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ModuleAdapter refactor

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- Remove ModuleAdapters. Will be replaced later.
- Refactor method name with typo
This commit is contained in:
Fabian Becker 2014-10-21 20:18:43 +02:00
parent 0eaddb26ef
commit 857fd35977
25 changed files with 22 additions and 2010 deletions
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18
src/eva2/OptimizerRunnable.java
View File

@ -82,7 +82,7 @@ public class OptimizerRunnable implements Runnable {
rnblID = cntID;
cntID++;
proc = new Processor(stats, null, params);
proc = new Processor(stats, params);
if (proc.getStatistics() instanceof AbstractStatistics) {
((AbstractStatistics) proc.getStatistics()).setSaveParams(false);
}
@ -107,7 +107,7 @@ public class OptimizerRunnable implements Runnable {
}
public InterfaceOptimizationParameters getGOParams() {
return proc.getOptimizationParameterss();
return proc.getOptimizationParameters();
}
public InterfaceStatistics getStats() {
@ -118,8 +118,8 @@ public class OptimizerRunnable implements Runnable {
if (proc.isOptimizationRunning()) {
throw new RuntimeException("Error - cannot change statistics instance during optimization.");
}
InterfaceOptimizationParameters params = proc.getOptimizationParameterss();
proc = new Processor(stats, null, params);
InterfaceOptimizationParameters params = proc.getOptimizationParameters();
proc = new Processor(stats, params);
if (proc.getStatistics() instanceof AbstractStatistics) {
((AbstractStatistics) proc.getStatistics()).setSaveParams(false);
}
@ -149,7 +149,7 @@ public class OptimizerRunnable implements Runnable {
try {
proc.setSaveParams(false);
if (postProcessOnly) {
proc.performPostProcessing((PostProcessParams) proc.getOptimizationParameterss().getPostProcessParams(), listener);
proc.performPostProcessing((PostProcessParams) proc.getOptimizationParameters().getPostProcessParams(), listener);
} else {
if (doRestart) {
proc.restartOptimization();
@ -199,15 +199,15 @@ public class OptimizerRunnable implements Runnable {
}
public SolutionSet getSolutionSet() {
return (SolutionSet) proc.getOptimizationParameterss().getOptimizer().getAllSolutions();
return (SolutionSet) proc.getOptimizationParameters().getOptimizer().getAllSolutions();
}
public void setPostProcessingParams(InterfacePostProcessParams ppp) {
proc.getOptimizationParameterss().setPostProcessParams(ppp);
proc.getOptimizationParameters().setPostProcessParams(ppp);
}
public int getProgress() {
return proc.getOptimizationParameterss().getOptimizer().getPopulation().getFunctionCalls();
return proc.getOptimizationParameters().getOptimizer().getPopulation().getFunctionCalls();
}
public String terminatedBecause() {
@ -215,7 +215,7 @@ public class OptimizerRunnable implements Runnable {
if (postProcessOnly) {
return "Post processing finished";
} else {
InterfaceTerminator term = proc.getOptimizationParameterss().getTerminator();
InterfaceTerminator term = proc.getOptimizationParameters().getTerminator();
return term.lastTerminationMessage();
}
} else {

3
src/eva2/optimization/ModuleServer.java
View File

@ -1,7 +1,6 @@
package eva2.optimization;
import eva2.optimization.go.InterfaceOptimizationParameters;
import eva2.optimization.modules.GOModuleAdapter;
import eva2.optimization.modules.ModuleAdapter;
import eva2.tools.EVAERROR;
import eva2.tools.ReflectPackage;
@ -121,7 +120,7 @@ public final class ModuleServer {
/* create a module instance */
int constrIndex = 0;
if ((optimizationParameters == null && noGuiLogFile == null) || !module.equals(GOModuleAdapter.class)) {
if ((optimizationParameters == null && noGuiLogFile == null)) {
if (optimizationParameters != null) {
System.err.println("Cant set params - no matching constructor found for " + adapterName + " (ModuleServer)");
}

6
src/eva2/optimization/modules/AbstractModuleAdapter.java
View File

@ -60,7 +60,7 @@ abstract public class AbstractModuleAdapter implements ModuleAdapter, Serializab
*/
@Override
public boolean hasPostProcessing() {
return ((processor instanceof Processor) && ((Processor) processor).getOptimizationParameterss().getPostProcessParams().isDoPostProcessing());
return ((processor instanceof Processor) && ((Processor) processor).getOptimizationParameters().getPostProcessParams().isDoPostProcessing());
}
/**
@ -70,7 +70,7 @@ abstract public class AbstractModuleAdapter implements ModuleAdapter, Serializab
*/
@Override
public boolean startPostProcessing() {
if (hasPostProcessing() && ((Processor) processor).getOptimizationParameterss().getPostProcessParams().isDoPostProcessing()) {
if (hasPostProcessing() && ((Processor) processor).getOptimizationParameters().getPostProcessParams().isDoPostProcessing()) {
((Processor) processor).performPostProcessing();
return true;
} else {
@ -80,7 +80,7 @@ abstract public class AbstractModuleAdapter implements ModuleAdapter, Serializab
public InterfaceOptimizationParameters getOptimizationParameters() {
if ((processor != null) && (processor instanceof Processor)) {
return ((Processor) processor).getOptimizationParameterss();
return ((Processor) processor).getOptimizationParameters();
} else {
return null;
}

22
src/eva2/optimization/modules/DEModuleAdapter.java
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@ -1,22 +0,0 @@
package eva2.optimization.modules;
/**
* This the DE module adapter necessary to access this implementation from the EvA top level.
*/
public class DEModuleAdapter extends GenericModuleAdapter implements ModuleAdapter {
private static final String moduleName = "Differential_Evolution";
public DEModuleAdapter(String adapterName) {
super(adapterName, "DE.html", DEParameters.getInstance(), true);
}
/**
* This method returns the name of the ModulAdapters.
*
* @return The name
*/
public static String getName() {
return moduleName;
}
}

171
src/eva2/optimization/modules/DEParameters.java
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@ -1,171 +0,0 @@
package eva2.optimization.modules;
import eva2.optimization.enums.DETypeEnum;
import eva2.optimization.go.InterfaceOptimizationParameters;
import eva2.optimization.operator.terminators.EvaluationTerminator;
import eva2.optimization.population.Population;
import eva2.problems.F1Problem;
import eva2.optimization.strategies.DifferentialEvolution;
import eva2.optimization.strategies.InterfaceOptimizer;
import eva2.tools.Serializer;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.Serializable;
import java.util.logging.Level;
/**
* The class gives access to all DE parameters for the EvA
* top level GUI.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 27.10.2004
* Time: 13:49:09
* To change this template use File | Settings | File Templates.
*/
public class DEParameters extends AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
/**
* Load or create a new instance of the class.
*
* @return A loaded (from file) or new instance of the class.
*/
public static DEParameters getInstance() {
DEParameters instance = null;
try {
FileInputStream fileStream = new FileInputStream("DEParameters.ser");
instance = (DEParameters) Serializer.loadObject(fileStream);
} catch (FileNotFoundException ex) {
LOGGER.log(Level.WARNING, "Could not load instance object.", ex);
}
if (instance == null) {
instance = new DEParameters();
}
return instance;
}
/**
*
*/
public DEParameters() {
super(new DifferentialEvolution(), new F1Problem(), new EvaluationTerminator(1000));
}
private DEParameters(DEParameters Source) {
super(Source);
}
@Override
public Object clone() {
return new DEParameters(this);
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a Differential Evolution optimization method, limit DE to real-valued genotypes.";
}
/**
* This method allows you to set the current optimizing algorithm
*
* @param optimizer The new optimizing algorithm
*/
@Override
public void setOptimizer(InterfaceOptimizer optimizer) {
// *pff* i'll ignore that!
}
/**
* Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
*
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
return this.optimizer.getPopulation();
}
public void setPopulation(Population pop) {
this.optimizer.setPopulation(pop);
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
/**
* This method will set the amplication factor f.
*
* @param f
*/
public void setF(double f) {
((DifferentialEvolution) this.optimizer).setDifferentialWeight(f);
}
public double getF() {
return ((DifferentialEvolution) this.optimizer).getDifferentialWeight();
}
public String differentialWeightTipText() {
return "F is a real and constant factor which controlls the amplification of the differential variation.";
}
/**
* This method will set the crossover probability
*
* @param k
*/
public void setK(double k) {
((DifferentialEvolution) this.optimizer).setCrossoverRate(k);
}
public double getK() {
return ((DifferentialEvolution) this.optimizer).getCrossoverRate();
}
public String crossoverRateTipText() {
return "Probability of alteration through DE1.";
}
/**
* This method will set greediness to move towards the best
*
* @param l
*/
public void setLambda(double l) {
((DifferentialEvolution) this.optimizer).setLambda(l);
}
public double getLambda() {
return ((DifferentialEvolution) this.optimizer).getLambda();
}
public String lambdaTipText() {
return "Enhance greediness through amplification of the differential vector to the best individual for DE2.";
}
/**
* This method allows you to choose the type of Differential Evolution.
*
* @param s The type.
*/
public void setDEType(DETypeEnum s) {
((DifferentialEvolution) this.optimizer).setDEType(s);
}
public DETypeEnum getDEType() {
return ((DifferentialEvolution) this.optimizer).getDEType();
}
public String dETypeTipText() {
return "Choose the type of Differential Evolution.";
}
}

22
src/eva2/optimization/modules/EPModuleAdapter.java
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@ -1,22 +0,0 @@
package eva2.optimization.modules;
/**
* This the EP module adapter necessary to access this implementation from the EvA top level.
*/
public class EPModuleAdapter extends GenericModuleAdapter implements ModuleAdapter {
private static final String moduleName = "Evolutionary_Programming";
public EPModuleAdapter(String adapterName) {
super(adapterName, "EP.html", EPParameters.getInstance(), true);
}
/**
* This method returns the name of the ModulAdapter
*
* @return The name
*/
public static String getName() {
return moduleName;
}
}

119
src/eva2/optimization/modules/EPParameters.java
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@ -1,119 +0,0 @@
package eva2.optimization.modules;
import eva2.optimization.go.InterfaceOptimizationParameters;
import eva2.optimization.operator.selection.InterfaceSelection;
import eva2.optimization.operator.terminators.EvaluationTerminator;
import eva2.optimization.population.Population;
import eva2.problems.F1Problem;
import eva2.optimization.strategies.EvolutionaryProgramming;
import eva2.optimization.strategies.InterfaceOptimizer;
import eva2.tools.Serializer;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.Serializable;
import java.util.logging.Level;
import java.util.logging.Logger;
/**
*
*/
public class EPParameters extends AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
private static final Logger LOGGER = Logger.getLogger(EPParameters.class.getName());
/**
* Load or create a new instance of the class.
*
* @return A loaded (from file) or new instance of the class.
*/
public static EPParameters getInstance() {
EPParameters instance = null;
try {
FileInputStream fileStream = new FileInputStream("EPParameters.ser");
instance = (EPParameters) Serializer.loadObject(fileStream);
} catch (FileNotFoundException ex) {
LOGGER.log(Level.WARNING, "Could not load instance object.", ex);
}
if (instance == null) {
instance = new EPParameters();
}
return instance;
}
/**
*
*/
public EPParameters() {
super(new EvolutionaryProgramming(), new F1Problem(), new EvaluationTerminator());
}
/**
*
*/
private EPParameters(EPParameters Source) {
super(Source);
}
/**
*
*/
@Override
public Object clone() {
return new EPParameters(this);
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a Evolutionary Programming optimization method, limit EP to mutation operators only.";
}
@Override
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
/**
* Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
*
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
return this.optimizer.getPopulation();
}
public void setPopulation(Population pop) {
this.optimizer.setPopulation(pop);
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
/**
* Choose the type of environment selection to use.
*
* @param selection
*/
public void setEnvironmentSelection(InterfaceSelection selection) {
((EvolutionaryProgramming) this.optimizer).setEnvironmentSelection(selection);
}
public InterfaceSelection getEnvironmentSelection() {
return ((EvolutionaryProgramming) this.optimizer).getEnvironmentSelection();
}
public String environmentSelectionTipText() {
return "Choose a method for selecting the reduced population.";
}
}

23
src/eva2/optimization/modules/GAModuleAdapter.java
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@ -1,23 +0,0 @@
package eva2.optimization.modules;
/**
* This the GA module adapter necessary to access this implementation from the EvA top level.
*/
public class GAModuleAdapter extends GenericModuleAdapter implements ModuleAdapter {
private static final String moduleName = "Genetic_Algorithm";
public GAModuleAdapter(String adapterName) {
super(adapterName, "GA.html", GAParameters.getInstance(), true);
}
/**
* This method returns the name of the ModulAdapters
*
* @return The name
*/
public static String getName() {
return moduleName;
}
}

190
src/eva2/optimization/modules/GAParameters.java
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@ -1,190 +0,0 @@
package eva2.optimization.modules;
import eva2.optimization.go.InterfaceOptimizationParameters;
import eva2.optimization.operator.selection.InterfaceSelection;
import eva2.optimization.operator.terminators.EvaluationTerminator;
import eva2.optimization.population.Population;
import eva2.problems.B1Problem;
import eva2.optimization.strategies.GeneticAlgorithm;
import eva2.optimization.strategies.InterfaceOptimizer;
import eva2.tools.Serializer;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.Serializable;
import java.util.logging.Level;
import java.util.logging.Logger;
/**
* The class gives access to all GA parameters for the EvA
* top level GUI.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 08.06.2004
* Time: 21:29:34
* To change this template use File | Settings | File Templates.
*/
public class GAParameters extends AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
private static final Logger LOGGER = Logger.getLogger(GAParameters.class.getName());
/**
* Load or create a new instance of the class.
*
* @return A loaded (from file) or new instance of the class.
*/
public static GAParameters getInstance() {
GAParameters instance = null;
try {
FileInputStream fileStream = new FileInputStream("GAParameters.ser");
instance = (GAParameters) Serializer.loadObject(fileStream);
} catch (FileNotFoundException ex) {
LOGGER.log(Level.WARNING, "Could not load instance object.", ex);
}
if (instance == null) {
instance = new GAParameters();
}
return instance;
}
/**
*
*/
public GAParameters() {
super(new GeneticAlgorithm(), new B1Problem(), new EvaluationTerminator());
}
/**
*
*/
private GAParameters(GAParameters Source) {
super(Source);
}
/**
*
*/
@Override
public Object clone() {
return new GAParameters(this);
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a Genetic Algorithm, which transforms into a GP or GE if the appropriate problem and genotype is used.";
}
@Override
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
/**
* Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
*
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
return this.optimizer.getPopulation();
}
public void setPopulation(Population pop) {
this.optimizer.setPopulation(pop);
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
// /** This method will set the normation method that is to be used.
// * @param normation
// */
// public void setNormationMethod (InterfaceNormation normation) {
// this.m_NormationOperator = normation;
// }
// public InterfaceNormation getNormationMethod () {
// return this.m_NormationOperator;
// }
// public String normationMethodTipText() {
// return "Select the normation method.";
// }
/**
* Choose a parent selection method.
*
* @param selection
*/
public void setParentSelection(InterfaceSelection selection) {
((GeneticAlgorithm) this.optimizer).setParentSelection(selection);
}
public InterfaceSelection getParentSelection() {
return ((GeneticAlgorithm) this.optimizer).getParentSelection();
}
public String parentSelectionTipText() {
return "Choose a parent selection method.";
}
/**
* Enable/disable elitism.
*
* @param elitism
*/
public void setElitism(boolean elitism) {
((GeneticAlgorithm) this.optimizer).setElitism(elitism);
}
public boolean getElitism() {
return ((GeneticAlgorithm) this.optimizer).getElitism();
}
public String elitismTipText() {
return "Enable/disable elitism.";
}
/**
* The number of mating partners needed to create offsprings.
*
* @param partners
*/
public void setNumberOfPartners(int partners) {
if (partners < 0) {
partners = 0;
}
((GeneticAlgorithm) this.optimizer).setNumberOfPartners(partners);
}
public int getNumberOfPartners() {
return ((GeneticAlgorithm) this.optimizer).getNumberOfPartners();
}
public String numberOfPartnersTipText() {
return "The number of mating partners needed to create offsprings.";
}
/**
* Choose a selection method for selecting recombination partners for given parents.
*
* @param selection
*/
public void setPartnerSelection(InterfaceSelection selection) {
((GeneticAlgorithm) this.optimizer).setPartnerSelection(selection);
}
public InterfaceSelection getPartnerSelection() {
return ((GeneticAlgorithm) this.optimizer).getPartnerSelection();
}
public String partnerSelectionTipText() {
return "Choose a selection method for selecting recombination partners for given parents.";
}
}

9
src/eva2/optimization/modules/GenericModuleAdapter.java
View File

@ -38,7 +38,8 @@ public class GenericModuleAdapter extends AbstractModuleAdapter implements Seria
} else {
statisticsModule = new StatisticsStandalone(noGUIStatOut);
}
processor = new Processor(statisticsModule, this, params);
processor = new Processor(statisticsModule, params);
processor.addListener(this);
// the statistics want to be informed if the strategy or the optimizer (which provide statistical data as InterfaceAdditionalInformer) change.
// THIS is now done directly in the constructor of a Processor
@ -87,7 +88,7 @@ public class GenericModuleAdapter extends AbstractModuleAdapter implements Seria
EvAModuleButtonPanelMaker buttonPanel = new EvAModuleButtonPanelMaker(remoteModuleAdapter, ((Processor) processor).isOptimizationRunning());
buttonPanel.setHelperFilename(helperFilename);
frmMkr.addPanelMaker(buttonPanel);
InterfaceOptimizationParameters optimizationParameters = ((Processor) processor).getOptimizationParameterss();
InterfaceOptimizationParameters optimizationParameters = ((Processor) processor).getOptimizationParameters();
frmMkr.addPanelMaker(paramPanel = new JParaPanel(optimizationParameters, optimizationParameters.getName()));
@ -102,7 +103,7 @@ public class GenericModuleAdapter extends AbstractModuleAdapter implements Seria
frmMkr.addPanelMaker(jobPanel);
((Processor) processor).getOptimizationParameterss().addInformableInstance(frmMkr);
((Processor) processor).getOptimizationParameters().addInformableInstance(frmMkr);
return frmMkr;
}
@ -133,7 +134,7 @@ public class GenericModuleAdapter extends AbstractModuleAdapter implements Seria
@Override
public OptimizationJob scheduleJob() {
OptimizationJob job = jobList.addJob(((Processor) processor).getOptimizationParameterss(), (AbstractStatistics) (((Processor) processor).getStatistics()));
OptimizationJob job = jobList.addJob(((Processor) processor).getOptimizationParameters(), (AbstractStatistics) (((Processor) processor).getStatistics()));
jobPanel.getEditor().setValue(jobList);
return job;
}

28
src/eva2/optimization/modules/HCModuleAdapter.java
View File

@ -1,28 +0,0 @@
package eva2.optimization.modules;
/**
* This the HC module adapter necessary to access this implementation from the EvA top level.
*/
public class HCModuleAdapter extends GenericModuleAdapter implements ModuleAdapter {
private static final String moduleName = "Hill_Climber";
/**
* Constructor of the ModuleAdapter
*
* @param AdapterName The AdapterName
* @param Client The client to serve
*/
public HCModuleAdapter(String adapterName) {
super(adapterName, "HC.html", HCParameters.getInstance(), true);
}
/**
* This method returns the name of the ModulAdapters
*
* @return The name
*/
public static String getName() {
return moduleName;
}
}

103
src/eva2/optimization/modules/HCParameters.java
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@ -1,103 +0,0 @@
package eva2.optimization.modules;
import eva2.optimization.go.InterfaceOptimizationParameters;
import eva2.optimization.operator.terminators.EvaluationTerminator;
import eva2.optimization.population.Population;
import eva2.problems.B1Problem;
import eva2.optimization.strategies.HillClimbing;
import eva2.optimization.strategies.InterfaceOptimizer;
import eva2.tools.Serializer;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.Serializable;
import java.util.logging.Level;
/**
* The class gives access to all HC parameters for the EvA
* top level GUI.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 08.06.2004
* Time: 21:19:20
* To change this template use File | Settings | File Templates.
*/
public class HCParameters extends AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
/**
* Load or create a new instance of the class.
*
* @return A loaded (from file) or new instance of the class.
*/
public static HCParameters getInstance() {
HCParameters instance = null;
try {
FileInputStream fileStream = new FileInputStream("HCParameters.ser");
instance = (HCParameters) Serializer.loadObject(fileStream);
} catch (FileNotFoundException ex) {
LOGGER.log(Level.WARNING, "Could not load instance object.", ex);
}
if (instance == null) {
instance = new HCParameters();
}
return instance;
}
/**
*
*/
public HCParameters() {
super(new HillClimbing(), new B1Problem(), new EvaluationTerminator());
}
/**
*
*/
private HCParameters(HCParameters Source) {
super(Source);
}
/**
*
*/
@Override
public Object clone() {
return new HCParameters(this);
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a Hill-Climber, use a population size > 1 for a Multi-Start Hill-Climber.";
}
@Override
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
/**
* Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
*
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
return this.optimizer.getPopulation();
}
public void setPopulation(Population pop) {
this.optimizer.setPopulation(pop);
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
}

28
src/eva2/optimization/modules/MCModuleAdapter.java
View File

@ -1,28 +0,0 @@
package eva2.optimization.modules;
/**
* This the MC module adapter necessary to access this implementation from the EvA top level.
*/
public class MCModuleAdapter extends GenericModuleAdapter implements ModuleAdapter {
private static final String moduleName = "Monte_Carlo_Search";
/**
* Constructor of the ModuleAdapter.
*
* @param adapterName The AdapterName
* @param client The client to serve
*/
public MCModuleAdapter(String adapterName) {
super(adapterName, "MC.html", MCParameters.getInstance(), true);
}
/**
* This method returns the name of the ModulAdapters
*
* @return The name
*/
public static String getName() {
return moduleName;
}
}

99
src/eva2/optimization/modules/MCParameters.java
View File

@ -1,99 +0,0 @@
package eva2.optimization.modules;
import eva2.optimization.go.InterfaceOptimizationParameters;
import eva2.optimization.operator.terminators.EvaluationTerminator;
import eva2.optimization.population.Population;
import eva2.problems.B1Problem;
import eva2.optimization.strategies.InterfaceOptimizer;
import eva2.optimization.strategies.MonteCarloSearch;
import eva2.tools.Serializer;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.Serializable;
import java.util.logging.Level;
/**
* The class gives access to all HC parameters for the EvA
* top level GUI.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 08.06.2004
* Time: 21:07:40
* To change this template use File | Settings | File Templates.
*/
public class MCParameters extends AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
/**
* Load or create a new instance of the class.
*
* @return A loaded (from file) or new instance of the class.
*/
public static MCParameters getInstance() {
MCParameters instance = null;
try {
FileInputStream fileStream = new FileInputStream("MCParameters.ser");
instance = (MCParameters) Serializer.loadObject(fileStream);
} catch (FileNotFoundException ex) {
LOGGER.log(Level.WARNING, "Could not load instance object.", ex);
}
if (instance == null) {
instance = new MCParameters();
}
return instance;
}
/**
*
*/
public MCParameters() {
super(new MonteCarloSearch(), new B1Problem(), new EvaluationTerminator());
}
private MCParameters(MCParameters Source) {
super(Source);
}
/**
*
*/
@Override
public Object clone() {
return new MCParameters(this);
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a simple Monte-Carlo Search, use big populations sizes for faster drawing.";
}
@Override
public void setOptimizer(InterfaceOptimizer optimizer) {
// *pff* i'll ignore that!
}
/**
* Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
*
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
return this.optimizer.getPopulation();
}
public void setPopulation(Population pop) {
this.optimizer.setPopulation(pop);
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
}

31
src/eva2/optimization/modules/MOEAModuleAdapter.java
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@ -1,31 +0,0 @@
package eva2.optimization.modules;
import java.io.Serializable;
/**
* This the MOEA module adapter necessary to access this implementation from the EvA top level.
*/
public class MOEAModuleAdapter extends GenericModuleAdapter implements Serializable, ModuleAdapter {
private static final String moduleName = "Multi-Objective_Evolutionary_Algorithms";
/**
* Constructor of the ModuleAdapter.
*
* @param AdapterName The AdapterName
* @param Client The client to serve
*/
public MOEAModuleAdapter(String adapterName) {
super(adapterName, "MOEA.html", MOEAParameters.getInstance(), true);
}
/**
* This method returns the name of the ModulAdapters
*
* @return The name
*/
public static String getName() {
return moduleName;
}
}

169
src/eva2/optimization/modules/MOEAParameters.java
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@ -1,169 +0,0 @@
package eva2.optimization.modules;
import eva2.optimization.go.InterfaceOptimizationParameters;
import eva2.optimization.operator.archiving.InterfaceArchiving;
import eva2.optimization.operator.archiving.InterfaceInformationRetrieval;
import eva2.optimization.operator.terminators.EvaluationTerminator;
import eva2.optimization.population.Population;
import eva2.problems.TF1Problem;
import eva2.optimization.strategies.InterfaceOptimizer;
import eva2.optimization.strategies.MultiObjectiveEA;
import eva2.tools.Serializer;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.Serializable;
import java.util.logging.Level;
/**
* The class gives access to all MOEA parameters for the EvA
* top level GUI.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 27.10.2004
* Time: 13:49:09
* To change this template use File | Settings | File Templates.
*/
public class MOEAParameters extends AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
/**
* Load or create a new instance of the class.
*
* @return A loaded (from file) or new instance of the class.
*/
public static MOEAParameters getInstance() {
MOEAParameters instance = null;
try {
FileInputStream fileStream = new FileInputStream("MOEAParameters.ser");
instance = (MOEAParameters) Serializer.loadObject(fileStream);
} catch (FileNotFoundException ex) {
LOGGER.log(Level.WARNING, "Could not load instance object.", ex);
}
if (instance == null) {
instance = new MOEAParameters();
}
return instance;
}
public MOEAParameters() {
super(new MultiObjectiveEA(), new TF1Problem(), new EvaluationTerminator(1000));
}
private MOEAParameters(MOEAParameters Source) {
super(Source);
}
@Override
public Object clone() {
return new MOEAParameters(this);
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a multi-objective evoluationary algorithm, limit MOEA to multi-objective problems (due to the given framework only the fitness of objective one will be plotted).";
}
/**
* Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
*
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
return this.optimizer.getPopulation();
}
public void setPopulation(Population pop) {
this.optimizer.setPopulation(pop);
}
public String populationTipText() {
return "Edit the properties of the Population used.";
}
/**
* This method allows you to set/get the optimizing technique to use.
*
* @return The current optimizing method
*/
@Override
public InterfaceOptimizer getOptimizer() {
return ((MultiObjectiveEA) this.optimizer).getOptimizer();
}
@Override
public void setOptimizer(InterfaceOptimizer b) {
((MultiObjectiveEA) this.optimizer).setOptimizer(b);
}
public String optimizerTipText() {
return "Choose a population based optimizing technique to use.";
}
/**
* This method allows you to set/get the archiving strategy to use.
*
* @return The current optimizing method
*/
public InterfaceArchiving getArchivingStrategy() {
return ((MultiObjectiveEA) this.optimizer).getArchivingStrategy();
}
public void setArchivingStrategy(InterfaceArchiving b) {
((MultiObjectiveEA) this.optimizer).setArchivingStrategy(b);
}
public String archivingStrategyTipText() {
return "Choose the archiving strategy.";
}
/**
* This method allows you to set/get the Information Retrieval strategy to use.
*
* @return The current optimizing method
*/
public InterfaceInformationRetrieval getInformationRetrieval() {
return ((MultiObjectiveEA) this.optimizer).getInformationRetrieval();
}
public void setInformationRetrieval(InterfaceInformationRetrieval b) {
((MultiObjectiveEA) this.optimizer).setInformationRetrieval(b);
}
public String informationRetrievalTipText() {
return "Choose the Information Retrieval strategy.";
}
/**
* This method allows you to set/get the size of the archive.
*
* @return The current optimizing method
*/
public int getArchiveSize() {
Population archive = this.optimizer.getPopulation().getArchive();
if (archive == null) {
archive = new Population();
this.optimizer.getPopulation().SetArchive(archive);
}
return ((MultiObjectiveEA) this.optimizer).getArchiveSize();
}
public void setArchiveSize(int b) {
Population archive = this.optimizer.getPopulation().getArchive();
if (archive == null) {
archive = new Population();
this.optimizer.getPopulation().SetArchive(archive);
}
this.optimizer.getPopulation().getArchive().setTargetSize(b);
}
public String archiveSizeTipText() {
return "Choose the size of the archive.";
}
}

29
src/eva2/optimization/modules/PBILModuleAdapter.java
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@ -1,29 +0,0 @@
package eva2.optimization.modules;
/**
* This the PBIL module adapter necessary to access this implementation from the EvA top level.
*/
public class PBILModuleAdapter extends GenericModuleAdapter implements ModuleAdapter {
private static final String moduleName = "Population_Based_Incremental_Learning";
/**
* Constructor of the ModuleAdapter
*
* @param AdapterName The AdapterName
* @param Client The client to serve
*/
public PBILModuleAdapter(String adapterName) {
super(adapterName, "PBIL.html", PBILParameters.getInstance(), true);
}
/**
* This method returns the name of the ModulAdapters
*
* @return The name
*/
public static String getName() {
return moduleName;
}
}

212
src/eva2/optimization/modules/PBILParameters.java
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@ -1,212 +0,0 @@
package eva2.optimization.modules;
import eva2.optimization.go.InterfaceOptimizationParameters;
import eva2.optimization.operator.selection.InterfaceSelection;
import eva2.optimization.operator.terminators.EvaluationTerminator;
import eva2.optimization.population.Population;
import eva2.problems.B1Problem;
import eva2.optimization.strategies.InterfaceOptimizer;
import eva2.optimization.strategies.PopulationBasedIncrementalLearning;
import eva2.tools.Serializer;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.Serializable;
import java.util.logging.Level;
/**
* The class gives access to all PBIL parameters for the EvA
* top level GUI.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 08.06.2004
* Time: 21:53:29
* To change this template use File | Settings | File Templates.
*/
public class PBILParameters extends AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
/**
* Load or create a new instance of the class.
*
* @return A loaded (from file) or new instance of the class.
*/
public static PBILParameters getInstance() {
PBILParameters instance = null;
try {
FileInputStream fileStream = new FileInputStream("PBILParameters.ser");
instance = (PBILParameters) Serializer.loadObject(fileStream);
} catch (FileNotFoundException ex) {
LOGGER.log(Level.WARNING, "Could not load instance object.", ex);
}
if (instance == null) {
instance = new PBILParameters();
}
return instance;
}
public PBILParameters() {
super(new PopulationBasedIncrementalLearning(), new B1Problem(), new EvaluationTerminator(1000));
}
private PBILParameters(PBILParameters Source) {
super(Source);
}
@Override
public Object clone() {
return new PBILParameters(this);
}
/**
* This method returns a global info string
*
* @return description
*/
// public static String globalInfo() {
// return ((PopulationBasedIncrementalLearning)this.optimizer).globalInfo();
// }
@Override
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
/**
* Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
*
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
return this.optimizer.getPopulation();
}
public void setPopulation(Population pop) {
this.optimizer.setPopulation(pop);
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
/**
* This method will set the selection method that is to be used
*
* @param selection
*/
public void setSelectionMethod(InterfaceSelection selection) {
((PopulationBasedIncrementalLearning) this.optimizer).setSelectionMethod(selection);
}
public InterfaceSelection getSelectionMethod() {
return ((PopulationBasedIncrementalLearning) this.optimizer).getSelectionMethod();
}
public String selectionMethodTipText() {
return "Choose a selection method.";
}
/**
* Enable/disable elitism.
*
* @param elitism
*/
public void setElitism(boolean elitism) {
((PopulationBasedIncrementalLearning) this.optimizer).setElitism(elitism);
}
public boolean getElitism() {
return ((PopulationBasedIncrementalLearning) this.optimizer).getElitism();
}
public String elitismTipText() {
return "Enable/disable elitism.";
}
/**
* This method will set the learning rate for PBIL
*
* @param LearningRate
*/
public void setLearningRate(double LearningRate) {
if (LearningRate < 0) {
LearningRate = 0;
}
((PopulationBasedIncrementalLearning) this.optimizer).setLearningRate(LearningRate);
}
public double getLearningRate() {
return ((PopulationBasedIncrementalLearning) this.optimizer).getLearningRate();
}
public String learningRateTipText() {
return "The learing rate of PBIL.";
}
/**
* This method will set the mutation rate for PBIL
*
* @param m
*/
public void setMutationRate(double m) {
if (m < 0) {
m = 0;
}
if (m > 1) {
m = 1;
}
((PopulationBasedIncrementalLearning) this.optimizer).setMutationRate(m);
}
public double getMutationRate() {
return ((PopulationBasedIncrementalLearning) this.optimizer).getMutationRate();
}
public String mutationRateTipText() {
return "The mutation rate of PBIL.";
}
/**
* This method will set the mutation sigma for PBIL
*
* @param m
*/
public void setMutateSigma(double m) {
if (m < 0) {
m = 0;
}
((PopulationBasedIncrementalLearning) this.optimizer).setMutateSigma(m);
}
public double getMutateSigma() {
return ((PopulationBasedIncrementalLearning) this.optimizer).getMutateSigma();
}
public String mutateSigmaTipText() {
return "Set the sigma for the mutation of the probability vector.";
}
/**
* This method will set the number of positive samples for PBIL
*
* @param PositiveSamples
*/
public void setPositiveSamples(int PositiveSamples) {
if (PositiveSamples < 1) {
PositiveSamples = 1;
}
((PopulationBasedIncrementalLearning) this.optimizer).setPositiveSamples(PositiveSamples);
}
public int getPositiveSamples() {
return ((PopulationBasedIncrementalLearning) this.optimizer).getPositiveSamples();
}
public String positiveSamplesTipText() {
return "The number of positive samples that update the PBIL vector.";
}
}

29
src/eva2/optimization/modules/PSOModuleAdapter.java
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@ -1,29 +0,0 @@
package eva2.optimization.modules;
/**
* This the PSO module adapter necessary to access this implementation from the EvA top level.
*/
public class PSOModuleAdapter extends GenericModuleAdapter implements ModuleAdapter {
private static final String moduleName = "Particle_Swarm_Optimization";
/**
* Constructor of the ModulAdapter.
*
* @param AdapterName The AdapterName
* @param Client The client to serve
*/
public PSOModuleAdapter(String adapterName) {
super(adapterName, "PSO.html", PSOParameters.getInstance(), true);
}
/**
* This method returns the name of the ModulAdapter
*
* @return The name
*/
public static String getName() {
return moduleName;
}
}

333
src/eva2/optimization/modules/PSOParameters.java
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@ -1,333 +0,0 @@
package eva2.optimization.modules;
import eva2.gui.editor.GenericObjectEditor;
import eva2.optimization.enums.PSOTopologyEnum;
import eva2.optimization.go.InterfaceOptimizationParameters;
import eva2.optimization.operator.terminators.EvaluationTerminator;
import eva2.optimization.population.Population;
import eva2.problems.F1Problem;
import eva2.optimization.strategies.InterfaceOptimizer;
import eva2.optimization.strategies.ParticleSwarmOptimization;
import eva2.tools.SelectedTag;
import eva2.tools.Serializer;
import eva2.util.annotation.Description;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.Serializable;
import java.util.logging.Level;
/**
*/
@Description("Particle Swarm Optimization by Kennedy and Eberhart.")
public class PSOParameters extends AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
/**
* Load or create a new instance of the class.
*
* @return A loaded (from file) or new instance of the class.
*/
public static PSOParameters getInstance() {
PSOParameters instance = null;
try {
FileInputStream fileStream = new FileInputStream("PSOParameters.ser");
instance = (PSOParameters) Serializer.loadObject(fileStream);
} catch (FileNotFoundException ex) {
LOGGER.log(Level.WARNING, "Could not load instance object.", ex);
}
if (instance == null) {
instance = new PSOParameters();
}
return instance;
}
/**
*
*/
public PSOParameters() {
super(new ParticleSwarmOptimization(), new F1Problem(), new EvaluationTerminator());
}
private PSOParameters(PSOParameters Source) {
super(Source);
}
@Override
public Object clone() {
return new PSOParameters(this);
}
/**
* Take care that all properties which may be hidden (and currently are) send a "hide" message to the Java Bean properties.
* This is called by PropertySheetPanel in use with the GenericObjectEditor.
*/
public void hideHideable() {
setCheckSpeedLimit(isCheckSpeedLimit());
setTopology(getTopology());
}
@Override
public void setOptimizer(InterfaceOptimizer optimizer) {
// *pff* i'll ignore that!
}
/**
* Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
*
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
return this.optimizer.getPopulation();
}
public void setPopulation(Population pop) {
this.optimizer.setPopulation(pop);
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
/**
* This method will set the initial velocity
*
* @param f
*/
public void setInitialVelocity(double f) {
((ParticleSwarmOptimization) this.optimizer).setInitialVelocity(f);
}
public double getInitialVelocity() {
return ((ParticleSwarmOptimization) this.optimizer).getInitialVelocity();
}
public String initialVelocityTipText() {
return ((ParticleSwarmOptimization) this.optimizer).initialVelocityTipText();
}
/**
* This method will set the speed limit
*
* @param k
*/
public void setSpeedLimit(double k) {
((ParticleSwarmOptimization) this.optimizer).setSpeedLimit(k);
}
public double getSpeedLimit() {
return ((ParticleSwarmOptimization) this.optimizer).getSpeedLimit();
}
public String speedLimitTipText() {
return ((ParticleSwarmOptimization) this.optimizer).speedLimitTipText();
}
/**
* This method will set the inertness
*
* @param k
*/
public void setInertnessOrChi(double k) {
// if (k < 0) k = 0;
// if (k > 1) k = 1;
((ParticleSwarmOptimization) this.optimizer).setInertnessOrChi(k);
}
public double getInertnessOrChi() {
return ((ParticleSwarmOptimization) this.optimizer).getInertnessOrChi();
}
public String inertnessOrChiTipText() {
return ((ParticleSwarmOptimization) this.optimizer).inertnessOrChiTipText();
}
/**
* This method will set greediness to move towards the best solution
* based on the cognition model
*
* @param l
*/
public void setPhi1(double l) {
((ParticleSwarmOptimization) this.optimizer).setPhi1(l);
}
public double getPhi1() {
return ((ParticleSwarmOptimization) this.optimizer).getPhi1();
}
public String phi1TipText() {
return ((ParticleSwarmOptimization) this.optimizer).phi1TipText();
}
/**
* This method will set greediness to move towards the best solution
* based on the social model
*
* @param l
*/
public void setPhi2(double l) {
((ParticleSwarmOptimization) this.optimizer).setPhi2(l);
}
public double getPhi2() {
return ((ParticleSwarmOptimization) this.optimizer).getPhi2();
}
public String phi2TipText() {
return ((ParticleSwarmOptimization) this.optimizer).phi2TipText();
}
/**
* Toggle Check Constraints.
*
* @param s Check Constraints.
*/
public void setCheckRange(boolean s) {
((ParticleSwarmOptimization) this.optimizer).setCheckRange(s);
}
public boolean isCheckRange() {
return ((ParticleSwarmOptimization) this.optimizer).isCheckRange();
}
public String checkConstraintsTipText() {
return ((ParticleSwarmOptimization) this.optimizer).checkRangeTipText();
}
/**
* This method allows you to choose the topology type.
*
* @param t The type.
*/
public void setTopology(PSOTopologyEnum t) {
((ParticleSwarmOptimization) this.optimizer).setTopology(t);
((ParticleSwarmOptimization) this.optimizer).setGOEShowProperties(getClass());
}
public PSOTopologyEnum getTopology() {
return ((ParticleSwarmOptimization) this.optimizer).getTopology();
}
public String topologyTipText() {
return ((ParticleSwarmOptimization) this.optimizer).topologyTipText();
}
/**
* The range of the local neighbourhood.
*
* @param s The range.
*/
public void setTopologyRange(int s) {
((ParticleSwarmOptimization) this.optimizer).setTopologyRange(s);
}
public int getTopologyRange() {
return ((ParticleSwarmOptimization) this.optimizer).getTopologyRange();
}
public String topologyRangeTipText() {
return ((ParticleSwarmOptimization) this.optimizer).topologyRangeTipText();
}
public double getSubSwarmRadius() {
return ((ParticleSwarmOptimization) this.optimizer).getSubSwarmRadius();
}
public void setSubSwarmRadius(double radius) {
((ParticleSwarmOptimization) this.optimizer).setSubSwarmRadius(radius);
}
public String subSwarmRadiusTipText() {
return ((ParticleSwarmOptimization) this.optimizer).subSwarmRadiusTipText();
}
public int getMaxSubSwarmSize() {
return ((ParticleSwarmOptimization) this.optimizer).getMaxSubSwarmSize();
}
public void setMaxSubSwarmSize(int subSize) {
((ParticleSwarmOptimization) this.optimizer).setMaxSubSwarmSize(subSize);
}
public String maxSubSwarmSizeTipText() {
return ((ParticleSwarmOptimization) this.optimizer).maxSubSwarmSizeTipText();
}
/**
* @return the checkSpeedLimit
*/
public boolean isCheckSpeedLimit() {
return ((ParticleSwarmOptimization) this.optimizer).isCheckSpeedLimit();
}
/**
* @param checkSpeedLimit the checkSpeedLimit to set
*/
public void setCheckSpeedLimit(boolean checkSpeedLimit) {
((ParticleSwarmOptimization) this.optimizer).setCheckSpeedLimit(checkSpeedLimit);
GenericObjectEditor.setHideProperty(getClass(), "speedLimit", !checkSpeedLimit);
}
public String checkSpeedLimitTipText() {
return ((ParticleSwarmOptimization) this.optimizer).checkSpeedLimitTipText();
}
/**
* This method allows you to choose the algorithm type.
*
* @param s The type.
*/
public void setAlgoType(SelectedTag s) {
((ParticleSwarmOptimization) this.optimizer).setAlgoType(s);
}
public SelectedTag getAlgoType() {
return ((ParticleSwarmOptimization) this.optimizer).getAlgoType();
}
public String algoTypeTipText() {
return ((ParticleSwarmOptimization) this.optimizer).algoTypeTipText();
}
// /**
// * @return the treeBranchDeg
// */
// public int getTreeBranchDegree() {
// return ((ParticleSwarmOptimization)this.optimizer).getTreeBranchDegree();
// }
//
// /**
// * @param treeBranchDeg the treeBranchDeg to set
// */
// public void setTreeBranchDegree(int treeBranchDeg) {
// ((ParticleSwarmOptimization)this.optimizer).setTreeBranchDegree(treeBranchDeg);
// }
//
// public String treeBranchDegreeTipText() {
// return ((ParticleSwarmOptimization)this.optimizer).treeBranchDegreeTipText();
// }
/**
* @return the wrapTopology
*/
public boolean isWrapTopology() {
return ((ParticleSwarmOptimization) this.optimizer).isWrapTopology();
}
/**
* @param wrapTopology the wrapTopology to set
*/
public void setWrapTopology(boolean wrapTopology) {
((ParticleSwarmOptimization) this.optimizer).setWrapTopology(wrapTopology);
}
public String wrapTopologyTipText() {
return ((ParticleSwarmOptimization) this.optimizer).wrapTopologyTipText();
}
}

13
src/eva2/optimization/modules/Processor.java
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@ -69,10 +69,9 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
*
* @see InterfaceNotifyOnInformers
*/
public Processor(InterfaceStatistics statistics, ModuleAdapter moduleAdapter, InterfaceOptimizationParameters optimizationParameters) {
public Processor(InterfaceStatistics statistics, InterfaceOptimizationParameters optimizationParameters) {
this.optimizationParameters = optimizationParameters;
this.statistics = statistics;
optimizationStateListener = moduleAdapter;
// the statistics want to be informed if the strategy or the optimizer (which provide statistical data as InterfaceAdditionalInformer) change.
if (statistics != null && (optimizationParameters != null)) {
@ -211,7 +210,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
Population resultPop = null;
if (!isOptimizationRunning()) {
System.err.println("warning, this shouldnt happen in processor! Was startOptimization called?");
LOGGER.warning("Was startOptimization already called?");
setOptimizationRunning(true);
}
@ -407,11 +406,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
@Override
public String getInfoString() {
//StringBuffer sb = new StringBuffer("processing ");
StringBuilder sb = new StringBuilder(this.optimizationParameters.getProblem().getName());
sb.append("+");
sb.append(this.optimizationParameters.getOptimizer().getName());
return sb.toString();
return this.optimizationParameters.getProblem().getName() + "+" + this.optimizationParameters.getOptimizer().getName();
}
/**
@ -424,7 +419,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
/**
* These methods allow you to get and set the Module Parameters.
*/
public InterfaceOptimizationParameters getOptimizationParameterss() {
public InterfaceOptimizationParameters getOptimizationParameters() {
return optimizationParameters;
}

29
src/eva2/optimization/modules/SAModuleAdapter.java
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@ -1,29 +0,0 @@
package eva2.optimization.modules;
/**
* This the SA module adapter necessary to access this implementation from the
* EvA top level.
*/
public class SAModuleAdapter extends GenericModuleAdapter implements ModuleAdapter {
private static final String moduleName = "Simulated_Annealing";
/**
* Constructor of the ModulAdapter.
*
* @param AdapterName The AdapterName
* @param Client The client to serve
*/
public SAModuleAdapter(String adapterName) {
super(adapterName, "SA.html", SAParameters.getInstance(), true);
}
/**
* This method returns the name of the ModulAdapter
*
* @return The name
*/
public static String getName() {
return moduleName;
}
}

135
src/eva2/optimization/modules/SAParameters.java
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@ -1,135 +0,0 @@
package eva2.optimization.modules;
import eva2.optimization.go.InterfacePopulationChangedEventListener;
import eva2.optimization.operator.terminators.EvaluationTerminator;
import eva2.optimization.operator.terminators.InterfaceTerminator;
import eva2.optimization.population.Population;
import eva2.problems.B1Problem;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.optimization.strategies.InterfaceOptimizer;
import eva2.optimization.strategies.SimulatedAnnealing;
import eva2.tools.Serializer;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.Serializable;
import java.util.logging.Level;
/**
*
*/
public class SAParameters extends AbstractOptimizationParameters implements Serializable {
// Opt. Algorithms and Parameters
private InterfaceOptimizer optimizer = new SimulatedAnnealing();
private InterfaceOptimizationProblem problem = new B1Problem();
private InterfaceTerminator terminator = new EvaluationTerminator();
transient private InterfacePopulationChangedEventListener changedEventListener;
/**
* Load or create a new instance of the class.
*
* @return A loaded (from file) or new instance of the class.
*/
public static SAParameters getInstance() {
SAParameters instance = null;
try {
FileInputStream fileStream = new FileInputStream("SAParameters.ser");
instance = (SAParameters) Serializer.loadObject(fileStream);
} catch (FileNotFoundException ex) {
LOGGER.log(Level.WARNING, "Could not load instance object.", ex);
}
if (instance == null) {
instance = new SAParameters();
}
return instance;
}
/**
*
*/
public SAParameters() {
super(new SimulatedAnnealing(), new B1Problem(), new EvaluationTerminator());
}
private SAParameters(SAParameters Source) {
super(Source);
}
@Override
public Object clone() {
return new SAParameters(this);
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a simple Simulated Annealing algorithm.";
}
@Override
public void setOptimizer(InterfaceOptimizer optimizer) {
// *pff* i'll ignore that!
}
/**
* Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
*
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
return this.optimizer.getPopulation();
}
public void setPopulation(Population pop) {
this.optimizer.setPopulation(pop);
}
public String populationTipText() {
return "Change the number of best individuals stored (MS-SA).";
}
/**
* This methods allow you to set/get the temperatur of the simulated
* annealing procedure
*
* @return The initial temperature.
*/
public double getInitialTemperature() {
return ((SimulatedAnnealing) this.optimizer).getInitialTemperature();
}
public void setInitialTemperature(double pop) {
((SimulatedAnnealing) this.optimizer).setInitialTemperature(pop);
}
public String initialTemperatureTipText() {
return "Set the initial temperature.";
}
/**
* This methods allow you to set/get the temperatur of the simulated
* annealing procedure
*
* @return The initial temperature.
*/
public double getAlpha() {
return ((SimulatedAnnealing) this.optimizer).getAlpha();
}
public void setAlpha(double a) {
if (a > 1) {
a = 1.0;
}
((SimulatedAnnealing) this.optimizer).setAlpha(a);
}
public String alphaTipText() {
return "Set alpha, which is used to degrade the temperaure.";
}
}

29
src/eva2/optimization/modules/SSGAModuleAdapter.java
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@ -1,29 +0,0 @@
package eva2.optimization.modules;
/**
* This the SSGA module adapter necessary to access this implementation from the
* EvA top level.
*/
public class SSGAModuleAdapter extends GenericModuleAdapter implements ModuleAdapter {
private static final String moduleName = "Steady_State_Genetic_Algorithm";
/**
* Constructor of the ModuleAdapter.
*
* @param AdapterName The AdapterName
* @param Client The client to serve
*/
public SSGAModuleAdapter(String adapterName) {
super(adapterName, "SSGA.html", SSGAParameters.getInstance(), true);
}
/**
* This method returns the name of the ModulAdapters
*
* @return The name
*/
public static String getName() {
return moduleName;
}
}

182
src/eva2/optimization/modules/SSGAParameters.java
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@ -1,182 +0,0 @@
package eva2.optimization.modules;
import eva2.optimization.go.InterfaceOptimizationParameters;
import eva2.optimization.operator.selection.InterfaceSelection;
import eva2.optimization.operator.selection.replacement.InterfaceReplacement;
import eva2.optimization.operator.terminators.EvaluationTerminator;
import eva2.optimization.population.Population;
import eva2.problems.B1Problem;
import eva2.optimization.strategies.InterfaceOptimizer;
import eva2.optimization.strategies.SteadyStateGA;
import eva2.tools.Serializer;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.Serializable;
import java.util.logging.Level;
/**
* The class gives access to all SSGA parameters for the EvA
* top level GUI.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 19.07.2005
* Time: 15:44:34
* To change this template use File | Settings | File Templates.
*/
public class SSGAParameters extends AbstractOptimizationParameters implements InterfaceOptimizationParameters, Serializable {
/**
* Load or create a new instance of the class.
*
* @return A loaded (from file) or new instance of the class.
*/
public static SSGAParameters getInstance() {
SSGAParameters instance = null;
try {
FileInputStream fileStream = new FileInputStream("SSGAParameters.ser");
instance = (SSGAParameters) Serializer.loadObject(fileStream);
} catch (FileNotFoundException ex) {
LOGGER.log(Level.WARNING, "Could not load instance object.", ex);
}
if (instance == null) {
instance = new SSGAParameters();
}
return instance;
}
/**
*
*/
public SSGAParameters() {
super(new SteadyStateGA(), new B1Problem(), new EvaluationTerminator());
}
private SSGAParameters(SSGAParameters Source) {
super(Source);
}
@Override
public Object clone() {
return new SSGAParameters(this);
}
/**
* This method returns a global info string.
*
* @return description
*/
public static String globalInfo() {
return "This is a steady-state GA.";
}
@Override
public void setOptimizer(InterfaceOptimizer optimizer) {
// *pff* i'll ignore that!
}
/**
* Assuming that all optimizer will store their data in a population
* we will allow access to this population to query to current state
* of the optimizer.
*
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
return this.optimizer.getPopulation();
}
public void setPopulation(Population pop) {
this.optimizer.setPopulation(pop);
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
// /** This method will set the normation method that is to be used.
// * @param normation
// */
// public void setNormationMethod (InterfaceNormation normation) {
// this.m_NormationOperator = normation;
// }
// public InterfaceNormation getNormationMethod () {
// return this.m_NormationOperator;
// }
// public String normationMethodTipText() {
// return "Select the normation method.";
// }
/**
* Choose a parent selection method.
*
* @param selection
*/
public void setParentSelection(InterfaceSelection selection) {
((SteadyStateGA) this.optimizer).setParentSelection(selection);
}
public InterfaceSelection getParentSelection() {
return ((SteadyStateGA) this.optimizer).getParentSelection();
}
public String parentSelectionTipText() {
return "Choose a parent selection method.";
}
/**
* This method will set the number of partners that are needed to create
* offsprings by mating.
*
* @param partners Number of partners needed for mating
*/
public void setNumberOfPartners(int partners) {
if (partners < 0) {
partners = 0;
}
((SteadyStateGA) this.optimizer).setNumberOfPartners(partners);
}
public int getNumberOfPartners() {
return ((SteadyStateGA) this.optimizer).getNumberOfPartners();
}
public String numberOfPartnersTipText() {
return "The number of mating partners needed to create offsprings.";
}
/**
* Choose a selection method for selecting recombination partners for given parents.
*
* @param selection
*/
public void setPartnerSelection(InterfaceSelection selection) {
((SteadyStateGA) this.optimizer).setPartnerSelection(selection);
}
public InterfaceSelection getPartnerSelection() {
return ((SteadyStateGA) this.optimizer).getPartnerSelection();
}
public String partnerSelectionTipText() {
return "Choose a selection method for selecting recombination partners for given parents.";
}
/**
* Choose a replacement strategy.
*
* @param s A InterfaceReplacement strategy.
*/
public void setReplacementSelection(InterfaceReplacement s) {
((SteadyStateGA) this.optimizer).setReplacementSelection(s);
}
public InterfaceReplacement getReplacementSelection() {
return ((SteadyStateGA) this.optimizer).getReplacementSelection();
}
public String replacementSelectionTipText() {
return "Choose a replacement strategy.";
}
}