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Little fixes in LTGA (Logging, deprecated method)

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Refactored InterfaceOptimizer (Set* to set*)
This commit is contained in:
Fabian Becker 2012-12-22 12:27:10 +00:00
parent 7a6c814a56
commit 0f553039e4
32 changed files with 385 additions and 385 deletions
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2
src/eva2/OptimizerFactory.java
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@ -373,7 +373,7 @@ public class OptimizerFactory {
setTemplateOperators(problem, mutator, 1., new NoCrossover(), 0);
HillClimbing hc = new HillClimbing();
hc.SetIdentifier("-"+popSize+"-"+mutator.getStringRepresentation());
hc.setIdentifier("-"+popSize+"-"+mutator.getStringRepresentation());
hc.getPopulation().setTargetSize(popSize);
hc.addPopulationChangedEventListener(listener);
hc.SetProblem(problem);

2
src/eva2/server/go/strategies/BOA.java
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@ -592,7 +592,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
return new SolutionSet(this.population);
}
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}

2
src/eva2/server/go/strategies/BinaryScatterSearch.java
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@ -723,7 +723,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
return new SolutionSet(this.refSet);
}
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}

2
src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java
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@ -261,7 +261,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/ClusterBasedNichingEA.java
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@ -996,7 +996,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/ClusteringHillClimbing.java
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@ -97,7 +97,7 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
setLocalSearchMethod(getLocalSearchMethod());
}
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/DifferentialEvolution.java
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@ -687,7 +687,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
/** This method allows you to set an identifier for the algorithm
* @param name The identifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/EsDpiNiching.java
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@ -1062,7 +1062,7 @@ public class EsDpiNiching implements InterfaceOptimizer, Serializable, Interface
return identifier;
}
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
identifier = name;
}

2
src/eva2/server/go/strategies/EvolutionStrategies.java
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@ -323,7 +323,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
*
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.identifier = name;
}

2
src/eva2/server/go/strategies/EvolutionaryProgramming.java
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@ -153,7 +153,7 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/FloodAlgorithm.java
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@ -200,7 +200,7 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/GeneticAlgorithm.java
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@ -225,7 +225,7 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/GradientDescentAlgorithm.java
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@ -316,7 +316,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}

2
src/eva2/server/go/strategies/HillClimbing.java
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@ -208,7 +208,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

3
src/eva2/server/go/strategies/InterfaceOptimizer.java
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@ -83,8 +83,9 @@ public interface InterfaceOptimizer {
/**
* This method allows you to set an identifier for the algorithm
* @param name The identifier
* @deprecated
*/
public void SetIdentifier(String name);
public void setIdentifier(String name);
public String getIdentifier();
/**

10
src/eva2/server/go/strategies/IslandModelEA.java
View File

@ -109,7 +109,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Islands = new InterfaceOptimizer[this.m_numLocalCPUs];
for (int i = 0; i < this.m_numLocalCPUs; i++) {
this.m_Islands[i] = (InterfaceOptimizer) this.m_Optimizer.clone();
this.m_Islands[i].SetIdentifier(""+i);
this.m_Islands[i].setIdentifier(""+i);
this.m_Islands[i].init();
if (this.m_LogLocalChanges)
this.m_Islands[i].addPopulationChangedEventListener(this);
@ -129,7 +129,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Islands = new InterfaceOptimizer[nodesList.length];
for (int i = 0; i < nodesList.length; i++) {
this.m_Islands[i] = (InterfaceOptimizer) RMIProxyRemoteThread.newInstance(this.m_Optimizer, nodesList[i]);
this.m_Islands[i].SetIdentifier(""+i);
this.m_Islands[i].setIdentifier(""+i);
this.m_Islands[i].init();
if (this.m_LogLocalChanges)
this.m_Islands[i].addPopulationChangedEventListener(myLocal);
@ -178,7 +178,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Islands = new InterfaceOptimizer[this.m_numLocalCPUs];
for (int i = 0; i < this.m_numLocalCPUs; i++) {
this.m_Islands[i] = (InterfaceOptimizer) this.m_Optimizer.clone();
this.m_Islands[i].SetIdentifier(""+i);
this.m_Islands[i].setIdentifier(""+i);
this.m_Islands[i].init();
if (this.m_LogLocalChanges)
this.m_Islands[i].addPopulationChangedEventListener(this);
@ -196,7 +196,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Islands = new InterfaceOptimizer[nodesList.length];
for (int i = 0; i < nodesList.length; i++) {
this.m_Islands[i] = (InterfaceOptimizer) RMIProxyRemoteThread.newInstance(this.m_Optimizer, nodesList[i]);
this.m_Islands[i].SetIdentifier(""+i);
this.m_Islands[i].setIdentifier(""+i);
this.m_Islands[i].init();
if (this.m_LogLocalChanges)
this.m_Islands[i].addPopulationChangedEventListener(myLocal);
@ -390,7 +390,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

697
src/eva2/server/go/strategies/LTGA.java
View File

@ -23,375 +23,374 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.Pair;
import eva2.tools.math.SpecialFunction;
public class LTGA implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener{
private static final Logger LOGGER = Logger.getLogger(BOA.class.getName());
transient private InterfacePopulationChangedEventListener m_Listener = null;
private String m_Identifier = "LTGA";
private int probDim = 8;
private int fitCrit = -1;
private int popSize = 50;
private Population population = new Population();
private AbstractOptimizationProblem problem = new BKnapsackProblem();
private AbstractEAIndividual template = null;
private int generationCycle = 500;
public LTGA(){
}
public LTGA(LTGA l){
this.m_Listener = l.m_Listener;
this.m_Identifier = l.m_Identifier;
this.probDim = l.probDim;
this.popSize = l.popSize;
this.population = (Population)l.population.clone();
this.problem = (AbstractOptimizationProblem)l.problem.clone();
public class LTGA implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener {
private static final Logger LOGGER = Logger.getLogger(LTGA.class.getName());
transient private InterfacePopulationChangedEventListener m_Listener = null;
private String m_Identifier = "LTGA";
private int probDim = 8;
private int fitCrit = -1;
private int popSize = 50;
private Population population = new Population();
private AbstractOptimizationProblem problem = new BKnapsackProblem();
private AbstractEAIndividual template = null;
private int generationCycle = 500;
public LTGA() {
}
public LTGA(LTGA l) {
this.m_Listener = l.m_Listener;
this.m_Identifier = l.m_Identifier;
this.probDim = l.probDim;
this.popSize = l.popSize;
this.population = (Population) l.population.clone();
this.problem = (AbstractOptimizationProblem) l.problem.clone();
this.template = (AbstractEAIndividual) template.clone();
}
public Object clone() {
return new LTGA(this);
}
}
@Override
public String getName() {
return "Linkage Tree Genetic Algorithm";
}
public static String globalInfo() {
return "Basic implementation of the Linkage Tree Genetic Algorithm based on the works by Dirk Thierens.";
}
@Override
public Object clone() {
return new LTGA(this);
}
@Override
public void addPopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public String getName() {
return "Linkage Tree Genetic Algorithm";
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener == ea) {
m_Listener = null;
return true;
} else
return false;
}
private void defaultInit(){
if (population == null) {
this.population = new Population(this.popSize);
} else {
this.population.setTargetPopSize(this.popSize);
}
this.template = this.problem.getIndividualTemplate();
if (!(template instanceof InterfaceDataTypeBinary)) {
LOGGER.log(Level.WARNING, "Requiring binary data!");
} else {
Object dim = BeanInspector.callIfAvailable(problem,
"getProblemDimension", null);
if (dim == null)
LOGGER.log(Level.WARNING, "Coudn't get problem dimension!");
probDim = (Integer) dim;
((InterfaceDataTypeBinary) this.template).SetBinaryGenotype(new BitSet(probDim));
}
this.population.addPopulationChangedEventListener(this);
this.population.setNotifyEvalInterval(this.generationCycle);
}
private static BitSet getBinaryData(AbstractEAIndividual indy) {
if (indy instanceof InterfaceGAIndividual)
return ((InterfaceGAIndividual) indy).getBGenotype();
else if (indy instanceof InterfaceDataTypeBinary)
return ((InterfaceDataTypeBinary) indy).getBinaryData();
else {
throw new RuntimeException(
"Unable to get binary representation for "
+ indy.getClass());
}
}
public static String globalInfo() {
return "Basic implementation of the Linkage Tree Genetic Algorithm based on the works by Dirk Thierens.";
}
@Override
public void init() {
defaultInit();
this.problem.initPopulation(this.population);
this.evaluatePopulation(this.population);
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
private void evaluatePopulation(Population pop) {
for (int i = 0; i < pop.size(); i++) {
evaluate(pop.getEAIndividual(i));
}
}
/**
* evaluate the given Individual and increments the counter. if the
* individual is null, only the counter is incremented
*
* @param indy
* the individual you want to evaluate
*/
private void evaluate(AbstractEAIndividual indy) {
// evaluate the given individual if it is not null
if (indy == null) {
LOGGER.log(Level.WARNING, "tried to evaluate null");
return;
}
this.problem.evaluate(indy);
// increment the number of evaluations
this.population.incrFunctionCalls();
}
@Override
public void addPopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
@Override
public void initByPopulation(Population pop, boolean reset) {
if (reset) {
init();
} else {
defaultInit();
this.population = pop;
}
}
private Stack<Set<Integer>> buildLinkageTree(){
// the final tree
Stack<Set<Integer>> linkageTree = new Stack<Set<Integer>>();
// the stack to cluster here clusters can be removed
Stack<Set<Integer>> workingStack = new Stack<Set<Integer>>();
// add the problem variables to the stacks
for(int i=0; i<this.probDim; i++){
Set<Integer> s1 = new HashSet<Integer>();
Set<Integer> s2 = new HashSet<Integer>();
s1.add(i);
s2.add(i);
linkageTree.add(s1);
workingStack.add(s2);
}
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
private void defaultInit() {
if (population == null) {
this.population = new Population(this.popSize);
} else {
this.population.setTargetPopSize(this.popSize);
}
this.template = this.problem.getIndividualTemplate();
if (!(template instanceof InterfaceDataTypeBinary)) {
LOGGER.log(Level.WARNING, "Requiring binary data!");
} else {
Object dim = BeanInspector.callIfAvailable(problem,
"getProblemDimension", null);
if (dim == null) {
LOGGER.log(Level.WARNING, "Couldn't get problem dimension!");
}
probDim = (Integer) dim;
((InterfaceDataTypeBinary) this.template).SetBinaryGenotype(new BitSet(probDim));
}
this.population.addPopulationChangedEventListener(this);
this.population.setNotifyEvalInterval(this.generationCycle);
}
private static BitSet getBinaryData(AbstractEAIndividual indy) {
if (indy instanceof InterfaceGAIndividual) {
return ((InterfaceGAIndividual) indy).getBGenotype();
} else if (indy instanceof InterfaceDataTypeBinary) {
return ((InterfaceDataTypeBinary) indy).getBinaryData();
} else {
throw new RuntimeException(
"Unable to get binary representation for "
+ indy.getClass());
}
}
@Override
public void init() {
defaultInit();
this.problem.initPopulation(this.population);
this.evaluatePopulation(this.population);
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
private void evaluatePopulation(Population pop) {
for (int i = 0; i < pop.size(); i++) {
evaluate(pop.getEAIndividual(i));
}
}
/**
* evaluate the given Individual and increments the counter. if the
* individual is null, only the counter is incremented
*
* @param indy the individual you want to evaluate
*/
private void evaluate(AbstractEAIndividual indy) {
// evaluate the given individual if it is not null
if (indy == null) {
LOGGER.log(Level.WARNING, "tried to evaluate null");
return;
}
this.problem.evaluate(indy);
// increment the number of evaluations
this.population.incrFunctionCalls();
}
@Override
public void initByPopulation(Population pop, boolean reset) {
if (reset) {
init();
} else {
defaultInit();
this.population = pop;
}
}
private Stack<Set<Integer>> buildLinkageTree() {
// the final tree
Stack<Set<Integer>> linkageTree = new Stack<Set<Integer>>();
// the stack to cluster here clusters can be removed
Stack<Set<Integer>> workingStack = new Stack<Set<Integer>>();
// add the problem variables to the stacks
for (int i = 0; i < this.probDim; i++) {
Set<Integer> s1 = new HashSet<Integer>();
Set<Integer> s2 = new HashSet<Integer>();
s1.add(i);
s2.add(i);
linkageTree.add(s1);
workingStack.add(s2);
}
// double[] probMass = calculateProbabilityMassFunction();
// until there is only one cluster left
while(workingStack.size()>1){
Pair<Set<Integer>, Set<Integer>> toCluster = findNearestClusters(workingStack);
// remove the second cluster from the working set
workingStack.remove(toCluster.tail);
// add all elements from the second cluster to the first one
toCluster.head.addAll(toCluster.tail);
// add the combined cluster to the linkage tree
linkageTree.add(toCluster.head);
}
return linkageTree;
}
private Pair<Set<Integer>, Set<Integer>> findNearestClusters(Stack<Set<Integer>> stack) {
Set<Integer> bestI = new HashSet<Integer>();
Set<Integer> bestJ = new HashSet<Integer>();
double bestScore = Double.MAX_VALUE;
for(int i=0; i<stack.size(); i++){
Set<Integer> s1 = stack.get(i);
for(int j=i+1; j<stack.size(); j++){
Set<Integer> s2 = stack.get(j);
double currDist = calculateDistance(s1, s2);
// better cluster found
if(currDist < bestScore){
bestI = s1;
bestJ = s2;
bestScore = currDist;
}
}
}
// return the best pair
return new Pair<Set<Integer>, Set<Integer>>(bestI, bestJ);
}
// until there is only one cluster left
while (workingStack.size() > 1) {
Pair<Set<Integer>, Set<Integer>> toCluster = findNearestClusters(workingStack);
// remove the second cluster from the working set
workingStack.remove(toCluster.tail);
// add all elements from the second cluster to the first one
toCluster.head.addAll(toCluster.tail);
// add the combined cluster to the linkage tree
linkageTree.add(toCluster.head);
}
return linkageTree;
}
private double calculateDistance(Set<Integer> s1, Set<Integer> s2) {
double entropy1 = calculateEntropy(s1);
double entropy2 = calculateEntropy(s2);
Set<Integer> combined = new HashSet<Integer>();
combined.addAll(s1);
combined.addAll(s2);
double entropy3 = calculateEntropy(combined);
return 2 - ((entropy1 + entropy2) / (entropy3));
}
private Pair<Set<Integer>, Set<Integer>> findNearestClusters(Stack<Set<Integer>> stack) {
Set<Integer> bestI = new HashSet<Integer>();
Set<Integer> bestJ = new HashSet<Integer>();
double bestScore = Double.MAX_VALUE;
for (int i = 0; i < stack.size(); i++) {
Set<Integer> s1 = stack.get(i);
for (int j = i + 1; j < stack.size(); j++) {
Set<Integer> s2 = stack.get(j);
double currDist = calculateDistance(s1, s2);
// better cluster found
if (currDist < bestScore) {
bestI = s1;
bestJ = s2;
bestScore = currDist;
}
}
}
// return the best pair
return new Pair<Set<Integer>, Set<Integer>>(bestI, bestJ);
}
private double calculateEntropy(Set<Integer> s){
double entropy = 0.0;
// for possible states {0,1} do
for(int i=0; i<=1; i++){
int count = 0;
// for every individual
for(int k=0; k<this.popSize; k++){
BitSet b = getBinaryData(this.population.getEAIndividual(k));
boolean addCount = true;
// for every specified Bit
for(Integer value: s){
// is the bit not set correctly
if(b.get(value) != (i==1)){
addCount = false;
break;
}
}
if(addCount){
count++;
}
addCount = true;
}
entropy += ((double)count) * SpecialFunction.logb((double)count, 2.0);
count = 0;
}
return entropy;
}
private double calculateDistance(Set<Integer> s1, Set<Integer> s2) {
double entropy1 = calculateEntropy(s1);
double entropy2 = calculateEntropy(s2);
Set<Integer> combined = new HashSet<Integer>();
combined.addAll(s1);
combined.addAll(s2);
double entropy3 = calculateEntropy(combined);
return 2 - ((entropy1 + entropy2) / (entropy3));
}
@Override
public void optimize() {
this.problem.evaluatePopulationStart(this.population);
Stack<Set<Integer>> linkageTree = buildLinkageTree();
Population newPop = new Population(this.popSize);
for(int i=0; i<(this.popSize/2); i++){
Population indies = this.population.getRandNIndividuals(2);
Population newIndies = buildNewIndies(indies, linkageTree);
newPop.addAll(newIndies);
}
this.population.clear();
this.population.addAll(newPop);
this.problem.evaluatePopulationEnd(this.population);
}
private Population buildNewIndies(Population indies,
Stack<Set<Integer>> linkageTree) {
if(indies.size() != 2){
return indies;
}
AbstractEAIndividual indy1 = indies.getEAIndividual(0);
AbstractEAIndividual indy2 = indies.getEAIndividual(1);
BitSet gen1 = getBinaryData(indy1);
BitSet gen2 = getBinaryData(indy2);
for(Set<Integer> mask: linkageTree){
BitSet newGene1 = (BitSet) gen1.clone();
BitSet newGene2 = (BitSet) gen2.clone();
boolean same = true;
for(Integer exchange: mask){
if(newGene1.get(exchange) != newGene2.get(exchange)){
same = false;
}
newGene1.set(exchange, gen2.get(exchange));
newGene2.set(exchange, gen1.get(exchange));
}
if(!same){
AbstractEAIndividual newIndy1 = (AbstractEAIndividual) this.template.clone();
AbstractEAIndividual newIndy2 = (AbstractEAIndividual) this.template.clone();
((InterfaceDataTypeBinary) newIndy1).SetBinaryGenotype(newGene1);
((InterfaceDataTypeBinary) newIndy2).SetBinaryGenotype(newGene2);
evaluate(newIndy1);
evaluate(newIndy2);
if(Math.min(newIndy1.getFitness(0), newIndy2.getFitness(0)) < Math.min(indy1.getFitness(0), indy2.getFitness(0))){
indy1 = newIndy1;
indy2 = newIndy2;
}
}
}
Population result = new Population(2);
result.add(indy1);
result.add(indy2);
return result;
}
private double calculateEntropy(Set<Integer> s) {
double entropy = 0.0;
// for possible states {0,1} do
for (int i = 0; i <= 1; i++) {
int count = 0;
// for every individual
for (int k = 0; k < this.popSize; k++) {
BitSet b = getBinaryData(this.population.getEAIndividual(k));
boolean addCount = true;
// for every specified Bit
for (Integer value : s) {
// is the bit not set correctly
if (b.get(value) != (i == 1)) {
addCount = false;
break;
}
}
if (addCount) {
count++;
}
addCount = true;
}
entropy += ((double) count) * SpecialFunction.logb((double) count, 2.0);
count = 0;
}
return entropy;
}
/**
* Something has changed
*/
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null)
this.m_Listener.registerPopulationStateChanged(this, name);
}
@Override
public void optimize() {
this.problem.evaluatePopulationStart(this.population);
Stack<Set<Integer>> linkageTree = buildLinkageTree();
Population newPop = new Population(this.popSize);
for (int i = 0; i < (this.popSize / 2); i++) {
Population indies = this.population.getRandNIndividuals(2);
Population newIndies = buildNewIndies(indies, linkageTree);
newPop.addAll(newIndies);
}
this.population.clear();
this.population.addAll(newPop);
this.problem.evaluatePopulationEnd(this.population);
}
@Override
public Population getPopulation() {
return this.population;
}
private Population buildNewIndies(Population indies,
Stack<Set<Integer>> linkageTree) {
if (indies.size() != 2) {
return indies;
}
AbstractEAIndividual indy1 = indies.getEAIndividual(0);
AbstractEAIndividual indy2 = indies.getEAIndividual(1);
BitSet gen1 = getBinaryData(indy1);
BitSet gen2 = getBinaryData(indy2);
for (Set<Integer> mask : linkageTree) {
BitSet newGene1 = (BitSet) gen1.clone();
BitSet newGene2 = (BitSet) gen2.clone();
boolean same = true;
for (Integer exchange : mask) {
if (newGene1.get(exchange) != newGene2.get(exchange)) {
same = false;
}
newGene1.set(exchange, gen2.get(exchange));
newGene2.set(exchange, gen1.get(exchange));
}
if (!same) {
AbstractEAIndividual newIndy1 = (AbstractEAIndividual) this.template.clone();
AbstractEAIndividual newIndy2 = (AbstractEAIndividual) this.template.clone();
((InterfaceDataTypeBinary) newIndy1).SetBinaryGenotype(newGene1);
((InterfaceDataTypeBinary) newIndy2).SetBinaryGenotype(newGene2);
evaluate(newIndy1);
evaluate(newIndy2);
if (Math.min(newIndy1.getFitness(0), newIndy2.getFitness(0)) < Math.min(indy1.getFitness(0), indy2.getFitness(0))) {
indy1 = newIndy1;
indy2 = newIndy2;
}
}
}
Population result = new Population(2);
result.add(indy1);
result.add(indy2);
return result;
}
@Override
public void setPopulation(Population pop) {
this.population = pop;
}
/**
* Something has changed
*/
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
@Override
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(this.population);
}
@Override
public Population getPopulation() {
return this.population;
}
@Override
public void SetIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public void setPopulation(Population pop) {
this.population = pop;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
@Override
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(this.population);
}
@Override
public void SetProblem(InterfaceOptimizationProblem problem) {
this.problem = (AbstractOptimizationProblem) problem;
}
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.problem;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
@Override
public String getStringRepresentation() {
return "Linkage Tree GA";
}
@Override
public void SetProblem(InterfaceOptimizationProblem problem) {
this.problem = (AbstractOptimizationProblem) problem;
}
@Override
public void freeWilly() {
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.problem;
}
@SuppressWarnings("deprecation")
@Override
public void registerPopulationStateChanged(Object source, String name) {
// The events of the interim hill climbing population will be caught here
if (name.compareTo(Population.funCallIntervalReached) == 0) {
// set funcalls to real value
this.population.SetFunctionCalls(((Population)source).getFunctionCalls());
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
}
public static void main(String[] args) {
LTGA ltga = new LTGA();
ltga.init();
ltga.optimize();
System.out.println(ltga.popSize);
Population p = ltga.getPopulation();
System.out.println(p.getFunctionCalls()+"\t"+p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
ltga.optimize();
p = ltga.getPopulation();
System.out.println(p.getFunctionCalls()+"\t"+p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
ltga.optimize();
p = ltga.getPopulation();
System.out.println(p.getFunctionCalls()+"\t"+p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
ltga.optimize();
p = ltga.getPopulation();
System.out.println(p.getFunctionCalls()+"\t"+p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
ltga.optimize();
p = ltga.getPopulation();
System.out.println(p.getFunctionCalls()+"\t"+p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
ltga.optimize();
p = ltga.getPopulation();
System.out.println(p.getFunctionCalls()+"\t"+p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
ltga.optimize();
p = ltga.getPopulation();
System.out.println(p.getFunctionCalls()+"\t"+p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
}
@Override
public String getStringRepresentation() {
return "Linkage Tree GA";
}
@Override
public void freeWilly() {
}
@Override
public void registerPopulationStateChanged(Object source, String name) {
// The events of the interim hill climbing population will be caught here
if (name.compareTo(Population.funCallIntervalReached) == 0) {
// set funcalls to real value
this.population.setFunctionCalls(((Population) source).getFunctionCalls());
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
}
public static void main(String[] args) {
LTGA ltga = new LTGA();
ltga.init();
ltga.optimize();
System.out.println(ltga.popSize);
Population p = ltga.getPopulation();
System.out.println(p.getFunctionCalls() + "\t" + p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
ltga.optimize();
p = ltga.getPopulation();
System.out.println(p.getFunctionCalls() + "\t" + p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
ltga.optimize();
p = ltga.getPopulation();
System.out.println(p.getFunctionCalls() + "\t" + p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
ltga.optimize();
p = ltga.getPopulation();
System.out.println(p.getFunctionCalls() + "\t" + p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
ltga.optimize();
p = ltga.getPopulation();
System.out.println(p.getFunctionCalls() + "\t" + p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
ltga.optimize();
p = ltga.getPopulation();
System.out.println(p.getFunctionCalls() + "\t" + p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
ltga.optimize();
p = ltga.getPopulation();
System.out.println(p.getFunctionCalls() + "\t" + p.size());
System.out.println(p.getBestEAIndividual().getStringRepresentation());
}
}

2
src/eva2/server/go/strategies/MemeticAlgorithm.java
View File

@ -255,7 +255,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
* @param name
* The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}

2
src/eva2/server/go/strategies/MonteCarloSearch.java
View File

@ -182,7 +182,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

4
src/eva2/server/go/strategies/MultiObjectiveCMAES.java
View File

@ -74,10 +74,10 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable {
* (non-Javadoc)
*
* @see
* eva2.server.go.strategies.InterfaceOptimizer#SetIdentifier(java.lang.
* eva2.server.go.strategies.InterfaceOptimizer#setIdentifier(java.lang.
* String)
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
m_Identifier = name;
}

2
src/eva2/server/go/strategies/MultiObjectiveEA.java
View File

@ -201,7 +201,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/NelderMeadSimplex.java
View File

@ -58,7 +58,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
return new NelderMeadSimplex(this);
}
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
m_Identifier = name;
}

2
src/eva2/server/go/strategies/NichePSO.java
View File

@ -1234,7 +1234,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac
* This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}

2
src/eva2/server/go/strategies/ParticleFilterOptimization.java
View File

@ -282,7 +282,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/ParticleSwarmOptimization.java
View File

@ -1712,7 +1712,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
*
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}

2
src/eva2/server/go/strategies/PopulationBasedIncrementalLearning.java
View File

@ -180,7 +180,7 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/ScatterSearch.java
View File

@ -664,7 +664,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
///////////// Trivials...
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
m_Identifier = name;
}

2
src/eva2/server/go/strategies/SimulatedAnnealing.java
View File

@ -201,7 +201,7 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/SteadyStateGA.java
View File

@ -152,7 +152,7 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/ThresholdAlgorithm.java
View File

@ -192,7 +192,7 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/Tribes.java
View File

@ -715,7 +715,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
public void freeWilly() {}
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {

2
src/eva2/server/go/strategies/WingedMultiObjectiveEA.java
View File

@ -246,7 +246,7 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
*/
public void SetIdentifier(String name) {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {