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Fixing lots of smaller bugs and code inconsitencies.

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Added several annotations and removed globalInfo/TipText methods.
This commit is contained in:
Fabian Becker 2014-01-21 16:50:18 +01:00
parent 531bbe50ed
commit 63fe74d2e0
13 changed files with 153 additions and 170 deletions
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6
src/eva2/cli/Main.java
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@ -324,6 +324,10 @@ public class Main implements OptimizationStateListener, InterfacePopulationChang
break;
case "EvolutionStrategies":
//double cm, cr;
//int mu, lambda;
boolean plusStrategy;
//this.optimizer = OptimizerFactory.createEvolutionStrategy()
break;
default:
throw new Exception("Unsupported Optimizer");
@ -349,7 +353,7 @@ public class Main implements OptimizationStateListener, InterfacePopulationChang
OptimizerFactory.setEvaluationTerminator(50000);
OptimizerFactory.addTerminator(new FitnessValueTerminator(new double[]{0.00001}), CombinedTerminator.OR);
LOGGER.log(Level.INFO, "Running {0}", "Differential Evolution");
LOGGER.log(Level.INFO, "Running {0}", optimizer.getName());
OptimizationParameters params = OptimizerFactory.makeParams(optimizer, this.populationSize, this.problem);
double[] result = OptimizerFactory.optimizeToDouble(params);

53
src/eva2/optimization/go/InterfaceOptimizationParameters.java
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@ -7,35 +7,29 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
import eva2.optimization.strategies.InterfaceOptimizer;
/**
* Created by IntelliJ IDEA.
* User: streiche
* Date: 11.05.2003
* Time: 13:14:06
* To change this template use Options | File Templates.
*
*/
public interface InterfaceOptimizationParameters {
/**
* This method allows you to serialize the current parameters into a *.ser file
*/
public void saveInstance();
void saveInstance();
/**
* This method returns the name
*
* @return string
*/
public String getName();
String getName();
/**
* This methods allow you to set and get the Seed for the Random Number Generator.
*
* @param x Long seed.
*/
public void setSeed(long x);
void setSeed(long x);
public long getSeed();
public String seedTipText();
long getSeed();
/**
* This method allows you to choose a termination criteria for the
@ -43,48 +37,33 @@ public interface InterfaceOptimizationParameters {
*
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term);
void setTerminator(InterfaceTerminator term);
public InterfaceTerminator getTerminator();
public String terminatorTipText();
InterfaceTerminator getTerminator();
/**
* This method allows you to set the current optimizing algorithm
*
* @param optimizer The new optimizing algorithm
*/
public void setOptimizer(InterfaceOptimizer optimizer);
void setOptimizer(InterfaceOptimizer optimizer);
public InterfaceOptimizer getOptimizer();
// public String optimizerTipText();
InterfaceOptimizer getOptimizer();
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
public void setProblem(InterfaceOptimizationProblem problem);
void setProblem(InterfaceOptimizationProblem problem);
public InterfaceOptimizationProblem getProblem();
InterfaceOptimizationProblem getProblem();
public String problemTipText();
InterfacePostProcessParams getPostProcessParams();
/**
* This method will set the output filename
*
* @param name TODO invalidate these!
*/
// public void setOutputFileName (String name);
// public String getOutputFileName ();
// public String outputFileNameTipText();
public InterfacePostProcessParams getPostProcessParams();
void setPostProcessParams(InterfacePostProcessParams ppp);
public void setPostProcessParams(InterfacePostProcessParams ppp);
public String postProcessParamsTipText();
public void setDoPostProcessing(boolean doPP);
void setDoPostProcessing(boolean doPP);
/**
* Give an instance which should be informed about elements which are additional informers.
@ -92,7 +71,7 @@ public interface InterfaceOptimizationParameters {
* @param o
* @see InterfaceAdditionalPopulationInformer
*/
public void addInformableInstance(InterfaceNotifyOnInformers o);
void addInformableInstance(InterfaceNotifyOnInformers o);
public boolean removeInformableInstance(InterfaceNotifyOnInformers o);
boolean removeInformableInstance(InterfaceNotifyOnInformers o);
}

53
src/eva2/optimization/modules/AbstractOptimizationParameters.java
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@ -11,6 +11,7 @@ import eva2.optimization.problems.InterfaceAdditionalPopulationInformer;
import eva2.optimization.problems.InterfaceOptimizationProblem;
import eva2.optimization.strategies.InterfaceOptimizer;
import eva2.tools.Serializer;
import eva2.util.annotation.Parameter;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
@ -23,13 +24,39 @@ import java.util.logging.Logger;
public abstract class AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
protected static final Logger LOGGER = Logger.getLogger(AbstractOptimizationParameters.class.getName());
@Parameter(name = "Random Seed", description = "Random number seed, set to zero to use current system time.")
protected long randomSeed = (long) 0.0;
// Opt. Algorithms and Parameters
/**
* The optimizer to be executed.
*/
@Parameter(name = "Optimizer", description = "Choose an optimization strategy.")
protected InterfaceOptimizer optimizer;
/**
* The optimization problem to be optimized.
* When changed it is automatically applied to the
* selected optimizer.
*/
@Parameter(name = "Problem", description = "Choose the problem that is to optimize and the EA individual parameters.")
protected InterfaceOptimizationProblem problem;
/**
* The termination criteria that terminated an
* optimization run.
*/
@Parameter(name = "Terminator", description = "Choose a termination criterion.")
protected InterfaceTerminator terminator;
/**
* Post processing parameters.
* This can be enabled in the UI and will perform additional
* optimization e.g. with Hill Climbing.
*/
@Parameter(name = "Post Processing", description = "Parameters for the post processing step.")
protected InterfacePostProcessParams postProcessing = new PostProcessParams(false);
transient protected InterfacePopulationChangedEventListener populationChangedEventListener;
transient private List<InterfaceNotifyOnInformers> toInformAboutInformers = null;
@ -181,10 +208,6 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
return this.optimizer;
}
public String optimizerTipText() {
return "Choose an optimization strategy.";
}
@Override
public String getName() {
return "Optimization parameters";
@ -207,11 +230,6 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
return this.problem;
}
@Override
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
/**
* This methods allow you to set and get the Seed for the Random Number Generator.
*
@ -232,11 +250,6 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
return randomSeed;
}
@Override
public String seedTipText() {
return "Random number seed, set to zero to use current system time.";
}
/**
* This method allows you to choose a termination criteria for the
* evolutionary algorithm.
@ -253,11 +266,6 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
return this.terminator;
}
@Override
public String terminatorTipText() {
return "Choose a termination criterion.";
}
@Override
public InterfacePostProcessParams getPostProcessParams() {
return postProcessing;
@ -268,11 +276,6 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
postProcessing = ppp;
}
@Override
public String postProcessParamsTipText() {
return "Parameters for the post processing step";
}
@Override
public void setDoPostProcessing(boolean doPP) {
postProcessing.setDoPostProcessing(doPP);

1
src/eva2/optimization/modules/MOEAParameters.java
View File

@ -102,7 +102,6 @@ public class MOEAParameters extends AbstractOptimizationParameters implements In
((MultiObjectiveEA) this.optimizer).setOptimizer(b);
}
@Override
public String optimizerTipText() {
return "Choose a population based optimizing technique to use.";
}

22
src/eva2/optimization/modules/OptimizationParameters.java
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@ -8,6 +8,7 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
import eva2.optimization.strategies.GeneticAlgorithm;
import eva2.optimization.strategies.InterfaceOptimizer;
import eva2.tools.Serializer;
import eva2.util.annotation.Description;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
@ -16,16 +17,24 @@ import java.util.logging.Level;
/**
* OptimizationParamers for configuration of an
* OptimizationParameters for configuration of an
* optimization run.
* <p/>
* This class is used to generate the default GUI
* configuration panel for optimizations.
*/
@Description("Select the optimization parameters.")
public class OptimizationParameters extends AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
/**
* Should be removed and replaced by a more solid
* serialization. (EvAScript?)
*
* @deprecated
* @return
*/
public static OptimizationParameters getInstance() {
return getInstance("OptimizationParameters.ser", true);
return getInstance("OptimizationParameters.set", true);
}
/**
@ -80,13 +89,4 @@ public class OptimizationParameters extends AbstractOptimizationParameters imple
public Object clone() {
return new OptimizationParameters(this);
}
/**
* This method returns a global info string.
*
* @return description
*/
public static String globalInfo() {
return "Select the optimization parameters.";
}
}

6
src/eva2/optimization/operator/crossover/CM2.java
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@ -16,14 +16,14 @@ import java.util.BitSet;
*/
public class CM2 implements InterfaceCrossover, java.io.Serializable {
private InterfaceOptimizationProblem m_OptimizationProblem;
private InterfaceOptimizationProblem optimizationProblem;
public CM2() {
}
public CM2(CM2 c) {
this.m_OptimizationProblem = c.m_OptimizationProblem;
this.optimizationProblem = c.optimizationProblem;
}
@Override
@ -58,7 +58,7 @@ public class CM2 implements InterfaceCrossover, java.io.Serializable {
@Override
public void init(AbstractEAIndividual individual,
InterfaceOptimizationProblem opt) {
this.m_OptimizationProblem = opt;
this.optimizationProblem = opt;
}
@Override

8
src/eva2/optimization/operator/crossover/CrossoverEAMixer.java
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@ -10,11 +10,7 @@ import java.util.ArrayList;
/**
* Created by IntelliJ IDEA.
* User: Dante Alighieri
* Date: 21.05.2005
* Time: 11:36:38
* To change this template use File | Settings | File Templates.
*
*/
public class CrossoverEAMixer implements InterfaceCrossover, InterfaceEvaluatingCrossoverOperator, java.io.Serializable {
public static final String CROSSOVER_EA_MIXER_OPERATOR_KEY = "CrossoverEAMixerOperatorKey";
@ -60,7 +56,7 @@ public class CrossoverEAMixer implements InterfaceCrossover, InterfaceEvaluating
this.m_Crossers.setSelectedCrossers(tmpList);
this.m_Crossers.normalizeWeights();
this.m_Crossers.setDescriptiveString("Combining alternative mutation operators, please norm the weights!");
this.m_Crossers.setWeightsLabel("Weigths");
this.m_Crossers.setWeightsLabel("Weights");
}
public CrossoverEAMixer(CrossoverEAMixer mutator) {

3
src/eva2/optimization/operator/crossover/TestESCrossover.java
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@ -86,7 +86,8 @@ public class TestESCrossover implements java.io.Serializable {
double[] tmpD = new double[2];
tmpD[0] = 0;
tmpD[1] = 0;
this.m_Plot = new Plot("ES Crossover Testert", "x", "y", tmpD, tmpD);
// ToDo: Fix plot (it's internal and not showing)
this.m_Plot = new Plot("ES Crossover Tester", "x", "y", tmpD, tmpD);
// validate and show
this.m_Frame.validate();
this.m_Frame.setVisible(true);

2
src/eva2/optimization/operator/selection/SelectAll.java
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@ -6,7 +6,7 @@ import eva2.optimization.population.Population;
/**
* Simple method to selecet all.
* In case of multiple fitness values the selection
* critria is selected randomly for each selection event. pff
* criteria is selected randomly for each selection event. pff
* Created by IntelliJ IDEA.
* User: streiche
* Date: 31.03.2004

2
src/eva2/optimization/operator/selection/SelectBestIndividuals.java
View File

@ -211,6 +211,6 @@ public class SelectBestIndividuals implements InterfaceSelection, java.io.Serial
}
public String obeyDebsConstViolationPrincipleToolTip() {
return "Toggle the use of Deb's coonstraint violation principle.";
return "Toggle the use of Deb's constraint violation principle.";
}
}

22
src/eva2/optimization/operator/selection/SelectTournament.java
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@ -17,8 +17,8 @@ import eva2.tools.math.RNG;
*/
public class SelectTournament implements InterfaceSelection, java.io.Serializable {
private int m_TournamentSize = 4;
private boolean m_ObeyDebsConstViolationPrinciple = true;
private int tournamentSize = 4;
private boolean obeyDebsConstViolationPrinciple = true;
public SelectTournament() {
}
@ -29,8 +29,8 @@ public class SelectTournament implements InterfaceSelection, java.io.Serializabl
}
public SelectTournament(SelectTournament a) {
this.m_TournamentSize = a.m_TournamentSize;
this.m_ObeyDebsConstViolationPrinciple = a.m_ObeyDebsConstViolationPrinciple;
this.tournamentSize = a.tournamentSize;
this.obeyDebsConstViolationPrinciple = a.obeyDebsConstViolationPrinciple;
}
@Override
@ -82,9 +82,9 @@ public class SelectTournament implements InterfaceSelection, java.io.Serializabl
try {
critSize = ((AbstractEAIndividual) population.get(0)).getFitness().length;
currentCriteria = RNG.randomInt(0, critSize - 1);
if (this.m_ObeyDebsConstViolationPrinciple) {
if (this.obeyDebsConstViolationPrinciple) {
Population tournamentGroup = new Population();
for (int i = 0; i < this.m_TournamentSize; i++) {
for (int i = 0; i < this.tournamentSize; i++) {
tournamentGroup.add(population.get(RNG.randomInt(0, population.size() - 1)));
}
SelectBestIndividuals best = new SelectBestIndividuals();
@ -92,7 +92,7 @@ public class SelectTournament implements InterfaceSelection, java.io.Serializabl
result = (AbstractEAIndividual) best.selectFrom(tournamentGroup, 1).get(0);
} else {
result = (AbstractEAIndividual) population.get(RNG.randomInt(0, population.size() - 1));
for (int i = 1; i < this.m_TournamentSize; i++) {
for (int i = 1; i < this.tournamentSize; i++) {
tmpIndy = (AbstractEAIndividual) population.get(RNG.randomInt(0, population.size() - 1));
if (tmpIndy.getFitness(currentCriteria) < result.getFitness(currentCriteria)) {
result = tmpIndy;
@ -152,11 +152,11 @@ public class SelectTournament implements InterfaceSelection, java.io.Serializabl
}
public int getTournamentSize() {
return m_TournamentSize;
return tournamentSize;
}
public void setTournamentSize(int g) {
m_TournamentSize = g;
tournamentSize = g;
}
/**
@ -167,11 +167,11 @@ public class SelectTournament implements InterfaceSelection, java.io.Serializabl
*/
@Override
public void setObeyDebsConstViolationPrinciple(boolean b) {
this.m_ObeyDebsConstViolationPrinciple = b;
this.obeyDebsConstViolationPrinciple = b;
}
public boolean getObeyDebsConstViolationPrinciple() {
return this.m_ObeyDebsConstViolationPrinciple;
return this.obeyDebsConstViolationPrinciple;
}
public String obeyDebsConstViolationPrincipleToolTip() {

139
src/eva2/optimization/operator/selection/SelectXProbRouletteWheel.java
View File

@ -6,81 +6,28 @@ import eva2.optimization.operator.selection.probability.SelProbStandard;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
class treeElement implements java.io.Serializable {
public double separator = 0;
public int m_Index = -1;
public treeElement m_Left = null, m_Right = null;
public treeElement(double[][] d, int list, int low, int high) {
//System.out.println("Calling Low/high: "+low+"/"+high);
if (low == high) {
// end reached
//System.out.println("This: "+low);
this.m_Index = low;
} else {
if (low == high - 1) {
//System.out.println("This: "+high);
this.m_Index = high;
} else {
int midPoint = (int) ((high + low) / 2);
this.separator = d[midPoint - 1][list];
//System.out.println("Branching: "+midPoint + " : " + this.separator);
this.m_Left = new treeElement(d, list, low, midPoint);
this.m_Right = new treeElement(d, list, midPoint, high);
}
}
}
public int getIndexFor(double d) {
if (this.m_Index >= 0) {
return this.m_Index - 1;
} else {
if (d < this.separator) {
return this.m_Left.getIndexFor(d);
} else {
return this.m_Right.getIndexFor(d);
}
}
}
@Override
public String toString() {
if (this.m_Index >= 0) {
return "Ind:" + this.m_Index;
} else {
return "{" + this.m_Left.toString() + "} X<" + this.separator + " {" + this.m_Right.toString() + "}";
}
}
}
/**
* The RoulettWheel selection requires a selection probability calculator.
* The RouletteWheel selection requires a selection probability calculator.
* In case of multiple fitness values the selection
* critria is selected randomly for each selection event.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 18.03.2003
* Time: 16:36:11
* To change this template use Options | File Templates.
* criteria is selected randomly for each selection event.
*/
public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Serializable {
private transient treeElement[] m_TreeRoot = null;
private InterfaceSelectionProbability m_SelProbCalculator = new SelProbStandard();
private boolean m_ObeyDebsConstViolationPrinciple = true;
private transient TreeElement[] treeRoot = null;
private InterfaceSelectionProbability selectionProbability = new SelProbStandard();
private boolean obeyDebsConstViolationPrinciple = true;
public SelectXProbRouletteWheel() {
}
public SelectXProbRouletteWheel(SelectXProbRouletteWheel a) {
this.m_ObeyDebsConstViolationPrinciple = a.m_ObeyDebsConstViolationPrinciple;
this.m_SelProbCalculator = (InterfaceSelectionProbability) a.m_SelProbCalculator.clone();
this.obeyDebsConstViolationPrinciple = a.obeyDebsConstViolationPrinciple;
this.selectionProbability = (InterfaceSelectionProbability) a.selectionProbability.clone();
}
@Override
public Object clone() {
return (Object) new SelectXProbRouletteWheel(this);
return new SelectXProbRouletteWheel(this);
}
/**
@ -93,8 +40,8 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
*/
@Override
public void prepareSelection(Population population) {
this.m_SelProbCalculator.computeSelectionProbability(population, "Fitness", this.m_ObeyDebsConstViolationPrinciple);
this.m_TreeRoot = this.buildSelectionTree(population);
this.selectionProbability.computeSelectionProbability(population, "Fitness", this.obeyDebsConstViolationPrinciple);
this.treeRoot = this.buildSelectionTree(population);
}
/**
@ -123,8 +70,8 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
*
* @param p The population
*/
private treeElement[] buildSelectionTree(Population p) {
treeElement result[];
private TreeElement[] buildSelectionTree(Population p) {
TreeElement result[];
double[][] tmpList = new double[p.size()][];
for (int i = 0; i < p.size(); i++) {
@ -137,12 +84,12 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
}
}
result = new treeElement[tmpList[0].length];
result = new TreeElement[tmpList[0].length];
for (int i = 0; i < tmpList[0].length; i++) {
//String s = "Input: {";
//for (int j = 0; j < tmpList.length; j++) s += tmpList[j][i] +"; ";
//System.out.println(s+"}");
result[i] = new treeElement(tmpList, i, 0, tmpList.length);
result[i] = new TreeElement(tmpList, i, 0, tmpList.length);
//System.out.println("Resulting Tree: " + result[i].toString());
}
@ -160,7 +107,7 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
critSize = ((AbstractEAIndividual) population.get(0)).getSelectionProbability().length;
currentCriteria = RNG.randomInt(0, critSize - 1);
double d = RNG.randomDouble();
int index = this.m_TreeRoot[currentCriteria].getIndexFor(d);
int index = this.treeRoot[currentCriteria].getIndexFor(d);
//System.out.println("Looking for: " + d + " found " +index);
return ((AbstractEAIndividual) (population.get(index)));
}
@ -234,11 +181,11 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
* @param normation
*/
public void setSelProbCalculator(InterfaceSelectionProbability normation) {
this.m_SelProbCalculator = normation;
this.selectionProbability = normation;
}
public InterfaceSelectionProbability getSelProbCalculator() {
return this.m_SelProbCalculator;
return this.selectionProbability;
}
public String selProbCalculatorTipText() {
@ -253,14 +200,62 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
*/
@Override
public void setObeyDebsConstViolationPrinciple(boolean b) {
this.m_ObeyDebsConstViolationPrinciple = b;
this.obeyDebsConstViolationPrinciple = b;
}
public boolean getObeyDebsConstViolationPrinciple() {
return this.m_ObeyDebsConstViolationPrinciple;
return this.obeyDebsConstViolationPrinciple;
}
public String obeyDebsConstViolationPrincipleToolTip() {
return "Toggle the use of Deb's coonstraint violation principle.";
}
}
class TreeElement implements java.io.Serializable {
public double separator = 0;
public int m_Index = -1;
public TreeElement m_Left = null, m_Right = null;
public TreeElement(double[][] d, int list, int low, int high) {
//System.out.println("Calling Low/high: "+low+"/"+high);
if (low == high) {
// end reached
//System.out.println("This: "+low);
this.m_Index = low;
} else {
if (low == high - 1) {
//System.out.println("This: "+high);
this.m_Index = high;
} else {
int midPoint = (int) ((high + low) / 2);
this.separator = d[midPoint - 1][list];
//System.out.println("Branching: "+midPoint + " : " + this.separator);
this.m_Left = new TreeElement(d, list, low, midPoint);
this.m_Right = new TreeElement(d, list, midPoint, high);
}
}
}
public int getIndexFor(double d) {
if (this.m_Index >= 0) {
return this.m_Index - 1;
} else {
if (d < this.separator) {
return this.m_Left.getIndexFor(d);
} else {
return this.m_Right.getIndexFor(d);
}
}
}
@Override
public String toString() {
if (this.m_Index >= 0) {
return "Ind:" + this.m_Index;
} else {
return "{" + this.m_Left.toString() + "} X<" + this.separator + " {" + this.m_Right.toString() + "}";
}
}
}

6
src/eva2/optimization/strategies/EvolutionStrategies.java
View File

@ -12,6 +12,7 @@ import eva2.optimization.population.SolutionSet;
import eva2.optimization.problems.B1Problem;
import eva2.optimization.problems.InterfaceOptimizationProblem;
import eva2.util.annotation.Description;
import eva2.util.annotation.Parameter;
/**
* Evolution strategies by Rechenberg and Schwefel, but please remember that
@ -26,8 +27,13 @@ import eva2.util.annotation.Description;
@Description(value = "This is an Evolution Strategy. Note that the population size depends on mu (number of parents) and lambda (number of offspring)")
public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializable {
@Parameter(description = "Mu", name = "mu")
protected int mu = 5;
@Parameter(description = "Lambda", name = "lambda")
protected int lambda = 20;
@Parameter(description = "Determines whether the +-Strategy should be used.", name = "usePlus")
protected boolean usePlusStrategy = false;
protected Population population = new Population();
protected InterfaceOptimizationProblem optimizationProblem = new B1Problem();