaceProblemDouble) m_Problem).evaluate(personalBestPos)[0]); // absolute diff in this case
-            }//			if (personalBestfit[0]!=((InterfaceProblemDouble)problem).eval(personalBestPos)[0]) {
+            }//			if (personalBestfit[0]!=((InterfaceProblemDouble)problem).evaluate(personalBestPos)[0]) {
             if (Math.abs(relDiff) > 1e-20) {
                 System.err.println("Warning: mismatching best fitness by " + relDiff);
                 System.err.println("partInfo: " + i + " - " + getParticleInfo(population.getEAIndividual(i)));
             }
             if (Math.abs(relDiff) > 1e-10) {
                 System.err.println("partInfo: " + i + " - " + getParticleInfo(population.getEAIndividual(i)));
-                throw new RuntimeException("Mismatching best fitness!! " + personalBestfit[0] + " vs. " + ((InterfaceProblemDouble) m_Problem).eval(personalBestPos)[0]);
+                throw new RuntimeException("Mismatching best fitness!! " + personalBestfit[0] + " vs. " + ((InterfaceProblemDouble) m_Problem).evaluate(personalBestPos)[0]);
             }
             ((InterfaceDataTypeDouble) indy).setDoubleGenotype(personalBestPos);
             indy.setFitness(personalBestfit);

src/eva2/optimization/strategies/ParticleSwarmOptimizationGCPSO.java

@@ -112,7 +112,7 @@ public class ParticleSwarmOptimizationGCPSO extends ParticleSwarmOptimization {
             }
         }
         for (int i = 0; i < lastVelocity.length; i++) {
-            curVelocity[i] = this.m_InertnessOrChi * lastVelocity[i];
+            curVelocity[i] = this.inertnessOrChi * lastVelocity[i];
             curVelocity[i] += accel[i];
         }
         return curVelocity;

src/eva2/optimization/strategies/ScatterSearch.java

@@ -185,7 +185,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
         }
     }
 
-    //	public double eval(double[] x) {
+    //	public double evaluate(double[] x) {
 //		AbstractEAIndividual indy = (AbstractEAIndividual)template.clone();
 //		((InterfaceDataTypeDouble)indy).setDoubleGenotype(x);
 //		problem.evaluate(indy);

src/eva2/optimization/strategies/Tribes.java

@@ -515,7 +515,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
      * }
      * // -----------OPTIMIZE END }
      *
-     * // Result of the run print("\nBest: eval.= " + evalF + "\n", displayPb);
+     * // Result of the run print("\nBest: evaluate.= " + evalF + "\n", displayPb);
      * swarm.Best.displayMemory(displayPb);
      *
      * save(" " + iter + " " + evalF+ " ", synthSave);

src/eva2/optimization/strategies/tribes/Tribe.java

@@ -94,7 +94,7 @@ public class Tribe implements java.io.Serializable {
         for (n = 0; n < explorerNb; n++) {
 
             boolean eval = explorer[n].moveExplorer(tribeRank, n, swarm, informOption, prob);
-//			if (eval) evals++;
+//			if (evaluate) evals++;
 
             if (pb.constraint) {
                 explorer[n].constraint(pb.gNb, pb.hNb, pb.ups);

src/eva2/optimization/strategies/tribes/TribesExplorer.java

@@ -139,7 +139,7 @@ public class TribesExplorer extends AbstractEAIndividual implements InterfaceDat
 //    public int generateExplorer(TribesParam pb,
 //    		TribesSwarm swarm, TribesPosition center, double radius,
 //    		int option, int fromTribe,
-//    		int initType, int eval, int label) {
+//    		int initType, int evaluate, int label) {
 //    	/*
 //         Generation of a new explorer ("scout")
 //         If fromTribe=-1, this is the very first generation
@@ -148,7 +148,7 @@ public class TribesExplorer extends AbstractEAIndividual implements InterfaceDat
 //    	TribesExplorer expl = this;
 //    	
 //    	int d, dmax, dmod;
-//    	int evalF = eval;
+//    	int evalF = evaluate;
 //    	int m;
 //    	//  int rank;
 //    	// int shaman;
@@ -433,7 +433,7 @@ public class TribesExplorer extends AbstractEAIndividual implements InterfaceDat
         if (Tribes.blind > 0 && strategy == 6) {
             if (RNG.randomDouble() < Tribes.blind) {
                 fitnessEval = false;
-                //System.out.print("\n no fitness eval");
+                //System.out.print("\n no fitness evaluate");
             }
         }
 
@@ -444,7 +444,7 @@ public class TribesExplorer extends AbstractEAIndividual implements InterfaceDat
                 prob.evaluate(this);
                 swarm.masterTribe.incEvalCnt();
 //                evalF = position.fitnessEval(pb.function, pb.objective,
-//                                             pb.fitnessSize, eval);
+//                                             pb.fitnessSize, evaluate);
             } else { // Artificial fitness by using penalties
                 for (n = 0; n < position.fitness.length; n++) {
                     SetFitness(n, swarm.tribes[fromTribe].memory[

src/eva2/optimization/strategies/tribes/TribesPosition.java

@@ -117,9 +117,9 @@ public class TribesPosition implements java.io.Serializable {
 //
 ////  =================================================================== FITNESS EVALUATION
 //    public int fitnessEval(int[] codeFunction, double objective,
-//    		int fitnessSize,int eval) {
+//    		int fitnessSize,int evaluate) {
 //
-//    	//Tribes.eval++; // Incrémente le nombre total d'évaluations
+//    	//Tribes.evaluate++; // Incrémente le nombre total d'évaluations
 //    	int n;
 //    	double r;
 //
@@ -301,7 +301,7 @@ public class TribesPosition implements java.io.Serializable {
 //    	 */
 //    	totalError = totalError(fitness, fitnessSize);
 //
-//    	return eval+1;
+//    	return evaluate+1;
 //    }
 //
 //    public double totalError(double[] fitness, int fitnessSize) {

src/eva2/tools/matlab/JMatLink.java

@@ -675,7 +675,7 @@ public class JMatLink extends Thread {
         // evaluate expression "evalS" in specified engine Ep
 
         if (debugB) {
-            System.out.println("eval(ep,String) in  " + epI + " " + evalS);
+            System.out.println("evaluate(ep,String) in  " + epI + " " + evalS);
         }
 
         lockEngineLock();
@@ -693,7 +693,7 @@ public class JMatLink extends Thread {
         releaseEngineLock();
 
         if (debugB) {
-            System.out.println("eval(ep,String) out " + epI + " " + evalS);
+            System.out.println("evaluate(ep,String) out " + epI + " " + evalS);
         }
 
         // return retValI; Return value indicates success