diff --git a/src/eva2/optimization/operator/archiving/ArchivingSPEAII.java b/src/eva2/optimization/operator/archiving/ArchivingSPEAII.java index 4673874f..5c7e9766 100644 --- a/src/eva2/optimization/operator/archiving/ArchivingSPEAII.java +++ b/src/eva2/optimization/operator/archiving/ArchivingSPEAII.java @@ -288,7 +288,7 @@ public class ArchivingSPEAII extends AbstractArchiving implements java.io.Serial // result[i] = Double.POSITIVE_INFINITY; // for (int j = 0; j < result.length; j++) { // if (i != j) { -// tmpD = this.m_Metric.distance((AbstractEAIndividual)pop.get(i), (AbstractEAIndividual)pop.get(j)); +// tmpD = this.distanceMetric.distance((AbstractEAIndividual)pop.get(i), (AbstractEAIndividual)pop.get(j)); // if (tmpD < result[i]) result[i] = tmpD; // } // } diff --git a/src/eva2/optimization/operator/cluster/ClusteringDensityBased.java b/src/eva2/optimization/operator/cluster/ClusteringDensityBased.java index 3e2e6520..fabfbfe2 100644 --- a/src/eva2/optimization/operator/cluster/ClusteringDensityBased.java +++ b/src/eva2/optimization/operator/cluster/ClusteringDensityBased.java @@ -210,7 +210,7 @@ public class ClusteringDensityBased implements InterfaceClusteringDistanceParam, // */ // public boolean belongsToSpecies(AbstractEAIndividual indy, Population species, Population pop) { // if (this.m_TestConvergingSpeciesOnBestOnly) { -// if (this.m_Metric.distance(indy, species.getBestEAIndividual()) < this.m_ClusterDistance) return true; +// if (this.distanceMetric.distance(indy, species.getBestEAIndividual()) < this.m_ClusterDistance) return true; // else return false; // } else { // Population tmpPop = (Population)species.clone(); diff --git a/src/eva2/optimization/operator/migration/MOClusteringSeparation.java b/src/eva2/optimization/operator/migration/MOClusteringSeparation.java index e42f7a47..6814c193 100644 --- a/src/eva2/optimization/operator/migration/MOClusteringSeparation.java +++ b/src/eva2/optimization/operator/migration/MOClusteringSeparation.java @@ -186,7 +186,7 @@ public class MOClusteringSeparation implements InterfaceMigration, java.io.Seria if (prob instanceof AbstractMultiObjectiveOptimizationProblem) { // set the boundaries to perform the constrained // domain principle introduced by Deb et al. - ((AbstractMultiObjectiveOptimizationProblem) prob).m_AreaConst4Parallelization.clear(); + ((AbstractMultiObjectiveOptimizationProblem) prob).areaConst4Parallelization.clear(); double[] myClass = c[i]; double[][] myOtherClass = new double[c.length - 1][]; int index = 0; @@ -198,7 +198,7 @@ public class MOClusteringSeparation implements InterfaceMigration, java.io.Seria index++; } ConstBelongsToDifferentClass b = new ConstBelongsToDifferentClass(myClass, myOtherClass, this.m_KMeans.getUseSearchSpace()); - ((AbstractMultiObjectiveOptimizationProblem) prob).m_AreaConst4Parallelization.add(b); + ((AbstractMultiObjectiveOptimizationProblem) prob).areaConst4Parallelization.add(b); // if (this.debug) { // String out = ""; // out += i+ ". MyClass: {"; diff --git a/src/eva2/optimization/operator/migration/MOConeSeparation.java b/src/eva2/optimization/operator/migration/MOConeSeparation.java index 401fcd9b..70d49fdd 100644 --- a/src/eva2/optimization/operator/migration/MOConeSeparation.java +++ b/src/eva2/optimization/operator/migration/MOConeSeparation.java @@ -272,18 +272,18 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl if (prob instanceof AbstractMultiObjectiveOptimizationProblem) { // set the boundaries to perform the constrained // domain principle introduced by Deb et al. - ((AbstractMultiObjectiveOptimizationProblem) prob).m_AreaConst4Parallelization.clear(); + ((AbstractMultiObjectiveOptimizationProblem) prob).areaConst4Parallelization.clear(); if (i > 0) { // add the lower boundary ConstObjectivesInEqualityBiggerThanLinear b = new ConstObjectivesInEqualityBiggerThanLinear(boundaries[i - 1][0], boundaries[i - 1][1]); - ((AbstractMultiObjectiveOptimizationProblem) prob).m_AreaConst4Parallelization.add(b); + ((AbstractMultiObjectiveOptimizationProblem) prob).areaConst4Parallelization.add(b); } if (i < islands.length - 1) { // add the upper boundary ConstObjectivesInEqualityLesserThanLinear b = new ConstObjectivesInEqualityLesserThanLinear(boundaries[i][0], boundaries[i][1]); - ((AbstractMultiObjectiveOptimizationProblem) prob).m_AreaConst4Parallelization.add(b); + ((AbstractMultiObjectiveOptimizationProblem) prob).areaConst4Parallelization.add(b); } islands[i].setProblem(prob); } @@ -380,11 +380,11 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl if (prob instanceof AbstractMultiObjectiveOptimizationProblem) { // set the boundaries to perform the constrained // domain principle introduced by Deb et al. - ((AbstractMultiObjectiveOptimizationProblem) prob).m_AreaConst4Parallelization.clear(); + ((AbstractMultiObjectiveOptimizationProblem) prob).areaConst4Parallelization.clear(); ConstObjectivesInEqualitySmallerThanSurface sts = new ConstObjectivesInEqualitySmallerThanSurface(curBoundingPlane[0], curBoundingPlane[1]); ConstObjectivesInEqualityBiggerThanSurface bts = new ConstObjectivesInEqualityBiggerThanSurface(lastBoundingPlane[0], lastBoundingPlane[1]); - ((AbstractMultiObjectiveOptimizationProblem) prob).m_AreaConst4Parallelization.add(sts); - ((AbstractMultiObjectiveOptimizationProblem) prob).m_AreaConst4Parallelization.add(bts); + ((AbstractMultiObjectiveOptimizationProblem) prob).areaConst4Parallelization.add(sts); + ((AbstractMultiObjectiveOptimizationProblem) prob).areaConst4Parallelization.add(bts); } islands[i].setProblem(prob); // if (true) { @@ -393,7 +393,7 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl // for (int j = 0; j < newIPOP[i].size(); j++) { // if(((AbstractEAIndividual)newIPOP[i].get(j)).areaConst4ParallelViolated) { // System.out.print(j+", "); -// ((AbstractEAIndividual)newIPOP[i].get(j)).checkAreaConst4Parallelization(((AbstractMultiObjectiveOptimizationProblem)prob).m_AreaConst4Parallelization); +// ((AbstractEAIndividual)newIPOP[i].get(j)).checkAreaConst4Parallelization(((AbstractMultiObjectiveOptimizationProblem)prob).areaConst4Parallelization); // } // } // System.out.println(""); @@ -421,11 +421,11 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl // if (prob instanceof AbstractMultiObjectiveOptimizationProblem) { // // set the boundaries to perform the constrained // // domain principle introduced by Deb et al. -// ((AbstractMultiObjectiveOptimizationProblem)prob).m_AreaConst4Parallelization.clear(); +// ((AbstractMultiObjectiveOptimizationProblem)prob).areaConst4Parallelization.clear(); // ConstObjectivesInEqualitySmallerThanSurface sts = new ConstObjectivesInEqualitySmallerThanSurface(curBoundingPlane[0], curBoundingPlane[1]); // ConstObjectivesInEqualityBiggerThanSurface bts = new ConstObjectivesInEqualityBiggerThanSurface(lastBoundingPlane[0], lastBoundingPlane[1]); -// ((AbstractMultiObjectiveOptimizationProblem)prob).m_AreaConst4Parallelization.add(sts); -// ((AbstractMultiObjectiveOptimizationProblem)prob).m_AreaConst4Parallelization.add(bts); +// ((AbstractMultiObjectiveOptimizationProblem)prob).areaConst4Parallelization.add(sts); +// ((AbstractMultiObjectiveOptimizationProblem)prob).areaConst4Parallelization.add(bts); // } // islands[last].SetProblem(prob); // } diff --git a/src/eva2/optimization/operator/migration/MOXMeansSeparation.java b/src/eva2/optimization/operator/migration/MOXMeansSeparation.java index d61a009a..e17dc337 100644 --- a/src/eva2/optimization/operator/migration/MOXMeansSeparation.java +++ b/src/eva2/optimization/operator/migration/MOXMeansSeparation.java @@ -188,7 +188,7 @@ public class MOXMeansSeparation implements InterfaceMigration, java.io.Serializa if (prob instanceof AbstractMultiObjectiveOptimizationProblem) { // set the boundaries to perform the constrained // domain principle introduced by Deb et al. - ((AbstractMultiObjectiveOptimizationProblem) prob).m_AreaConst4Parallelization.clear(); + ((AbstractMultiObjectiveOptimizationProblem) prob).areaConst4Parallelization.clear(); double[] myClass = c[i]; double[][] myOtherClass = new double[c.length - 1][]; int index = 0; @@ -200,7 +200,7 @@ public class MOXMeansSeparation implements InterfaceMigration, java.io.Serializa index++; } ConstBelongsToDifferentClass b = new ConstBelongsToDifferentClass(myClass, myOtherClass, this.m_XMeans.getUseSearchSpace()); - ((AbstractMultiObjectiveOptimizationProblem) prob).m_AreaConst4Parallelization.add(b); + ((AbstractMultiObjectiveOptimizationProblem) prob).areaConst4Parallelization.add(b); // if (this.debug) { // String out = ""; // out += i+ ". MyClass: {"; diff --git a/src/eva2/optimization/problems/AbstractDynTransProblem.java b/src/eva2/optimization/problems/AbstractDynTransProblem.java index 822aa84b..3b77733a 100644 --- a/src/eva2/optimization/problems/AbstractDynTransProblem.java +++ b/src/eva2/optimization/problems/AbstractDynTransProblem.java @@ -64,9 +64,6 @@ public abstract class AbstractDynTransProblem extends AbstractSynchronousOptimiz // myplot.addGraph(0, 1); myplot.jump(); } else { - if (TRACE) { - System.out.println("creating myplot instance"); - } double[] tmpD = new double[2]; tmpD[0] = 0; tmpD[1] = 0; @@ -135,12 +132,9 @@ public abstract class AbstractDynTransProblem extends AbstractSynchronousOptimiz */ @Override public void initPopulationAt(Population population, double time) { - if (TRACE) { - System.out.println("DynTransProblem at " + this + " initPop, problem is " + getProblem()); - } getProblem().initializePopulation(population); - for (int i = 0; i < population.size(); i++) { - ((AbstractEAIndividual) population.get(i)).setAge(0); + for (Object individuum : population) { + ((AbstractEAIndividual) individuum).setAge(0); } } diff --git a/src/eva2/optimization/problems/AbstractDynamicOptimizationProblem.java b/src/eva2/optimization/problems/AbstractDynamicOptimizationProblem.java index 7d01bbe5..49e38572 100644 --- a/src/eva2/optimization/problems/AbstractDynamicOptimizationProblem.java +++ b/src/eva2/optimization/problems/AbstractDynamicOptimizationProblem.java @@ -10,10 +10,6 @@ import eva2.optimization.population.Population; * changes in the problem environment (the target function). The latter may be measured in absolute * function evaluations or relative to the population size. The necessary transmutations are to be * implemented, however. - *

- * User: MK - * Date: 19.12.2006 - * Time: 11:02:12 */ public abstract class AbstractDynamicOptimizationProblem extends AbstractOptimizationProblem implements java.io.Serializable { @@ -47,10 +43,6 @@ public abstract class AbstractDynamicOptimizationProblem extends AbstractOptimiz * Switch controling extra problem visualization */ private boolean bExtraPlot; - /** - * Debug switch - */ - protected boolean TRACE; /** * the plot instance for problem data visualization */ @@ -62,7 +54,6 @@ public abstract class AbstractDynamicOptimizationProblem extends AbstractOptimiz public AbstractDynamicOptimizationProblem() { //System.out.println("AbstractDynamicOptimizationProblem()"); bExtraPlot = false; - TRACE = false; myplot = null; idealInd = null; // initialize(0, 1., 0.1); @@ -169,9 +160,6 @@ public abstract class AbstractDynamicOptimizationProblem extends AbstractOptimiz */ public void setSeverity(double sev) { severity = sev; - if (TRACE) { - System.out.println("severity to " + sev); - } } /** @@ -228,9 +216,6 @@ public abstract class AbstractDynamicOptimizationProblem extends AbstractOptimiz protected void incProblemTime(double dt) { currentProblemTime += dt; resetProblem(dt); - if (TRACE) { - System.out.println("new problem time is " + currentProblemTime); - } } /** @@ -344,9 +329,6 @@ public abstract class AbstractDynamicOptimizationProblem extends AbstractOptimiz } private void makePlot() { - if (TRACE) { - System.out.println("creating myplot instance"); - } double[] tmpD = new double[2]; tmpD[0] = 0; tmpD[1] = 0; diff --git a/src/eva2/optimization/problems/AbstractMultiModalProblemKnown.java b/src/eva2/optimization/problems/AbstractMultiModalProblemKnown.java index 7ed80409..89edf25d 100644 --- a/src/eva2/optimization/problems/AbstractMultiModalProblemKnown.java +++ b/src/eva2/optimization/problems/AbstractMultiModalProblemKnown.java @@ -16,7 +16,7 @@ import java.util.List; public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDouble implements Interface2DBorderProblem, InterfaceMultimodalProblemKnown { - protected static InterfaceDistanceMetric m_Metric = new PhenotypeMetric(); + protected static InterfaceDistanceMetric distanceMetric = new PhenotypeMetric(); private double globalOptimum = 0; protected Population listOfOptima; protected double epsilon = 0.05; diff --git a/src/eva2/optimization/problems/AbstractMultiObjectiveOptimizationProblem.java b/src/eva2/optimization/problems/AbstractMultiObjectiveOptimizationProblem.java index 6686c9f8..2f3d80da 100644 --- a/src/eva2/optimization/problems/AbstractMultiObjectiveOptimizationProblem.java +++ b/src/eva2/optimization/problems/AbstractMultiObjectiveOptimizationProblem.java @@ -25,11 +25,7 @@ import java.util.Vector; import java.util.concurrent.Semaphore; /** - * Created by IntelliJ IDEA. - * User: streiche - * Date: 15.04.2004 - * Time: 13:32:08 - * To change this template use File | Settings | File Templates. + * */ public abstract class AbstractMultiObjectiveOptimizationProblem extends AbstractOptimizationProblem { @@ -47,14 +43,14 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract AbstractEAIndividual ind; Vector resultrep; Population pop; - Semaphore m_Semaphore = null; + Semaphore semaphore = null; public MultiObjectiveEvalThread(AbstractMultiObjectiveOptimizationProblem prob, AbstractEAIndividual ind, Vector resultrep, Population pop, Semaphore sema) { this.ind = ind; this.prob = prob; this.resultrep = resultrep; this.pop = pop; - this.m_Semaphore = sema; + this.semaphore = sema; } @Override @@ -66,47 +62,47 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract fitness = ind.getFitness(); // check and update border if necessary - if (m_Border == null) { - prob.m_Border = new double[fitness.length][2]; - } else if (fitness.length != prob.m_Border.length) { - //System.out.println("AbstractMOOptimizationProblem: Warning fitness.length("+fitness.length+") doesn't fit border.length("+this.m_Border.length+")"); + if (border == null) { + prob.border = new double[fitness.length][2]; + } else if (fitness.length != prob.border.length) { + //System.out.println("AbstractMOOptimizationProblem: Warning fitness.length("+fitness.length+") doesn't fit border.length("+this.border.length+")"); //System.out.println("Resetting the border!"); - prob.m_Border = new double[fitness.length][2]; + prob.border = new double[fitness.length][2]; } for (int j = 0; j < fitness.length; j++) { -// if ((this.m_Border[j][0] > fitness[j]) || (this.m_Border[j][1] < fitness[j])) { +// if ((this.border[j][0] > fitness[j]) || (this.border[j][1] < fitness[j])) { // System.out.println("border... " + j); -// System.out.println(this.m_Border[j][0]+" > "+fitness[j]); -// System.out.println(this.m_Border[j][1]+" < "+fitness[j]); +// System.out.println(this.border[j][0]+" > "+fitness[j]); +// System.out.println(this.border[j][1]+" < "+fitness[j]); // } - prob.m_Border[j][0] = Math.min(prob.m_Border[j][0], fitness[j]); - prob.m_Border[j][1] = Math.max(prob.m_Border[j][1], fitness[j]); + prob.border[j][0] = Math.min(prob.border[j][0], fitness[j]); + prob.border[j][1] = Math.max(prob.border[j][1], fitness[j]); } pop.incrFunctionCalls(); - m_Semaphore.release(); + semaphore.release(); } } - protected InterfaceMOSOConverter m_MOSOConverter = new MOSONoConvert(); - protected InterfaceParetoFrontMetric m_Metric = new MetricS(); - transient protected Population m_ParetoFront = new Population(); - public ArrayList m_AreaConst4Parallelization = new ArrayList(); - protected int m_OutputDimension = 2; - double m_defaultBorderLow = 0; - double m_defaultBorderHigh = 5; + protected InterfaceMOSOConverter mosoConverter = new MOSONoConvert(); + protected InterfaceParetoFrontMetric metric = new MetricS(); + transient protected Population paretoFront = new Population(); + public ArrayList areaConst4Parallelization = new ArrayList(); + protected int outputDimension = 2; + double defaultBorderLow = 0; + double defaultBorderHigh = 5; - transient protected double[][] m_Border; - transient protected Plot m_Plot; - transient protected JFrame m_Result; - protected transient boolean m_Show = false; + transient protected double[][] border; + transient protected Plot plot; + transient protected JFrame resultFrame; + protected transient boolean show = false; public AbstractMultiObjectiveOptimizationProblem(double borderHigh) { super(); - m_defaultBorderHigh = borderHigh; + defaultBorderHigh = borderHigh; this.template = new ESIndividualDoubleData(); makeBorder(); - if (this.m_Show) { + if (this.show) { this.initProblemFrame(); } } @@ -121,17 +117,17 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract * @param b True if the pareto-front is to be shown. */ public void setShowParetoFront(boolean b) { - this.m_Show = b; - if (this.m_Show) { + this.show = b; + if (this.show) { this.initProblemFrame(); - } else if (this.m_Plot != null) { - this.m_Plot.dispose(); - this.m_Plot = null; + } else if (this.plot != null) { + this.plot.dispose(); + this.plot = null; } } public boolean isShowParetoFront() { - return this.m_Show; + return this.show; } public String showParetoFrontTipText() { @@ -155,28 +151,28 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract @Override public void initializeProblem() { makeBorder(); - this.m_ParetoFront = new Population(); - if (this.m_Show) { + this.paretoFront = new Population(); + if (this.show) { this.initProblemFrame(); } } protected void makeBorder() { - if (this.m_Border == null) { - this.m_Border = new double[m_OutputDimension][2]; + if (this.border == null) { + this.border = new double[outputDimension][2]; } - for (int i = 0; i < this.m_Border.length; i++) { - this.m_Border[i][0] = getLowerBorder(i); - this.m_Border[i][1] = getUpperBorder(i); + for (int i = 0; i < this.border.length; i++) { + this.border[i][0] = getLowerBorder(i); + this.border[i][1] = getUpperBorder(i); } } protected double getUpperBorder(int i) { - return m_defaultBorderHigh; + return defaultBorderHigh; } protected double getLowerBorder(int i) { - return m_defaultBorderLow; + return defaultBorderLow; } /** @@ -212,13 +208,13 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract * This caused a lot of trouble for the DeNovo Approach of MOCCO */ public void resetParetoFront() { - this.m_ParetoFront = new Population(); + this.paretoFront = new Population(); } @Override public void evaluatePopulationStart(Population population) { super.evaluatePopulationStart(population); - if (this.m_Show && (this.m_Plot == null)) { + if (this.show && (this.plot == null)) { this.initProblemFrame(); } } @@ -233,21 +229,21 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract AbstractEAIndividual tmpIndy = (AbstractEAIndividual) population.get(i); fitness = tmpIndy.getFitness(); // check and update border if necessary - if (m_Border == null) { - this.m_Border = new double[fitness.length][2]; - } else if (fitness.length != this.m_Border.length) { - //System.out.println("AbstractMOOptimizationProblem: Warning fitness.length("+fitness.length+") doesn't fit border.length("+this.m_Border.length+")"); + if (border == null) { + this.border = new double[fitness.length][2]; + } else if (fitness.length != this.border.length) { + //System.out.println("AbstractMOOptimizationProblem: Warning fitness.length("+fitness.length+") doesn't fit border.length("+this.border.length+")"); //System.out.println("Resetting the border!"); - this.m_Border = new double[fitness.length][2]; + this.border = new double[fitness.length][2]; } for (int j = 0; j < fitness.length; j++) { -// if ((this.m_Border[j][0] > fitness[j]) || (this.m_Border[j][1] < fitness[j])) { +// if ((this.border[j][0] > fitness[j]) || (this.border[j][1] < fitness[j])) { // System.out.println("border... " + j); -// System.out.println(this.m_Border[j][0]+" > "+fitness[j]); -// System.out.println(this.m_Border[j][1]+" < "+fitness[j]); +// System.out.println(this.border[j][0]+" > "+fitness[j]); +// System.out.println(this.border[j][1]+" < "+fitness[j]); // } - this.m_Border[j][0] = Math.min(this.m_Border[j][0], fitness[j]); - this.m_Border[j][1] = Math.max(this.m_Border[j][1], fitness[j]); + this.border[j][0] = Math.min(this.border[j][0], fitness[j]); + this.border[j][1] = Math.max(this.border[j][1], fitness[j]); } } @@ -260,16 +256,16 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract // could be pretty many // currently the problem should be multi-criteria - logPopToParetoFront(m_ParetoFront, population); + logPopToParetoFront(paretoFront, population); // Sometimes you want to transform a multiobjective optimization problem // into a single objective one, this way single objective optimization // algorithms can be applied more easily - this.m_MOSOConverter.convertMultiObjective2SingleObjective(population); + this.mosoConverter.convertMultiObjective2SingleObjective(population); - if (this.m_Show) { - if (m_Plot.isValid()) { - AbstractMultiObjectiveOptimizationProblem.drawProblem(population, m_Plot, this); + if (this.show) { + if (plot.isValid()) { + AbstractMultiObjectiveOptimizationProblem.drawProblem(population, plot, this); } } } @@ -307,12 +303,12 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract double[] tmpD = new double[2]; tmpD[0] = 0; tmpD[1] = 0; - if (this.m_Plot == null) { - m_Plot = new Plot("Multiobjective Optimization", "Y1", "Y2", tmpD, tmpD); + if (this.plot == null) { + plot = new Plot("Multiobjective Optimization", "Y1", "Y2", tmpD, tmpD); } // plot init stuff - this.initAdditionalData(this.m_Plot, 10); + this.initAdditionalData(this.plot, 10); } /** @@ -388,7 +384,7 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract // MOOpt was converted into a SOOpt if (AbstractMultiObjectiveOptimizationProblem.isPopulationMultiObjective(p)) { // in this case i have to use my local archive - tmpPop = moProblem.m_ParetoFront; + tmpPop = moProblem.paretoFront; } else { // in this case i use the population of the optimizer // and eventually the population.archive if there is one @@ -427,8 +423,8 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract } plot.setUnconnectedPoint(tmpFitness[0], tmpFitness[1], index); } - plot.setUnconnectedPoint(this.m_Border[0][1], this.m_Border[1][1], index); - plot.setUnconnectedPoint(this.m_Border[0][0], this.m_Border[1][0], index); + plot.setUnconnectedPoint(this.border[0][1], this.border[1][1], index); + plot.setUnconnectedPoint(this.border[0][0], this.border[1][0], index); } /** @@ -526,8 +522,8 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract return new Double(this.calculateMetric(pop)); } else { // in this case the local Pareto-Front could be multi-objective - if (AbstractMultiObjectiveOptimizationProblem.isPopulationMultiObjective(this.m_ParetoFront)) { - return new Double(this.calculateMetric(this.m_ParetoFront)); + if (AbstractMultiObjectiveOptimizationProblem.isPopulationMultiObjective(this.paretoFront)) { + return new Double(this.calculateMetric(this.paretoFront)); } else { return new Double(pop.getBestEAIndividual().getFitness(0)); } @@ -540,7 +536,7 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract * @return the local Pareto-front log */ public Population getLocalParetoFront() { - return this.m_ParetoFront; + return this.paretoFront; } @Override @@ -552,7 +548,7 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract @Override public Object[] getAdditionalDataValue(PopulationInterface pop) { Object[] result = new Object[2]; - if (m_MOSOConverter != null && !(m_MOSOConverter instanceof MOSONoConvert)) { + if (mosoConverter != null && !(mosoConverter instanceof MOSONoConvert)) { result[0] = Double.NaN; result[1] = Double.NaN; } else { @@ -591,7 +587,7 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract if (pop == null || (pop.size() == 0)) { return Double.NaN; } - return this.m_Metric.calculateMetricOn(pop, this); + return this.metric.calculateMetricOn(pop, this); } /** @@ -600,7 +596,7 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract * @return The objective space range */ public double[][] getObjectiveSpaceRange() { - return this.m_Border; + return this.border; } /** @@ -609,7 +605,7 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract * @return The output dimension */ public int getOutputDimension() { - return this.m_OutputDimension; + return this.outputDimension; } // /** This method will calculate the s-Metric if an archive population is present. @@ -619,8 +615,8 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract // public double tcalculateSMetric(Population population) { // double result = 0; // -// ((SMetric)this.m_Metric).setObjectiveSpaceRange(this.m_Border); -// result = this.m_Metric.calculateMetricOn(population); +// ((SMetric)this.distanceMetric).setObjectiveSpaceRange(this.border); +// result = this.distanceMetric.calculateMetricOn(population); // // return result; // } @@ -632,7 +628,7 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract // public double calculateRelativeSMetric(Population population, double[][] ref) { // double result = 0; // SMetricWithReference tmpMetric = new SMetricWithReference(); -// tmpMetric.setObjectiveSpaceRange(this.m_Border); +// tmpMetric.setObjectiveSpaceRange(this.border); // tmpMetric.setReferenceFront(ref); // result = tmpMetric.calculateMetricOn(population); // return result; @@ -645,12 +641,12 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract * @param b The new MO2SO converter. */ public void setMOSOConverter(InterfaceMOSOConverter b) { - this.m_MOSOConverter = b; - this.m_MOSOConverter.setOutputDimension(this.m_OutputDimension); + this.mosoConverter = b; + this.mosoConverter.setOutputDimension(this.outputDimension); } public InterfaceMOSOConverter getMOSOConverter() { - return this.m_MOSOConverter; + return this.mosoConverter; } public String mOSOConverterTipText() { @@ -663,11 +659,11 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract * @param b The new metric. */ public void setMetric(InterfaceParetoFrontMetric b) { - this.m_Metric = b; + this.metric = b; } public InterfaceParetoFrontMetric getMetric() { - return this.m_Metric; + return this.metric; } public String metricTipText() { diff --git a/src/eva2/optimization/problems/AbstractOptimizationProblem.java b/src/eva2/optimization/problems/AbstractOptimizationProblem.java index 74bdb968..8fa65dd5 100644 --- a/src/eva2/optimization/problems/AbstractOptimizationProblem.java +++ b/src/eva2/optimization/problems/AbstractOptimizationProblem.java @@ -34,11 +34,7 @@ import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; /** - * Created by IntelliJ IDEA. - * User: streiche - * Date: 29.08.2003 - * Time: 13:40:12 - * To change this template use Options | File Templates. + * */ public abstract class AbstractOptimizationProblem implements InterfaceOptimizationProblem, Serializable { class EvalThread extends Thread { diff --git a/src/eva2/optimization/problems/AbstractParallelOptimizationProblem.java b/src/eva2/optimization/problems/AbstractParallelOptimizationProblem.java index 1e39d3b0..df42d439 100644 --- a/src/eva2/optimization/problems/AbstractParallelOptimizationProblem.java +++ b/src/eva2/optimization/problems/AbstractParallelOptimizationProblem.java @@ -2,44 +2,15 @@ package eva2.optimization.problems; /** * This class is under construction. - *

- * Created by IntelliJ IDEA. - * User: streiche - * Date: 17.12.2004 - * Time: 14:43:35 - * To change this template use File | Settings | File Templates. */ public abstract class AbstractParallelOptimizationProblem extends AbstractOptimizationProblem { - - // private PropertyRemoteServers m_Servers = new PropertyRemoteServers(); - private int m_LocalCPUs = 4; - private boolean m_Parallelize = false; - private AbstractOptimizationProblem[] m_Slaves; + private int localCPUs = 4; + private boolean parallelize = false; @Override public void initializeProblem() { - /* if (this.m_Parallelize) { - // this is running on remote maschines - String[] nodesList = this.m_Servers.getCheckedServerNodes(); - if ((nodesList == null) || (nodesList.length == 0)) { - return; - } - this.m_Slaves = new AbstractOptimizationProblem[nodesList.length]; - for (int i = 0; i < nodesList.length; i++) { - this.m_Slaves[i] = (AbstractOptimizationProblem) RMIProxyRemoteThread.newInstance(this, nodesList[i]); - } - } else { - // this is running on the local machine - this.m_Slaves = new AbstractOptimizationProblem[this.m_LocalCPUs]; - for (int i = 0; i < this.m_LocalCPUs; i++) { - this.m_Slaves[i] = (AbstractOptimizationProblem) this.clone(); - } - }*/ } -/********************************************************************************************************************** - * These are for GUI - */ /** * This method returns a global info string * @@ -66,42 +37,28 @@ public abstract class AbstractParallelOptimizationProblem extends AbstractOptimi * @return The current optimzation mode */ public boolean getParallelize() { - return this.m_Parallelize; + return this.parallelize; } public void setParallelize(boolean b) { - this.m_Parallelize = b; + this.parallelize = b; } public String parallelizeTipText() { return "Toggle between parallel and serial implementation."; } - /** This method allows you to managae the available servers - * @return The current servers - */ - /* public PropertyRemoteServers getServers() { - return this.m_Servers; - } - public void setServers(PropertyRemoteServers b){ - this.m_Servers = b; - } - public String serversTipText() { - return "Choose and manage the servers (only active in parallelized mode)."; - } -*/ - /** * This method allows you to set the number of processors in local mode * * @param n Number of processors. */ public void setNumberLocalCPUs(int n) { - this.m_LocalCPUs = n; + this.localCPUs = n; } public int getNumberLocalCPUs() { - return this.m_LocalCPUs; + return this.localCPUs; } public String numberLocalCPUsTipText() { diff --git a/src/eva2/optimization/problems/AbstractProblemInteger.java b/src/eva2/optimization/problems/AbstractProblemInteger.java index 50205fa5..b2394de5 100644 --- a/src/eva2/optimization/problems/AbstractProblemInteger.java +++ b/src/eva2/optimization/problems/AbstractProblemInteger.java @@ -11,8 +11,8 @@ import eva2.optimization.strategies.InterfaceOptimizer; */ public abstract class AbstractProblemInteger extends AbstractOptimizationProblem implements java.io.Serializable { - protected AbstractEAIndividual m_OverallBest = null; - protected int m_ProblemDimension = 10; + protected AbstractEAIndividual bestIndividuum = null; + protected int problemDimension = 10; public AbstractProblemInteger() { initTemplate(); @@ -31,22 +31,22 @@ public abstract class AbstractProblemInteger extends AbstractOptimizationProblem if (o.template != null) { template = (AbstractEAIndividual) o.template.clone(); } - if (o.m_OverallBest != null) { - m_OverallBest = (AbstractEAIndividual) ((AbstractEAIndividual) o.m_OverallBest).clone(); + if (o.bestIndividuum != null) { + bestIndividuum = (AbstractEAIndividual) ((AbstractEAIndividual) o.bestIndividuum).clone(); } - this.m_ProblemDimension = o.m_ProblemDimension; + this.problemDimension = o.problemDimension; } @Override public void initializeProblem() { initTemplate(); - this.m_OverallBest = null; + this.bestIndividuum = null; } @Override public void initializePopulation(Population population) { - this.m_OverallBest = null; - ((InterfaceDataTypeInteger) this.template).setIntegerDataLength(this.m_ProblemDimension); + this.bestIndividuum = null; + ((InterfaceDataTypeInteger) this.template).setIntegerDataLength(this.problemDimension); AbstractOptimizationProblem.defaultInitPopulation(population, template, this); } @@ -64,8 +64,8 @@ public abstract class AbstractProblemInteger extends AbstractOptimizationProblem // set the fitness of the individual individual.SetFitness(i, fitness[i]); } - if ((this.m_OverallBest == null) || (this.m_OverallBest.getFitness(0) > individual.getFitness(0))) { - this.m_OverallBest = (AbstractEAIndividual) individual.clone(); + if ((this.bestIndividuum == null) || (this.bestIndividuum.getFitness(0) > individual.getFitness(0))) { + this.bestIndividuum = (AbstractEAIndividual) individual.clone(); } } @@ -120,11 +120,11 @@ public abstract class AbstractProblemInteger extends AbstractOptimizationProblem * @param n The problem dimension */ public void setProblemDimension(int n) { - this.m_ProblemDimension = n; + this.problemDimension = n; } public int getProblemDimension() { - return this.m_ProblemDimension; + return this.problemDimension; } public String problemDimensionTipText() { diff --git a/src/eva2/optimization/problems/B1Problem.java b/src/eva2/optimization/problems/B1Problem.java index 230d3c29..62a22ab7 100644 --- a/src/eva2/optimization/problems/B1Problem.java +++ b/src/eva2/optimization/problems/B1Problem.java @@ -16,7 +16,7 @@ import java.util.BitSet; */ @Description("The task in this problem is to maximize the number of false bits in a BitSet.") public class B1Problem extends AbstractProblemBinary implements java.io.Serializable { - public int m_ProblemDimension = 30; + public int problemDimension = 30; public B1Problem() { super(); @@ -26,7 +26,7 @@ public class B1Problem extends AbstractProblemBinary implements java.io.Serializ public B1Problem(B1Problem b) { super.cloneObjects(b); - this.m_ProblemDimension = b.m_ProblemDimension; + this.problemDimension = b.problemDimension; } /** @@ -73,7 +73,7 @@ public class B1Problem extends AbstractProblemBinary implements java.io.Serializ this.evaluate(individual); String result = "Minimize Number of Bits problem:\n"; result += individual.getStringRepresentation() + "\n"; - result += "Scores " + (this.m_ProblemDimension - individual.getFitness(0)) + " zero bits!"; + result += "Scores " + (this.problemDimension - individual.getFitness(0)) + " zero bits!"; return result; } @@ -90,7 +90,7 @@ public class B1Problem extends AbstractProblemBinary implements java.io.Serializ result += "Minimize Bits Problem:\n"; result += "The task is to reduce the number of TRUE Bits in the given bit string.\n"; result += "Parameters:\n"; - result += "Number of Bits: " + this.m_ProblemDimension + "\n"; + result += "Number of Bits: " + this.problemDimension + "\n"; result += "Solution representation:\n"; //result += this.template.getSolutionRepresentationFor(); return result; @@ -116,12 +116,12 @@ public class B1Problem extends AbstractProblemBinary implements java.io.Serializ * @param dim The problem dimension. */ public void setProblemDimension(int dim) { - this.m_ProblemDimension = dim; + this.problemDimension = dim; } @Override public int getProblemDimension() { - return this.m_ProblemDimension; + return this.problemDimension; } public String multiRunsTipText() { diff --git a/src/eva2/optimization/problems/BKnapsackProblem.java b/src/eva2/optimization/problems/BKnapsackProblem.java index 4a778e85..2b3db71b 100644 --- a/src/eva2/optimization/problems/BKnapsackProblem.java +++ b/src/eva2/optimization/problems/BKnapsackProblem.java @@ -15,11 +15,11 @@ import java.util.BitSet; @Description("Maximize the value of the knapsack without exceeding the weight limit") public class BKnapsackProblem extends AbstractProblemBinary implements java.io.Serializable { - private int m_Limit = 5000; - private double m_Punish = 2.0; - private double m_LocalSearch = 0.0; - private boolean m_Lamarckism = false; - private double m_ProblemSpecificInit = 0.0; + private int limit = 5000; + private double punish = 2.0; + private double localSearch = 0.0; + private boolean lamarckism = false; + private double problemSpecificInit = 0.0; static final int[][] items = { {334, -328}, {303, -291}, @@ -130,10 +130,10 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S //AbstractOptimizationProblem cloneObjects(b); // BKnapsackProblem - this.m_Limit = b.m_Limit; - this.m_Punish = b.m_Punish; - this.m_LocalSearch = b.m_LocalSearch; - this.m_Lamarckism = b.m_Lamarckism; + this.limit = b.limit; + this.punish = b.punish; + this.localSearch = b.localSearch; + this.lamarckism = b.lamarckism; } @Override @@ -161,7 +161,7 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S protected void initIndy(int k, AbstractEAIndividual indy) { indy.init(this); - if (RNG.flipCoin(this.m_ProblemSpecificInit)) { + if (RNG.flipCoin(this.problemSpecificInit)) { BitSet tmpSet = new BitSet(); tmpSet.clear(); @@ -184,7 +184,7 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S tmpBitSet = ((InterfaceDataTypeBinary) individual).getBinaryData(); result = this.eval(tmpBitSet); - if (RNG.flipCoin(this.m_LocalSearch)) { + if (RNG.flipCoin(this.localSearch)) { // first remove surplus assets while (result[1] > 0) { // search for an element to replace @@ -232,7 +232,7 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S } for (int i = 0; i < items.length; i++) { - if (items[i][0] < this.m_Limit - weight) { + if (items[i][0] < this.limit - weight) { // possible candidate if (stronger < 0) { stronger = i; @@ -249,7 +249,7 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S result = this.eval(tmpBitSet); } - if (this.m_Lamarckism) { + if (this.lamarckism) { ((InterfaceDataTypeBinary) individual).setBinaryGenotype(tmpBitSet); } } @@ -285,8 +285,8 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S } } // write the solution - result[1] = Math.max(0, result[1] - this.m_Limit); - result[0] = (this.m_Punish * result[1]) + result[2]; + result[1] = Math.max(0, result[1] - this.limit); + result[0] = (this.punish * result[1]) + result[2]; return result; } @@ -330,7 +330,7 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S result.append("Knapsack Problem:\n"); result.append("The task is to find a packing for a knapsack with limited size("); - result.append(this.m_Limit); + result.append(this.limit); result.append(") and maximal value.\n"); result.append("The default setting with limit=5000 allows a knapsack with value 5100. Note: value reads negative."); result.append("Available items {(weight/value),...}: {"); @@ -344,7 +344,7 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S result.append("}\n"); result.append("Parameters:\n"); result.append("Punish rate: "); - result.append(this.m_Punish); + result.append(this.punish); result.append("\n"); result.append("Solution representation:\n"); //result += this.template.getSolutionRepresentationFor(); @@ -369,11 +369,11 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S * @param punish The number of multiruns that are to be performed */ public void setPunishment(double punish) { - this.m_Punish = punish; + this.punish = punish; } public double getPunishment() { - return this.m_Punish; + return this.punish; } public String punishmentTipText() { @@ -405,11 +405,11 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S * @param b gives the chance of problemspecific initialization. */ public void setProblemSpecificInit(double b) { - this.m_ProblemSpecificInit = b; + this.problemSpecificInit = b; } public double getProblemSpecificInit() { - return this.m_ProblemSpecificInit; + return this.problemSpecificInit; } public String problemSpecificInitTipText() { @@ -424,11 +424,11 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S * @param b gives the chance of problemspecific local search. */ public void setLocalSearch(double b) { - this.m_LocalSearch = b; + this.localSearch = b; } public double getLocalSearch() { - return this.m_LocalSearch; + return this.localSearch; } public String localSearchTipText() { @@ -441,11 +441,11 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S * @param b toggles lamarckism. */ public void setLamarckism(boolean b) { - this.m_Lamarckism = b; + this.lamarckism = b; } public boolean getLamarckism() { - return this.m_Lamarckism; + return this.lamarckism; } public String lamarckismTipText() { @@ -453,11 +453,11 @@ public class BKnapsackProblem extends AbstractProblemBinary implements java.io.S } public int getWeightLimit() { - return m_Limit; + return limit; } public void setWeightLimit(int mLimit) { - m_Limit = mLimit; + limit = mLimit; } public String weightLimitTipText() { diff --git a/src/eva2/optimization/problems/ExternalRuntimeProblem.java b/src/eva2/optimization/problems/ExternalRuntimeProblem.java index 447d7ede..5c3d19df 100644 --- a/src/eva2/optimization/problems/ExternalRuntimeProblem.java +++ b/src/eva2/optimization/problems/ExternalRuntimeProblem.java @@ -23,14 +23,14 @@ import java.util.List; public class ExternalRuntimeProblem extends AbstractOptimizationProblem implements Interface2DBorderProblem, InterfaceProblemDouble, InterfaceHasInitRange { - protected AbstractEAIndividual m_OverallBest = null; - protected int m_ProblemDimension = 10; - protected String m_Command = ""; - protected String m_WorkingDir = ""; - PropertyDoubleArray m_Range = new PropertyDoubleArray(m_ProblemDimension, 2, -10, 10); - PropertyDoubleArray m_initRange = new PropertyDoubleArray(m_ProblemDimension, 2, -10, 10); + protected AbstractEAIndividual bestIndividuum = null; + protected int problemDimension = 10; + protected String command = ""; + protected String workingDir = ""; + PropertyDoubleArray range = new PropertyDoubleArray(problemDimension, 2, -10, 10); + PropertyDoubleArray initializationRange = new PropertyDoubleArray(problemDimension, 2, -10, 10); private String additionalArg = ""; - protected InterfaceMOSOConverter m_MosoConverter = new MOSONoConvert(); + protected InterfaceMOSOConverter mosoConverter = new MOSONoConvert(); // Private Subclass to redirect Streams within an extra Thread to avoid dead // locks @@ -70,7 +70,7 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem public ExternalRuntimeProblem() { this.template = new ESIndividualDoubleData(); - ((ESIndividualDoubleData) this.template).setDoubleDataLength(m_ProblemDimension); + ((ESIndividualDoubleData) this.template).setDoubleDataLength(problemDimension); ((ESIndividualDoubleData) this.template).setDoubleRange(makeRange()); } @@ -80,27 +80,27 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem this.template = (AbstractEAIndividual) ((AbstractEAIndividual) b.template).clone(); } //ExternalRuntimeProblem - if (b.m_OverallBest != null) { - this.m_OverallBest = (AbstractEAIndividual) ((AbstractEAIndividual) b.m_OverallBest).clone(); + if (b.bestIndividuum != null) { + this.bestIndividuum = (AbstractEAIndividual) ((AbstractEAIndividual) b.bestIndividuum).clone(); } - this.m_ProblemDimension = b.m_ProblemDimension; - this.m_Command = b.m_Command; - if (b.m_Range != null) { - this.m_Range = (PropertyDoubleArray) b.m_Range.clone(); + this.problemDimension = b.problemDimension; + this.command = b.command; + if (b.range != null) { + this.range = (PropertyDoubleArray) b.range.clone(); } else { - this.m_Range = null; + this.range = null; } - if (b.m_initRange != null) { - this.m_initRange = (PropertyDoubleArray) b.m_initRange.clone(); + if (b.initializationRange != null) { + this.initializationRange = (PropertyDoubleArray) b.initializationRange.clone(); } else { - this.m_initRange = null; + this.initializationRange = null; } - if (b.m_MosoConverter != null) { - this.m_MosoConverter = (InterfaceMOSOConverter) b.m_MosoConverter.clone(); + if (b.mosoConverter != null) { + this.mosoConverter = (InterfaceMOSOConverter) b.mosoConverter.clone(); } else { - this.m_MosoConverter = null; + this.mosoConverter = null; } - this.m_WorkingDir = b.m_WorkingDir; + this.workingDir = b.workingDir; } /** @@ -118,21 +118,21 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem */ @Override public void initializeProblem() { - this.m_OverallBest = null; - File f = new File(m_Command); + this.bestIndividuum = null; + File f = new File(command); if (f.exists()) { - m_Command = f.getAbsolutePath(); + command = f.getAbsolutePath(); } else { String sep = System.getProperty("file.separator"); - if (m_WorkingDir.endsWith(sep)) { - f = new File(m_WorkingDir + m_Command); + if (workingDir.endsWith(sep)) { + f = new File(workingDir + command); } else { - f = new File(m_WorkingDir + sep + m_Command); + f = new File(workingDir + sep + command); } if (f.exists()) { - m_Command = f.getAbsolutePath(); + command = f.getAbsolutePath(); } else { - System.err.println("Warning, " + this.getClass() + " could not find command " + m_Command + " in " + m_WorkingDir); + System.err.println("Warning, " + this.getClass() + " could not find command " + command + " in " + workingDir); } } } @@ -144,9 +144,9 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem */ @Override public void initializePopulation(Population population) { - this.m_OverallBest = null; + this.bestIndividuum = null; - ((InterfaceDataTypeDouble) this.template).setDoubleDataLength(this.m_ProblemDimension); + ((InterfaceDataTypeDouble) this.template).setDoubleDataLength(this.problemDimension); ((InterfaceDataTypeDouble) this.template).setDoubleRange(makeRange()); AbstractOptimizationProblem.defaultInitPopulation(population, template, this); @@ -154,13 +154,13 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem @Override public double[][] makeRange() { - if (m_Range == null) { + if (range == null) { System.err.println("Warning, range not set in ExternalRuntimeProblem.makeRange!"); } - if (m_Range.getNumRows() != getProblemDimension()) { + if (range.getNumRows() != getProblemDimension()) { System.err.println("Warning, problem dimension and range dimension dont match in ExternalRuntimeProblem.makeRange!"); } - return m_Range.getDoubleArrayShallow().clone(); + return range.getDoubleArrayShallow().clone(); } public void setRange(double[][] range) { @@ -174,14 +174,14 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem * @param range */ public void setRange(PropertyDoubleArray range) { - if (range.getNumRows() < this.m_ProblemDimension) { - System.err.println("Warning, expected range of dimension " + m_ProblemDimension + " in setRange!"); + if (range.getNumRows() < this.problemDimension) { + System.err.println("Warning, expected range of dimension " + problemDimension + " in setRange!"); } - m_Range.setDoubleArray(range.getDoubleArrayShallow()); + this.range.setDoubleArray(range.getDoubleArrayShallow()); } public PropertyDoubleArray getRange() { - return m_Range; + return range; } public String rangeTipText() { @@ -190,12 +190,12 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem @Override public double getRangeLowerBound(int dim) { - return m_Range.getValue(dim, 0); + return range.getValue(dim, 0); } @Override public double getRangeUpperBound(int dim) { - return m_Range.getValue(dim, 1); + return range.getValue(dim, 1); } @@ -217,16 +217,16 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem // if (this.m_UseTestConstraint) { // if (x[0] < 1) individual.addConstraintViolation(1-x[0]); // } - if ((this.m_OverallBest == null) || (this.m_OverallBest.getFitness(0) > individual.getFitness(0))) { - this.m_OverallBest = (AbstractEAIndividual) individual.clone(); + if ((this.bestIndividuum == null) || (this.bestIndividuum.getFitness(0) > individual.getFitness(0))) { + this.bestIndividuum = (AbstractEAIndividual) individual.clone(); } } @Override public void evaluatePopulationEnd(Population population) { super.evaluatePopulationEnd(population); - if (m_MosoConverter != null) { - m_MosoConverter.convertMultiObjective2SingleObjective(population); + if (mosoConverter != null) { + mosoConverter.convertMultiObjective2SingleObjective(population); } } @@ -292,22 +292,22 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem ArrayList fitList = new ArrayList(); List parameters = new ArrayList(); - parameters.add(this.m_Command); + parameters.add(this.command); if (additionalArg != null && (additionalArg.length() > 0)) { parameters.add(additionalArg); } - for (int i = 0; i < this.m_ProblemDimension; i++) { + for (int i = 0; i < this.problemDimension; i++) { String p = prepareParameter(x, i); parameters.add(p); } - List res = runProcess(parameters, m_WorkingDir); + List res = runProcess(parameters, workingDir); try { for (String str : res) { fitList.add(new Double(str)); } } catch (NumberFormatException e) { - System.err.println("Error: " + m_Command + " delivered malformatted output for " + BeanInspector.toString(x)); + System.err.println("Error: " + command + " delivered malformatted output for " + BeanInspector.toString(x)); e.printStackTrace(); } double[] fit = new double[fitList.size()]; @@ -341,7 +341,7 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem sb.append("External Runtime Problem:\n"); sb.append("Here the individual codes a vector of real number x is to be minimized on a user given external problem.\nParameters:\n"); sb.append("Dimension : "); - sb.append(this.m_ProblemDimension); + sb.append(this.problemDimension); return sb.toString(); } @@ -368,14 +368,14 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem * @param t Length of the x vector that is to be optimized */ public void setProblemDimension(int t) { - this.m_ProblemDimension = t; - this.m_Range.adaptRowCount(t); - this.m_initRange.adaptRowCount(t); + this.problemDimension = t; + this.range.adaptRowCount(t); + this.initializationRange.adaptRowCount(t); } @Override public int getProblemDimension() { - return this.m_ProblemDimension; + return this.problemDimension; } public String problemDimensionTipText() { @@ -388,11 +388,11 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem * @param t Length of the x vector at is to be optimized */ public void setCommand(String t) { - this.m_Command = t; + this.command = t; } public String getCommand() { - return this.m_Command; + return this.command; } public String commandTipText() { @@ -405,11 +405,11 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem * @param t working directory */ public void setWorkingDirectory(String t) { - this.m_WorkingDir = t; + this.workingDir = t; } public String getWorkingDirectory() { - return this.m_WorkingDir; + return this.workingDir; } public String workingDirectoryTipText() { @@ -417,11 +417,11 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem } public InterfaceMOSOConverter getMosoConverter() { - return m_MosoConverter; + return mosoConverter; } public void setMosoConverter(InterfaceMOSOConverter mMosoConverter) { - m_MosoConverter = mMosoConverter; + mosoConverter = mMosoConverter; } public String mosoConverterTipText() { @@ -472,13 +472,13 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem // @Override @Override public Object getInitRange() { - if (m_initRange == null) { - if (m_Range == null) { + if (initializationRange == null) { + if (range == null) { System.err.println("Warning, neither range nor initRange has been set in ExternalRuntimeProblem!"); } - return m_Range.getDoubleArrayShallow(); + return range.getDoubleArrayShallow(); } else { - return m_initRange.getDoubleArrayShallow(); + return initializationRange.getDoubleArrayShallow(); } } @@ -488,14 +488,14 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem } public void setInitialRange(PropertyDoubleArray range) { - if (range.getNumRows() < this.m_ProblemDimension) { - System.err.println("Warning, expected range of dimension " + m_ProblemDimension + " in setInitRange!"); + if (range.getNumRows() < this.problemDimension) { + System.err.println("Warning, expected range of dimension " + problemDimension + " in setInitRange!"); } - m_initRange = new PropertyDoubleArray(range); + initializationRange = new PropertyDoubleArray(range); } public PropertyDoubleArray getInitialRange() { - return m_initRange; + return initializationRange; } public String initialRangeTipText() { diff --git a/src/eva2/optimization/problems/F10Problem.java b/src/eva2/optimization/problems/F10Problem.java index b550d97e..5bee6c4b 100644 --- a/src/eva2/optimization/problems/F10Problem.java +++ b/src/eva2/optimization/problems/F10Problem.java @@ -9,9 +9,9 @@ import eva2.util.annotation.Description; @Description("Weierstrass-Mandelbrot Fractal Function") public class F10Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, java.io.Serializable { - private double m_D = 1.5; - private double m_b = 2.3; - private int m_Iterations = 20; + private double d = 1.5; + private double b = 2.3; + private int iterations = 20; public F10Problem() { this.template = new ESIndividualDoubleData(); @@ -19,9 +19,9 @@ public class F10Problem extends AbstractProblemDoubleOffset implements Interface public F10Problem(F10Problem b) { super(b); - this.m_D = b.m_D; - this.m_b = b.m_b; - this.m_Iterations = b.m_Iterations; + this.d = b.d; + this.b = b.b; + this.iterations = b.iterations; } /** @@ -48,7 +48,7 @@ public class F10Problem extends AbstractProblemDoubleOffset implements Interface result[0] = yOffset; for (int i = 0; i < x.length - 1; i++) { double xi = x[i] - xOffset; - result[0] += ((this.calculateC(xi)) / (c1 * Math.pow(Math.abs(xi), 2 - this.m_D))) + Math.pow(xi, 2) - 1; + result[0] += ((this.calculateC(xi)) / (c1 * Math.pow(Math.abs(xi), 2 - this.d))) + Math.pow(xi, 2) - 1; } return result; } @@ -56,8 +56,8 @@ public class F10Problem extends AbstractProblemDoubleOffset implements Interface private double calculateC(double x) { double result = 0; - for (int i = -this.m_Iterations; i < this.m_Iterations + 1; i++) { - result += (1 - Math.cos(Math.pow(this.m_b, i) * x)) / (Math.pow(this.m_b, (2 - this.m_D) * i)); + for (int i = -this.iterations; i < this.iterations + 1; i++) { + result += (1 - Math.cos(Math.pow(this.b, i) * x)) / (Math.pow(this.b, (2 - this.d) * i)); } return result; @@ -103,11 +103,11 @@ public class F10Problem extends AbstractProblemDoubleOffset implements Interface if (d > 2) { d = 2; } - this.m_D = d; + this.d = d; } public double getD() { - return this.m_D; + return this.d; } public String dTipText() { @@ -123,11 +123,11 @@ public class F10Problem extends AbstractProblemDoubleOffset implements Interface if (b < 1.000001) { b = 1.000001; } - this.m_b = b; + this.b = b; } public double getb() { - return this.m_b; + return this.b; } public String bTipText() { @@ -143,11 +143,11 @@ public class F10Problem extends AbstractProblemDoubleOffset implements Interface if (iters < 2) { iters = 2; } - this.m_Iterations = iters; + this.iterations = iters; } public int getIterations() { - return this.m_Iterations; + return this.iterations; } public String iterationsTipText() { diff --git a/src/eva2/optimization/problems/F11Problem.java b/src/eva2/optimization/problems/F11Problem.java index 0ffa7fbf..83c12c5a 100644 --- a/src/eva2/optimization/problems/F11Problem.java +++ b/src/eva2/optimization/problems/F11Problem.java @@ -9,7 +9,7 @@ import eva2.util.annotation.Description; @Description("Griewank Function") public class F11Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, java.io.Serializable { - private double m_D = 4000; + private double d = 4000; public F11Problem() { this.problemDimension = 10; @@ -19,7 +19,7 @@ public class F11Problem extends AbstractProblemDoubleOffset implements Interface public F11Problem(F11Problem b) { super(b); - this.m_D = b.m_D; + this.d = b.d; } /** @@ -48,7 +48,7 @@ public class F11Problem extends AbstractProblemDoubleOffset implements Interface result[0] += Math.pow(xi, 2); tmpProd *= Math.cos((xi) / Math.sqrt(i + 1)); } - result[0] = ((result[0] / this.m_D) - tmpProd + 1) + yOffset; + result[0] = ((result[0] / this.d) - tmpProd + 1) + yOffset; return result; } @@ -88,11 +88,11 @@ public class F11Problem extends AbstractProblemDoubleOffset implements Interface public void setD(double d) { // if (d < 1) d = 1;// how can this be limited to [1,2] if 4000 is default? // if (d > 2) d = 2;// MK FIXED: this obviously was a copy-paste error from F10 - this.m_D = d; + this.d = d; } public double getD() { - return this.m_D; + return this.d; } public String dTipText() { diff --git a/src/eva2/optimization/problems/F6Problem.java b/src/eva2/optimization/problems/F6Problem.java index 159fbed1..0d0050e2 100644 --- a/src/eva2/optimization/problems/F6Problem.java +++ b/src/eva2/optimization/problems/F6Problem.java @@ -14,8 +14,8 @@ import java.io.Serializable; @Description("Generalized Rastrigin's function.") public class F6Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, InterfaceFirstOrderDerivableProblem, InterfaceLocalSearchable, Serializable, InterfaceInterestingHistogram { - private double m_A = 10; - private double m_Omega = 2 * Math.PI; + private double a = 10; + private double omega = 2 * Math.PI; private transient GradientDescentAlgorithm localSearchOptimizer = null; public F6Problem() { @@ -24,8 +24,8 @@ public class F6Problem extends AbstractProblemDoubleOffset public F6Problem(F6Problem b) { super(b); - this.m_A = b.m_A; - this.m_Omega = b.m_Omega; + this.a = b.a; + this.omega = b.omega; } public F6Problem(int dim) { @@ -52,10 +52,10 @@ public class F6Problem extends AbstractProblemDoubleOffset public double[] evaluate(double[] x) { x = rotateMaybe(x); double[] result = new double[1]; - result[0] = x.length * this.m_A + yOffset; + result[0] = x.length * this.a + yOffset; for (int i = 0; i < x.length; i++) { double xi = x[i] - xOffset; - result[0] += Math.pow(xi, 2) - this.m_A * Math.cos(this.m_Omega * xi); + result[0] += Math.pow(xi, 2) - this.a * Math.cos(this.omega * xi); } return result; } @@ -67,7 +67,7 @@ public class F6Problem extends AbstractProblemDoubleOffset for (int j = 0; j < x.length; j++) { result[j] = 0; double xj = x[j] - xOffset; - result[j] += 2 * xj + this.m_Omega * this.m_A * Math.sin(this.m_Omega * xj); + result[j] += 2 * xj + this.omega * this.a * Math.sin(this.omega * xj); } return result; } @@ -106,11 +106,11 @@ public class F6Problem extends AbstractProblemDoubleOffset * @param a The offset for the decision variables. */ public void setA(double a) { - this.m_A = a; + this.a = a; } public double getA() { - return this.m_A; + return this.a; } public String aTipText() { @@ -124,11 +124,11 @@ public class F6Problem extends AbstractProblemDoubleOffset * @param Omega The offset for the objective value. */ public void setOmega(double Omega) { - this.m_Omega = Omega; + this.omega = Omega; } public double getOmega() { - return this.m_Omega; + return this.omega; } public String omegaTipText() { diff --git a/src/eva2/optimization/problems/F7Problem.java b/src/eva2/optimization/problems/F7Problem.java index 648b3e07..fe6903c3 100644 --- a/src/eva2/optimization/problems/F7Problem.java +++ b/src/eva2/optimization/problems/F7Problem.java @@ -15,24 +15,24 @@ import java.io.Serializable; @Description("Sphere Model, changing Environment.") public class F7Problem extends AbstractProblemDoubleOffset implements Serializable { - private double m_t = 250; - private double m_Change = 4; - protected SelectedTag m_TimeIntervalType; - private int m_CurrentTimeStamp; + private double t = 250; + private double change = 4; + protected SelectedTag timeIntervalType; + private int currentTimeStamp; public F7Problem() { Tag[] tag = new Tag[2]; tag[0] = new Tag(0, "Function Calls"); tag[1] = new Tag(1, "Generation"); - this.m_TimeIntervalType = new SelectedTag(0, tag); + this.timeIntervalType = new SelectedTag(0, tag); this.template = new ESIndividualDoubleData(); } public F7Problem(F7Problem b) { super(b); - this.m_Change = b.m_Change; - this.m_t = b.m_t; - this.m_TimeIntervalType = (SelectedTag) b.m_TimeIntervalType.clone(); + this.change = b.change; + this.t = b.t; + this.timeIntervalType = (SelectedTag) b.timeIntervalType.clone(); } /** @@ -59,10 +59,10 @@ public class F7Problem extends AbstractProblemDoubleOffset implements Serializab for (int i = 0; i < population.size(); i++) { tmpIndy = (AbstractEAIndividual) population.get(i); tmpIndy.resetConstraintViolation(); - if (this.m_TimeIntervalType.getSelectedTag().getID() == 0) { - this.m_CurrentTimeStamp = population.getFunctionCalls(); + if (this.timeIntervalType.getSelectedTag().getID() == 0) { + this.currentTimeStamp = population.getFunctionCalls(); } else { - this.m_CurrentTimeStamp = population.getGeneration(); + this.currentTimeStamp = population.getGeneration(); } this.evaluate(tmpIndy); population.incrFunctionCalls(); @@ -81,13 +81,13 @@ public class F7Problem extends AbstractProblemDoubleOffset implements Serializab x = rotateMaybe(x); double[] result = new double[1]; result[0] = yOffset; - if ((Math.floor(this.m_CurrentTimeStamp / this.m_t) % 2) == 0) { + if ((Math.floor(this.currentTimeStamp / this.t) % 2) == 0) { for (int i = 0; i < x.length - 1; i++) { result[0] += Math.pow(x[i] - xOffset, 2); } } else { for (int i = 0; i < x.length - 1; i++) { - result[0] += Math.pow(x[i] - xOffset - this.m_Change, 2); + result[0] += Math.pow(x[i] - xOffset - this.change, 2); } } return result; @@ -130,11 +130,11 @@ public class F7Problem extends AbstractProblemDoubleOffset implements Serializab if (d < 1) { d = 1; } - this.m_t = d; + this.t = d; } public double gett() { - return this.m_t; + return this.t; } public String tTipText() { @@ -147,11 +147,11 @@ public class F7Problem extends AbstractProblemDoubleOffset implements Serializab * @param d The mutation operator. */ public void setChange(double d) { - this.m_Change = d; + this.change = d; } public double getChange() { - return this.m_Change; + return this.change; } public String changeTipText() { @@ -164,11 +164,11 @@ public class F7Problem extends AbstractProblemDoubleOffset implements Serializab * @param d The mutation operator. */ public void setTimeIntervalType(SelectedTag d) { - this.m_TimeIntervalType = d; + this.timeIntervalType = d; } public SelectedTag getTimeIntervalType() { - return this.m_TimeIntervalType; + return this.timeIntervalType; } public String timeIntervalTypeTipText() { diff --git a/src/eva2/optimization/problems/F8Problem.java b/src/eva2/optimization/problems/F8Problem.java index c4a5f237..49ea3382 100644 --- a/src/eva2/optimization/problems/F8Problem.java +++ b/src/eva2/optimization/problems/F8Problem.java @@ -20,8 +20,8 @@ public class F8Problem extends AbstractProblemDoubleOffset implements InterfaceInterestingHistogram, InterfaceMultimodalProblem, //InterfaceFirstOrderDerivableProblem, InterfaceMultimodalProblemKnown, java.io.Serializable { - transient protected Population m_ListOfOptima = null; - private static transient boolean state_initializing_optima = false; + transient protected Population listOfOptima = null; + private static transient boolean stateInitializingOptima = false; private double a = 20; private double b = 0.2; private double c = 2 * Math.PI; @@ -133,7 +133,7 @@ public class F8Problem extends AbstractProblemDoubleOffset @Override public double getMaximumPeakRatio(Population pop) { - return AbstractMultiModalProblemKnown.getMaximumPeakRatioMinimization(m_ListOfOptima, pop, getDefaultAccuracy(), 0, 5); + return AbstractMultiModalProblemKnown.getMaximumPeakRatioMinimization(listOfOptima, pop, getDefaultAccuracy(), 0, 5); } @Override @@ -143,7 +143,7 @@ public class F8Problem extends AbstractProblemDoubleOffset @Override public Population getRealOptima() { - return m_ListOfOptima; + return listOfOptima; } @Override @@ -172,8 +172,8 @@ public class F8Problem extends AbstractProblemDoubleOffset @Override public void initListOfOptima() { if (listOfOptimaNeedsUpdate()) { - state_initializing_optima = true; - m_ListOfOptima = new Population(); + stateInitializingOptima = true; + listOfOptima = new Population(); // ingeniously avoid recursive calls during refinement! double[] pos = new double[getProblemDimension()]; Arrays.fill(pos, getXOffset()); @@ -192,7 +192,7 @@ public class F8Problem extends AbstractProblemDoubleOffset // ab dim 18/20 oder so finde ich plötzlich keine optima bei x[i]<1 mehr??? dir = inverseRotateMaybe(dir); pos = refineSolution(this, pos, dir, 0.0005, 1e-20, 0); - if (EuclideanMetric.euclideanDistance(pos, m_ListOfOptima.getEAIndividual(0).getDoublePosition()) < 0.5) { + if (EuclideanMetric.euclideanDistance(pos, listOfOptima.getEAIndividual(0).getDoublePosition()) < 0.5) { System.err.println("Warning, possibly converged to a wrong optimum in F8Problem.initListOfOptima!"); } pos = rotateMaybe(pos); @@ -204,7 +204,7 @@ public class F8Problem extends AbstractProblemDoubleOffset } } } - state_initializing_optima = false; + stateInitializingOptima = false; } } @@ -237,13 +237,13 @@ public class F8Problem extends AbstractProblemDoubleOffset } private boolean listOfOptimaNeedsUpdate() { - if (state_initializing_optima) { + if (stateInitializingOptima) { return false; } // avoid recursive call during refining with GDA - if (m_ListOfOptima == null || (m_ListOfOptima.size() != (1 + 2 * getProblemDimension()))) { + if (listOfOptima == null || (listOfOptima.size() != (1 + 2 * getProblemDimension()))) { 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); + AbstractEAIndividual indy = listOfOptima.getEAIndividual(1); double[] curFit = evaluate(indy.getDoublePosition()); if (Math.abs(Mathematics.dist(curFit, indy.getFitness(), 2)) > 1e-10) { return true; @@ -254,6 +254,6 @@ public class F8Problem extends AbstractProblemDoubleOffset } private void addOptimum(double[] pos) { - AbstractProblemDouble.addUnrotatedOptimum(m_ListOfOptima, this, pos); + AbstractProblemDouble.addUnrotatedOptimum(listOfOptima, this, pos); } } \ No newline at end of file diff --git a/src/eva2/optimization/problems/FLensProblem.java b/src/eva2/optimization/problems/FLensProblem.java index 17e60478..6bf235e1 100644 --- a/src/eva2/optimization/problems/FLensProblem.java +++ b/src/eva2/optimization/problems/FLensProblem.java @@ -23,8 +23,8 @@ class MyLensViewer extends JPanel implements InterfaceSolutionViewer { Population indiesToPaint = new Population(); // private double[] m_BestVariables; // private double m_BestFitness; - private int m_Height, m_Width; - FLensProblem m_LensProblem; + private int theHeight, theWidth; + FLensProblem lensProblem; public MyLensViewer(FLensProblem f) { initView(f); @@ -36,13 +36,11 @@ class MyLensViewer extends JPanel implements InterfaceSolutionViewer { @Override public void initView(AbstractOptimizationProblem prob) { - this.m_LensProblem = (FLensProblem) prob; + this.lensProblem = (FLensProblem) prob; } @Override public void resetView() { -// this.m_BestFitness = Double.POSITIVE_INFINITY; -// this.m_BestVariables = new double[10]; ESIndividualDoubleData dummy = new ESIndividualDoubleData(); dummy.setFitness(new double[]{Double.POSITIVE_INFINITY}); indiesToPaint = new Population(); @@ -68,8 +66,8 @@ class MyLensViewer extends JPanel implements InterfaceSolutionViewer { } // Create a buffered image in which to draw // try { -// this.m_Height = (int)g.getClipBounds().getHeight(); -// this.m_Width = (int)g.getClipBounds().getWidth(); +// this.theHeight = (int)g.getClipBounds().getHeight(); +// this.theWidth = (int)g.getClipBounds().getWidth(); // this.m_CenterX = (int)g.getClipBounds().getCenterX(); // this.m_CenterY = (int)g.getClipBounds().getCenterY(); // } catch (java.lang.NullPointerException npe) { @@ -77,34 +75,34 @@ class MyLensViewer extends JPanel implements InterfaceSolutionViewer { // } // This might cure the eternal display problems: just ignore clipping and leave it up to swing Dimension winDim = getSize(); - m_Height = winDim.height; - m_Width = winDim.width; -// m_CenterX = m_Width/2; -// m_CenterY = m_Height/2; + theHeight = winDim.height; + theWidth = winDim.width; +// m_CenterX = theWidth/2; +// m_CenterY = theHeight/2; -// if (this.m_Height == 0) this.m_Height = 250; -// if (this.m_Width == 0) this.m_Width = 350; -// System.out.println(" h w cx cy " + m_Height + " " + m_Width + " " + m_CenterX + " " + m_CenterY ); - bufferedImage = new BufferedImage(this.m_Width, this.m_Height, BufferedImage.TYPE_INT_RGB); +// if (this.theHeight == 0) this.theHeight = 250; +// if (this.theWidth == 0) this.theWidth = 350; +// System.out.println(" h w cx cy " + theHeight + " " + theWidth + " " + m_CenterX + " " + m_CenterY ); + bufferedImage = new BufferedImage(this.theWidth, this.theHeight, BufferedImage.TYPE_INT_RGB); // Create a graphics contents on the buffered image Graphics2D g2D = bufferedImage.createGraphics(); g2D.setPaint(Color.white); - tmpShape = new Rectangle(0, 0, this.m_Width, this.m_Height); + tmpShape = new Rectangle(0, 0, this.theWidth, this.theHeight); g2D.fill(tmpShape); // now start to plot some interesting stuff //draw the mid line g2D.setPaint(Color.black); - g2D.drawLine(0, this.m_Height / 2, this.m_Width, this.m_Height / 2); + g2D.drawLine(0, this.theHeight / 2, this.theWidth, this.theHeight / 2); centerLens = 5 + 50; - centerScreen = centerLens + (int) this.m_LensProblem.focalLength * 10; - segment = 10 * (int) this.m_LensProblem.radius * 2 / (this.m_LensProblem.problemDimension - 1); + centerScreen = centerLens + (int) this.lensProblem.focalLength * 10; + segment = 10 * (int) this.lensProblem.radius * 2 / (this.lensProblem.problemDimension - 1); g2D.setStroke(dashStroke); - g2D.drawLine(centerLens, this.m_Height / 2 + (int) this.m_LensProblem.radius * 10, centerLens, this.m_Height / 2 - (int) this.m_LensProblem.radius * 10); - g2D.drawLine(centerScreen, this.m_Height / 2 + (int) this.m_LensProblem.radius * 10 + 10, centerScreen, this.m_Height / 2 - (int) this.m_LensProblem.radius * 10 - 10); + g2D.drawLine(centerLens, this.theHeight / 2 + (int) this.lensProblem.radius * 10, centerLens, this.theHeight / 2 - (int) this.lensProblem.radius * 10); + g2D.drawLine(centerScreen, this.theHeight / 2 + (int) this.lensProblem.radius * 10 + 10, centerScreen, this.theHeight / 2 - (int) this.lensProblem.radius * 10 - 10); g2D.setStroke(ds); // System.out.println("indies to paint: " + indiesToPaint.size()); - paintLens(m_LensProblem.problemDimension, m_Height, m_LensProblem.radius, mag, centerLens, centerScreen, segment, g2D); + paintLens(lensProblem.problemDimension, theHeight, lensProblem.radius, mag, centerLens, centerScreen, segment, g2D); // Now put everything on the screen g.drawImage(bufferedImage, 0, 0, this); } @@ -112,13 +110,13 @@ class MyLensViewer extends JPanel implements InterfaceSolutionViewer { private void paintLens(int dim, int height, double radius, int mag, int centerLens, int centerScreen, int segment, Graphics2D g2D) { for (int i = 0; i < indiesToPaint.size(); i++) { AbstractEAIndividual indy = indiesToPaint.getEAIndividual(i); - paintLens(indy.getDoublePosition(), m_LensProblem.testLens(indy.getDoublePosition()), indy.getFitness(0), dim, height, radius, mag, centerLens, centerScreen, segment, g2D); + paintLens(indy.getDoublePosition(), lensProblem.testLens(indy.getDoublePosition()), indy.getFitness(0), dim, height, radius, mag, centerLens, centerScreen, segment, g2D); } } private void paintLens(AbstractEAIndividual indy, int dim, int height, double radius, int mag, int centerLens, int centerScreen, int segment, Graphics2D g2D) { if (indy != null) { - paintLens(indy.getDoublePosition(), m_LensProblem.testLens(indy.getDoublePosition()), indy.getFitness(0), dim, height, radius, mag, centerLens, centerScreen, segment, g2D); + paintLens(indy.getDoublePosition(), lensProblem.testLens(indy.getDoublePosition()), indy.getFitness(0), dim, height, radius, mag, centerLens, centerScreen, segment, g2D); } } @@ -145,12 +143,12 @@ class MyLensViewer extends JPanel implements InterfaceSolutionViewer { g2D.drawLine(centerLens, currentXPos + segment / 2, centerScreen, height / 2 + (int) (dots[i - 1] * mag)); currentXPos += segment; -// tmpShape = new Rectangle(currentPos-width/2, this.m_Height/2, width, (int)(variables[i]*10)); +// tmpShape = new Rectangle(currentPos-width/2, this.theHeight/2, width, (int)(variables[i]*10)); // g2D.setPaint(Color.red); // g2D.fill(tmpShape); // g2D.setPaint(Color.black); // g2D.draw(tmpShape); -// g2D.drawLine(currentPos, this.m_Height/2+5, currentPos, this.m_Height/2-5); +// g2D.drawLine(currentPos, this.theHeight/2+5, currentPos, this.theHeight/2-5); } } @@ -164,7 +162,7 @@ class MyLensViewer extends JPanel implements InterfaceSolutionViewer { if (showAllIfPossible) { // indiesToPaint=population; for (int i = 0; i < pop.size(); i++) { - MyLensViewer newView = new MyLensViewer(m_LensProblem); + MyLensViewer newView = new MyLensViewer(lensProblem); Population newPop = new Population(); newPop.add(pop.getEAIndividual(i)); @@ -208,7 +206,7 @@ public class FLensProblem extends AbstractOptimizationProblem protected double noise = 0.0; protected double xOffset = 0.0; protected double yOffset = 0.0; - transient protected boolean m_Show = false; + transient protected boolean show = false; //protected int sleepTime = 0; transient private JFrame problemFrame; @@ -220,7 +218,7 @@ public class FLensProblem extends AbstractOptimizationProblem public FLensProblem() { this.template = new ESIndividualDoubleData(); - if (this.m_Show) { + if (this.show) { this.initProblemFrame(); } } @@ -294,7 +292,7 @@ public class FLensProblem extends AbstractOptimizationProblem @Override public void initializeProblem() { this.overallBest = null; - if (this.m_Show) { + if (this.show) { this.initProblemFrame(); } } @@ -317,14 +315,14 @@ public class FLensProblem extends AbstractOptimizationProblem ((InterfaceDataTypeDouble) this.template).setDoubleRange(range); AbstractOptimizationProblem.defaultInitPopulation(population, template, this); - if (this.m_Show) { + if (this.show) { this.initProblemFrame(); } } @Override public void evaluatePopulationEnd(Population pop) { - if (this.m_Show) { + if (this.show) { this.updateProblemFrame(pop); } } @@ -402,13 +400,13 @@ public class FLensProblem extends AbstractOptimizationProblem * @return double[] */ public double[] testLens(double[] x) { - double m_SegmentHight = 2 * radius / (x.length - 1); + double segmentHeight = 2 * radius / (x.length - 1); double[] result = new double[x.length - 1]; // Computation of fitness. Uses an approximation for very thin lenses. // The fitness is the sum over all segments of the deviation from the center // of focus of a beam running through a segment. for (int i = 1; i < x.length; i++) { - result[i - 1] = radius - m_SegmentHight / 2 - m_SegmentHight * (i - 1) - focalLength / m_SegmentHight * (epsilon - 1) * (x[i] - x[i - 1]); + result[i - 1] = radius - segmentHeight / 2 - segmentHeight * (i - 1) - focalLength / segmentHeight * (epsilon - 1) * (x[i] - x[i - 1]); } return result; } @@ -581,8 +579,8 @@ public class FLensProblem extends AbstractOptimizationProblem * @param show Whether to show the result or not */ public void setShow(boolean show) { - this.m_Show = show; - if (this.m_Show) { + this.show = show; + if (this.show) { this.initProblemFrame(); } else { this.disposeProblemFrame(); @@ -590,7 +588,7 @@ public class FLensProblem extends AbstractOptimizationProblem } public boolean getShow() { - return this.m_Show; + return this.show; } public String showTipText() { diff --git a/src/eva2/optimization/problems/I1Problem.java b/src/eva2/optimization/problems/I1Problem.java index 43eabe1c..0c4e29ea 100644 --- a/src/eva2/optimization/problems/I1Problem.java +++ b/src/eva2/optimization/problems/I1Problem.java @@ -57,7 +57,7 @@ public class I1Problem extends AbstractProblemInteger implements java.io.Seriali result += "I1 Problem:\n"; result += "Here the individual codes a vector of int numbers x and F1(x)= x^2 is to be minimized.\n"; result += "Parameters:\n"; - result += "Dimension : " + this.m_ProblemDimension + "\n"; + result += "Dimension : " + this.problemDimension + "\n"; result += "Solution representation:\n"; return result; } diff --git a/src/eva2/optimization/problems/PSymbolicRegression.java b/src/eva2/optimization/problems/PSymbolicRegression.java index 72178f25..b19765f4 100644 --- a/src/eva2/optimization/problems/PSymbolicRegression.java +++ b/src/eva2/optimization/problems/PSymbolicRegression.java @@ -20,22 +20,22 @@ import java.io.Serializable; @Description("The task is to infer the equation of a system that can only be observed at a number of checkpoints.") public class PSymbolicRegression extends AbstractOptimizationProblem implements InterfaceProgramProblem, InterfaceAdditionalPopulationInformer, Serializable { - private double[] m_X = new double[1]; - private int m_NumberOfConstants = 3; - private double m_LowerBound = -1; - private double m_UpperBound = 1; - private int m_NumberOfCheckPoints = 20; - transient private InterfaceRegressionFunction m_TargetFunction = new RFKoza_GPI_7_3(); - private double[] m_C = new double[m_NumberOfConstants]; - private boolean m_UseInnerConst = false; - private boolean m_UseLocalHillClimbing = false; - private GPArea m_GPArea = new GPArea(); - protected AbstractEAIndividual m_OverallBest = null; - protected double m_Noise = 0.0; + private double[] x = new double[1]; + private int numberOfConstants = 3; + private double lowerBound = -1; + private double upperBound = 1; + private int numberOfCheckPoints = 20; + transient private InterfaceRegressionFunction targetFunction = new RFKoza_GPI_7_3(); + private double[] constants = new double[numberOfConstants]; + private boolean useInnerConst = false; + private boolean useLocalHillClimbing = false; + private GPArea gpArea = new GPArea(); + protected AbstractEAIndividual overallBestIndividuum = null; + protected double noise = 0.0; // This is graphics stuff - transient private Plot m_Plot; - private boolean m_Show = false; + transient private Plot plot; + private boolean show = false; public PSymbolicRegression() { this.template = new GPIndividualProgramData(); @@ -49,35 +49,35 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements this.template = (AbstractEAIndividual) ((AbstractEAIndividual) b.template).clone(); } //F1Problem - if (b.m_OverallBest != null) { - this.m_OverallBest = (AbstractEAIndividual) ((AbstractEAIndividual) b.m_OverallBest).clone(); + if (b.overallBestIndividuum != null) { + this.overallBestIndividuum = (AbstractEAIndividual) ((AbstractEAIndividual) b.overallBestIndividuum).clone(); } - if (b.m_GPArea != null) { - this.m_GPArea = (GPArea) b.m_GPArea.clone(); + if (b.gpArea != null) { + this.gpArea = (GPArea) b.gpArea.clone(); } - if (b.m_TargetFunction != null) { - this.m_TargetFunction = (InterfaceRegressionFunction) b.m_TargetFunction.clone(); + if (b.targetFunction != null) { + this.targetFunction = (InterfaceRegressionFunction) b.targetFunction.clone(); } - if (b.m_X != null) { - this.m_X = new double[b.m_X.length]; - for (int i = 0; i < this.m_X.length; i++) { - this.m_X[i] = b.m_X[i]; + if (b.x != null) { + this.x = new double[b.x.length]; + for (int i = 0; i < this.x.length; i++) { + this.x[i] = b.x[i]; } } - if (b.m_C != null) { - this.m_C = new double[b.m_C.length]; - for (int i = 0; i < this.m_C.length; i++) { - this.m_C[i] = b.m_C[i]; + if (b.constants != null) { + this.constants = new double[b.constants.length]; + for (int i = 0; i < this.constants.length; i++) { + this.constants[i] = b.constants[i]; } } - this.m_Noise = b.m_Noise; - this.m_UseInnerConst = b.m_UseInnerConst; - this.m_UseLocalHillClimbing = b.m_UseLocalHillClimbing; - this.m_NumberOfConstants = b.m_NumberOfConstants; - this.m_LowerBound = b.m_LowerBound; - this.m_UpperBound = b.m_UpperBound; - this.m_NumberOfCheckPoints = b.m_NumberOfCheckPoints; - this.m_LowerBound = b.m_LowerBound; + this.noise = b.noise; + this.useInnerConst = b.useInnerConst; + this.useLocalHillClimbing = b.useLocalHillClimbing; + this.numberOfConstants = b.numberOfConstants; + this.lowerBound = b.lowerBound; + this.upperBound = b.upperBound; + this.numberOfCheckPoints = b.numberOfCheckPoints; + this.lowerBound = b.lowerBound; } /** @@ -95,13 +95,13 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements */ @Override public void initializeProblem() { - if (m_TargetFunction == null) { - m_TargetFunction = new RFKoza_GPI_7_3(); + if (targetFunction == null) { + targetFunction = new RFKoza_GPI_7_3(); } - this.m_OverallBest = null; - this.m_C = new double[this.m_NumberOfConstants]; - for (int i = 0; i < this.m_C.length; i++) { - this.m_C[i] = RNG.randomDouble(-10, 10); + this.overallBestIndividuum = null; + this.constants = new double[this.numberOfConstants]; + for (int i = 0; i < this.constants.length; i++) { + this.constants[i] = RNG.randomDouble(-10, 10); } } @@ -111,34 +111,34 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements */ private void compileArea() { // unfortunately this must be cloned or the GUI wont update. - GPArea oldArea = m_GPArea; - m_GPArea = new GPArea(); + GPArea oldArea = gpArea; + gpArea = new GPArea(); - if (m_GPArea.isEmpty()) { - this.m_GPArea.add2CompleteList(new GPNodeOne()); - this.m_GPArea.add2CompleteList(new GPNodePi(), false); - this.m_GPArea.add2CompleteList(new GPNodeAdd()); - this.m_GPArea.add2CompleteList(new GPNodeSub()); - this.m_GPArea.add2CompleteList(new GPNodeDiv()); - this.m_GPArea.add2CompleteList(new GPNodeMult()); - this.m_GPArea.add2CompleteList(new GPNodeAbs(), false); - this.m_GPArea.add2CompleteList(new GPNodeSin(), false); - this.m_GPArea.add2CompleteList(new GPNodeCos(), false); - this.m_GPArea.add2CompleteList(new GPNodeExp(), false); - this.m_GPArea.add2CompleteList(new GPNodeSqrt(), false); - this.m_GPArea.add2CompleteList(new GPNodePow2(), false); - this.m_GPArea.add2CompleteList(new GPNodePow3(), false); - for (int i = 0; i < this.m_X.length; i++) { - this.m_GPArea.add2CompleteList(new GPNodeInput("X" + i)); + if (gpArea.isEmpty()) { + this.gpArea.add2CompleteList(new GPNodeOne()); + this.gpArea.add2CompleteList(new GPNodePi(), false); + this.gpArea.add2CompleteList(new GPNodeAdd()); + this.gpArea.add2CompleteList(new GPNodeSub()); + this.gpArea.add2CompleteList(new GPNodeDiv()); + this.gpArea.add2CompleteList(new GPNodeMult()); + this.gpArea.add2CompleteList(new GPNodeAbs(), false); + this.gpArea.add2CompleteList(new GPNodeSin(), false); + this.gpArea.add2CompleteList(new GPNodeCos(), false); + this.gpArea.add2CompleteList(new GPNodeExp(), false); + this.gpArea.add2CompleteList(new GPNodeSqrt(), false); + this.gpArea.add2CompleteList(new GPNodePow2(), false); + this.gpArea.add2CompleteList(new GPNodePow3(), false); + for (int i = 0; i < this.x.length; i++) { + this.gpArea.add2CompleteList(new GPNodeInput("X" + i)); } - for (int i = 0; i < this.m_C.length; i++) { - this.m_GPArea.add2CompleteList(new GPNodeInput("C" + i), false); + for (int i = 0; i < this.constants.length; i++) { + this.gpArea.add2CompleteList(new GPNodeInput("C" + i), false); } } - if ((oldArea != null) && (oldArea.getBlackList() != null) && (oldArea.getBlackList().size() == m_GPArea.getBlackList().size())) { - m_GPArea.SetBlackList(oldArea.getBlackList()); + if ((oldArea != null) && (oldArea.getBlackList() != null) && (oldArea.getBlackList().size() == gpArea.getBlackList().size())) { + gpArea.SetBlackList(oldArea.getBlackList()); } - this.m_GPArea.compileReducedList(); + this.gpArea.compileReducedList(); } /** @@ -148,7 +148,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements */ @Override public void initializePopulation(Population population) { - initPopulation(population, this, m_UseInnerConst, m_NumberOfConstants); + initPopulation(population, this, useInnerConst, numberOfConstants); } /** @@ -174,8 +174,8 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements * This method init the enviroment panel if necessary. */ private void initEnvironmentPanel() { - if (this.m_Plot == null) { - this.m_Plot = new Plot("Symbolic Regression", "x", "y", true); + if (this.plot == null) { + this.plot = new Plot("Symbolic Regression", "x", "y", true); } } @@ -193,7 +193,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements for (int i = 0; i < population.size(); i++) { tmpIndy = (AbstractEAIndividual) population.get(i); tmpIndy.resetConstraintViolation(); - if ((this.m_UseLocalHillClimbing) && (tmpIndy instanceof GAPIndividualProgramData)) { + if ((this.useLocalHillClimbing) && (tmpIndy instanceof GAPIndividualProgramData)) { AbstractEAIndividual tmpBestConst = (AbstractEAIndividual) ((GAPIndividualProgramData) tmpIndy).getNumbers(); AbstractEAIndividual tmpConst; this.evaluate(tmpIndy); @@ -213,7 +213,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements ((GAPIndividualProgramData) tmpIndy).setNumbers((InterfaceDataTypeDouble) tmpBestConst); tmpIndy.SetFitness(0, tmpBestConst.getFitness(0)); } else { - if (m_UseLocalHillClimbing) { + if (useLocalHillClimbing) { EVAERROR.errorMsgOnce("Error: local hill climbing only works on GAPIndividualProgramData individuals!"); } this.evaluate(tmpIndy); @@ -237,40 +237,40 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements tmpIndy = (InterfaceDataTypeProgram) individual; program = tmpIndy.getProgramData()[0]; - if ((tmpIndy instanceof GAPIndividualProgramData) && (this.m_UseInnerConst)) { - this.m_C = ((GAPIndividualProgramData) tmpIndy).getDoubleData(); + if ((tmpIndy instanceof GAPIndividualProgramData) && (this.useInnerConst)) { + this.constants = ((GAPIndividualProgramData) tmpIndy).getDoubleData(); } fitness = 0; - for (int j = 0; j < this.m_NumberOfCheckPoints; j++) { - setCheckPoint(m_X, j); + for (int j = 0; j < this.numberOfCheckPoints; j++) { + setCheckPoint(x, j); tmpValue = ((Double) program.evaluate(this)).doubleValue(); - fitness += Math.pow((this.m_TargetFunction.evaluateFunction(this.m_X) - ((Double) program.evaluate(this)).doubleValue()), 2); + fitness += Math.pow((this.targetFunction.evaluateFunction(this.x) - ((Double) program.evaluate(this)).doubleValue()), 2); } - fitness /= (double) this.m_NumberOfCheckPoints; + fitness /= (double) this.numberOfCheckPoints; // add noise to the fitness - fitness += RNG.gaussianDouble(this.m_Noise); + fitness += RNG.gaussianDouble(this.noise); // set the fitness of the individual individual.SetFitness(0, fitness); - if ((this.m_Plot != null) && (this.m_Plot.getFunctionArea().getContainerSize() == 0)) { - this.m_OverallBest = null; + if ((this.plot != null) && (this.plot.getFunctionArea().getContainerSize() == 0)) { + this.overallBestIndividuum = null; } - if ((this.m_OverallBest == null) || (this.m_OverallBest.getFitness(0) > individual.getFitness(0))) { - this.m_OverallBest = (AbstractEAIndividual) individual.clone(); - if (this.m_Show) { - if (m_Plot == null) { + if ((this.overallBestIndividuum == null) || (this.overallBestIndividuum.getFitness(0) > individual.getFitness(0))) { + this.overallBestIndividuum = (AbstractEAIndividual) individual.clone(); + if (this.show) { + if (plot == null) { this.initEnvironmentPanel(); } - this.m_Plot.clearAll(); - program = ((InterfaceDataTypeProgram) this.m_OverallBest).getProgramData()[0]; - for (int i = 0; i < this.m_NumberOfCheckPoints; i++) { - setCheckPoint(m_X, i); + this.plot.clearAll(); + program = ((InterfaceDataTypeProgram) this.overallBestIndividuum).getProgramData()[0]; + for (int i = 0; i < this.numberOfCheckPoints; i++) { + setCheckPoint(x, i); tmpValue = ((Double) program.evaluate(this)).doubleValue(); - this.m_Plot.setConnectedPoint(this.m_X[0], tmpValue, 0); - tmpValue = this.m_TargetFunction.evaluateFunction(this.m_X); - this.m_Plot.setConnectedPoint(this.m_X[0], tmpValue, 1); - this.m_Plot.setInfoString(1, program.getStringRepresentation(), 1.0f); + this.plot.setConnectedPoint(this.x[0], tmpValue, 0); + tmpValue = this.targetFunction.evaluateFunction(this.x); + this.plot.setConnectedPoint(this.x[0], tmpValue, 1); + this.plot.setInfoString(1, program.getStringRepresentation(), 1.0f); } } } @@ -284,7 +284,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements */ private void setCheckPoint(double[] x, int j) { for (int i = 0; i < x.length; i++) { - x[i] = this.m_LowerBound + (j * (this.m_UpperBound - this.m_LowerBound) / (this.m_NumberOfCheckPoints - 1)); + x[i] = this.lowerBound + (j * (this.upperBound - this.lowerBound) / (this.numberOfCheckPoints - 1)); } } @@ -312,8 +312,8 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements */ @Override public Object getSensorValue(String sensor) { - return PSymbolicRegression.getSensorValue(sensor, m_X, m_C); -// for (int i = 0; i < this.m_X.length; i++) if (sensor.equalsIgnoreCase("X"+i)) return new Double(this.m_X[i]); + return PSymbolicRegression.getSensorValue(sensor, x, constants); +// for (int i = 0; i < this.x.length; i++) if (sensor.equalsIgnoreCase("X"+i)) return new Double(this.x[i]); // for (int i = 0; i < this.c.length; i++) if (sensor.equalsIgnoreCase("C"+i)) return new Double(this.c[i]); // return new Double(0); } @@ -350,7 +350,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements } else { return new Double(0); } -// for (int i = 0; i < this.m_X.length; i++) if (sensor.equalsIgnoreCase("X"+i)) return new Double(this.m_X[i]); +// for (int i = 0; i < this.x.length; i++) if (sensor.equalsIgnoreCase("X"+i)) return new Double(this.x[i]); // for (int i = 0; i < this.c.length; i++) if (sensor.equalsIgnoreCase("C"+i)) return new Double(this.c[i]); // return new Double(0); } @@ -390,11 +390,11 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements if (noise < 0) { noise = 0; } - this.m_Noise = noise; + this.noise = noise; } public double getNoise() { - return this.m_Noise; + return this.noise; } public String noiseTipText() { @@ -407,11 +407,11 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements * @param b The flag to use inner constants. */ public void setUseInnerConst(boolean b) { - this.m_UseInnerConst = b; + this.useInnerConst = b; } public boolean getUseInnerConst() { - return this.m_UseInnerConst; + return this.useInnerConst; } public String useInnerConstTipText() { @@ -424,11 +424,11 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements * @param b The flag to use local hill climbing for inner constants. */ public void setUseLocalHillClimbing(boolean b) { - this.m_UseLocalHillClimbing = b; + this.useLocalHillClimbing = b; } public boolean getUseLocalHillClimbing() { - return this.m_UseLocalHillClimbing; + return this.useLocalHillClimbing; } public String useLocalHillClimbingTipText() { @@ -441,14 +441,14 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements * @param b The new number of ephremial constants. */ public void setNumberOfConstants(int b) { - this.m_NumberOfConstants = b; + this.numberOfConstants = b; this.initializeProblem(); - m_GPArea.clear(); + gpArea.clear(); this.compileArea(); } public int getNumberOfConstants() { - return this.m_NumberOfConstants; + return this.numberOfConstants; } public String numberOfConstantsTipText() { @@ -464,11 +464,11 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements if (b < 0) { b = 1; } - this.m_NumberOfCheckPoints = b; + this.numberOfCheckPoints = b; } public int getNumberOfCheckPoints() { - return this.m_NumberOfCheckPoints; + return this.numberOfCheckPoints; } public String numberOfCheckPointsTipText() { @@ -481,19 +481,19 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements * @param i Number of maximal steps. */ public void setArea(GPArea i) { - this.m_GPArea = i; + this.gpArea = i; GPArea tmpArea[] = new GPArea[1]; - tmpArea[0] = this.m_GPArea; + tmpArea[0] = this.gpArea; ((InterfaceDataTypeProgram) this.template).setProgramDataLength(1); ((InterfaceDataTypeProgram) this.template).SetFunctionArea(tmpArea); } @Override public GPArea getArea() { - if (m_GPArea == null) { + if (gpArea == null) { initializeProblem(); } - return this.m_GPArea; + return this.gpArea; } public String areaTipText() { @@ -506,17 +506,17 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements * @param b True if the path is to be shown. */ public void setShowResult(boolean b) { - this.m_Show = b; - if (this.m_Show) { + this.show = b; + if (this.show) { this.initEnvironmentPanel(); - } else if (this.m_Plot != null) { - this.m_Plot.dispose(); - this.m_Plot = null; + } else if (this.plot != null) { + this.plot.dispose(); + this.plot = null; } } public boolean getShowResult() { - return this.m_Show; + return this.show; } public String showResultTipText() { @@ -529,14 +529,14 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements * @param b The target function. */ public void setTargetFunction(InterfaceRegressionFunction b) { - this.m_TargetFunction = b; + this.targetFunction = b; } public InterfaceRegressionFunction getTargetFunction() { - if (m_TargetFunction == null) { + if (targetFunction == null) { initializeProblem(); } - return this.m_TargetFunction; + return this.targetFunction; } public String targetFunctionTipText() { @@ -561,11 +561,11 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements } public double getLowerBound() { - return m_LowerBound; + return lowerBound; } public void setLowerBound(double mLowerBound) { - m_LowerBound = mLowerBound; + lowerBound = mLowerBound; } public String lowerBoundTipText() { @@ -573,11 +573,11 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements } public double getUpperBound() { - return m_UpperBound; + return upperBound; } public void setUpperBound(double mUpperBound) { - m_UpperBound = mUpperBound; + upperBound = mUpperBound; } public String upperBoundTipText() { diff --git a/src/eva2/optimization/problems/TF1Problem.java b/src/eva2/optimization/problems/TF1Problem.java index 121a590e..4539db7b 100644 --- a/src/eva2/optimization/problems/TF1Problem.java +++ b/src/eva2/optimization/problems/TF1Problem.java @@ -20,12 +20,12 @@ import java.util.ArrayList; */ @Description("T1 is to be minimized.") public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implements java.io.Serializable { - protected int m_ProblemDimension = 30; - protected int m_OutputDimension = 2; - protected double m_Noise = 0.0; - protected double m_XOffSet = 0.0; - protected double m_YOffSet = 0.0; - protected boolean m_ApplyConstraints = false; + protected int problemDimension = 30; + protected int outputDimension = 2; + protected double noise = 0.0; + protected double xOffset = 0.0; + protected double yOffset = 0.0; + protected boolean applyConstraints = false; // transient private GraphPointSet mySet; @@ -43,35 +43,35 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem this.template = (AbstractEAIndividual) ((AbstractEAIndividual) b.template).clone(); } //AbstractMultiObjectiveOptimizationProblem - if (b.m_MOSOConverter != null) { - this.m_MOSOConverter = (InterfaceMOSOConverter) b.m_MOSOConverter.clone(); + if (b.mosoConverter != null) { + this.mosoConverter = (InterfaceMOSOConverter) b.mosoConverter.clone(); } - if (b.m_Metric != null) { - this.m_Metric = (InterfaceParetoFrontMetric) b.m_Metric.clone(); + if (b.metric != null) { + this.metric = (InterfaceParetoFrontMetric) b.metric.clone(); } - if (b.m_ParetoFront != null) { - this.m_ParetoFront = (Population) b.m_ParetoFront.clone(); + if (b.paretoFront != null) { + this.paretoFront = (Population) b.paretoFront.clone(); } - if (b.m_Border != null) { - this.m_Border = new double[b.m_Border.length][2]; - for (int i = 0; i < this.m_Border.length; i++) { - this.m_Border[i][0] = b.m_Border[i][0]; - this.m_Border[i][1] = b.m_Border[i][1]; + if (b.border != null) { + this.border = new double[b.border.length][2]; + for (int i = 0; i < this.border.length; i++) { + this.border[i][0] = b.border[i][0]; + this.border[i][1] = b.border[i][1]; } } - if (b.m_AreaConst4Parallelization != null) { - this.m_AreaConst4Parallelization = new ArrayList(); - for (int i = 0; i < b.m_AreaConst4Parallelization.size(); i++) { - this.m_AreaConst4Parallelization.add(((InterfaceConstraint) b.m_AreaConst4Parallelization.get(i)).clone()); + if (b.areaConst4Parallelization != null) { + this.areaConst4Parallelization = new ArrayList(); + for (int i = 0; i < b.areaConst4Parallelization.size(); i++) { + this.areaConst4Parallelization.add(((InterfaceConstraint) b.areaConst4Parallelization.get(i)).clone()); } } // TF1Problem - this.m_ApplyConstraints = b.m_ApplyConstraints; - this.m_ProblemDimension = b.m_ProblemDimension; - this.m_OutputDimension = b.m_OutputDimension; - this.m_Noise = b.m_Noise; - this.m_XOffSet = b.m_XOffSet; - this.m_YOffSet = b.m_YOffSet; + this.applyConstraints = b.applyConstraints; + this.problemDimension = b.problemDimension; + this.outputDimension = b.outputDimension; + this.noise = b.noise; + this.xOffset = b.xOffset; + this.yOffset = b.yOffset; } /** @@ -91,11 +91,11 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem */ @Override public void initializePopulation(Population population) { - this.m_ParetoFront = new Population(); + this.paretoFront = new Population(); double[][] newRange = makeRange(); - ((InterfaceDataTypeDouble) this.template).setDoubleDataLength(this.m_ProblemDimension); + ((InterfaceDataTypeDouble) this.template).setDoubleDataLength(this.problemDimension); ((InterfaceDataTypeDouble) this.template).setDoubleRange(newRange); AbstractOptimizationProblem.defaultInitPopulation(population, template, this); @@ -106,8 +106,8 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem } protected double[][] makeRange(double lower, double upper) { - double[][] newRange = new double[this.m_ProblemDimension][2]; - for (int i = 0; i < this.m_ProblemDimension; i++) { + double[][] newRange = new double[this.problemDimension][2]; + for (int i = 0; i < this.problemDimension; i++) { newRange[i][0] = lower; newRange[i][1] = upper; } @@ -127,17 +127,17 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem x = new double[((InterfaceDataTypeDouble) individual).getDoubleData().length]; System.arraycopy(((InterfaceDataTypeDouble) individual).getDoubleData(), 0, x, 0, x.length); for (int i = 0; i < x.length; i++) { - x[i] -= this.m_XOffSet; + x[i] -= this.xOffset; } fitness = this.doEvaluation(x); for (int i = 0; i < fitness.length; i++) { // add noise to the fitness - fitness[i] += RNG.gaussianDouble(this.m_Noise); - fitness[i] += this.m_YOffSet; + fitness[i] += RNG.gaussianDouble(this.noise); + fitness[i] += this.yOffset; // set the fitness of the individual individual.SetFitness(i, fitness[i]); } - if (this.m_ApplyConstraints) { + if (this.applyConstraints) { if (fitness[0] > 0.5) { individual.addConstraintViolation(fitness[0] - 0.5); } @@ -151,7 +151,7 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem individual.addConstraintViolation(x[3] - 0.1); } } - individual.checkAreaConst4Parallelization(this.m_AreaConst4Parallelization); + individual.checkAreaConst4Parallelization(this.areaConst4Parallelization); } /** @@ -203,7 +203,7 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem @Override public void drawAdditionalData(Plot plot, Population pop, int index) { - AbstractMultiObjectiveOptimizationProblem.drawWithConstraints(plot, pop, m_Border, index); + AbstractMultiObjectiveOptimizationProblem.drawWithConstraints(plot, pop, border, index); } /** @@ -219,8 +219,8 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem result += "T1 Problem:\n"; result += "Here the individual codes a vector of real number x and T1(x)= x is to be minimized.\n"; result += "Parameters:\n"; - result += "Dimension : " + this.m_ProblemDimension + "\n"; - result += "Noise level : " + this.m_Noise + "\n"; + result += "Dimension : " + this.problemDimension + "\n"; + result += "Noise level : " + this.noise + "\n"; result += "Solution representation:\n"; //result += this.template.getSolutionRepresentationFor(); return result; @@ -281,11 +281,11 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem if (noise < 0) { noise = 0; } - this.m_Noise = noise; + this.noise = noise; } public double getNoise() { - return this.m_Noise; + return this.noise; } public String noiseTipText() { @@ -298,11 +298,11 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem * @param XOffSet The offset for the decision variables. */ public void setXOffSet(double XOffSet) { - this.m_XOffSet = XOffSet; + this.xOffset = XOffSet; } public double getXOffSet() { - return this.m_XOffSet; + return this.xOffset; } public String xOffSetTipText() { @@ -316,11 +316,11 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem * @param YOffSet The offset for the objective value. */ public void setYOffSet(double YOffSet) { - this.m_YOffSet = YOffSet; + this.yOffset = YOffSet; } public double getYOffSet() { - return this.m_YOffSet; + return this.yOffset; } public String yOffSetTipText() { @@ -333,11 +333,11 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem * @param d The number of input dimensions */ public void setProblemDimension(int d) { - this.m_ProblemDimension = d; + this.problemDimension = d; } public int getProblemDimension() { - return this.m_ProblemDimension; + return this.problemDimension; } public String problemDimensionTipText() { @@ -348,15 +348,15 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem // * @param a The number of objective variables // */ // public void setOutputDimension(int a) { -// this.m_OutputDimension = a; -// this.m_Border = new double[this.m_OutputDimension][2]; -// for (int i = 0; i < this.m_Border.length; i++) { -// this.m_Border[i][0] = 0; -// this.m_Border[i][1] = 5; +// this.outputDimension = a; +// this.border = new double[this.outputDimension][2]; +// for (int i = 0; i < this.border.length; i++) { +// this.border[i][0] = 0; +// this.border[i][1] = 5; // } // } // public int getOutputDimension() { -// return this.m_OutputDimension; +// return this.outputDimension; // } // public String outputDimensionTipText() { // return "Number of objective variables."; @@ -383,13 +383,13 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem */ @Override public void setMOSOConverter(InterfaceMOSOConverter b) { - this.m_MOSOConverter = b; - this.m_MOSOConverter.setOutputDimension(this.m_OutputDimension); + this.mosoConverter = b; + this.mosoConverter.setOutputDimension(this.outputDimension); } @Override public InterfaceMOSOConverter getMOSOConverter() { - return this.m_MOSOConverter; + return this.mosoConverter; } @Override @@ -401,10 +401,10 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem // * @param b state. // */ // public void setApplyConstraints(boolean b) { -// this.m_ApplyConstraints = b; +// this.applyConstraints = b; // } // public boolean getApplyConstraints() { -// return this.m_ApplyConstraints; +// return this.applyConstraints; // } // public String applyConstraintsTipText() { // return "Toggle application of constraint (works only for T1)."; @@ -419,11 +419,11 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem * @param pop The pareto-front archive. */ public void setParetoFront(Population pop) { - this.m_ParetoFront = pop; + this.paretoFront = pop; } public Population getParetoFront() { - return this.m_ParetoFront; + return this.paretoFront; } public String paretoFrontTipText() { diff --git a/src/eva2/optimization/stat/StatisticsStandalone.java b/src/eva2/optimization/stat/StatisticsStandalone.java index 4c5bc14c..076fa0f7 100644 --- a/src/eva2/optimization/stat/StatisticsStandalone.java +++ b/src/eva2/optimization/stat/StatisticsStandalone.java @@ -81,7 +81,7 @@ public class StatisticsStandalone extends AbstractStatistics implements Interfac double[] specificData = pop.getSpecificData(); if (specificData != null) { for (int i = 0; i < specificData.length; i++) { -// ((ArrayList[]) m_Result.get(i))[optRunsPerformed].add(new Double[] {new Double(functionCalls), specificData[i]}); +// ((ArrayList[]) resultFrame.get(i))[optRunsPerformed].add(new Double[] {new Double(functionCalls), specificData[i]}); m_ResultData.get(optRunsPerformed).add(new Object[]{new Double(functionCalls), specificData}); } }