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refs #24 and #25

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Last RMI occurences have been removed. Free Willy is officially dead. Hooray!

Also, code formatting :)
This commit is contained in:
Fabian Becker
2013-01-31 13:42:59 +00:00
parent b0ab7aba0c
commit d474eebfa2
35 changed files with 10532 additions and 10157 deletions
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22
src/eva2/client/EvAClient.java
View File

@@ -772,15 +772,15 @@ public class EvAClient extends JFrame implements OptimizationStateListener {
menuBar = new JMenuBar();
setJMenuBar(menuBar);
menuModule = new JExtMenu("&Module");
menuModule.add(actModuleLoad);
//menuModule.add(actModuleLoad);
menuSelHosts = new JExtMenu("&Select Hosts");
menuSelHosts.setToolTipText("Select a host for the server application");
menuSelHosts.add(actHost);
menuSelHosts.add(actAvailableHost);
menuSelHosts.addSeparator();
menuSelHosts.add(actKillHost);
menuSelHosts.add(actKillAllHosts);
//menuSelHosts.setToolTipText("Select a host for the server application");
//menuSelHosts.add(actHost);
//menuSelHosts.add(actAvailableHost);
//menuSelHosts.addSeparator();
//menuSelHosts.add(actKillHost);
//menuSelHosts.add(actKillAllHosts);
menuHelp = new JExtMenu("&Help");
menuHelp.add(actHelp);
@@ -790,13 +790,13 @@ public class EvAClient extends JFrame implements OptimizationStateListener {
menuOptions = new JExtMenu("&Options");
menuOptions.add(actPreferences);
menuOptions.add(menuSelHosts);
//menuOptions.add(menuSelHosts);
menuOptions.addSeparator();
menuOptions.add(actQuit);
// this is accessible if no default module is given
if (showLoadModules) {
menuBar.add(menuModule);
}
//if (showLoadModules) {
// menuBar.add(menuModule);
//}
menuBar.add(menuOptions);
menuBar.add(((JExtDesktopPane) desktopPane).getWindowMenu());

4
src/eva2/server/go/InterfaceGOStandalone.java
View File

@@ -8,6 +8,6 @@ package eva2.server.go;
* To change this template use Options | File Templates.
*/
public interface InterfaceGOStandalone {
public void startExperiment();
public void setShow(boolean t);
void startExperiment();
void setShow(boolean t);
}

2
src/eva2/server/go/InterfacePopulationChangedEventListener.java
View File

@@ -14,5 +14,5 @@ public interface InterfacePopulationChangedEventListener {
* @param source The source of the event.
* @param name Could be used to indicate the nature of the event.
*/
public void registerPopulationStateChanged(Object source, String name);
void registerPopulationStateChanged(Object source, String name);
}

33
src/eva2/server/go/strategies/BOA.java
View File

@@ -47,7 +47,6 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
private static final Logger LOGGER = Logger.getLogger(BOA.class.getName());
transient private InterfacePopulationChangedEventListener m_Listener = null;
private String m_Identifier = "BOA";
private int probDim = 8;
private int fitCrit = -1;
private int PopSize = 50;
@@ -63,7 +62,6 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
private int count = 0;
private String netFolder = "BOAOutput";
private int[][] edgeRate = null;
private BOAScoringMethods scoringMethod = BOAScoringMethods.BDM;
private boolean printNetworks = false;
private boolean printEdgeRate = false;
@@ -72,7 +70,6 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
// private boolean printExtraOutput = false;
public BOA() {
}
public BOA(int numberOfParents, int popSize, BOAScoringMethods method,
@@ -166,7 +163,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
File theDir = new File(directoryName);
// if the directory does not exist, create it
if (!theDir.exists()) {
LOGGER.log(Level.INFO, "creating directory: "+directoryName);
LOGGER.log(Level.INFO, "creating directory: " + directoryName);
theDir.mkdir();
}
}
@@ -185,11 +182,9 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
private static BitSet getBinaryData(AbstractEAIndividual indy) {
if (indy instanceof InterfaceGAIndividual) {
return ((InterfaceGAIndividual) indy).getBGenotype();
}
else if (indy instanceof InterfaceDataTypeBinary) {
} else if (indy instanceof InterfaceDataTypeBinary) {
return ((InterfaceDataTypeBinary) indy).getBinaryData();
}
else {
} else {
throw new RuntimeException(
"Unable to get binary representation for "
+ indy.getClass());
@@ -200,8 +195,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
* 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
* @param indy the individual you want to evaluate
*/
private void evaluate(AbstractEAIndividual indy) {
// evaluate the given individual if it is not null
@@ -343,8 +337,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
* Generate a Bayesian network with the individuals of the population as a
* reference Point
*
* @param pop
* the individuals the network is based on
* @param pop the individuals the network is based on
*/
private void constructNetwork(Population pop) {
generateGreedy(pop);
@@ -394,6 +387,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
/**
* remove the individuals in pop from the population
*
* @param pop
*/
public void remove(Population pop) {
@@ -546,7 +540,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
// generate the network with these individuals
constructNetwork(best);
// if(this.printExtraOutput && this.printEdgeRate){
if(this.printEdgeRate){
if (this.printEdgeRate) {
this.edgeRate = this.network.adaptEdgeRate(this.edgeRate);
}
// sample new individuals from the network
@@ -627,15 +621,9 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
return "Bayesian Network";
}
@Override
public void freeWilly() {
}
// -------------------------------
// -------------GUI---------------
// -------------------------------
public int getNumberOfParents() {
return this.numberOfParents;
}
@@ -725,13 +713,12 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
}
public String[] customPropertyOrder() {
return new String[] { "learningRatio", "resamplingRatio" };
return new String[]{"learningRatio", "resamplingRatio"};
}
// public boolean isPrintExtraOutput() {
// return this.printExtraOutput;
// }
// public void setPrintExtraOutput(boolean b) {
// this.printExtraOutput = b;
// GenericObjectEditor.setHideProperty(getClass(), "printNetworks",
@@ -743,11 +730,9 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
// GenericObjectEditor.setHideProperty(getClass(), "printTimestamps",
// !printExtraOutput);
// }
// public String printExtraOutputTipText() {
// return "do you want to print extra output files";
// }
public boolean isPrintNetworks() {
return this.printNetworks;
}
@@ -796,7 +781,6 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
return "Print the time starting time and a timestamp after each generation";
}
public static void main(String[] args) {
Population pop = new Population();
GAIndividualBinaryData indy1 = new GAIndividualBinaryData();
@@ -980,5 +964,4 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
// b.print();
// b.printNetworkToFile("test");
}
}

428
src/eva2/server/go/strategies/BinaryScatterSearch.java
View File

@@ -30,15 +30,15 @@ import java.util.BitSet;
*
* @author Alex
*
* F. Gortazar, A. Duarte, M. Laguna and R. Marti: Black Box Scatter Search for General Classes of Binary Optimization Problems
* Computers and Operations research, vol. 37, no. 11, pp. 1977-1986 (2010)
* F. Gortazar, A. Duarte, M. Laguna and R. Marti: Black Box Scatter Search for
* General Classes of Binary Optimization Problems Computers and Operations
* research, vol. 37, no. 11, pp. 1977-1986 (2010)
*/
public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener {
private static boolean TRACE = false;
transient private InterfacePopulationChangedEventListener m_Listener = null;
private String m_Identifier = "BinaryScatterSearch";
private int MaxImpIter = 5;
private int poolSize = 100;
private int refSetSize = 10;
@@ -47,8 +47,8 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
private int generationCycle = 500;
private double th1 = 0.5;
private double th2 = 0.5;
private double g1 = 1.0/3.0;
private double g2 = 1.0/3.0;
private double g1 = 1.0 / 3.0;
private double g2 = 1.0 / 3.0;
private boolean firstTime = true;
private AbstractEAIndividual template = null;
private AbstractOptimizationProblem problem = new B1Problem();
@@ -59,14 +59,16 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/**
* Create a new BinaryScatterSearch with default values
*/
public BinaryScatterSearch(){
public BinaryScatterSearch() {
}
/**
* Create a new BinaryScatterSearch with the same parameters as the given BinaryScatterSearch
* Create a new BinaryScatterSearch with the same parameters as the given
* BinaryScatterSearch
*
* @param b
*/
public BinaryScatterSearch(BinaryScatterSearch b){
public BinaryScatterSearch(BinaryScatterSearch b) {
this.m_Listener = b.m_Listener;
this.m_Identifier = b.m_Identifier;
this.MaxImpIter = b.MaxImpIter;
@@ -74,7 +76,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
this.refSetSize = b.refSetSize;
this.fitCrit = b.fitCrit;
this.probDim = b.probDim;
this.generationCycle= b.generationCycle;
this.generationCycle = b.generationCycle;
this.th1 = b.th1;
this.th2 = b.th2;
this.g1 = b.g1;
@@ -89,19 +91,22 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/**
* Create a new BinaryScatterSearch with the given Parameters
*
* @param refSetS the refSetSize
* @param poolS the poolSize
* @param lowerThreshold the lower Boundary for the local Search
* @param upperThreshold the upper Boundary for the local Search
* @param perCentFirstIndGenerator how many individuals (in prospect of the poolSize) are generated through the first Generator
* @param perCentSecondIndGenerator how many individuals (in prospect of the poolSize) are generated through the second Generator
* @param perCentFirstIndGenerator how many individuals (in prospect of the
* poolSize) are generated through the first Generator
* @param perCentSecondIndGenerator how many individuals (in prospect of the
* poolSize) are generated through the second Generator
* @param prob the Problem
*/
public BinaryScatterSearch(
int refSetS, int poolS, double lowerThreshold, double upperThreshold,
double perCentFirstIndGenerator, double perCentSecondIndGenerator, AbstractOptimizationProblem prob) {
this.refSetSize = refSetS;
this.poolSize= poolS;
this.poolSize = poolS;
this.th1 = lowerThreshold;
this.th2 = upperThreshold;
this.g1 = perCentFirstIndGenerator;
@@ -111,12 +116,15 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/**
* Create a new BinaryScatterSearch with the given Parameters
*
* @param refSetS the refSetSize
* @param poolS the poolSize
* @param lowerThreshold the lower Boundary for the local Search
* @param upperThreshold the upper Boundary for the local Search
* @param perCentFirstIndGenerator how many individuals (in prospect of the poolSize) are generated through the first Generator
* @param perCentSecondIndGenerator how many individuals (in prospect of the poolSize) are generated through the second Generator
* @param perCentFirstIndGenerator how many individuals (in prospect of the
* poolSize) are generated through the first Generator
* @param perCentSecondIndGenerator how many individuals (in prospect of the
* poolSize) are generated through the second Generator
* @param prob the Problem
* @param cross the Crossover-Operators
*/
@@ -125,7 +133,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
double perCentFirstIndGenerator, double perCentSecondIndGenerator,
AbstractOptimizationProblem prob, AdaptiveCrossoverEAMixer cross) {
this.refSetSize = refSetS;
this.poolSize= poolS;
this.poolSize = poolS;
this.th1 = lowerThreshold;
this.th2 = upperThreshold;
this.g1 = perCentFirstIndGenerator;
@@ -138,7 +146,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
* @return a copy of the current BinaryScatterSearch
*/
@Override
public Object clone(){
public Object clone() {
return new BinaryScatterSearch(this);
}
@@ -156,8 +164,8 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
@@ -165,12 +173,14 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
}
/**
* evaluate the given Individual and increments the counter. if the individual is null, only the counter is incremented
* 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){
private void evaluate(AbstractEAIndividual indy) {
// evaluate the given individual if it is not null
if(indy == null){
if (indy == null) {
System.err.println("tried to evaluate null");
return;
}
@@ -182,18 +192,18 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/**
* Default initialization
*/
private void defaultInit(){
private void defaultInit() {
this.refSet = new Population();
this.template = this.problem.getIndividualTemplate();
if (!(template instanceof InterfaceDataTypeBinary)){
if (!(template instanceof InterfaceDataTypeBinary)) {
System.err.println("Requiring binary data!");
}else{
} else {
Object dim = BeanInspector.callIfAvailable(problem, "getProblemDimension", null);
if (dim==null) {
if (dim == null) {
System.err.println("Couldnt get problem dimension!");
}
probDim = (Integer)dim;
((InterfaceDataTypeBinary)this.template).SetBinaryGenotype(new BitSet(probDim));
probDim = (Integer) dim;
((InterfaceDataTypeBinary) this.template).SetBinaryGenotype(new BitSet(probDim));
}
this.firstTime = true;
this.cross.init(this.template, problem, refSet, Double.MAX_VALUE);
@@ -219,27 +229,28 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
*
* @return a new diversified Population
*/
private Population diversify(){
private Population diversify() {
return diversify(new Population());
}
/**
*
* @param pop the initial Population
* @return a diversified Population with all the Individuals in the initial Population
* @return a diversified Population with all the Individuals in the initial
* Population
*/
private Population diversify(Population pop){
private Population diversify(Population pop) {
int numToInit = this.poolSize - pop.size();
if (numToInit>0) {
pop.addAll(generateG1((int)(numToInit * this.g1)));
if (numToInit > 0) {
pop.addAll(generateG1((int) (numToInit * this.g1)));
if (TRACE) {
System.out.println("s1: " + pop.size());
}
generateG2(pop, (int)(numToInit * this.g2));
generateG2(pop, (int) (numToInit * this.g2));
if (TRACE) {
System.out.println("s2: " + pop.size());
}
generateG3(pop, poolSize-pop.size());
generateG3(pop, poolSize - pop.size());
if (TRACE) {
System.out.println("s3: " + pop.size());
}
@@ -248,49 +259,52 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
}
/**
* Generate a new Population with diverse Individuals starting with 000...000,
* then 010101...01, 101010...10, 001001001...001, 110110110...110 and so on
* The returned population is evaluated.
* Generate a new Population with diverse Individuals starting with
* 000...000, then 010101...01, 101010...10, 001001001...001,
* 110110110...110 and so on The returned population is evaluated.
*
* @param pop the initial Population
* @return the new Population
*/
private Population generateG1(int numToInit){
private Population generateG1(int numToInit) {
Population pop = generateG1Pop(numToInit, this.template);
for (int i=0; i<pop.size(); i++) {
for (int i = 0; i < pop.size(); i++) {
evaluate(pop.getEAIndividual(i));
}
return pop;
}
/**
* Generate a new Population with diverse Individuals starting with 000...000, then 010101...01,
* 101010...10, 001001001...001, 110110110...110 and so on
* Generate a new Population with diverse Individuals starting with
* 000...000, then 010101...01, 101010...10, 001001001...001,
* 110110110...110 and so on
*
* @param pop the initial Population
* @return the new Population
*/
public static Population generateG1Pop(int targetSize, AbstractEAIndividual template){
public static Population generateG1Pop(int targetSize, AbstractEAIndividual template) {
boolean method1 = true;
int i=1;
int i = 1;
Population pop = new Population(targetSize);
while(pop.size() < targetSize) {
while (pop.size() < targetSize) {
AbstractEAIndividual indy = (AbstractEAIndividual) template.clone();
BitSet data = getBinaryData(indy);
if(method1){
if (method1) {
method1 = !method1;
data.set(0, data.size(), true);
for(int j=0; j<data.size(); j += i){
for (int j = 0; j < data.size(); j += i) {
data.flip(j);
}
((InterfaceDataTypeBinary) indy).SetBinaryGenotype(data);
if(i==1){
if (i == 1) {
i++;
method1 = !method1;
}
}else{
} else {
method1 = !method1;
if(i!=1){
if (i != 1) {
data.set(0, data.size(), false);
for(int j=0; j<data.size(); j += i){
for (int j = 0; j < data.size(); j += i) {
data.flip(j);
}
((InterfaceDataTypeBinary) indy).SetBinaryGenotype(data);
@@ -303,24 +317,26 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
}
/**
* Generate new Individuals that have the individuals of the given Population as a base
* Generate new Individuals that have the individuals of the given
* Population as a base
*
* @param pop the population
* @return the new Population
*/
private Population generateG2(Population pop, int numToInit){
private Population generateG2(Population pop, int numToInit) {
int origSize = pop.size();
while(pop.size() < origSize+numToInit){
AbstractEAIndividual indy = (AbstractEAIndividual)template.clone();
while (pop.size() < origSize + numToInit) {
AbstractEAIndividual indy = (AbstractEAIndividual) template.clone();
InterfaceDataTypeBinary dblIndy = (InterfaceDataTypeBinary) indy;
BitSet data = dblIndy.getBinaryData();
data.set(0, data.size(), false);
for(int i=0; i<data.size(); i++){
if(RNG.flipCoin(Math.min(0.1+score(i, pop),1))){
for (int i = 0; i < data.size(); i++) {
if (RNG.flipCoin(Math.min(0.1 + score(i, pop), 1))) {
data.set(i, true);
}
}
dblIndy.SetBinaryGenotype(data);
if(!contains(dblIndy, pop)){
if (!contains(dblIndy, pop)) {
pop.add(dblIndy);
evaluate(indy);
}
@@ -329,24 +345,26 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
}
/**
* Generate new Individuals that have the individuals of the given Population as a base
* Generate new Individuals that have the individuals of the given
* Population as a base
*
* @param pop the population
* @return the new Population
*/
private Population generateG3(Population pop, int numToInit){
private Population generateG3(Population pop, int numToInit) {
int origSize = pop.size();
while(pop.size() < origSize+numToInit){
AbstractEAIndividual indy = (AbstractEAIndividual)template.clone();
InterfaceDataTypeBinary dblIndy = (InterfaceDataTypeBinary)indy;
while (pop.size() < origSize + numToInit) {
AbstractEAIndividual indy = (AbstractEAIndividual) template.clone();
InterfaceDataTypeBinary dblIndy = (InterfaceDataTypeBinary) indy;
BitSet data = dblIndy.getBinaryData();
data.set(0, data.size(), true);
for(int i=0; i<data.size(); i++){
if(RNG.flipCoin(Math.max(0, 1-score(i, pop)))){//???
for (int i = 0; i < data.size(); i++) {
if (RNG.flipCoin(Math.max(0, 1 - score(i, pop)))) {//???
data.set(i, false);
}
}
dblIndy.SetBinaryGenotype(data);
if(!contains(dblIndy, pop)){
if (!contains(dblIndy, pop)) {
pop.add(dblIndy);
evaluate(indy);
}
@@ -355,17 +373,19 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
}
/**
* calculate the number of individuals in the given Population that have a 1 at the i-th position
* calculate the number of individuals in the given Population that have a 1
* at the i-th position
*
* @param i the position
* @param pop the population
* @return The number of individuals that have a '1' on the i-th position
*/
private static double calculateNumberOFPI1(int i, Population pop){
private static double calculateNumberOFPI1(int i, Population pop) {
int result = 0;
for(int j=0; j<pop.size(); j++){
for (int j = 0; j < pop.size(); j++) {
AbstractEAIndividual indy = pop.getEAIndividual(j);
BitSet binData = getBinaryData(indy);
if(binData.get(i)){
if (binData.get(i)) {
result++;
}
}
@@ -373,17 +393,19 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
}
/**
* calculate the number of individuals in the given Population that have a 0 at the i-th position
* calculate the number of individuals in the given Population that have a 0
* at the i-th position
*
* @param i the position
* @param pop the population
* @return The number of individuals that have a '0' on the i-th position
*/
private static double calculateNumberOFPI0(int i, Population pop){
private static double calculateNumberOFPI0(int i, Population pop) {
int result = 0;
for(int j=0; j<pop.size(); j++){
for (int j = 0; j < pop.size(); j++) {
AbstractEAIndividual indy = pop.getEAIndividual(j);
BitSet binData = getBinaryData(indy);
if(!binData.get(i)){
if (!binData.get(i)) {
result++;
}
}
@@ -392,28 +414,28 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
private static BitSet getBinaryData(AbstractEAIndividual indy) {
if (indy instanceof InterfaceGAIndividual) {
return ((InterfaceGAIndividual)indy).getBGenotype();
}
else if (indy instanceof InterfaceDataTypeBinary) {
return ((InterfaceDataTypeBinary)indy).getBinaryData();
}
else {
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());
}
}
/**
* calculate the sum of all the FitnessValues of the individuals that have a '0' at the i-th position
* calculate the sum of all the FitnessValues of the individuals that have a
* '0' at the i-th position
*
* @param i the position
* @param pop the population
* @return the sum
*/
private static double calculateSumPI0(int i, Population pop){
private static double calculateSumPI0(int i, Population pop) {
double result = 0;
for(int j=0; j<pop.size(); j++){
for (int j = 0; j < pop.size(); j++) {
AbstractEAIndividual indy = pop.getEAIndividual(j);
BitSet binData = getBinaryData(indy);
if(binData.get(i)){
if (binData.get(i)) {
result += indy.getFitness(0);
}
}
@@ -421,17 +443,19 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
}
/**
* calculate the sum of all the FitnessValues of the individuals that have a '0' at the i-th position
* calculate the sum of all the FitnessValues of the individuals that have a
* '0' at the i-th position
*
* @param i the position
* @param pop the population
* @return the sum
*/
private static double calculateSumPI1(int i, Population pop){
private static double calculateSumPI1(int i, Population pop) {
double result = 0;
for(int j=0; j<pop.size(); j++){
for (int j = 0; j < pop.size(); j++) {
AbstractEAIndividual indy = pop.getEAIndividual(j);
BitSet binData = getBinaryData(indy);
if(binData.get(i)){
if (binData.get(i)) {
result += indy.getFitness(0);
}
}
@@ -439,37 +463,39 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
}
/**
* calculates a score that gives a reference Point if the Bit on the i-th position is right.
* If the bit is set to '1' and you get a high score then the Bit is probably set correct.
* If the bit is set to '0' and you get a low score then the Bit is probably set correct.
* calculates a score that gives a reference Point if the Bit on the i-th
* position is right. If the bit is set to '1' and you get a high score then
* the Bit is probably set correct. If the bit is set to '0' and you get a
* low score then the Bit is probably set correct.
*
* @param i the position
* @param pop the population
* @return the score
*/
public static double score(int i, Population pop){
public static double score(int i, Population pop) {
double sumPI1 = calculateSumPI1(i, pop);
double sumPI0 = calculateSumPI0(i, pop);
double numberOfPI1 = calculateNumberOFPI1(i, pop);
double numberOfPI0 = calculateNumberOFPI0(i, pop);
double v= (sumPI1/numberOfPI1)/((sumPI1/numberOfPI1)+(sumPI0/numberOfPI0));
double v = (sumPI1 / numberOfPI1) / ((sumPI1 / numberOfPI1) + (sumPI0 / numberOfPI0));
return v;
}
/**
* calculate the first RefSet with the given Population as a reference Point
*
* @param pop the generated Pool
*/
private void initRefSet(Population pop){
private void initRefSet(Population pop) {
this.problem.evaluatePopulationStart(this.refSet);
this.pool = pop;
refSetUpdate(true);
Population best = this.refSet.getBestNIndividuals(this.refSetSize/2, fitCrit);
for(int i=0; i<best.size(); i++){
Population best = this.refSet.getBestNIndividuals(this.refSetSize / 2, fitCrit);
for (int i = 0; i < best.size(); i++) {
//improve(best.getEAIndividual(i));
AbstractEAIndividual indy = best.getEAIndividual(i);
AbstractEAIndividual x = improve((AbstractEAIndividual)indy.clone());
if(x.getFitness(0)<indy.getFitness(0) && !contains((InterfaceDataTypeBinary)x, this.refSet)){
AbstractEAIndividual x = improve((AbstractEAIndividual) indy.clone());
if (x.getFitness(0) < indy.getFitness(0) && !contains((InterfaceDataTypeBinary) x, this.refSet)) {
this.refSet.remove(indy);
this.refSet.add(x);
}
@@ -479,45 +505,46 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/**
* Update the reference Set
*
* @param replaceWorstHalf replaces the worst half of the RefSet if set
* @return has the Population changed
*/
private boolean refSetUpdate(boolean replaceWorstHalf){
private boolean refSetUpdate(boolean replaceWorstHalf) {
boolean refSetChanged = false;
Population rest = (Population) this.pool.clone();
if(this.firstTime){
if (this.firstTime) {
Population tmp = this.pool.getBestNIndividuals(this.pool.size(), this.fitCrit);
int i=0;
while(this.refSet.size() < this.refSetSize){
int i = 0;
while (this.refSet.size() < this.refSetSize) {
this.refSet.add(tmp.get(i));
i++;
}
rest = this.pool.getWorstNIndividuals(this.poolSize-i, this.fitCrit);
rest = this.pool.getWorstNIndividuals(this.poolSize - i, this.fitCrit);
refSetChanged = true;
}
if(replaceWorstHalf){
if (replaceWorstHalf) {
refSetChanged = true;
// delete the worst half of refSet
this.refSet.removeMembers(this.refSet.getWorstNIndividuals(this.refSet.size()-this.refSetSize/2, this.fitCrit), false);
while(this.refSet.size()<this.refSetSize){
ArrayList<Pair<Integer,Double>> list = new ArrayList<Pair<Integer,Double>>();
for(int i=0; i< this.refSet.size(); i++){
this.refSet.removeMembers(this.refSet.getWorstNIndividuals(this.refSet.size() - this.refSetSize / 2, this.fitCrit), false);
while (this.refSet.size() < this.refSetSize) {
ArrayList<Pair<Integer, Double>> list = new ArrayList<Pair<Integer, Double>>();
for (int i = 0; i < this.refSet.size(); i++) {
AbstractEAIndividual indy = this.refSet.getEAIndividual(i);
list.add(Population.getClosestFarthestIndy(indy, rest, new GenotypeMetricBitSet(), false));
}
Pair<Integer,Double> pair = list.get(0);
for(Pair<Integer,Double> p: list){
if(p.getTail()<pair.getTail()){
pair=p;
Pair<Integer, Double> pair = list.get(0);
for (Pair<Integer, Double> p : list) {
if (p.getTail() < pair.getTail()) {
pair = p;
}
}
this.refSet.add(rest.getEAIndividual(pair.getHead()));
rest.remove(pair.getHead());
}
}else{
} else {
Population best = this.pool.getBestNIndividuals(this.refSetSize, this.fitCrit);
while(best.size()>0 && best.getBestEAIndividual().getFitness(0)<this.refSet.getWorstEAIndividual().getFitness(0)){
if(!contains((InterfaceDataTypeBinary)best.getBestIndividual(), this.refSet)){
while (best.size() > 0 && best.getBestEAIndividual().getFitness(0) < this.refSet.getWorstEAIndividual().getFitness(0)) {
if (!contains((InterfaceDataTypeBinary) best.getBestIndividual(), this.refSet)) {
refSetChanged = true;
this.refSet.remove(this.refSet.getWorstEAIndividual());
this.refSet.add(best.getBestIndividual());
@@ -531,23 +558,24 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/**
* Order the given List according to the score of the given values
*
* @param list the initial List
* @return the ordered List
*/
private ArrayList<Integer> order(ArrayList<Integer> list){
private ArrayList<Integer> order(ArrayList<Integer> list) {
ArrayList<Integer> result = new ArrayList<Integer>();
for(Integer s: list){
for (Integer s : list) {
boolean done = false;
if(result.isEmpty()){
if (result.isEmpty()) {
result.add(s);
}else{
for(int i=0; i<result.size(); i++){
if(score(s, this.refSet)>score(result.get(i), this.refSet)&&!done){
result.add(i,s);
} else {
for (int i = 0; i < result.size(); i++) {
if (score(s, this.refSet) > score(result.get(i), this.refSet) && !done) {
result.add(i, s);
done = true;
}
}
if(!done){
if (!done) {
result.add(s);
}
}
@@ -557,58 +585,59 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/**
* Do a local search
*
* @param indy the individual that will be improved
* @return the new improved individual
*/
private AbstractEAIndividual improve(AbstractEAIndividual indy){
private AbstractEAIndividual improve(AbstractEAIndividual indy) {
AbstractEAIndividual tmpIndy = (AbstractEAIndividual) indy.clone();
BitSet data = ((InterfaceDataTypeBinary)tmpIndy).getBinaryData();
BitSet data = ((InterfaceDataTypeBinary) tmpIndy).getBinaryData();
ArrayList<Integer> cl = new ArrayList<Integer>();
int localIter = 0;
for(int i=0; i<data.size(); i++){
if(data.get(i)){
if(score(i, this.refSet)<=this.th2){
for (int i = 0; i < data.size(); i++) {
if (data.get(i)) {
if (score(i, this.refSet) <= this.th2) {
cl.add(i);
}
}else{
if(score(i, this.refSet)>=this.th1){
} else {
if (score(i, this.refSet) >= this.th1) {
cl.add(i);
}
}
}
cl = order(cl);
boolean improvement = true;
while(improvement && localIter < this.MaxImpIter){
while (improvement && localIter < this.MaxImpIter) {
improvement = false;
for(int i:cl){
for (int i : cl) {
data.flip(i);
((InterfaceDataTypeBinary) tmpIndy).SetBinaryGenotype(data);
evaluate(tmpIndy);
if(tmpIndy.getFitness(0)<indy.getFitness(0)){
if (tmpIndy.getFitness(0) < indy.getFitness(0)) {
improvement = true;
indy = (AbstractEAIndividual) tmpIndy.clone();
data = ((InterfaceDataTypeBinary)tmpIndy).getBinaryData();
}else{
data = ((InterfaceDataTypeBinary) tmpIndy).getBinaryData();
} else {
tmpIndy = (AbstractEAIndividual) indy.clone();
}
}
cl = order(cl);
for(int i=0; i<cl.size()-1; i++){
for (int i = 0; i < cl.size() - 1; i++) {
boolean valI = ((InterfaceDataTypeBinary) tmpIndy).getBinaryData().get(i);
for(int j=i+1; j<cl.size(); j++){
for (int j = i + 1; j < cl.size(); j++) {
boolean valJ = ((InterfaceDataTypeBinary) tmpIndy).getBinaryData().get(i);
if(valJ != valI){
if (valJ != valI) {
data.set(i, valJ);
data.set(j, valI);
((InterfaceDataTypeBinary) tmpIndy).SetBinaryGenotype(data);
evaluate(tmpIndy);
if(tmpIndy.getFitness(0)<indy.getFitness(0)){
if (tmpIndy.getFitness(0) < indy.getFitness(0)) {
improvement = true;
indy = (AbstractEAIndividual) tmpIndy.clone();
data = ((InterfaceDataTypeBinary)tmpIndy).getBinaryData();
i=cl.size();
j=cl.size();
}else{
data = ((InterfaceDataTypeBinary) tmpIndy).getBinaryData();
i = cl.size();
j = cl.size();
} else {
tmpIndy = (AbstractEAIndividual) indy.clone();
}
}
@@ -621,12 +650,13 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/**
* Combine all the individuals in the reference Set (always 2)
*
* @return the List with all the combinations
*/
public ArrayList<Population> generateSubsets(){
public ArrayList<Population> generateSubsets() {
ArrayList<Population> result = new ArrayList<Population>();
for(int i=0; i<this.refSet.size(); i++){
for(int j=i+1; j<this.refSet.size(); j++){
for (int i = 0; i < this.refSet.size(); i++) {
for (int j = i + 1; j < this.refSet.size(); j++) {
Population tmp = new Population();
tmp.add(this.refSet.getIndividual(i));
tmp.add(this.refSet.getIndividual(j));
@@ -638,24 +668,25 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/**
* combine the first individual with the second one
*
* @param pop the Population
* @return the new Individual
*/
public AbstractEAIndividual combineSolution(Population pop){
public AbstractEAIndividual combineSolution(Population pop) {
AbstractEAIndividual result = (AbstractEAIndividual) template.clone();
if(pop.size()>=2){
if (pop.size() >= 2) {
AbstractEAIndividual indy1 = pop.getEAIndividual(0);
pop.remove(0);
// Because some Crossover-Methods need the score, we need to give them the RefSet
for(int i=0; i<this.refSet.size(); i++){
for (int i = 0; i < this.refSet.size(); i++) {
pop.add(this.refSet.getEAIndividual(i));
}
this.cross.update(indy1, problem, refSet, indy1.getFitness(0));
result = this.cross.mate(indy1, pop)[0];
//result = indy1.mateWith(s)[0];
}else if(pop.size()>0){
} else if (pop.size() > 0) {
result = pop.getBestEAIndividual();
}else{
} else {
System.err.println("Population empty");
//return null;
}
@@ -664,18 +695,19 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/**
* look if the individual is already in the population
*
* @param indy the Individual to be tested
* @param pop the population in where to search
* @return is the individual already in the Population
*/
private boolean contains(InterfaceDataTypeBinary indy, Population pop){
if(pop.size()<=0){
private boolean contains(InterfaceDataTypeBinary indy, Population pop) {
if (pop.size() <= 0) {
return false;
}else{
} else {
BitSet data = indy.getBinaryData();
for(int i=0; i<pop.size(); i++){
for (int i = 0; i < pop.size(); i++) {
BitSet tmpData = ((InterfaceDataTypeBinary) pop.getEAIndividual(i)).getBinaryData();
if(tmpData.equals(data)){
if (tmpData.equals(data)) {
return true;
}
}
@@ -691,27 +723,27 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
// generate a new Pool
this.pool = new Population();
ArrayList<Population> newSubsets = generateSubsets();
for(int i=0; i<newSubsets.size(); i++){
for (int i = 0; i < newSubsets.size(); i++) {
Population s = newSubsets.get(i);
AbstractEAIndividual x = combineSolution(s);
if(!contains((InterfaceDataTypeBinary)x,this.pool)&&this.pool.size()<=this.poolSize){
if (!contains((InterfaceDataTypeBinary) x, this.pool) && this.pool.size() <= this.poolSize) {
this.pool.add(x);
}
}
this.refSet.incrFunctionCallsBy(this.pool.size());
// increase the number of evaluations by the number of evaluations that are performed in the crossover-step
for(int i=0; i<this.cross.getEvaluations(); i++){
for (int i = 0; i < this.cross.getEvaluations(); i++) {
this.refSet.incrFunctionCalls();
}
// reset the extra evaluations
this.cross.resetEvaluations();
// get the best half of the Populations
Population best = this.refSet.getBestNIndividuals(this.refSetSize/2, fitCrit);
Population best = this.refSet.getBestNIndividuals(this.refSetSize / 2, fitCrit);
// do a local search on the better half of the reference Set
for(int i=0; i<best.size(); i++){
for (int i = 0; i < best.size(); i++) {
AbstractEAIndividual indy = best.getEAIndividual(i);
AbstractEAIndividual x = improve((AbstractEAIndividual)indy.clone());
if(x.getFitness(0)<indy.getFitness(0) && !contains((InterfaceDataTypeBinary)x, this.refSet)){
AbstractEAIndividual x = improve((AbstractEAIndividual) indy.clone());
if (x.getFitness(0) < indy.getFitness(0) && !contains((InterfaceDataTypeBinary) x, this.refSet)) {
this.refSet.remove(indy);
this.refSet.add(x);
}
@@ -719,10 +751,10 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
// update the referenceSet
boolean changed = refSetUpdate(false);
// if no change occurred, replace the worst half of the referenceSet
if(!changed){
if (!changed) {
refSetUpdate(true);
}
} while (refSet.getFunctionCalls()-funCallsStart < generationCycle);
} while (refSet.getFunctionCalls() - funCallsStart < generationCycle);
problem.evaluatePopulationEnd(refSet);
}
@@ -737,7 +769,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
this.refSetSize = pop.getTargetSize();
}
public String populationTipText(){
public String populationTipText() {
return "The Population";
}
@@ -771,13 +803,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
return "BinaryScatterSearch";
}
@Override
public void freeWilly() {
// TODO Auto-generated method stub
}
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
@@ -788,7 +814,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
// The events of the interim hill climbing population will be caught here
if (name.compareTo(Population.funCallIntervalReached) == 0) {
// set funcalls to real value
refSet.SetFunctionCalls(((Population)source).getFunctionCalls());
refSet.SetFunctionCalls(((Population) source).getFunctionCalls());
// System.out.println("FunCallIntervalReached at " + (((Population)source).getFunctionCalls()));
@@ -797,105 +823,103 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
}
//----------GUI----------
public int getPoolSize(){
public int getPoolSize() {
return this.poolSize;
}
public void setPoolSize(int i){
public void setPoolSize(int i) {
this.poolSize = i;
}
public String poolSizeTipText(){
public String poolSizeTipText() {
return "The number of individuals created in the diversification step";
}
public double getThresholdHigh(){
public double getThresholdHigh() {
return th2;
}
public void setThresholdHigh(double t){
public void setThresholdHigh(double t) {
this.th2 = t;
}
public String thresholdHighTipText(){
public String thresholdHighTipText() {
return "Only scores set to 0 with a value below this value will be improved";
}
public double getThresholdLow(){
public double getThresholdLow() {
return th1;
}
public void setThresholdLow(double t){
public void setThresholdLow(double t) {
this.th1 = t;
}
public String thresholdLowTipText(){
public String thresholdLowTipText() {
return "Only scores set to 1 with a value above this value will be improved";
}
public int getLocalSearchSteps(){
public int getLocalSearchSteps() {
return this.MaxImpIter;
}
public void setLocalSearchSteps(int i){
public void setLocalSearchSteps(int i) {
this.MaxImpIter = i;
}
public String localSearchStepsTipText(){
public String localSearchStepsTipText() {
return "Maximum number of local search iterations";
}
public AdaptiveCrossoverEAMixer getCrossoverMethods(){
public AdaptiveCrossoverEAMixer getCrossoverMethods() {
return this.cross;
}
public void setCrossoverMethods(AdaptiveCrossoverEAMixer c){
public void setCrossoverMethods(AdaptiveCrossoverEAMixer c) {
this.cross = c;
}
public String crossoverMethodsTipText(){
public String crossoverMethodsTipText() {
return "The crossover Methods used to create the pool";
}
public int getGenerationCycle(){
public int getGenerationCycle() {
return this.generationCycle;
}
public void setGenerationCycle(int i){
public void setGenerationCycle(int i) {
this.generationCycle = i;
}
public String generationCycleTipText(){
public String generationCycleTipText() {
return "The number of evaluations done in every generation Cycle";
}
public double getPerCentFirstGenMethod(){
public double getPerCentFirstGenMethod() {
return this.g1;
}
public void setPerCentFirstGenMethod(double d){
public void setPerCentFirstGenMethod(double d) {
this.g1 = d;
}
public String perCentFirstGenMethodTipText(){
public String perCentFirstGenMethodTipText() {
return "The number of individuals generated with the first Generation Method. The percentage, that is not covered with the first and the second method will be covered with a third method";
}
public double getPerCentSecondGenMethod(){
public double getPerCentSecondGenMethod() {
return this.g2;
}
public void setPerCentSecondGenMethod(double d){
public void setPerCentSecondGenMethod(double d) {
this.g2 = d;
}
public String perCentSecondGenMethodTipText(){
public String perCentSecondGenMethodTipText() {
return "The number of individuals generated with the second Generation Method. The percentage, that is not covered with the first and the second method will be covered with a third method";
}
public static String globalInfo(){
public static String globalInfo() {
return "A basic implementation of a Binary ScatterSearch";
}
}

162
src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java
View File

@@ -1,6 +1,5 @@
package eva2.server.go.strategies;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.InterfaceGAIndividual;
@@ -15,22 +14,22 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import java.util.BitSet;
/** This is an implementation of the CHC Adaptive Search Algorithm by Eshelman. It is
* limited to binary data and is based on massively disruptive crossover. I'm not
* sure whether i've implemented this correctly, but i definitely wasn't able to make
* it competitive to a standard GA.. *sigh*
* This is a implementation of the CHC Adaptive Search Algorithm (Cross generational
/**
* This is an implementation of the CHC Adaptive Search Algorithm by Eshelman.
* It is limited to binary data and is based on massively disruptive crossover.
* I'm not sure whether i've implemented this correctly, but i definitely wasn't
* able to make it competitive to a standard GA.. *sigh* This is a
* implementation of the CHC Adaptive Search Algorithm (Cross generational
* elitist selection, Heterogeneous recombination and Cataclysmic mutation).
* Citation:
*
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
* Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
* Architecture
*
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.Serializable {
private double m_InitialDifferenceThreshold = 0.25;
@@ -42,7 +41,6 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private InterfaceSelection m_RecombSelectionOperator = new SelectRandom();
private InterfaceSelection m_PopulSelectionOperator = new SelectBestSingle();
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -50,15 +48,15 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
}
public CHCAdaptiveSearchAlgorithm(CHCAdaptiveSearchAlgorithm a) {
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.m_InitialDifferenceThreshold = a.m_InitialDifferenceThreshold;
this.m_DifferenceThreshold = a.m_DifferenceThreshold;
this.m_DivergenceRate = a.m_DivergenceRate;
this.m_NumberOfPartners = a.m_NumberOfPartners;
this.m_UseElitism = a.m_UseElitism;
this.m_RecombSelectionOperator = (InterfaceSelection)a.m_RecombSelectionOperator.clone();
this.m_PopulSelectionOperator = (InterfaceSelection)a.m_PopulSelectionOperator.clone();
this.m_RecombSelectionOperator = (InterfaceSelection) a.m_RecombSelectionOperator.clone();
this.m_PopulSelectionOperator = (InterfaceSelection) a.m_PopulSelectionOperator.clone();
}
@Override
@@ -69,9 +67,9 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
@Override
public void init() {
this.m_Problem.initPopulation(this.m_Population);
AbstractEAIndividual tmpIndy = ((AbstractEAIndividual)(this.m_Population.get(0)));
AbstractEAIndividual tmpIndy = ((AbstractEAIndividual) (this.m_Population.get(0)));
if (tmpIndy instanceof InterfaceGAIndividual) {
this.m_DifferenceThreshold = (int)(((InterfaceGAIndividual)tmpIndy).getGenotypeLength()*this.m_InitialDifferenceThreshold);
this.m_DifferenceThreshold = (int) (((InterfaceGAIndividual) tmpIndy).getGenotypeLength() * this.m_InitialDifferenceThreshold);
} else {
System.out.println("Problem does not apply InterfaceGAIndividual, which is the only individual type valid for CHC!");
}
@@ -80,19 +78,21 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
this.m_Population = (Population)pop.clone();
this.m_Population = (Population) pop.clone();
if (reset) {
this.m_Population.init();
}
AbstractEAIndividual tmpIndy = ((AbstractEAIndividual)(this.m_Population.get(0)));
AbstractEAIndividual tmpIndy = ((AbstractEAIndividual) (this.m_Population.get(0)));
if (tmpIndy instanceof InterfaceGAIndividual) {
this.m_DifferenceThreshold = (int)(((InterfaceGAIndividual)tmpIndy).getGenotypeLength()*this.m_InitialDifferenceThreshold);
this.m_DifferenceThreshold = (int) (((InterfaceGAIndividual) tmpIndy).getGenotypeLength() * this.m_InitialDifferenceThreshold);
} else {
System.out.println("Problem does not apply InterfaceGAIndividual, which is the only individual type valid for CHC!");
}
@@ -103,8 +103,9 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
}
}
/** This method will evaluate the current population using the
* given problem.
/**
* This method will evaluate the current population using the given problem.
*
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
@@ -112,8 +113,9 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
population.incrGeneration();
}
/** This method will generate the offspring population from the
* given population of evaluated individuals.
/**
* This method will generate the offspring population from the given
* population of evaluated individuals.
*/
private Population generateChildren() {
Population result = this.m_Population.cloneWithoutInds(), parents, partners;
@@ -129,12 +131,12 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
//System.out.println("Parents:"+parents.getSolutionRepresentationFor());
for (int i = 0; i < parents.size(); i++) {
tmpIndy = ((AbstractEAIndividual)parents.get(i));
tmpIndy = ((AbstractEAIndividual) parents.get(i));
if (tmpIndy == null) {
System.out.println("Individual null "+i);
System.out.println("Individual null " + i);
}
if (this.m_Population == null) {
System.out.println("population null "+i);
System.out.println("population null " + i);
}
partners = this.m_RecombSelectionOperator.findPartnerFor(tmpIndy, this.m_Population, this.m_NumberOfPartners);
@@ -149,7 +151,9 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return result;
}
/** This method computes the Hamming Distance between n-Individuals
/**
* This method computes the Hamming Distance between n-Individuals
*
* @param dad
* @param partners
* @return The maximal Hamming Distance between dad and the partners
@@ -158,11 +162,11 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
int result = 0, tmpDist;
BitSet tmpB1, tmpB2;
tmpB1 = ((InterfaceGAIndividual)dad).getBGenotype();
tmpB1 = ((InterfaceGAIndividual) dad).getBGenotype();
for (int i = 0; i < partners.size(); i++) {
tmpB2 = ((InterfaceGAIndividual) partners.get(i)).getBGenotype();
tmpDist = 0;
for (int j = 0; j < ((InterfaceGAIndividual)dad).getGenotypeLength(); j++) {
for (int j = 0; j < ((InterfaceGAIndividual) dad).getGenotypeLength(); j++) {
if (tmpB1.get(j) == tmpB2.get(j)) {
tmpDist++;
}
@@ -172,8 +176,10 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return result;
}
/** This method method replaces the current population with copies of the current
* best individual but all but one are randomized with a very high mutation rate.
/**
* This method method replaces the current population with copies of the
* current best individual but all but one are randomized with a very high
* mutation rate.
*/
private void diverge() {
AbstractEAIndividual best = this.m_Population.getBestEAIndividual();
@@ -183,14 +189,13 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
this.m_Population.clear();
this.m_Population.add(best);
for (int i = 1; i < this.m_Population.getTargetSize(); i++) {
mutant = (InterfaceGAIndividual)best.clone();
mutant = (InterfaceGAIndividual) best.clone();
tmpBitSet = mutant.getBGenotype();
for (int j = 0; j < mutant.getGenotypeLength(); j++) {
if (RNG.flipCoin(this.m_DivergenceRate)) {
if (tmpBitSet.get(j)) {
tmpBitSet.clear(j);
}
else {
} else {
tmpBitSet.set(j);
}
}
@@ -199,7 +204,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
this.m_Population.add(mutant);
}
if (best instanceof InterfaceGAIndividual) {
this.m_DifferenceThreshold = (int)(this.m_DivergenceRate* (1-this.m_DivergenceRate) * ((InterfaceGAIndividual)best).getGenotypeLength());
this.m_DifferenceThreshold = (int) (this.m_DivergenceRate * (1 - this.m_DivergenceRate) * ((InterfaceGAIndividual) best).getGenotypeLength());
}
this.evaluatePopulation(this.m_Population);
@@ -237,39 +242,46 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -277,41 +289,42 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
String result = "";
result += "CHC Adaptive Search Algorithm:\n";
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is an implementation of the CHC Adaptive Search Algorithm by Eselman.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -319,19 +332,23 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return "CHC";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
@@ -353,21 +370,26 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
// public String normationMethodTipText() {
// return "Select the normation method.";
// }
/** Enable/disable elitism.
/**
* Enable/disable elitism.
*
* @param elitism
*/
public void setElitism (boolean elitism) {
public void setElitism(boolean elitism) {
this.m_UseElitism = elitism;
}
public boolean getElitism() {
return this.m_UseElitism;
}
public String elitismTipText() {
return "Enable/disable elitism.";
}
/** The number of mating partners needed to create offsprings.
/**
* The number of mating partners needed to create offsprings.
*
* @param partners
*/
public void setNumberOfPartners(int partners) {
@@ -376,9 +398,11 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
}
this.m_NumberOfPartners = partners;
}
public int getNumberOfPartners() {
return this.m_NumberOfPartners;
}
public String numberOfPartnersTipText() {
return "The number of mating partners needed to create offsprings.";
}

499
src/eva2/server/go/strategies/ClusterBasedNichingEA.java
View File

File diff suppressed because it is too large Load Diff

113
src/eva2/server/go/strategies/ClusteringHillClimbing.java
View File

@@ -16,27 +16,29 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.Pair;
import java.io.Serializable;
/**
* The clustering hill climber is similar to a multi-start hill climber. In addition so optimizing
* a set of individuals in parallel using a (1+1) strategy, the population is clustered in regular
* intervals. If several individuals have gathered together in the sense that they are interpreted
* as a cluster, only a subset of representatives of the cluster is taken over to the next HC step
* while the rest is discarded. This means that the population size may be reduced.
* The clustering hill climber is similar to a multi-start hill climber. In
* addition so optimizing a set of individuals in parallel using a (1+1)
* strategy, the population is clustered in regular intervals. If several
* individuals have gathered together in the sense that they are interpreted as
* a cluster, only a subset of representatives of the cluster is taken over to
* the next HC step while the rest is discarded. This means that the population
* size may be reduced.
*
* As soon as the improvement by HC lies below a threshold, the mutation step size is decreased.
* If the step size is decreased below a certain threshold, the current population is stored to
* an archive and reinitialized. Thus, the number of optima that may be found and returned by
* getAllSolutions is higher than the population size.
* As soon as the improvement by HC lies below a threshold, the mutation step
* size is decreased. If the step size is decreased below a certain threshold,
* the current population is stored to an archive and reinitialized. Thus, the
* number of optima that may be found and returned by getAllSolutions is higher
* than the population size.
*
* @author mkron
*
*/
public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener,
InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
transient private InterfacePopulationChangedEventListener m_Listener;
public static final boolean TRACE = false;
transient private String m_Identifier = "";
private Population m_Population = new Population();
private transient Population archive = new Population();
@@ -68,8 +70,8 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
public ClusteringHillClimbing(ClusteringHillClimbing other) {
hideHideable();
m_Population = (Population)other.m_Population.clone();
m_Problem = (InterfaceOptimizationProblem)other.m_Problem.clone();
m_Population = (Population) other.m_Population.clone();
m_Problem = (InterfaceOptimizationProblem) other.m_Problem.clone();
hcEvalCycle = other.hcEvalCycle;
initialPopSize = other.initialPopSize;
@@ -79,7 +81,7 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
stepSizeThreshold = other.stepSizeThreshold;
initialStepSize = other.initialStepSize;
reduceFactor = other.reduceFactor;
mutator = (MutateESFixedStepSize)other.mutator.clone();
mutator = (MutateESFixedStepSize) other.mutator.clone();
loopCnt = 0;
}
@@ -101,20 +103,24 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
@@ -122,16 +128,18 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
@Override
public void init() {
loopCnt = 0;
@@ -145,14 +153,16 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
loopCnt = 0;
this.m_Population = (Population)pop.clone();
this.m_Population = (Population) pop.clone();
m_Population.addPopulationChangedEventListener(null);
if (reset) {
this.m_Population.init();
@@ -161,9 +171,10 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
@@ -177,32 +188,31 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
m_Population.addPopulationChangedEventListener(this);
m_Population.setNotifyEvalInterval(notifyGuiEvery);
Pair<Population, Double> popD;
int funCallsBefore=m_Population.getFunctionCalls();
int funCallsBefore = m_Population.getFunctionCalls();
int evalsNow, lastOverhead = (m_Population.getFunctionCalls() % hcEvalCycle);
if (lastOverhead>0) {
evalsNow = (2*hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle));
}
else {
if (lastOverhead > 0) {
evalsNow = (2 * hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle));
} else {
evalsNow = hcEvalCycle;
}
do {
if (TRACE) {
System.out.println("evalCycle: " + hcEvalCycle + ", evals now: " + evalsNow);
}
popD = PostProcess.clusterLocalSearch(localSearchMethod, m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, evalsNow, 0.5, mutator);
popD = PostProcess.clusterLocalSearch(localSearchMethod, m_Population, (AbstractOptimizationProblem) m_Problem, sigmaClust, evalsNow, 0.5, mutator);
// (m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5);
if (popD.head().getFunctionCalls()==funCallsBefore) {
if (popD.head().getFunctionCalls() == funCallsBefore) {
System.err.println("Bad case, increasing allowed evaluations!");
evalsNow=Math.max(evalsNow++, (int)(evalsNow*1.2));
evalsNow = Math.max(evalsNow++, (int) (evalsNow * 1.2));
}
} while (popD.head().getFunctionCalls()==funCallsBefore);
} while (popD.head().getFunctionCalls() == funCallsBefore);
improvement = popD.tail();
m_Population = popD.head();
if (TRACE) {
System.out.println("num inds after clusterLS: " + m_Population.size());
}
popD.head().setGenerationTo(m_Population.getGeneration()+1);
popD.head().setGenerationTo(m_Population.getGeneration() + 1);
if (doReinitialization && (improvement < minImprovement)) {
if (TRACE) {
@@ -239,7 +249,7 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
if (localSearchMethod != PostProcessMethod.hillClimber) {
System.err.println("Invalid case in ClusteringHillClimbing!");
}
mutator.setSigma(mutator.getSigma()*reduceFactor);
mutator.setSigma(mutator.getSigma() * reduceFactor);
if (TRACE) {
System.out.println("mutation stepsize reduced to " + mutator.getSigma());
}
@@ -258,7 +268,7 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
// System.out.println("bla");
// }
// set funcalls to real value
m_Population.SetFunctionCalls(((Population)source).getFunctionCalls());
m_Population.SetFunctionCalls(((Population) source).getFunctionCalls());
// System.out.println("FunCallIntervalReached at " + (((Population)source).getFunctionCalls()));
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
@@ -267,19 +277,23 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Change the number of starting individuals stored (Cluster-HC).";
}
@@ -295,8 +309,10 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
return new SolutionSet(m_Population, tmp);
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -310,17 +326,14 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
return sbuf.toString();
}
@Override
public void freeWilly() {}
@Override
public String getName() {
return "ClustHC-"+initialPopSize+"-"+localSearchMethod;
return "ClustHC-" + initialPopSize + "-" + localSearchMethod;
}
public static String globalInfo() {
return "Similar to multi-start HC, but clusters the population during optimization to remove redundant individuals for efficiency." +
"If the local search step does not achieve a minimum improvement, the population may be reinitialized.";
return "Similar to multi-start HC, but clusters the population during optimization to remove redundant individuals for efficiency."
+ "If the local search step does not achieve a minimum improvement, the population may be reinitialized.";
}
/**
@@ -455,8 +468,8 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
public void setLocalSearchMethod(PostProcessMethod localSearchMethod) {
this.localSearchMethod = localSearchMethod;
GenericObjectEditor.setShowProperty(this.getClass(), "stepSizeInitial", localSearchMethod==PostProcessMethod.hillClimber);
GenericObjectEditor.setShowProperty(this.getClass(), "stepSizeThreshold", localSearchMethod==PostProcessMethod.hillClimber);
GenericObjectEditor.setShowProperty(this.getClass(), "stepSizeInitial", localSearchMethod == PostProcessMethod.hillClimber);
GenericObjectEditor.setShowProperty(this.getClass(), "stepSizeThreshold", localSearchMethod == PostProcessMethod.hillClimber);
}
public String localSearchMethodTipText() {

384
src/eva2/server/go/strategies/DifferentialEvolution.java
View File

@@ -21,12 +21,12 @@ import eva2.tools.math.RNG;
import java.util.Vector;
/**
* Differential evolution implementing DE1 and DE2 following the paper of Storm and
* Price and the Trigonometric DE published recently.
* Please note that DE will only work on real-valued genotypes and will ignore
* all mutation and crossover operators selected.
* Added aging mechanism to provide for dynamically changing problems. If an individual
* reaches the age limit, it is doomed and replaced by the next challenge vector, even if its worse.
* Differential evolution implementing DE1 and DE2 following the paper of Storm
* and Price and the Trigonometric DE published recently. Please note that DE
* will only work on real-valued genotypes and will ignore all mutation and
* crossover operators selected. Added aging mechanism to provide for
* dynamically changing problems. If an individual reaches the age limit, it is
* doomed and replaced by the next challenge vector, even if its worse.
*
*/
public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serializable {
@@ -44,11 +44,10 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// to log the parents of a newly created indy.
public boolean doLogParents = false; // deactivate for better performance
private transient Vector<AbstractEAIndividual> parents = null;
private boolean randomizeFKLambda = false;
private boolean generational = true;
private String m_Identifier = "";
transient private Vector<InterfacePopulationChangedEventListener> m_Listener=new Vector<InterfacePopulationChangedEventListener>();
transient private Vector<InterfacePopulationChangedEventListener> m_Listener = new Vector<InterfacePopulationChangedEventListener>();
private boolean forceRange = true;
private boolean cyclePop = false; // if true, individuals are used as parents in a cyclic sequence - otherwise randomly
private boolean compareToParent = true; // if true, the challenge indy is compared to its parent, otherwise to a random individual
@@ -63,7 +62,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
}
public DifferentialEvolution(int popSize, DETypeEnum type, double f, double k, double lambda, double mt) {
m_Population=new Population(popSize);
m_Population = new Population(popSize);
m_DEType = type;
m_F = f;
m_k = k;
@@ -78,8 +77,8 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
*/
public DifferentialEvolution(DifferentialEvolution a) {
this.m_DEType = a.m_DEType;
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (AbstractOptimizationProblem)a.m_Problem.clone();
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (AbstractOptimizationProblem) a.m_Problem.clone();
this.m_Identifier = a.m_Identifier;
this.m_F = a.m_F;
this.m_k = a.m_k;
@@ -110,13 +109,15 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
setDEType(getDEType());
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
this.m_Population = (Population)pop.clone();
this.m_Population = (Population) pop.clone();
if (reset) {
this.m_Population.init();
this.evaluatePopulation(this.m_Population);
@@ -126,8 +127,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// else children = new Population(m_Population.size());
}
/** This method will evaluate the current population using the
* given problem.
/**
* This method will evaluate the current population using the given problem.
*
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
@@ -136,8 +138,8 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
}
/**
* This method returns a difference vector between two random individuals from the population.
* This method should make sure that delta is not zero.
* This method returns a difference vector between two random individuals
* from the population. This method should make sure that delta is not zero.
*
* @param pop The population to choose from
* @return The delta vector
@@ -164,7 +166,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
for (int i = 0; i < x1.length; i++) {
result[i] = x1[i] - x2[i];
isEmpty = (isEmpty && (result[i]==0));
isEmpty = (isEmpty && (result[i] == 0));
}
iterations++;
}
@@ -176,13 +178,12 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// for n (popSize) iterations there were only zero vectors found
// so now the hard way: construct a random vector
for (int i = 0; i < x1.length; i++) {
if (RNG.flipCoin(1/(double)x1.length)) {
result[i] = 0.01*RNG.gaussianDouble(0.1);
}
else {
if (RNG.flipCoin(1 / (double) x1.length)) {
result[i] = 0.01 * RNG.gaussianDouble(0.1);
} else {
result[i] = 0;
}
isEmpty = (isEmpty && (result[i]==0));
isEmpty = (isEmpty && (result[i] == 0));
}
// single parent! dont add another one
}
@@ -191,13 +192,13 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
}
/**
* This method returns a difference vector between two random individuals from the population.
* This method should make sure that delta is not zero.
* This method returns a difference vector between two random individuals
* from the population. This method should make sure that delta is not zero.
*
* @param pop The population to choose from
* @return The delta vector
*/
private double[] fetchDeltaCurrentRandom(Population pop,InterfaceDataTypeDouble indy) {
private double[] fetchDeltaCurrentRandom(Population pop, InterfaceDataTypeDouble indy) {
double[] x1, x2;
double[] result;
boolean isEmpty;
@@ -208,7 +209,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
if (parents != null) {
parents.add((AbstractEAIndividual)indy);
parents.add((AbstractEAIndividual) indy);
}
result = new double[x1.length];
@@ -220,7 +221,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
for (int i = 0; i < x1.length; i++) {
result[i] = x1[i] - x2[i];
isEmpty = (isEmpty && (result[i]==0));
isEmpty = (isEmpty && (result[i] == 0));
}
iterations++;
}
@@ -232,13 +233,12 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// for n (popSize) iterations there were only zero vectors found
// so now the hard way: construct a random vector
for (int i = 0; i < x1.length; i++) {
if (RNG.flipCoin(1/(double)x1.length)) {
result[i] = 0.01*RNG.gaussianDouble(0.1);
}
else {
if (RNG.flipCoin(1 / (double) x1.length)) {
result[i] = 0.01 * RNG.gaussianDouble(0.1);
} else {
result[i] = 0;
}
isEmpty = (isEmpty && (result[i]==0));
isEmpty = (isEmpty && (result[i] == 0));
}
// single parent! dont add another one
}
@@ -261,7 +261,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
result = new double[x1.length];
if (m_Problem instanceof AbstractMultiObjectiveOptimizationProblem) {
// implements MODE for the multi-objective case: a dominating individual is selected for difference building
Population domSet = pop.getDominatingSet((AbstractEAIndividual)indy);
Population domSet = pop.getDominatingSet((AbstractEAIndividual) indy);
if (domSet.size() > 0) {
xbIndy = getRandomIndy(domSet);
} else {
@@ -283,7 +283,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return result;
}
/** This method returns two parents to the original individual
/**
* This method returns two parents to the original individual
*
* @param pop The population to choose from
* @return the delta vector
*/
@@ -301,8 +303,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// result[1] = indy2.getDGenotype();
// return result;
// }
/** This method will generate one new individual from the given population
/**
* This method will generate one new individual from the given population
*
* @param pop The current population
* @return AbstractEAIndividual
*/
@@ -313,17 +316,16 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
if (doLogParents) {
parents = new Vector<AbstractEAIndividual>();
}
else {
} else {
parents = null;
}
try {
// select one random indy as starting individual. its a parent in any case.
if (parentIndex<0) {
parentIndex = RNG.randomInt(0, pop.size()-1);
if (parentIndex < 0) {
parentIndex = RNG.randomInt(0, pop.size() - 1);
}
indy = (AbstractEAIndividual)(pop.getEAIndividual(parentIndex)).getClone();
esIndy = (InterfaceDataTypeDouble)indy;
indy = (AbstractEAIndividual) (pop.getEAIndividual(parentIndex)).getClone();
esIndy = (InterfaceDataTypeDouble) indy;
} catch (java.lang.ClassCastException e) {
throw new RuntimeException("Differential Evolution currently requires InterfaceESIndividual as basic data type!");
// return (AbstractEAIndividual)((AbstractEAIndividual)pop.get(RNG.randomInt(0, pop.size()-1))).getClone();
@@ -340,11 +342,11 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
parents.add(pop.getEAIndividual(parentIndex));
} // Add wherever oX is used directly
for (int i = 0; i < oX.length; i++) {
vX[i] = oX[i] + this.getCurrentF()*delta[i];
vX[i] = oX[i] + this.getCurrentF() * delta[i];
}
break;
}
case DE_CurrentToRand : {
case DE_CurrentToRand: {
// this is DE/current-to-rand/1
double[] rndDelta = this.fetchDeltaRandom(pop);
double[] bestDelta = this.fetchDeltaCurrentRandom(pop, esIndy);
@@ -356,7 +358,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
}
break;
}
case DE2_CurrentToBest : {
case DE2_CurrentToBest: {
// this is DE2 or DE/current-to-best/1
double[] rndDelta = this.fetchDeltaRandom(pop);
double[] bestDelta = this.fetchDeltaBest(pop, esIndy);
@@ -382,7 +384,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
}
break;
}
case TrigonometricDE : {
case TrigonometricDE: {
// this is trigonometric mutation
if (parents != null) {
parents.add(pop.getEAIndividual(parentIndex));
@@ -393,23 +395,23 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
InterfaceDataTypeDouble indy1 = null, indy2 = null;
try {
// and i got indy!
indy1 = (InterfaceDataTypeDouble)pop.get(RNG.randomInt(0, pop.size()-1));
indy2 = (InterfaceDataTypeDouble)pop.get(RNG.randomInt(0, pop.size()-1));
indy1 = (InterfaceDataTypeDouble) pop.get(RNG.randomInt(0, pop.size() - 1));
indy2 = (InterfaceDataTypeDouble) pop.get(RNG.randomInt(0, pop.size() - 1));
if (parents != null) {
parents.add((AbstractEAIndividual)indy1);
parents.add((AbstractEAIndividual)indy2);
parents.add((AbstractEAIndividual) indy1);
parents.add((AbstractEAIndividual) indy2);
}
} catch (java.lang.ClassCastException e) {
EVAERROR.errorMsgOnce("Differential Evolution currently requires InterfaceESIndividual as basic data type!");
}
xk = indy1.getDoubleData();
xl = indy2.getDoubleData();
p = Math.abs(((AbstractEAIndividual)esIndy).getFitness(0)) + Math.abs(((AbstractEAIndividual)indy1).getFitness(0)) + Math.abs(((AbstractEAIndividual)indy2).getFitness(0));
pj = Math.abs(((AbstractEAIndividual)esIndy).getFitness(0))/p;
pk = Math.abs(((AbstractEAIndividual)indy1).getFitness(0))/p;
pl = Math.abs(((AbstractEAIndividual)indy2).getFitness(0))/p;
p = Math.abs(((AbstractEAIndividual) esIndy).getFitness(0)) + Math.abs(((AbstractEAIndividual) indy1).getFitness(0)) + Math.abs(((AbstractEAIndividual) indy2).getFitness(0));
pj = Math.abs(((AbstractEAIndividual) esIndy).getFitness(0)) / p;
pk = Math.abs(((AbstractEAIndividual) indy1).getFitness(0)) / p;
pl = Math.abs(((AbstractEAIndividual) indy2).getFitness(0)) / p;
for (int i = 0; i < oX.length; i++) {
vX[i] = (oX[i] + xk[i] + xl[i])/3.0 + ((pk-pj)*(oX[i]-xk[i])) + ((pl-pk)*(xk[i]-xl[i])) + ((pj-pl)*(xl[i]-oX[i]));
vX[i] = (oX[i] + xk[i] + xl[i]) / 3.0 + ((pk - pj) * (oX[i] - xk[i])) + ((pl - pk) * (xk[i] - xl[i])) + ((pj - pl) * (xl[i] - oX[i]));
}
} else {
// this is DE1
@@ -418,15 +420,15 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
parents.add(pop.getEAIndividual(parentIndex));
} // Add wherever oX is used directly
for (int i = 0; i < oX.length; i++) {
vX[i] = oX[i] + this.getCurrentF()*delta[i];
vX[i] = oX[i] + this.getCurrentF() * delta[i];
}
}
break;
}
}
int k=RNG.randomInt(oX.length); // at least one position is changed
for (int i =0; i < oX.length; i++) {
if ((i==k) || RNG.flipCoin(this.getCurrentK())) {
int k = RNG.randomInt(oX.length); // at least one position is changed
for (int i = 0; i < oX.length; i++) {
if ((i == k) || RNG.flipCoin(this.getCurrentK())) {
// it is altered
nX[i] = vX[i];
} else {
@@ -452,48 +454,45 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
private double getCurrentK() {
if (randomizeFKLambda) {
return RNG.randomDouble(m_k*0.8, m_k * 1.2);
}
else {
return RNG.randomDouble(m_k * 0.8, m_k * 1.2);
} else {
return m_k;
}
}
private double getCurrentLambda() {
if (randomizeFKLambda) {
return RNG.randomDouble(m_Lambda*0.8, m_Lambda * 1.2);
}
else {
return RNG.randomDouble(m_Lambda * 0.8, m_Lambda * 1.2);
} else {
return m_Lambda;
}
}
private double getCurrentF() {
if (randomizeFKLambda) {
return RNG.randomDouble(m_F*0.8, m_F * 1.2);
}
else {
return RNG.randomDouble(m_F * 0.8, m_F * 1.2);
} else {
return m_F;
}
}
private AbstractEAIndividual getBestIndy(Population pop) {
return (AbstractEAIndividual)pop.getBestIndividual();
return (AbstractEAIndividual) pop.getBestIndividual();
}
private AbstractEAIndividual getRandomIndy(Population pop) {
if (pop.size()<1) {
if (pop.size() < 1) {
System.err.println("Error: invalid pop size in DE!");
System.err.println("DE: \n"+ BeanInspector.toString(this) + "\nPop: \n" + BeanInspector.toString(pop));
System.err.println("DE: \n" + BeanInspector.toString(this) + "\nPop: \n" + BeanInspector.toString(pop));
}
int randIndex = RNG.randomInt(0, pop.size()-1);
int randIndex = RNG.randomInt(0, pop.size() - 1);
return pop.getEAIndividual(randIndex);
}
private double[] getGenotype(AbstractEAIndividual indy) {
try {
return ((InterfaceDataTypeDouble)indy).getDoubleData();
return ((InterfaceDataTypeDouble) indy).getDoubleData();
} catch (java.lang.ClassCastException e) {
EVAERROR.errorMsgOnce("Differential Evolution currently requires InterfaceESIndividual as basic data type!");
return null;
@@ -504,16 +503,16 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
public void optimize() {
if (generational) {
optimizeGenerational();
}
else {
} else {
optimizeSteadyState();
}
}
/**
* This generational DE variant calls the method AbstractOptimizationProblem.evaluate(Population).
* Its performance may be slightly worse for schemes that rely on current best individuals,
* because improvements are not immediately incorporated as in the steady state DE.
* This generational DE variant calls the method
* AbstractOptimizationProblem.evaluate(Population). Its performance may be
* slightly worse for schemes that rely on current best individuals, because
* improvements are not immediately incorporated as in the steady state DE.
* However it may be easier to parallelize.
*
*/
@@ -522,18 +521,16 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
int parentIndex;
// required for dynamic problems especially
// m_Problem.evaluatePopulationStart(m_Population);
if (children==null) {
if (children == null) {
children = new Population(m_Population.size());
}
else {
} else {
children.clear();
}
for (int i = 0; i < this.m_Population.size(); i++) {
if (cyclePop) {
parentIndex=i;
}
else {
parentIndex=RNG.randomInt(0, this.m_Population.size()-1);
parentIndex = i;
} else {
parentIndex = RNG.randomInt(0, this.m_Population.size() - 1);
}
AbstractEAIndividual indy = generateNewIndividual(m_Population, parentIndex);
children.add(indy);
@@ -543,14 +540,15 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
m_Problem.evaluate(children);
/**
* MdP: added a reevalutation mechanism for dynamically changing problems
* MdP: added a reevalutation mechanism for dynamically changing
* problems
*/
if(isReEvaluate()){
for(int i=0;i<this.m_Population.size();i++){
if (isReEvaluate()) {
for (int i = 0; i < this.m_Population.size(); i++) {
if (((AbstractEAIndividual)m_Population.get(i)).getAge() >= maximumAge) {
this.m_Problem.evaluate(((AbstractEAIndividual)m_Population.get(i)));
((AbstractEAIndividual)m_Population.get(i)).SetAge(0);
if (((AbstractEAIndividual) m_Population.get(i)).getAge() >= maximumAge) {
this.m_Problem.evaluate(((AbstractEAIndividual) m_Population.get(i)));
((AbstractEAIndividual) m_Population.get(i)).SetAge(0);
m_Population.incrFunctionCalls();
}
}
@@ -560,24 +558,23 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
for (int i = 0; i < this.m_Population.size(); i++) {
AbstractEAIndividual indy = children.getEAIndividual(i);
if (cyclePop) {
parentIndex=i;
}
else {
parentIndex=RNG.randomInt(0, this.m_Population.size()-1);
parentIndex = i;
} else {
parentIndex = RNG.randomInt(0, this.m_Population.size() - 1);
}
if (nextDoomed >= 0) { // this one is lucky, may replace an 'old' one
m_Population.replaceIndividualAt(nextDoomed, indy);
nextDoomed = getNextDoomed(m_Population, nextDoomed+1);
nextDoomed = getNextDoomed(m_Population, nextDoomed + 1);
} else {
if (m_Problem instanceof AbstractMultiObjectiveOptimizationProblem&indy.getFitness().length>1) {
if (m_Problem instanceof AbstractMultiObjectiveOptimizationProblem & indy.getFitness().length > 1) {
ReplacementCrowding repl = new ReplacementCrowding();
repl.insertIndividual(indy, m_Population, null);
} else {
// index = RNG.randomInt(0, this.m_Population.size()-1);
if (!compareToParent) {
parentIndex = RNG.randomInt(0, this.m_Population.size()-1);
parentIndex = RNG.randomInt(0, this.m_Population.size() - 1);
}
AbstractEAIndividual orig = (AbstractEAIndividual)this.m_Population.get(parentIndex);
AbstractEAIndividual orig = (AbstractEAIndividual) this.m_Population.get(parentIndex);
if (indy.isDominatingDebConstraints(orig)) {
this.m_Population.replaceIndividualAt(parentIndex, indy);
}
@@ -600,15 +597,16 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
/**
* MdP: added a reevalutation mechanism for dynamically changing problems
* MdP: added a reevalutation mechanism for dynamically changing
* problems
*/
if(isReEvaluate()){
nextDoomed=-1;
for(int i=0;i<this.m_Population.size();i++){
if (isReEvaluate()) {
nextDoomed = -1;
for (int i = 0; i < this.m_Population.size(); i++) {
if (((AbstractEAIndividual)m_Population.get(i)).getAge() >= maximumAge) {
this.m_Problem.evaluate(((AbstractEAIndividual)m_Population.get(i)));
((AbstractEAIndividual)m_Population.get(i)).SetAge(0);
if (((AbstractEAIndividual) m_Population.get(i)).getAge() >= maximumAge) {
this.m_Problem.evaluate(((AbstractEAIndividual) m_Population.get(i)));
((AbstractEAIndividual) m_Population.get(i)).SetAge(0);
m_Population.incrFunctionCalls();
}
}
@@ -617,10 +615,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
for (int i = 0; i < this.m_Population.size(); i++) {
if (cyclePop) {
index=i;
}
else {
index=RNG.randomInt(0, this.m_Population.size()-1);
index = i;
} else {
index = RNG.randomInt(0, this.m_Population.size() - 1);
}
indy = generateNewIndividual(m_Population, index);
// if (cyclePop) indy = this.generateNewIndividual(this.m_Population, i);
@@ -629,14 +626,14 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
this.m_Population.incrFunctionCalls();
if (nextDoomed >= 0) { // this one is lucky, may replace an 'old' one
m_Population.replaceIndividualAt(nextDoomed, indy);
nextDoomed = getNextDoomed(m_Population, nextDoomed+1);
nextDoomed = getNextDoomed(m_Population, nextDoomed + 1);
} else {
if (m_Problem instanceof AbstractMultiObjectiveOptimizationProblem) {
if(indy.isDominatingDebConstraints(m_Population.getEAIndividual(index))){ //child dominates the parent replace the parent
if (indy.isDominatingDebConstraints(m_Population.getEAIndividual(index))) { //child dominates the parent replace the parent
m_Population.replaceIndividualAt(index, indy);
}else if(!(m_Population.getEAIndividual(index).isDominatingDebConstraints(indy))){ //do nothing if parent dominates the child use crowding if neither one dominates the other one
ReplacementNondominatedSortingDistanceCrowding repl =new ReplacementNondominatedSortingDistanceCrowding();
} else if (!(m_Population.getEAIndividual(index).isDominatingDebConstraints(indy))) { //do nothing if parent dominates the child use crowding if neither one dominates the other one
ReplacementNondominatedSortingDistanceCrowding repl = new ReplacementNondominatedSortingDistanceCrowding();
repl.insertIndividual(indy, m_Population, null);
}
// ReplacementCrowding repl = new ReplacementCrowding();
@@ -646,9 +643,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
} else {
// index = RNG.randomInt(0, this.m_Population.size()-1);
if (!compareToParent) {
index = RNG.randomInt(0, this.m_Population.size()-1);
index = RNG.randomInt(0, this.m_Population.size() - 1);
}
orig = (AbstractEAIndividual)this.m_Population.get(index);
orig = (AbstractEAIndividual) this.m_Population.get(index);
if (indy.isDominatingDebConstraints(orig)) {
this.m_Population.replaceIndividualAt(index, indy);
}
@@ -699,9 +696,10 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
}
/**
* Search for the first individual which is older than the age limit and return its index.
* If there is no age limit or all individuals are younger, -1 is returned. The start index
* of the search may be provided to make iterative search efficient.
* Search for the first individual which is older than the age limit and
* return its index. If there is no age limit or all individuals are
* younger, -1 is returned. The start index of the search may be provided to
* make iterative search efficient.
*
* @param pop Population to search
* @param startIndex index to start the search from
@@ -709,8 +707,8 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
*/
protected int getNextDoomed(Population pop, int startIndex) {
if (maximumAge > 0) {
for (int i=startIndex; i<pop.size(); i++) {
if (((AbstractEAIndividual)pop.get(i)).getAge() >= maximumAge) {
for (int i = startIndex; i < pop.size(); i++) {
if (((AbstractEAIndividual) pop.get(i)).getAge() >= maximumAge) {
return i;
}
}
@@ -718,51 +716,62 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return -1;
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
if(this.m_Listener ==null){
this.m_Listener=new Vector<InterfacePopulationChangedEventListener>();
if (this.m_Listener == null) {
this.m_Listener = new Vector<InterfacePopulationChangedEventListener>();
}
this.m_Listener.add(ea);
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener!=null&&m_Listener.removeElement(ea)) {
if (m_Listener != null && m_Listener.removeElement(ea)) {
return true;
} else {
return false;
}
}
/** Something has changed
/**
* Something has changed
*
* @param name
*/
protected void firePropertyChangedEvent (String name) {
if (this.m_Listener != null){
for(int i=0;i<this.m_Listener.size();i++){
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
for (int i = 0; i < this.m_Listener.size(); i++) {
this.m_Listener.get(i).registerPopulationStateChanged(this, name);
}
}
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = (AbstractOptimizationProblem)problem;
}
@Override
public InterfaceOptimizationProblem getProblem () {
return (InterfaceOptimizationProblem)this.m_Problem;
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = (AbstractOptimizationProblem) problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
@Override
public InterfaceOptimizationProblem getProblem() {
return (InterfaceOptimizationProblem) this.m_Problem;
}
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -770,39 +779,42 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
String result = "";
result += "Differential Evolution:\n";
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The identifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "Differential Evolution using a steady-state population scheme.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -810,19 +822,23 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return "DE";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
@@ -833,20 +849,28 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return new SolutionSet(pop, pop);
}
/** F is a real and constant factor which controls the amplification of the differential variation
/**
* F is a real and constant factor which controls the amplification of the
* differential variation
*
* @param f
*/
public void setF (double f) {
public void setF(double f) {
this.m_F = f;
}
public double getF() {
return this.m_F;
}
public String fTipText() {
return "F is a real and constant factor which controls the amplification of the differential variation.";
}
/** Probability of alteration through DE (something like a discrete uniform crossover is performed here)
/**
* Probability of alteration through DE (something like a discrete uniform
* crossover is performed here)
*
* @param k
*/
public void setK(double k) {
@@ -858,30 +882,39 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
}
this.m_k = k;
}
public double getK() {
return this.m_k;
}
public String kTipText() {
return "Probability of alteration through DE (a.k.a. CR, similar to discrete uniform crossover).";
}
/** Enhance greediness through amplification of the differential vector to the best individual for DE2
/**
* Enhance greediness through amplification of the differential vector to
* the best individual for DE2
*
* @param l
*/
public void setLambda (double l) {
public void setLambda(double l) {
this.m_Lambda = l;
}
public double getLambda() {
return this.m_Lambda;
}
public String lambdaTipText() {
return "Enhance greediness through amplification of the differential vector to the best individual for DE2.";
}
/** In case of trig. mutation DE, the TMO is applied wit probability Mt
/**
* In case of trig. mutation DE, the TMO is applied wit probability Mt
*
* @param l
*/
public void setMt (double l) {
public void setMt(double l) {
this.m_Mt = l;
if (this.m_Mt < 0) {
this.m_Mt = 0;
@@ -890,39 +923,47 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
this.m_Mt = 1;
}
}
public double getMt() {
return this.m_Mt;
}
public String mtTipText() {
return "In case of trigonometric mutation DE, the TMO is applied with probability Mt.";
}
/** This method allows you to choose the type of Differential Evolution.
/**
* This method allows you to choose the type of Differential Evolution.
*
* @param s The type.
*/
public void setDEType(DETypeEnum s) {
this.m_DEType = s;
// show mt for trig. DE only
GenericObjectEditor.setShowProperty(this.getClass(), "lambda", s==DETypeEnum.DE2_CurrentToBest);
GenericObjectEditor.setShowProperty(this.getClass(), "mt", s==DETypeEnum.TrigonometricDE);
GenericObjectEditor.setShowProperty(this.getClass(), "lambda", s == DETypeEnum.DE2_CurrentToBest);
GenericObjectEditor.setShowProperty(this.getClass(), "mt", s == DETypeEnum.TrigonometricDE);
}
public DETypeEnum getDEType() {
return this.m_DEType;
}
public String dETypeTipText() {
return "Choose the type of Differential Evolution.";
}
/**
* @return the maximumAge
**/
*
*/
public int getMaximumAge() {
return maximumAge;
}
/**
* @param maximumAge the maximumAge to set
**/
*
*/
public void setMaximumAge(int maximumAge) {
this.maximumAge = maximumAge;
}
@@ -933,6 +974,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
/**
* Check whether the problem range will be enforced.
*
* @return the forceRange
*/
public boolean isCheckRange() {
@@ -973,7 +1015,6 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// public String cyclePopTipText() {
// return "Use all individuals as parents in cyclic sequence instead of randomly.";
// }
public boolean isCompareToParent() {
return compareToParent;
}
@@ -1012,14 +1053,16 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
/**
* @return the maximumAge
**/
*
*/
public boolean isReEvaluate() {
return reEvaluate;
}
/**
* @param maximumAge the maximumAge to set
**/
*
*/
public void setReEvaluate(boolean reEvaluate) {
this.reEvaluate = reEvaluate;
}
@@ -1027,5 +1070,4 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
public String reEvaluateTipText() {
return "Reeavulates individuals which are older than maximum age instead of discarding them";
}
}

9
src/eva2/server/go/strategies/EsDpiNiching.java
View File

@@ -284,6 +284,8 @@ public class EsDpiNiching implements InterfaceOptimizer, Serializable, Interface
* with the given parameters. If windowLen <= 0, the deactivation mechanism
* is disabled. This provides for semi-sequential niching with DPI-ES
*
*
*
*
* @param threshold
@@ -948,7 +950,8 @@ public class EsDpiNiching implements InterfaceOptimizer, Serializable, Interface
/**
* Calculate the dynamic population size, which is the number of individuals
* that are currently "alive" in the peak set. This must be implemented in
* analogy to {@link #collectPopulationIncGen(Population, EvolutionStrategies[], Population)}
* analogy to
* {@link #collectPopulationIncGen(Population, EvolutionStrategies[], Population)}
*
* @return
*/
@@ -1048,10 +1051,6 @@ public class EsDpiNiching implements InterfaceOptimizer, Serializable, Interface
}
}
@Override
public void freeWilly() {
}
@Override
public InterfaceSolutionSet getAllSolutions() {
Population peaks = new Population(peakOpts.length);

33
src/eva2/server/go/strategies/EvolutionStrategies.java
View File

@@ -12,20 +12,20 @@ import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/** Evolution strategies by Rechenberg and Schwefel, but please remember that
/**
* Evolution strategies by Rechenberg and Schwefel, but please remember that
* this only gives the generation strategy and not the coding. But this is the
* only stategy that is able to utilize the 1/5 success rule mutation. Unfortunately,
* there is a minor problem with the interpretation of the population size in constrast
* to the parameters mu and lambda used by Rechenberg and Schwefel. Therefore, i'm
* afraid that the interpretation of the population size may be subject to future
* changes.
* This is a implementation of Evolution Strategies.
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
* only stategy that is able to utilize the 1/5 success rule mutation.
* Unfortunately, there is a minor problem with the interpretation of the
* population size in constrast to the parameters mu and lambda used by
* Rechenberg and Schwefel. Therefore, i'm afraid that the interpretation of the
* population size may be subject to future changes. This is a implementation of
* Evolution Strategies. Copyright: Copyright (c) 2003 Company: University of
* Tuebingen, Computer Architecture
*
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializable {
@@ -339,18 +339,9 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
return this.identifier;
}
/**
* This method is required to free the memory on a RMIServer, but there is
* nothing to implement.
*/
@Override
public void freeWilly() {
}
/**
* These are for GUI
*/
/**
* This method returns a global info string
*

123
src/eva2/server/go/strategies/EvolutionaryProgramming.java
View File

@@ -10,26 +10,25 @@ import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.F1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/** Evolutionary programming by Fogel. Works fine but is actually a quite greedy local search
* strategy solely based on mutation. To prevent any confusion, the mutation rate is temporaily
* set to 1.0.
* Potential citation: the PhD thesis of David B. Fogel (1992).
/**
* Evolutionary programming by Fogel. Works fine but is actually a quite greedy
* local search strategy solely based on mutation. To prevent any confusion, the
* mutation rate is temporaily set to 1.0. Potential citation: the PhD thesis of
* David B. Fogel (1992).
*
* Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
* Architecture
*
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Serializable {
private int m_PopulationSize = 0;
private Population m_Population = new Population();
private InterfaceOptimizationProblem m_Problem = new F1Problem();
private InterfaceSelection m_EnvironmentSelection = new SelectEPTournaments();
private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -37,10 +36,10 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
}
public EvolutionaryProgramming(EvolutionaryProgramming a) {
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.m_Identifier = a.m_Identifier;
this.m_EnvironmentSelection = (InterfaceSelection)a.m_EnvironmentSelection.clone();
this.m_EnvironmentSelection = (InterfaceSelection) a.m_EnvironmentSelection.clone();
}
@Override
@@ -56,12 +55,14 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param reset If true the population is reset.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
this.m_Population = (Population)pop.clone();
this.m_Population = (Population) pop.clone();
if (reset) {
this.m_Population.init();
this.evaluatePopulation(this.m_Population);
@@ -69,8 +70,9 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
}
}
/** This method will evaluate the current population using the
* given problem.
/**
* This method will evaluate the current population using the given problem.
*
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
@@ -78,16 +80,17 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
population.incrGeneration();
}
/** This method will generate the offspring population from the
* given population of evaluated individuals.
/**
* This method will generate the offspring population from the given
* population of evaluated individuals.
*/
private Population generateChildren() {
Population result = (Population)this.m_Population.cloneWithoutInds();
Population result = (Population) this.m_Population.cloneWithoutInds();
AbstractEAIndividual mutant;
result.clear();
for (int i = 0; i < this.m_Population.size(); i++) {
mutant = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Population.get(i)).clone();
mutant = (AbstractEAIndividual) ((AbstractEAIndividual) this.m_Population.get(i)).clone();
double tmpD = mutant.getMutationProbability();
mutant.setMutationProbability(1.0);
mutant.mutate();
@@ -113,45 +116,55 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -159,39 +172,42 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
String result = "";
result += "Evolutionary Programming:\n";
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a basic Evolutionary Programming scheme.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -199,19 +215,23 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
return "EP";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
@@ -220,15 +240,20 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** Choose a method for selecting the reduced population.
/**
* Choose a method for selecting the reduced population.
*
* @param selection
*/
public void setEnvironmentSelection(InterfaceSelection selection) {
this.m_EnvironmentSelection = selection;
}
public InterfaceSelection getEnvironmentSelection() {
return this.m_EnvironmentSelection;
}
public String environmentSelectionTipText() {
return "Choose a method for selecting the reduced population.";
}

159
src/eva2/server/go/strategies/FloodAlgorithm.java
View File

@@ -9,21 +9,20 @@ import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/** The flood algorithm, and alternative to the threshold algorithms. No really
* good but commonly known and sometimes even used. Here the problem is to choose
* the initial flood peak and the drain rate such that it fits the current optimization
* problem. But again this is a greedy local search strategy. Similar to the
* evolutionary programming strategy this strategy sets the mutation rate temporarily
* to 1.0.
* The algorithm regards only one-dimensional fitness.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 01.10.2004
* Time: 13:46:02
* To change this template use File | Settings | File Templates.
/**
* The flood algorithm, and alternative to the threshold algorithms. No really
* good but commonly known and sometimes even used. Here the problem is to
* choose the initial flood peak and the drain rate such that it fits the
* current optimization problem. But again this is a greedy local search
* strategy. Similar to the evolutionary programming strategy this strategy sets
* the mutation rate temporarily to 1.0. The algorithm regards only
* one-dimensional fitness. Created by IntelliJ IDEA. User: streiche Date:
* 01.10.2004 Time: 13:46:02 To change this template use File | Settings | File
* Templates.
*/
public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private int m_MultiRuns = 100;
private int m_FitnessCalls = 100;
@@ -31,7 +30,6 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
GAIndividualBinaryData m_Best, m_Test;
public double m_InitialFloodPeak = 2000.0, m_CurrentFloodPeak;
public double m_DrainRate = 1.0;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -43,8 +41,8 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
}
public FloodAlgorithm(FloodAlgorithm a) {
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.m_InitialFloodPeak = a.m_InitialFloodPeak;
this.m_DrainRate = a.m_DrainRate;
}
@@ -54,7 +52,8 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
return (Object) new FloodAlgorithm(this);
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
@Override
public void init() {
@@ -64,12 +63,14 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param reset If true the population is reset.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
this.m_Population = (Population)pop.clone();
this.m_Population = (Population) pop.clone();
if (reset) {
this.m_Population.init();
this.m_Problem.evaluate(this.m_Population);
@@ -78,12 +79,13 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
this.m_CurrentFloodPeak = this.m_InitialFloodPeak;
}
/** This method will optimize
/**
* This method will optimize
*/
@Override
public void optimize() {
AbstractEAIndividual indy;
Population original = (Population)this.m_Population.clone();
Population original = (Population) this.m_Population.clone();
double[] fitness;
for (int i = 0; i < this.m_Population.size(); i++) {
@@ -95,7 +97,7 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
}
this.m_Problem.evaluate(this.m_Population);
for (int i = 0; i < this.m_Population.size(); i++) {
fitness = ((AbstractEAIndividual)this.m_Population.get(i)).getFitness();
fitness = ((AbstractEAIndividual) this.m_Population.get(i)).getFitness();
if (fitness[0] > this.m_CurrentFloodPeak) {
this.m_Population.remove(i);
this.m_Population.add(i, original.get(i));
@@ -106,7 +108,9 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method calculates the difference between the fitness values
/**
* This method calculates the difference between the fitness values
*
* @param org The original
* @param mut The mutant
*/
@@ -121,19 +125,23 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
return result;
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
public void defaultInit() {
this.m_FitnessCallsNeeded = 0;
@@ -141,24 +149,26 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
this.m_Best.defaultInit(m_Problem);
}
/** This method will optimize
/**
* This method will optimize
*/
public void defaultOptimize() {
for (int i = 0; i < m_FitnessCalls; i++) {
this.m_Test = (GAIndividualBinaryData)((this.m_Best).clone());
this.m_Test = (GAIndividualBinaryData) ((this.m_Best).clone());
this.m_Test.defaultMutate();
if (this.m_Test.defaultEvaulateAsMiniBits() < this.m_Best.defaultEvaulateAsMiniBits()) {
this.m_Best = this.m_Test;
}
this.m_FitnessCallsNeeded = i;
if (this.m_Best.defaultEvaulateAsMiniBits() == 0) {
i = this.m_FitnessCalls +1;
i = this.m_FitnessCalls + 1;
}
}
}
/** This main method will start a simple hillclimber.
* No arguments necessary.
/**
* This main method will start a simple hillclimber. No arguments necessary.
*
* @param args
*/
public static void main(String[] args) {
@@ -172,36 +182,43 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
}
TmpMeanCalls /= program.m_MultiRuns;
TmpMeanFitness /= program.m_MultiRuns;
System.out.println("("+program.m_MultiRuns+"/"+program.m_FitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
System.out.println("(" + program.m_MultiRuns + "/" + program.m_FitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -209,44 +226,46 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
String result = "";
if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
}
else {
} else {
result += "Simulated Annealing:\n";
}
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The flood algorithm uses an declining flood peak to accpect new solutions (*shudder* check inital flood peak and drain very carefully!).";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -254,55 +273,67 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
return "MS-FA";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Change the number of best individuals stored (MS-FA).";
}
@Override
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This methods allow you to set/get the temperatur of the flood
* algorithm procedure
/**
* This methods allow you to set/get the temperatur of the flood algorithm
* procedure
*
* @return The initial flood level.
*/
public double getInitialFloodPeak() {
return this.m_InitialFloodPeak;
}
public void setInitialFloodPeak(double pop){
public void setInitialFloodPeak(double pop) {
this.m_InitialFloodPeak = pop;
}
public String initialFloodPeakTipText() {
return "Set the initial flood peak.";
}
/** This methods allow you to set/get the drain rate of the flood
* algorithm procedure
/**
* This methods allow you to set/get the drain rate of the flood algorithm
* procedure
*
* @return The drain rate.
*/
public double getDrainRate() {
return this.m_DrainRate;
}
public void setDrainRate(double a){
public void setDrainRate(double a) {
this.m_DrainRate = a;
if (this.m_DrainRate < 0) {
this.m_DrainRate = 0.0;
}
}
public String drainRateTipText() {
return "Set the drain rate that reduces the current flood level each generation.";
}

8
src/eva2/server/go/strategies/GeneticAlgorithm.java
View File

@@ -241,14 +241,6 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
return this.identifier;
}
/**
* This method is required to free the memory on a RMIServer, but there is
* nothing to implement.
*/
@Override
public void freeWilly() {
}
/**
* ********************************************************************************************************************
* These are for GUI

85
src/eva2/server/go/strategies/GradientDescentAlgorithm.java
View File

@@ -22,11 +22,9 @@ import eva2.tools.ReflectPackage;
* @author not attributable
* @version 1.0
*/
public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Serializable {
private InterfaceOptimizationProblem m_Problem;
InterfaceDataTypeDouble m_Best, m_Test;
private int iterations = 1;
private double wDecreaseStepSize = 0.5;
@@ -46,13 +44,10 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
transient private InterfacePopulationChangedEventListener m_Listener;
public double maximumabsolutechange = 0.2;
// Hashtable indyhash;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
private Population m_Population;
private static boolean TRACE=false;
private static boolean TRACE = false;
private static final String lockKey = "gdaLockDataKey";
private static final String lastFitnessKey = "gdaLastFitDataKey";
private static final String stepSizeKey = "gdaStepSizeDataKey";
@@ -87,8 +82,8 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
* @param maxAbsoluteChange
*/
public GradientDescentAlgorithm(double minStepSize, double maxStepSize, double maxAbsoluteChange) {
globalStepSizeAdaption=false;
localStepSizeAdaption=true;
globalStepSizeAdaption = false;
localStepSizeAdaption = true;
localminstepsize = minStepSize;
globalminstepsize = minStepSize;
localmaxstepsize = maxStepSize;
@@ -98,7 +93,9 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
@Override
public Object clone() {
/**@todo Implement InterfaceOptimizer method*/
/**
* @todo Implement InterfaceOptimizer method
*/
throw new java.lang.UnsupportedOperationException("Method clone() not yet implemented.");
}
@@ -126,7 +123,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
// if ((this.indyhash == null) || (this.indyhash.size() <1)) init();
for (int i = 0; i < this.m_Population.size(); i++) {
indy = ((AbstractEAIndividual)this.m_Population.get(i));
indy = ((AbstractEAIndividual) this.m_Population.get(i));
if (!indy.hasData(gradientKey)) {
//System.out.println("new indy to hash");
// Hashtable history = new Hashtable();
@@ -151,10 +148,10 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
// System.out.println("hashtable built");
for (int i = 0; i < this.m_Population.size(); i++) {
indy = ((AbstractEAIndividual)this.m_Population.get(i));
indy = ((AbstractEAIndividual) this.m_Population.get(i));
double[][] range = ((InterfaceDataTypeDouble) indy).getDoubleRange();
double[] params = ((InterfaceDataTypeDouble) indy).getDoubleData();
indy.putData(oldParamsKey , params);
indy.putData(oldParamsKey, params);
int[] lock = (int[]) indy.getData(lockKey);
double indystepsize = ((Double) indy.getData(stepSizeKey)).doubleValue();
@@ -191,7 +188,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
indy.putData(gradientKey, gradient);
double[] change = new double[params.length];
if (indy.hasData(changesKey)) {
oldchange =(double[]) indy.getData(changesKey);
oldchange = (double[]) indy.getData(changesKey);
}
boolean dograddesc = (this.momentumterm) && (oldchange != null);
@@ -204,7 +201,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
if (this.globalStepSizeAdaption) {
tempstepsize *= indystepsize;
}
double wchange = signum(tempstepsize * gradient[j]) * Math.min(maximumabsolutechange,Math.abs(tempstepsize * gradient[j])); //indystepsize * gradient[j];
double wchange = signum(tempstepsize * gradient[j]) * Math.min(maximumabsolutechange, Math.abs(tempstepsize * gradient[j])); //indystepsize * gradient[j];
if (this.manhattan) {
wchange = this.signum(wchange) * tempstepsize;
}
@@ -236,12 +233,12 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
} // end if ((this.m_Problem instanceof InterfaceFirstOrderDerivableProblem) && (indy instanceof InterfaceDataTypeDouble)) {
else {
String msg="Warning, problem of type InterfaceFirstOrderDerivableProblem and template of type InterfaceDataTypeDouble is required for " + this.getClass();
String msg = "Warning, problem of type InterfaceFirstOrderDerivableProblem and template of type InterfaceDataTypeDouble is required for " + this.getClass();
EVAERROR.errorMsgOnce(msg);
Class<?>[] clsArr = ReflectPackage.getAssignableClasses(InterfaceFirstOrderDerivableProblem.class.getName(), true, true);
msg += " (available: ";
for (Class<?> cls: clsArr) {
msg=msg+" "+cls.getSimpleName();
for (Class<?> cls : clsArr) {
msg = msg + " " + cls.getSimpleName();
}
msg += ")";
throw new RuntimeException(msg);
@@ -253,7 +250,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
if (this.recovery) {
for (int i = 0; i < this.m_Population.size(); i++) {
indy = ((AbstractEAIndividual)this.m_Population.get(i));
indy = ((AbstractEAIndividual) this.m_Population.get(i));
// Hashtable history = (Hashtable) indyhash.get(indy);
if (indy.getFitness()[0] > recoverythreshold) {
if (TRACE) {
@@ -284,7 +281,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
//System.out.println("gsa main");
for (int i = 0; i < this.m_Population.size(); i++) {
indy = ((AbstractEAIndividual)this.m_Population.get(i));
indy = ((AbstractEAIndividual) this.m_Population.get(i));
// Hashtable history = (Hashtable) indyhash.get(indy);
// if (history == null) break;
if (indy.getData(lastFitnessKey) != null) {
@@ -311,7 +308,6 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
private double momentumweigth = 0.1;
protected void firePropertyChangedEvent(String name) {
@@ -340,8 +336,9 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
this.m_Population = pop;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
@@ -374,16 +371,18 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
public static void main(String[] args) {
GradientDescentAlgorithm program = new GradientDescentAlgorithm();
InterfaceOptimizationProblem problem = new F1Problem();
@@ -399,22 +398,22 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
}
}
@Override
public void freeWilly() { }
public static String globalInfo() {
return "Gradient Descent can be applied to derivable functions ("+InterfaceFirstOrderDerivableProblem.class.getSimpleName()+").";
return "Gradient Descent can be applied to derivable functions (" + InterfaceFirstOrderDerivableProblem.class.getSimpleName() + ").";
}
//////////////// for global adaption
public boolean isAdaptStepSizeGlobally() {
return globalStepSizeAdaption;
}
public void setAdaptStepSizeGlobally(boolean globalstepsizeadaption) {
this.globalStepSizeAdaption = globalstepsizeadaption;
if (globalstepsizeadaption && localStepSizeAdaption) {
setAdaptStepSizeLocally(false);
}
}
public String adaptStepSizeGloballyTipText() {
return "Use a single step size per individual - (priority over local step size).";
}
@@ -422,9 +421,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getGlobalMaxStepSize() {
return globalmaxstepsize;
}
public void setGlobalMaxStepSize(double p) {
globalmaxstepsize = p;
}
public String globalMaxStepSizeTipText() {
return "Maximum step size for global adaption.";
}
@@ -432,9 +433,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getGlobalMinStepSize() {
return globalminstepsize;
}
public void setGlobalMinStepSize(double p) {
globalminstepsize = p;
}
public String globalMindStepSizeTipText() {
return "Minimum step size for global adaption.";
}
@@ -442,9 +445,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getGlobalInitStepSize() {
return globalinitstepsize;
}
public void setGlobalInitStepSize(double initstepsize) {
this.globalinitstepsize = initstepsize;
}
public String globalInitStepSizeTipText() {
return "Initial step size for global adaption.";
}
@@ -453,12 +458,14 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public boolean isAdaptStepSizeLocally() {
return localStepSizeAdaption;
}
public void setAdaptStepSizeLocally(boolean stepsizeadaption) {
this.localStepSizeAdaption = stepsizeadaption;
if (globalStepSizeAdaption && localStepSizeAdaption) {
setAdaptStepSizeGlobally(false);
}
}
public String adaptStepSizeLocallyTipText() {
return "Use a step size parameter in any dimension.";
}
@@ -466,6 +473,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getLocalMinStepSize() {
return localminstepsize;
}
public void setLocalMinStepSize(double localminstepsize) {
this.localminstepsize = localminstepsize;
}
@@ -473,6 +481,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getLocalMaxStepSize() {
return localmaxstepsize;
}
public void setLocalMaxStepSize(double localmaxstepsize) {
this.localmaxstepsize = localmaxstepsize;
}
@@ -480,9 +489,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public void setStepSizeIncreaseFact(double nplus) {
this.wIncreaseStepSize = nplus;
}
public double getStepSizeIncreaseFact() {
return wIncreaseStepSize;
}
public String stepSizeIncreaseFactTipText() {
return "Factor for increasing the step size in adaption.";
}
@@ -490,9 +501,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public void setStepSizeDecreaseFact(double nminus) {
this.wDecreaseStepSize = nminus;
}
public double getStepSizeDecreaseFact() {
return wDecreaseStepSize;
}
public String stepSizeDecreaseFactTipText() {
return "Factor for decreasing the step size in adaption.";
}
@@ -501,40 +514,47 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public boolean isRecovery() {
return recovery;
}
public void setRecovery(boolean recovery) {
this.recovery = recovery;
}
public int getRecoveryLocksteps() {
return recoverylocksteps;
}
public void setRecoveryLocksteps(int locksteps) {
this.recoverylocksteps = locksteps;
}
public double getRecoveryThreshold() {
return recoverythreshold;
}
public void setRecoveryThreshold(double recoverythreshold) {
this.recoverythreshold = recoverythreshold;
}
public String recoveryThresholdTipText() {
return "If the fitness exceeds this threshold, an unstable area is assumed and one step recovered.";
}
public int getIterations() {
return iterations;
}
public void setIterations(int iterations) {
this.iterations = iterations;
}
public String iterationsTipText() {
return "The number of GD-iterations per generation.";
}
public boolean isManhattan() {
return manhattan;
}
public void setManhattan(boolean manhattan) {
this.manhattan = manhattan;
}
@@ -542,6 +562,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public boolean isMomentumTerm() {
return momentumterm;
}
public void setMomentumTerm(boolean momentum) {
this.momentumterm = momentum;
}
@@ -549,6 +570,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getMomentumWeigth() {
return momentumweigth;
}
public void setMomentumWeigth(double momentumweigth) {
this.momentumweigth = momentumweigth;
}
@@ -556,11 +578,12 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getMaximumAbsoluteChange() {
return maximumabsolutechange;
}
public void setMaximumAbsoluteChange(double maximumabsolutechange) {
this.maximumabsolutechange = maximumabsolutechange;
}
public String maximumAbsoluteChangeTipText() {
return "The maximum change along a coordinate in one step.";
}
}

113
src/eva2/server/go/strategies/HillClimbing.java
View File

@@ -9,27 +9,25 @@ import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/** This is a Multi-Start Hill-Climber, here the population size gives the number of
* multi-starts. Similar to the evolutionary programming strategy this strategy sets the
* mutation rate temporarily to 1.0.
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
/**
* This is a Multi-Start Hill-Climber, here the population size gives the number
* of multi-starts. Similar to the evolutionary programming strategy this
* strategy sets the mutation rate temporarily to 1.0. Copyright: Copyright (c)
* 2003 Company: University of Tuebingen, Computer Architecture
*
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private InterfaceMutation mutator = null;
// private int m_MultiRuns = 100;
// private int m_FitnessCalls = 100;
// private int m_FitnessCallsNeeded = 0;
// GAIndividualBinaryData m_Best, m_Test;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -41,8 +39,8 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
}
public HillClimbing(HillClimbing a) {
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
}
@Override
@@ -50,7 +48,8 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
return (Object) new HillClimbing(this);
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
@Override
public void init() {
@@ -61,7 +60,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
@Override
public void initByPopulation(Population pop, boolean reset) {
this.m_Population = (Population)pop.clone();
this.m_Population = (Population) pop.clone();
if (reset) {
this.m_Population.init();
this.m_Problem.evaluate(this.m_Population);
@@ -69,12 +68,13 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
}
}
/** This method will optimize
/**
* This method will optimize
*/
@Override
public void optimize() {
AbstractEAIndividual indy;
Population original = (Population)this.m_Population.clone();
Population original = (Population) this.m_Population.clone();
double tmpD;
InterfaceMutation tmpMut;
@@ -84,15 +84,14 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
indy.setMutationProbability(1.0);
if (mutator == null) {
indy.mutate();
}
else {
} else {
mutator.mutate(indy);
}
indy.setMutationProbability(tmpD);
}
this.m_Problem.evaluate(this.m_Population);
for (int i = 0; i < this.m_Population.size(); i++) {
if (((AbstractEAIndividual)original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual)this.m_Population.get(i)))) {
if (((AbstractEAIndividual) original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual) this.m_Population.get(i)))) {
// this.m_Population.remove(i);
// throw away mutated one and replace by old one
this.m_Population.set(i, original.get(i));
@@ -123,8 +122,9 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
}
/**
* Allows to set a desired mutator by hand, which is used instead of the one in the individuals.
* Set it to null to use the one in the individuals, which is the default.
* Allows to set a desired mutator by hand, which is used instead of the one
* in the individuals. Set it to null to use the one in the individuals,
* which is the default.
*
* @param mute
*/
@@ -132,15 +132,18 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
mutator = mute;
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
@@ -163,7 +166,6 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
// if (this.m_Best.defaultEvaulateAsMiniBits() == 0) i = this.m_FitnessCalls +1;
// }
// }
// /** This main method will start a simple hillclimber.
// * No arguments necessary.
// * @param args
@@ -181,34 +183,40 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
// TmpMeanFitness = TmpMeanFitness/program.m_MultiRuns;
// System.out.println("("+program.m_MultiRuns+"/"+program.m_FitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
// }
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -216,56 +224,60 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
String result = "";
if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
}
else {
} else {
result += "Hill Climbing:\n";
}
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The Hill Climber uses the default EA mutation and initializing operators. If the population size is bigger than one a multi-start Hill Climber is performed.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
public String getName() {
return "MS-HC"+getIdentifier();
return "MS-HC" + getIdentifier();
}
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
@@ -273,6 +285,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
public String populationTipText() {
return "Change the number of best individuals stored (MS-HC).";
}

35
src/eva2/server/go/strategies/InterfaceOptimizer.java
View File

@@ -22,29 +22,29 @@ public interface InterfaceOptimizer {
/** This method will return deep clone of the optimizer
* @return The clone
*/
public Object clone();
Object clone();
/** This method will return a naming String
* @return The name of the algorithm
*/
public String getName();
String getName();
/**
* This method allows you to add a listener to the Optimizer.
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea);
void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea);
/**
* This method removes a listener from the Optimizer. It returns true on success,
* false if the listener could not be found.
* @param ea
*/
public boolean removePopulationChangedEventListener(InterfacePopulationChangedEventListener ea);
boolean removePopulationChangedEventListener(InterfacePopulationChangedEventListener ea);
/** This method will init the optimizer
*/
public void init();
void init();
/**
* This method will init the optimizer with a given population.
@@ -52,22 +52,22 @@ public interface InterfaceOptimizer {
* @param pop The initial population
* @param reset If true the population is reinitialized and reevaluated.
*/
public void initByPopulation(Population pop, boolean reset);
void initByPopulation(Population pop, boolean reset);
/** This method will optimize for a single iteration, after this step
* the population should be as big as possible (ie. the size of lambda
* and not mu) and all individual should be evaluated. This allows more
* usefull statistics on the population.
*/
public void optimize();
void optimize();
/** 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();
public void setPopulation(Population pop);
Population getPopulation();
void setPopulation(Population pop);
/**
* Return all found solutions (local optima) if they are not contained in the current population. Be
@@ -78,14 +78,14 @@ public interface InterfaceOptimizer {
*
* @return A solution set of the current population and possibly earlier solutions.
*/
public InterfaceSolutionSet getAllSolutions();
InterfaceSolutionSet getAllSolutions();
/**
* This method allows you to set an identifier for the algorithm
* @param name The identifier
*/
public void setIdentifier(String name);
public String getIdentifier();
void setIdentifier(String name);
String getIdentifier();
/**
* This method will set the problem that is to be optimized. The problem
@@ -93,17 +93,12 @@ public interface InterfaceOptimizer {
*
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem);
public InterfaceOptimizationProblem getProblem ();
void setProblem (InterfaceOptimizationProblem problem);
InterfaceOptimizationProblem getProblem ();
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
* @return A descriptive string
*/
public String getStringRepresentation();
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
public void freeWilly();
String getStringRepresentation();
}

236
src/eva2/server/go/strategies/IslandModelEA.java
View File

@@ -17,27 +17,26 @@ import eva2.server.go.problems.F8Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.server.go.problems.TF1Problem;
/** The one and only island model for parallelization. Since parallelization based
* on the RMIProxyRemoteThread is on the one hand much slower than benchmark function
* evaluation and on the other hand the GUI based distribution scheme is rather prone
* to config errors (the correct ssh version is required, the jar needs to be in
* the working dir and possible problem data must be on the servers to) an implicit
* island-model has been implemented too to allow fast and reliable computation.
* This is still usefull, since it is less prone to premature convergence and also
* an heterogenuous island model can be used.
/**
* The one and only island model for parallelization. Since parallelization
* based on the RMIProxyRemoteThread is on the one hand much slower than
* benchmark function evaluation and on the other hand the GUI based
* distribution scheme is rather prone to config errors (the correct ssh version
* is required, the jar needs to be in the working dir and possible problem data
* must be on the servers to) an implicit island-model has been implemented too
* to allow fast and reliable computation. This is still usefull, since it is
* less prone to premature convergence and also an heterogenuous island model
* can be used.
*
* A population of the same size is sent to all nodes and evaluated there independently
* for a cycle (more precisely: for MigrationRate generations) after which a communication
* step is performed according to the migration model. Only after migration is a main
* cycle complete, the statistics updated etc.
* A population of the same size is sent to all nodes and evaluated there
* independently for a cycle (more precisely: for MigrationRate generations)
* after which a communication step is performed according to the migration
* model. Only after migration is a main cycle complete, the statistics updated
* etc.
*
* Created by IntelliJ IDEA.
* User: streiche
* Date: 12.09.2004
* Time: 14:48:20
* To change this template use File | Settings | File Templates.
* Created by IntelliJ IDEA. User: streiche Date: 12.09.2004 Time: 14:48:20 To
* change this template use File | Settings | File Templates.
*/
public class IslandModelEA implements InterfacePopulationChangedEventListener, InterfaceOptimizer, java.io.Serializable {
private Population m_Population = new Population();
@@ -47,30 +46,26 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
// private String[] m_NodesList;
private int m_MigrationRate = 10;
private boolean m_HeterogenuousProblems = false;
// These are the processor to run on
private int m_numLocalCPUs = 1;
private boolean m_localOnly = false;
transient private InterfaceOptimizer[] m_Islands;
// This is for debugging
private boolean m_LogLocalChanges = true;
private boolean m_Show = false;
transient private Plot m_Plot = null;
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
transient private final boolean TRACE = false;
public IslandModelEA() {
}
public IslandModelEA(IslandModelEA a) {
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Optimizer = (InterfaceOptimizer)a.m_Optimizer.clone();
this.m_Migration = (InterfaceMigration)a.m_Migration.clone();
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.m_Optimizer = (InterfaceOptimizer) a.m_Optimizer.clone();
this.m_Migration = (InterfaceMigration) a.m_Migration.clone();
this.m_MigrationRate = a.m_MigrationRate;
this.m_HeterogenuousProblems = a.m_HeterogenuousProblems;
this.m_numLocalCPUs = a.m_numLocalCPUs;
@@ -98,14 +93,14 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Population.init();
this.m_Optimizer.init();
this.m_Optimizer.setProblem(this.m_Problem);
this.m_Optimizer.setPopulation((Population)m_Population.clone());
this.m_Optimizer.setPopulation((Population) m_Population.clone());
InterfacePopulationChangedEventListener myLocal = null;
if (this.m_localOnly) {
// this is running on the local machine
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);
@@ -142,17 +137,19 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Population.incrGeneration(); // the island-initialization has increased the island-pop generations.
for (int i = 0; i < this.m_Islands.length; i++) {
pop = (Population)this.m_Islands[i].getPopulation().clone();
pop = (Population) this.m_Islands[i].getPopulation().clone();
this.m_Population.addPopulation(pop);
this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
if (m_Islands[i].getPopulation().getGeneration()!=m_Population.getGeneration()) {
if (m_Islands[i].getPopulation().getGeneration() != m_Population.getGeneration()) {
System.err.println("Error, inconsistent generations!");
}
}
this.firePropertyChangedEvent(Population.nextGenerationPerformed, this.m_Optimizer.getPopulation());
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param reset If true the population is reset.
*/
@Override
@@ -167,7 +164,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
}
}
this.m_Population = (Population)tpop.clone();
this.m_Population = (Population) tpop.clone();
if (reset) {
this.m_Population.init();
this.m_Population.incrGeneration();
@@ -180,7 +177,7 @@ 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);
@@ -216,21 +213,22 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Migration.initMigration(this.m_Islands);
Population pop;
for (int i = 0; i < this.m_Islands.length; i++) {
pop = (Population)this.m_Islands[i].getPopulation().clone();
pop = (Population) this.m_Islands[i].getPopulation().clone();
this.m_Population.addPopulation(pop);
this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
}
this.firePropertyChangedEvent(Population.nextGenerationPerformed, this.m_Optimizer.getPopulation());
}
/** The optimize method will compute an 'improved' and evaluated population
/**
* The optimize method will compute an 'improved' and evaluated population
*/
@Override
public void optimize() {
for (int i = 0; i < this.m_Islands.length; i++) {
if (this.m_Islands[i].getPopulation().size() > 0) {
this.m_Islands[i].optimize();
if (TRACE ) {
if (TRACE) {
System.out.println(BeanInspector.toString(m_Islands[i].getPopulation()));
}
} else {
@@ -253,8 +251,8 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
System.gc();
}
/** This method will manage communication between the
* islands
/**
* This method will manage communication between the islands
*/
private void communicate() {
// Here i'll have to wait until all islands are finished
@@ -264,13 +262,13 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
allReachedG = true;
String gen = "[";
for (int i = 0; i < this.m_Islands.length; i++) {
gen += this.m_Islands[i].getPopulation().getGeneration()+"; ";
gen += this.m_Islands[i].getPopulation().getGeneration() + "; ";
if (this.m_Islands[i].getPopulation().getGeneration() != G) {
allReachedG = false;
}
}
if (!allReachedG) {
System.out.println("Waiting...."+gen+"] ?= " + G);
System.out.println("Waiting...." + gen + "] ?= " + G);
try {
Thread.sleep(1000);
} catch (Exception e) {
@@ -282,7 +280,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Population.setFunctionCalls(0);
Population pop;
for (int i = 0; i < this.m_Islands.length; i++) {
pop = (Population)this.m_Islands[i].getPopulation().clone();
pop = (Population) this.m_Islands[i].getPopulation().clone();
this.m_Population.addPopulation(pop);
this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
}
@@ -296,46 +294,56 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Migration.migrate(this.m_Islands);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent (String name, Population population) {
protected void firePropertyChangedEvent(String name, Population population) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.setProblem(problem);
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -350,11 +358,11 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
if (this.m_HeterogenuousProblems) {
result += " Heterogenuous Optimizers: \n";
for (int i = 0; i < this.m_Islands.length; i++) {
result += this.m_Islands[i].getStringRepresentation() +"\n";
result += this.m_Islands[i].getStringRepresentation() + "\n";
}
} else {
result += " Homogeneous Optimizer = " + this.m_Optimizer.getClass().toString() + "\n";
result += this.m_Optimizer.getStringRepresentation() +"\n";
result += this.m_Optimizer.getStringRepresentation() + "\n";
}
//result += "=> The Optimization Problem: ";
//result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
@@ -362,7 +370,8 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
return result;
}
/** This method is to test the parallelization scheme
/**
* This method is to test the parallelization scheme
*
* @param args
*/
@@ -374,10 +383,10 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
imea.m_localOnly = false;
if (false) {
imea.m_Optimizer = new MultiObjectiveEA();
((MultiObjectiveEA)imea.m_Optimizer).setArchiveSize(25);
((MultiObjectiveEA)imea.m_Optimizer).getPopulation().setTargetSize(50);
((MultiObjectiveEA) imea.m_Optimizer).setArchiveSize(25);
((MultiObjectiveEA) imea.m_Optimizer).getPopulation().setTargetSize(50);
imea.m_Problem = new TF1Problem();
((TF1Problem)imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
((TF1Problem) imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
// ((TF1Problem)imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
// imea.m_Problem = new TFPortfolioSelectionProblem();
// ((TFPortfolioSelectionProblem)imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
@@ -399,7 +408,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
}
} else {
imea.m_Problem = new F8Problem();
((F1Problem)imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
((F1Problem) imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
}
imea.m_MigrationRate = 15;
imea.init();
@@ -410,75 +419,82 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
//System.exit(0);
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method will return the Optimizers
/**
* This method will return the Optimizers
*
* @return An array of optimizers
*/
public InterfaceOptimizer[] getOptimizers() {
return this.m_Islands;
}
/** This method will allow you to toggel between homogenuous and heterogenuous problems.
* In case of heterogenuous problems the individuals need to be reevaluated after migration.
/**
* This method will allow you to toggel between homogenuous and
* heterogenuous problems. In case of heterogenuous problems the individuals
* need to be reevaluated after migration.
*
* @param t
*/
public void setHeterogenuousProblems(boolean t) {
this.m_HeterogenuousProblems = t;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
for (int i = 0; i < this.m_Islands.length; i++) {
this.m_Islands[i].freeWilly();
}
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for InterfacePopulationChangedEventListener
*/
/** This method allows an optimizer to register a change in the EA-lecture
/**
* This method allows an optimizer to register a change in the EA-lecture
*
* @param source The source of the event.
* @param name Could be used to indicate the nature of the event.
*/
@Override
public void registerPopulationStateChanged(Object source, String name) {
InterfaceOptimizer opt = (InterfaceOptimizer)source;
InterfaceOptimizer opt = (InterfaceOptimizer) source;
int sourceID = new Integer(opt.getIdentifier()).intValue();
double cFCOpt = opt.getPopulation().getFunctionCalls();
double plotValue = (this.m_Problem.getDoublePlotValue(opt.getPopulation())).doubleValue();
if (this.m_Show) {
this.m_Plot.setConnectedPoint(cFCOpt, plotValue, (sourceID+1));
this.m_Plot.setConnectedPoint(cFCOpt, plotValue, (sourceID + 1));
}
//System.out.println("Someone has finished, ("+this.m_Generation+"/"+this.m_Performed+")");
//System.out.println(sourceID + " is at generation "+ opt.getPopulation().getGeneration() +" i'm at " +this.m_Generation);
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is an island model EA distributing the individuals across several (remote) CPUs for optimization.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -486,8 +502,10 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
return "IslandEA";
}
/** This method allows you to toggle between a truly parallel
* and a serial implementation.
/**
* This method allows you to toggle between a truly parallel and a serial
* implementation.
*
* @return The current optimization mode
*/
// TODO Deactivated from GUI because the current implementation does not really parallelize on a multicore.
@@ -495,62 +513,79 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
// public boolean isLocalOnly() {
// return this.m_localOnly;
// }
public void setLocalOnly(boolean b){
public void setLocalOnly(boolean b) {
this.m_localOnly = b;
}
public String localOnlyTipText() {
return "Toggle between usage of local CPUs and remote servers.";
}
/** This will show the local performance
/**
* This will show the local performance
*
* @return The current optimzation mode
*/
public boolean getShow() {
return this.m_Show;
}
public void setShow(boolean b){
public void setShow(boolean b) {
this.m_Show = b;
this.m_LogLocalChanges = b;
}
public String showTipText() {
return "This will show the local performance.";
}
/** This method allows you to set/get the optimizing technique to use.
/**
* This method allows you to set/get the optimizing technique to use.
*
* @return The current optimizing method
*/
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
public void setOptimizer(InterfaceOptimizer b){
public void setOptimizer(InterfaceOptimizer b) {
this.m_Optimizer = b;
}
public String optimizerTipText() {
return "Choose a population based optimizing technique to use.";
}
/** This method allows you to set/get the migration strategy to use.
/**
* This method allows you to set/get the migration strategy to use.
*
* @return The current migration strategy
*/
public InterfaceMigration getMigrationStrategy() {
return this.m_Migration;
}
public void setMigrationStrategy(InterfaceMigration b){
public void setMigrationStrategy(InterfaceMigration b) {
this.m_Migration = b;
}
public String migrationStrategyTipText() {
return "Choose a migration strategy to use.";
}
/** This method allows you to set/get the migration rate to use.
/**
* This method allows you to set/get the migration rate to use.
*
* @return The current migration rate
*/
public int getMigrationRate() {
return this.m_MigrationRate;
}
public void setMigrationRate(int b){
public void setMigrationRate(int b) {
this.m_MigrationRate = b;
}
public String migrationRateTipText() {
return "Set the migration rate for communication between islands.";
}
@@ -559,20 +594,24 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
return "Choose and manage the servers (only active in parallelized mode).";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
// @todo Jetzt m<>sste ich die pop auch auf die Rechner verteilen...
this.m_Population = pop;
}
public String populationTipText() {
return "(Defunct)";
}
@@ -581,14 +620,16 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method allows you to set the number of processors in local mode
/**
* This method allows you to set the number of processors in local mode
*
* @param n Number of processors.
*/
public void setNumberLocalCPUs(int n) {
if (n>=1) {
if (n >= 1) {
this.m_numLocalCPUs = n;
}
else {
} else {
System.err.println("Number of CPUs must be at least 1!");
}
}
@@ -597,6 +638,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
// public int getNumberLocalCPUs() {
// return this.m_LocalCPUs;
// }
public String numberLocalCPUsTipText() {
return "Set the number of local CPUS (>=1, only used in local mode).";
}

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

@@ -364,10 +364,6 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface
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

14
src/eva2/server/go/strategies/MLTGA.java
View File

@@ -246,13 +246,13 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac
this.problem.evaluatePopulationStart(this.population);
Stack<Set<Integer>> linkageTree = buildLinkageTree();
Population newPop = new Population(this.popSize);
if(elitism){
if (elitism) {
AbstractEAIndividual firstIndy = this.population.getBestEAIndividual();
AbstractEAIndividual firstNewIndy = buildNewIndy(firstIndy, linkageTree);
newPop.add(firstNewIndy);
}
for (int i = 0; i < this.popSize; i++) {
if(this.elitism && i==0){
if (this.elitism && i == 0) {
continue;
}
Population indies = this.population.getRandNIndividuals(1);
@@ -321,15 +321,15 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac
this.problem = (AbstractOptimizationProblem) problem;
}
public boolean getElitism(){
public boolean getElitism() {
return this.elitism;
}
public void setElitism(boolean b){
public void setElitism(boolean b) {
this.elitism = b;
}
public String elitismTipText(){
public String elitismTipText() {
return "use elitism?";
}
@@ -343,10 +343,6 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac
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

77
src/eva2/server/go/strategies/MemeticAlgorithm.java
View File

@@ -12,28 +12,15 @@ import eva2.server.go.problems.InterfaceLocalSearchable;
import eva2.server.go.problems.InterfaceOptimizationProblem;
import java.util.Hashtable;
/**
* A memetic algorithm by hannes planatscher. The local search strategy can only
* be applied to problems which implement the InterfaceLocalSearchable else the
* local search will not be activated at all.
* <p>
* Title: EvA2
* </p>
* <p>
* Description:
* </p>
* <p>
* Copyright: Copyright (c) 2003
* </p>
* <p>
* Company:
* </p>
* local search will not be activated at all. <p> Title: EvA2 </p> <p>
* Description: </p> <p> Copyright: Copyright (c) 2003 </p> <p> Company: </p>
*
* @author not attributable
* @version 1.0
*/
public class MemeticAlgorithm implements InterfaceOptimizer,
java.io.Serializable {
@@ -41,32 +28,20 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
* serial version uid.
*/
private static final long serialVersionUID = -1730086430763348568L;
private int localSearchSteps = 1;
private int subsetsize = 5;
private int globalSearchIterations = 1;
private boolean lamarckism = true;
// int counter = 0; !?
// int maxfunctioncalls = 1000; !?
private boolean TRACE = false;
private String m_Identifier = "";
private InterfaceOptimizationProblem m_Problem = new F1Problem();
private InterfaceOptimizer m_GlobalOptimizer = new GeneticAlgorithm();
private InterfaceSelection selectorPlug = new SelectBestIndividuals();
transient private InterfacePopulationChangedEventListener m_Listener;
public MemeticAlgorithm() {
}
public MemeticAlgorithm(MemeticAlgorithm a) {
@@ -108,8 +83,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
/**
* This method will evaluate the current population using the given problem.
*
* @param population
* The population that is to be evaluated
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
this.m_Problem.evaluate(population);
@@ -124,7 +98,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
}
this.m_GlobalOptimizer.optimize();
if ((globalSearchIterations>0) && (((this.m_GlobalOptimizer.getPopulation().getGeneration() % this.globalSearchIterations) == 0))
if ((globalSearchIterations > 0) && (((this.m_GlobalOptimizer.getPopulation().getGeneration() % this.globalSearchIterations) == 0))
&& (this.localSearchSteps > 0)
&& (this.m_Problem instanceof InterfaceLocalSearchable)) {
// here the local search is performed
@@ -212,16 +186,18 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
/**
* Something has changed
*/
@@ -251,8 +227,8 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
}
/**
* This method will return a string describing all properties of the optimizer
* and the applied methods.
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@@ -269,8 +245,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
/**
* This method allows you to set an identifier for the algorithm
*
* @param name
* The indenifier
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
@@ -282,14 +257,6 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
return this.m_Identifier;
}
/**
* This method is required to free the memory on a RMIServer, but there is
* nothing to implement.
*/
@Override
public void freeWilly() {
}
/*
* ========================================================================================
@@ -301,9 +268,9 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
* @return description
*/
public static String globalInfo() {
return "This is a basic generational Memetic Algorithm. Local search steps are performed on a selected subset " +
"of individuals after certain numbers of global search iterations. Note " +
"that the problem class must implement InterfaceLocalSearchable.";
return "This is a basic generational Memetic Algorithm. Local search steps are performed on a selected subset "
+ "of individuals after certain numbers of global search iterations. Note "
+ "that the problem class must implement InterfaceLocalSearchable.";
}
/**
@@ -318,7 +285,8 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
/**
* 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.
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@@ -356,16 +324,19 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
}
/**
* Choose the number of local search steps to perform per selected individual.
* Choose the number of local search steps to perform per selected
* individual.
*
* @param localSearchSteps
*/
public void setLocalSearchSteps(int localSearchSteps) {
this.localSearchSteps = localSearchSteps;
}
public int getLocalSearchSteps() {
return localSearchSteps;
}
public String localSearchStepsTipText() {
return "Choose the number of local search steps to perform per selected individual.";
}
@@ -378,9 +349,11 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
public void setGlobalSearchIterations(int globalSearchSteps) {
this.globalSearchIterations = globalSearchSteps;
}
public int getGlobalSearchIterations() {
return globalSearchIterations;
}
public String globalSearchIterationsTipText() {
return "Choose the interval between the application of the local search.";
}
@@ -398,9 +371,11 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
public void setSubsetsize(int subsetsize) {
this.subsetsize = subsetsize;
}
public int getSubsetsize() {
return subsetsize;
}
public String subsetsizeTipText() {
return "Choose the number of individuals to be locally optimized.";
}
@@ -413,9 +388,11 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
public void setLamarckism(boolean lamarckism) {
this.lamarckism = lamarckism;
}
public String lamarckismTipText() {
return "Toggle between Lamarckism and the Baldwin Effect.";
}
public boolean isLamarckism() {
return lamarckism;
}
@@ -423,9 +400,11 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
public InterfaceSelection getSubSetSelector() {
return selectorPlug;
}
public void setSubSetSelector(InterfaceSelection selectorPlug) {
this.selectorPlug = selectorPlug;
}
public String subSetSelectorTipText() {
return "Selection method to select the subset on which local search is to be performed.";
}

130
src/eva2/server/go/strategies/MonteCarloSearch.java
View File

@@ -10,20 +10,20 @@ import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/**
* The simple random or Monte-Carlo search, simple but useful
* to evaluate the complexity of the search space.
* This implements a Random Walk Search using the initialization
* method of the problem instance, meaning that the random characteristics
* may be problem dependent.
* The simple random or Monte-Carlo search, simple but useful to evaluate the
* complexity of the search space. This implements a Random Walk Search using
* the initialization method of the problem instance, meaning that the random
* characteristics may be problem dependent.
*
* Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
* Architecture
*
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializable {
/**
* Generated serial version id.
*/
@@ -35,7 +35,6 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
private int m_FitnessCallsNeeded = 0;
private Population m_Population;
private GAIndividualBinaryData m_Best, m_Test;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -46,8 +45,8 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
}
public MonteCarloSearch(MonteCarloSearch a) {
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
}
@Override
@@ -55,7 +54,8 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
return (Object) new MonteCarloSearch(this);
}
/** This method will init the MonteCarloSearch
/**
* This method will init the MonteCarloSearch
*/
@Override
public void init() {
@@ -64,13 +64,15 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
this.m_Population = (Population)pop.clone();
this.m_Population = (Population) pop.clone();
if (reset) {
this.m_Population.init();
this.m_Problem.evaluate(this.m_Population);
@@ -79,22 +81,22 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
}
/**
* This method will optimize without specific operators, by just calling the individual method
* for initialization.
* This method will optimize without specific operators, by just calling the
* individual method for initialization.
*/
@Override
public void optimize() {
Population original = (Population)this.m_Population.clone();
Population original = (Population) this.m_Population.clone();
// this.m_Problem.initPopulation(this.m_Population);
for (int i=0; i<m_Population.size(); i++) {
for (int i = 0; i < m_Population.size(); i++) {
m_Population.getEAIndividual(i).defaultInit(null);
}
this.m_Population.SetFunctionCalls(original.getFunctionCalls());
this.m_Problem.evaluate(this.m_Population);
for (int i = 0; i < this.m_Population.size(); i++) {
if (((AbstractEAIndividual)original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual)this.m_Population.get(i)))) {
if (((AbstractEAIndividual) original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual) this.m_Population.get(i)))) {
this.m_Population.remove(i);
this.m_Population.add(i, original.get(i));
}
@@ -103,20 +105,23 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
public void defaultInit() {
this.m_FitnessCallsNeeded = 0;
@@ -124,7 +129,8 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
this.m_Best.defaultInit(m_Problem);
}
/** This method will optimize
/**
* This method will optimize
*/
public void defaultOptimize() {
for (int i = 0; i < m_FitnessCalls; i++) {
@@ -135,13 +141,14 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
}
this.m_FitnessCallsNeeded = i;
if (this.m_Best.defaultEvaulateAsMiniBits() == 0) {
i = this.m_FitnessCalls +1;
i = this.m_FitnessCalls + 1;
}
}
}
/** This main method will start a simple hillclimber.
* No arguments necessary.
/**
* This main method will start a simple hillclimber. No arguments necessary.
*
* @param args
*/
public static void main(String[] args) {
@@ -155,36 +162,43 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
}
TmpMeanCalls /= program.m_MultiRuns;
TmpMeanFitness /= program.m_MultiRuns;
System.out.println("("+program.m_MultiRuns+"/"+program.m_FitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
System.out.println("(" + program.m_MultiRuns + "/" + program.m_FitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -192,58 +206,66 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
String result = "";
result += "Monte-Carlo Search:\n";
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The Monte Carlo Search repeatively creates random individuals and stores the best individuals found.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
public String getName() {
return "MCS";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Change the number of best individuals stored.";
}

19
src/eva2/server/go/strategies/MultiObjectiveCMAES.java
View File

@@ -32,21 +32,17 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable {
*
*/
class CounterClass {
public CounterClass(int i) {
value = i;
}
public int value;
public boolean seen = false;
}
private String m_Identifier = "MOCMAES";
private Population m_Population;
private AbstractOptimizationProblem m_Problem;
transient private InterfacePopulationChangedEventListener m_Listener;
private int m_lambda = 1;
private int m_lambdamo = 1;
@@ -105,15 +101,6 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable {
this.m_Listener = ea;
}
/*
* (non-Javadoc)
*
* @see eva2.server.go.strategies.InterfaceOptimizer#freeWilly()
*/
@Override
public void freeWilly() {
}
/*
* (non-Javadoc)
*
@@ -221,8 +208,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable {
/**
* This method will evaluate the current population using the given problem.
*
* @param population
* The population that is to be evaluated
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
this.m_Problem.evaluate(population);
@@ -417,5 +403,4 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable {
*
* public void setLambdaMo(int mLambda) { m_lambdamo = mLambda; }
*/
}

148
src/eva2/server/go/strategies/MultiObjectiveEA.java
View File

@@ -16,28 +16,17 @@ import eva2.server.go.problems.FM0Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/**
* A generic framework for multi-objecitve optimization, you need
* to specify an optimization strategy (like GA), an archiver and
* an information retrival strategy. With this scheme you can realized:
* Vector Evaluated GA
* Random Weight GA
* Multiple Objective GA
* NSGA
* NSGA-II
* SPEA
* SPEA 2
* PESA
* PESA-II
* In case you address a multi-objective optimization problem with a single-
* objective optimizer instead of this MOEA, such an optimizer would randomly
* toggle between the objective for each selection and thus explore at least
* the extreme points of the objective space, but simpler methods like
* random search or hill-climbing might even fail on that.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 05.06.2003
* Time: 11:03:50
* To change this template use Options | File Templates.
* A generic framework for multi-objecitve optimization, you need to specify an
* optimization strategy (like GA), an archiver and an information retrival
* strategy. With this scheme you can realized: Vector Evaluated GA Random
* Weight GA Multiple Objective GA NSGA NSGA-II SPEA SPEA 2 PESA PESA-II In case
* you address a multi-objective optimization problem with a single- objective
* optimizer instead of this MOEA, such an optimizer would randomly toggle
* between the objective for each selection and thus explore at least the
* extreme points of the objective space, but simpler methods like random search
* or hill-climbing might even fail on that. Created by IntelliJ IDEA. User:
* streiche Date: 05.06.2003 Time: 11:03:50 To change this template use Options
* | File Templates.
*/
public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable {
@@ -50,15 +39,15 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
public MultiObjectiveEA() {
this.m_Optimizer.getPopulation().setTargetSize(100);
((GeneticAlgorithm)this.m_Optimizer).setParentSelection(new SelectMONonDominated());
((GeneticAlgorithm)this.m_Optimizer).setPartnerSelection(new SelectMONonDominated());
((GeneticAlgorithm) this.m_Optimizer).setParentSelection(new SelectMONonDominated());
((GeneticAlgorithm) this.m_Optimizer).setPartnerSelection(new SelectMONonDominated());
}
public MultiObjectiveEA(MultiObjectiveEA a) {
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Optimizer = (InterfaceOptimizer)a.m_Optimizer.clone();
this.m_Archiver = (InterfaceArchiving)a.m_Archiver.clone();
this.m_InformationRetrieval = (InterfaceInformationRetrieval)a.m_InformationRetrieval.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.m_Optimizer = (InterfaceOptimizer) a.m_Optimizer.clone();
this.m_Archiver = (InterfaceArchiving) a.m_Archiver.clone();
this.m_InformationRetrieval = (InterfaceInformationRetrieval) a.m_InformationRetrieval.clone();
}
public MultiObjectiveEA(InterfaceOptimizer subOpt, InterfaceArchiving archiving, int archiveSize,
@@ -82,8 +71,9 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@@ -94,7 +84,8 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** The optimize method will compute a 'improved' and evaluated population
/**
* The optimize method will compute a 'improved' and evaluated population
*/
@Override
public void optimize() {
@@ -144,7 +135,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
double[][] fitness = new double[tmp.size()][];
for (int i = 0; i < tmp.size(); i++) {
fitness[i] = ((AbstractEAIndividual)tmp.get(i)).getFitness();
fitness[i] = ((AbstractEAIndividual) tmp.get(i)).getFitness();
}
double[] minY, maxY;
minY = fitness[0];
@@ -172,35 +163,40 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.setProblem(problem);
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -212,42 +208,44 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
result += " Information Retrieval = " + this.m_InformationRetrieval.getClass().toString() + "\n";
result += " Information Retrieval = " + this.getClass().toString() + "\n";
result += " Optimizer = " + this.m_Optimizer.getClass().toString() + "\n";
result += this.m_Optimizer.getStringRepresentation() +"\n";
result += this.m_Optimizer.getStringRepresentation() + "\n";
//result += "=> The Optimization Problem: ";
//result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
//result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a general Multi-objective Evolutionary Optimization Framework.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -255,19 +253,23 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
return "MOEA";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Optimizer.getPopulation();
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Optimizer.setPopulation(pop);
}
public String populationTipText() {
return "Edit the properties of the Population used.";
}
@@ -277,47 +279,61 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
return new SolutionSet(getPopulation(), ArchivingNSGAII.getNonDominatedSortedFront(getPopulation().getArchive()).getSortedPop(new AbstractEAIndividualComparator(0)));
}
/** This method allows you to set/get the optimizing technique to use.
/**
* This method allows you to set/get the optimizing technique to use.
*
* @return The current optimizing method
*/
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
public void setOptimizer(InterfaceOptimizer b){
public void setOptimizer(InterfaceOptimizer b) {
this.m_Optimizer = 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.
/**
* This method allows you to set/get the archiving strategy to use.
*
* @return The current optimizing method
*/
public InterfaceArchiving getArchivingStrategy() {
return this.m_Archiver;
}
public void setArchivingStrategy(InterfaceArchiving b){
public void setArchivingStrategy(InterfaceArchiving b) {
this.m_Archiver = b;
}
public String archivingStrategyTipText() {
return "Choose the archiving strategy.";
}
/** This method allows you to set/get the Information Retrieval strategy to use.
/**
* This method allows you to set/get the Information Retrieval strategy to
* use.
*
* @return The current optimizing method
*/
public InterfaceInformationRetrieval getInformationRetrieval() {
return this.m_InformationRetrieval;
}
public void setInformationRetrieval(InterfaceInformationRetrieval b){
public void setInformationRetrieval(InterfaceInformationRetrieval b) {
this.m_InformationRetrieval = b;
}
public String informationRetrievalTipText() {
return "Choose the Information Retrieval strategy.";
}
/** This method allows you to set/get the size of the archive.
/**
* This method allows you to set/get the size of the archive.
*
* @return The current optimizing method
*/
public int getArchiveSize() {
@@ -328,7 +344,8 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
}
return archive.getTargetSize();
}
public void setArchiveSize(int b){
public void setArchiveSize(int b) {
Population archive = this.m_Optimizer.getPopulation().getArchive();
if (archive == null) {
archive = new Population();
@@ -336,6 +353,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
}
archive.setTargetSize(b);
}
public String archiveSizeTipText() {
return "Choose the size of the archive.";
}

148
src/eva2/server/go/strategies/NelderMeadSimplex.java
View File

@@ -15,9 +15,9 @@ import java.io.Serializable;
import java.util.Vector;
/**
* Nelder-Mead-Simplex does not guarantee an equal number of evaluations within each optimize call
* because of the different step types.
* Range check is now available by projection at the bounds.
* Nelder-Mead-Simplex does not guarantee an equal number of evaluations within
* each optimize call because of the different step types. Range check is now
* available by projection at the bounds.
*
* @author mkron
*
@@ -27,9 +27,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
private int populationSize = 100;
// simulating the generational cycle. Set rather small (eg 5) for use as local search, higher for global search (eg 50)
private int generationCycle = 50;
private int fitIndex = 0; // choose criterion for multi objective functions
private Population m_Population;
private AbstractOptimizationProblem m_Problem;
private transient Vector<InterfacePopulationChangedEventListener> m_Listener;
@@ -41,13 +39,13 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
}
public NelderMeadSimplex(int popSize) {
populationSize=popSize;
populationSize = popSize;
setPopulation(new Population(populationSize));
}
public NelderMeadSimplex(NelderMeadSimplex a) {
m_Problem = (AbstractOptimizationProblem)a.m_Problem.clone();
setPopulation((Population)a.m_Population.clone());
m_Problem = (AbstractOptimizationProblem) a.m_Problem.clone();
setPopulation((Population) a.m_Population.clone());
populationSize = a.populationSize;
generationCycle = a.generationCycle;
m_Identifier = a.m_Identifier;
@@ -65,18 +63,18 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
m_Problem = (AbstractOptimizationProblem)problem;
m_Problem = (AbstractOptimizationProblem) problem;
}
public boolean setProblemAndPopSize(InterfaceOptimizationProblem problem) {
setProblem(problem);
if (m_Problem instanceof AbstractProblemDouble) {
setPopulationSize(((AbstractProblemDouble)problem).getProblemDimension()+1);
setPopulationSize(((AbstractProblemDouble) problem).getProblemDimension() + 1);
return true;
} else {
Object ret=BeanInspector.callIfAvailable(problem, "getProblemDimension", null);
if (ret!=null) {
setPopulationSize(((Integer)ret)+1);
Object ret = BeanInspector.callIfAvailable(problem, "getProblemDimension", null);
if (ret != null) {
setPopulationSize(((Integer) ret) + 1);
return true;
}
}
@@ -86,7 +84,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
@Override
public void addPopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (ea!=null) {
if (ea != null) {
if (m_Listener == null) {
m_Listener = new Vector<InterfacePopulationChangedEventListener>();
}
@@ -99,21 +97,17 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==null) {
if (m_Listener == null) {
return false;
}
else {
} else {
return m_Listener.remove(ea);
}
}
@Override
public void freeWilly() {}
protected double[] calcChallengeVect(double[] centroid, double[] refX) {
double[] r = new double[centroid.length];
for (int i=0; i<r.length; i++) {
r[i] = 2*centroid[i] - refX[i];
for (int i = 0; i < r.length; i++) {
r[i] = 2 * centroid[i] - refX[i];
}
// double alpha = 1.3;
// for (int i=0; i<r.length; i++) r[i] = centroid[i] + alpha*(centroid[i] - refX[i]);
@@ -121,11 +115,11 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
}
protected boolean firstIsBetter(double[] first, double[] second) {
return first[fitIndex]<second[fitIndex];
return first[fitIndex] < second[fitIndex];
}
protected boolean firstIsBetterEqual(double[] first, double[] second) {
return first[fitIndex]<=second[fitIndex];
return first[fitIndex] <= second[fitIndex];
}
protected boolean firstIsBetter(AbstractEAIndividual first, AbstractEAIndividual second) {
@@ -141,10 +135,10 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
// hole die n-1 besten individuen der fitness dimension fitIndex
subpop.setSortingFitnessCriterion(fitIndex);
Population bestpop = subpop.getBestNIndividuals(subpop.size()-1, fitIndex);
Population bestpop = subpop.getBestNIndividuals(subpop.size() - 1, fitIndex);
// und das schlechteste
AbstractEAIndividual worst = subpop.getWorstEAIndividual(fitIndex);
AbstractEAIndividual best=subpop.getBestEAIndividual(fitIndex);
AbstractEAIndividual best = subpop.getBestEAIndividual(fitIndex);
double[][] range = ((InterfaceDataTypeDouble) worst).getDoubleRange();
double[] x_worst = ((InterfaceDataTypeDouble) worst).getDoubleData();
@@ -152,11 +146,11 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
// Centroid berechnen
double[] centroid = new double[dim];
for (int i=0; i<bestpop.size(); i++) {
for (int j=0; j<dim; j++) {
AbstractEAIndividual bestIndi= (AbstractEAIndividual) bestpop.getIndividual(i);
double[] bestIndyPos = ((InterfaceDataTypeDouble)bestIndi).getDoubleDataWithoutUpdate();
centroid[j] +=bestIndyPos[j]/bestpop.size(); // bug?
for (int i = 0; i < bestpop.size(); i++) {
for (int j = 0; j < dim; j++) {
AbstractEAIndividual bestIndi = (AbstractEAIndividual) bestpop.getIndividual(i);
double[] bestIndyPos = ((InterfaceDataTypeDouble) bestIndi).getDoubleDataWithoutUpdate();
centroid[j] += bestIndyPos[j] / bestpop.size(); // bug?
}
}
@@ -179,8 +173,8 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
return reflectedInd;
} else if (firstIsBetter(reflectedInd, best)) { //neues besser als bisher bestes => Expansion
double[] e = new double[dim];
for (int i=0; i<dim; i++) {
e[i] = 3*centroid[i] - 2*x_worst[i];
for (int i = 0; i < dim; i++) {
e[i] = 3 * centroid[i] - 2 * x_worst[i];
}
if (checkConstraints && !Mathematics.isInRange(e, range)) {
Mathematics.projectToRange(e, range);
@@ -197,8 +191,8 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
} else if (firstIsBetterEqual(bestpop.getWorstEAIndividual(fitIndex), reflectedInd)) {
//kontrahiere da neues indi keine verbesserung brachte
double[] c = new double[dim];
for (int i=0; i<dim; i++) {
c[i] = 0.5*centroid[i] + 0.5*x_worst[i];
for (int i = 0; i < dim; i++) {
c[i] = 0.5 * centroid[i] + 0.5 * x_worst[i];
}
if (checkConstraints && !Mathematics.isInRange(c, range)) {
Mathematics.projectToRange(c, range);
@@ -209,7 +203,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
// m_Problem.evaluate(c_ind);
AbstractEAIndividual c_ind = createEvalIndy(bestpop, c);
this.m_Population.incrFunctionCalls();
if(firstIsBetterEqual(c_ind, worst)) {
if (firstIsBetterEqual(c_ind, worst)) {
return c_ind;
}
}
@@ -217,11 +211,11 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
}
private AbstractEAIndividual createEvalIndy(Population pop, double[] newGenotype) {
AbstractEAIndividual e_ind = (AbstractEAIndividual)((AbstractEAIndividual)pop.getIndividual(1)).clone();
((InterfaceDataTypeDouble)e_ind).SetDoubleGenotype(newGenotype);
AbstractEAIndividual e_ind = (AbstractEAIndividual) ((AbstractEAIndividual) pop.getIndividual(1)).clone();
((InterfaceDataTypeDouble) e_ind).SetDoubleGenotype(newGenotype);
e_ind.resetConstraintViolation();
m_Problem.evaluate(e_ind);
if (e_ind.getFitness(0)<6000) {
if (e_ind.getFitness(0) < 6000) {
m_Problem.evaluate(e_ind);
}
return e_ind;
@@ -279,7 +273,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
private void fireNextGenerationPerformed() {
if (m_Listener != null) {
for (int i=0; i<m_Listener.size(); i++) {
for (int i = 0; i < m_Listener.size(); i++) {
m_Listener.elementAt(i).registerPopulationStateChanged(this, Population.nextGenerationPerformed);
}
}
@@ -294,9 +288,9 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
m_Problem.evaluatePopulationStart(m_Population);
do {
AbstractEAIndividual ind = simplexStep(m_Population);
if(ind!=null){ //Verbesserung gefunden
double[] x=((InterfaceDataTypeDouble)ind).getDoubleData();
double[][] range = ((InterfaceDataTypeDouble)ind).getDoubleRange();
if (ind != null) { //Verbesserung gefunden
double[] x = ((InterfaceDataTypeDouble) ind).getDoubleData();
double[][] range = ((InterfaceDataTypeDouble) ind).getDoubleRange();
if (!Mathematics.isInRange(x, range)) {
System.err.println("WARNING: nelder mead step produced indy out of range!");
// Mathematics.projectToRange(x, range);
@@ -305,14 +299,14 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
// this.m_Population.incrFunctionCalls();
}
m_Population.set(m_Population.getIndexOfWorstIndividualNoConstr(fitIndex), ind, fitIndex);
}else{//keine Verbesserung gefunden shrink!!
} else {//keine Verbesserung gefunden shrink!!
double[] u_1 = ((InterfaceDataTypeDouble) m_Population.getBestEAIndividual(fitIndex)).getDoubleData();
for(int j=0;j<m_Population.size();j++){
double [] c= ((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).getDoubleData();
for (int i=0; i<c.length; i++) {
c[i] = 0.5*c[i] + 0.5*u_1[i];
for (int j = 0; j < m_Population.size(); j++) {
double[] c = ((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).getDoubleData();
for (int i = 0; i < c.length; i++) {
c[i] = 0.5 * c[i] + 0.5 * u_1[i];
}
((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).SetDoubleGenotype(c);
// m_Population.getEAIndividual(j).resetConstraintViolation(); // not a good idea because during evaluation, a stats update may be performed which mustnt see indies which are evaluated, but possible constraints have been reset.
@@ -350,7 +344,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
*/
public void setPopulationSize(int populationSize) {
this.populationSize = populationSize;
if (m_Population!=null) {
if (m_Population != null) {
m_Population.setTargetSize(populationSize);
m_Population.setNotifyEvalInterval(m_Population.getTargetSize());
}
@@ -370,10 +364,8 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
/**
* This method creates a Nelder-Mead instance.
*
* @param pop
* The size of the population
* @param problem
* The problem to be optimized
* @param pop The size of the population
* @param problem The problem to be optimized
* @param listener
* @return An optimization procedure that performs nelder mead optimization.
*/
@@ -384,12 +376,12 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
NelderMeadSimplex nms = new NelderMeadSimplex();
nms.setProblemAndPopSize(problem);
if (listener!=null) {
if (listener != null) {
nms.addPopulationChangedEventListener(listener);
}
nms.init();
if (listener!=null) {
if (listener != null) {
listener.registerPopulationStateChanged(nms.getPopulation(), "");
}
@@ -398,15 +390,15 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
/**
* This method creates a Nelder-Mead instance with an initial population
* around a given candidate solution. The population is created as a simplex with given
* perturbation ratio or randomly across the search range if the perturbation ratio is
* zero or below zero.
* around a given candidate solution. The population is created as a simplex
* with given perturbation ratio or randomly across the search range if the
* perturbation ratio is zero or below zero.
*
*
* @param problem
* The problem to be optimized
* @param problem The problem to be optimized
* @param candidate starting point of the search
* @param perturbationRatio perturbation ratio relative to the problem range for the initial simplex creation
* @param perturbationRatio perturbation ratio relative to the problem range
* for the initial simplex creation
* @param listener
* @return An optimization procedure that performs nelder mead optimization.
*/
@@ -425,8 +417,8 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
problem.initPopulation(initialPop);
initialPop.set(0, candidate);
} else {
double[][] range = ((InterfaceDataTypeDouble)candidate).getDoubleRange();
if (range.length != nms.getPopulationSize()-1) {
double[][] range = ((InterfaceDataTypeDouble) candidate).getDoubleRange();
if (range.length != nms.getPopulationSize() - 1) {
System.err.println("Unexpected population size for nelder mead!");
}
initialPop = createNMSPopulation(candidate, perturbationRatio, range, true);
@@ -441,11 +433,12 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
}
/**
* From a given candidate solution, create n solutions around the candidate, where every i-th
* new candidate differs in i dimensions by a distance of perturbRatio relative to the range in
* that dimension (respecting the range).
* The new solutions are returned as a population, which, if includeCand is true,
* also contains the initial candidate. However, the new candidates have not been evaluated.
* From a given candidate solution, create n solutions around the candidate,
* where every i-th new candidate differs in i dimensions by a distance of
* perturbRatio relative to the range in that dimension (respecting the
* range). The new solutions are returned as a population, which, if
* includeCand is true, also contains the initial candidate. However, the
* new candidates have not been evaluated.
*
* @param candidate
* @param perturbRelative
@@ -467,20 +460,20 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
private static void addPerturbedPopulation(double perturbationRatio,
Population initialPop, double[][] range, AbstractEAIndividual candidate) {
AbstractEAIndividual indy = (AbstractEAIndividual)candidate.clone();
AbstractEAIndividual indy = (AbstractEAIndividual) candidate.clone();
// span by perturbation, every new individual i is modified in dimension i by
// a value of perturbRatio*range_i such that a simplex of relative side length perturbRatio is created.
for (int i=0; i<range.length; i+=1) {
double curPerturb = ((range[i][1]-range[i][0])*perturbationRatio);
double[] dat = ((InterfaceDataTypeDouble)indy).getDoubleData();
if (dat[i]==range[i][1]) { // in this case the bound is said to be too close
dat[i]=Math.max(dat[i]-curPerturb, range[i][0]);
for (int i = 0; i < range.length; i += 1) {
double curPerturb = ((range[i][1] - range[i][0]) * perturbationRatio);
double[] dat = ((InterfaceDataTypeDouble) indy).getDoubleData();
if (dat[i] == range[i][1]) { // in this case the bound is said to be too close
dat[i] = Math.max(dat[i] - curPerturb, range[i][0]);
} else {
dat[i] = Math.min(dat[i]+curPerturb, range[i][1]);
dat[i] = Math.min(dat[i] + curPerturb, range[i][1]);
}
((InterfaceDataTypeDouble)indy).SetDoubleGenotype(dat);
((InterfaceDataTypeDouble) indy).SetDoubleGenotype(dat);
indy.resetConstraintViolation();
initialPop.add((AbstractEAIndividual)indy.clone());
initialPop.add((AbstractEAIndividual) indy.clone());
}
initialPop.synchSize();
}
@@ -515,5 +508,4 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
public String critIndexTipText() {
return "For multi-criterial problems, set the index of the fitness to be used in 0..n-1. Default is 0";
}
}

1024
src/eva2/server/go/strategies/NichePSO.java
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File diff suppressed because it is too large Load Diff

175
src/eva2/server/go/strategies/ParticleFilterOptimization.java
View File

@@ -1,6 +1,5 @@
package eva2.server.go.strategies;
import eva2.gui.BeanInspector;
import eva2.gui.GenericObjectEditor;
import eva2.gui.Plot;
@@ -22,28 +21,27 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
/**
* This is a Particle Filter implemented by Frank Senke, only some documentation
* here and not completely checked whether this works on arbitrary problem
* instances. MK did some adaptations, this should work on real valued problems now.
* instances. MK did some adaptations, this should work on real valued problems
* now.
*
* This is a implementation of Genetic Algorithms.
* Copyright: Copyright (c) 2003
* This is a implementation of Genetic Algorithms. Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
*
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.Serializable {
/**
* Comment for <code>serialVersionUID</code>
* Comment for
* <code>serialVersionUID</code>
*/
private static final long serialVersionUID = 1L;
private Population m_Population = new Population();
private InterfaceOptimizationProblem m_Problem = new F1Problem();
private InterfaceSelection m_ParentSelection = new SelectParticleWheel(0.5);
//private boolean m_UseElitism = true;
private String m_Identifier = "";
private boolean withShow = false;
private double mutationSigma = 0.01;
@@ -51,9 +49,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
private double initialVelocity = 0.02;
private double rotationDeg = 20.;
private int popSize = 300;
private int sleepTime = 0;
transient private int indCount = 0;
transient private InterfacePopulationChangedEventListener m_Listener;
transient Plot myPlot = null;
@@ -77,13 +73,13 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
}
public ParticleFilterOptimization(ParticleFilterOptimization a) {
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.m_Identifier = a.m_Identifier;
//this.m_Plague = a.m_Plague;
//this.m_NumberOfPartners = a.m_NumberOfPartners;
//this.m_UseElitism = a.m_UseElitism;
this.m_ParentSelection = (InterfaceSelection)a.m_ParentSelection.clone();
this.m_ParentSelection = (InterfaceSelection) a.m_ParentSelection.clone();
if (a.withShow) {
setWithShow(true);
}
@@ -104,9 +100,9 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
//System.out.println("pops targ is " + m_Population.getPopulationSize());
if (initialVelocity <= 0.) {
(((AbstractOptimizationProblem)m_Problem).getIndividualTemplate()).setMutationOperator(new MutateESFixedStepSize(mutationSigma));
(((AbstractOptimizationProblem) m_Problem).getIndividualTemplate()).setMutationOperator(new MutateESFixedStepSize(mutationSigma));
} else {
(((AbstractOptimizationProblem)m_Problem).getIndividualTemplate()).setMutationOperator(new MutateESCorrVector(mutationSigma, initialVelocity, rotationDeg));
(((AbstractOptimizationProblem) m_Problem).getIndividualTemplate()).setMutationOperator(new MutateESCorrVector(mutationSigma, initialVelocity, rotationDeg));
}
m_Population.setTargetSize(popSize);
this.m_Problem.initPopulation(this.m_Population);
@@ -117,13 +113,15 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
this.m_Population = (Population)pop.clone();
this.m_Population = (Population) pop.clone();
if (reset) {
this.m_Population.init();
this.evaluatePopulation(this.m_Population);
@@ -131,8 +129,9 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
}
}
/** This method will evaluate the current population using the
* given problem.
/**
* This method will evaluate the current population using the given problem.
*
* @param population The population that is to be evaluated
*/
private Population evaluatePopulation(Population population) {
@@ -147,34 +146,33 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
*/
protected Population resample(Population pop) {
Population parents;
boolean doImmigr=false;
boolean doImmigr = false;
this.m_ParentSelection.prepareSelection(pop);
// Generate a Population of Parents with Parantselectionmethod.
// DONT forget cloning -> selection does only shallow copies!
int targetSize = this.m_Population.getTargetSize();
if (randomImmigrationQuota>0) {
if (randomImmigrationQuota>1.) {
if (randomImmigrationQuota > 0) {
if (randomImmigrationQuota > 1.) {
System.err.println("Error, invalid immigration quota!");
}
else {
targetSize = (int)(this.m_Population.getTargetSize() * (1.-randomImmigrationQuota));
} else {
targetSize = (int) (this.m_Population.getTargetSize() * (1. - randomImmigrationQuota));
targetSize = Math.max(1, targetSize); // guarantee at least one to be selected
if (targetSize < this.m_Population.getTargetSize()) {
doImmigr=true;
doImmigr = true;
}
}
}
parents = (Population)(this.m_ParentSelection.selectFrom(pop, targetSize)).clone();
parents = (Population) (this.m_ParentSelection.selectFrom(pop, targetSize)).clone();
if (doImmigr) {
// add immigrants
AbstractEAIndividual immi;
int i;
for (i=0; (i+parents.getTargetSize())<pop.getTargetSize(); i++) {
immi = (AbstractEAIndividual)pop.getEAIndividual(0).clone();
for (i = 0; (i + parents.getTargetSize()) < pop.getTargetSize(); i++) {
immi = (AbstractEAIndividual) pop.getEAIndividual(0).clone();
immi.init(getProblem());
parents.add(immi);
}
@@ -199,7 +197,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
drawPop(pop, 0, false);
}
for (int i = 0; i < pop.getTargetSize(); i++) {
applyMotionModel((AbstractEAIndividual)((AbstractEAIndividual)pop.get(i)), 0.);
applyMotionModel((AbstractEAIndividual) ((AbstractEAIndividual) pop.get(i)), 0.);
indCount++;
}
if (withShow) {
@@ -212,14 +210,13 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
if (graphLabel < 0) {
graphLabel = indCount;
}
for (int i=0; i<pop.size(); i++) {
for (int i = 0; i < pop.size(); i++) {
InterfaceDataTypeDouble endy = (InterfaceDataTypeDouble) pop.getEAIndividual(i);
double[] curPosition = endy.getDoubleData();
if (useCircles) {
myPlot.getFunctionArea().drawCircle("", curPosition, graphLabel);
}
else {
} else {
myPlot.setUnconnectedPoint(curPosition[0], curPosition[1], graphLabel);
}
// myPlot.setConnectedPoint(curPosition[0], curPosition[1], graphLabel);
@@ -251,8 +248,11 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
// resample using selection
nextGeneration = resample(m_Population);
if (sleepTime > 0 ) {
try { Thread.sleep(sleepTime); } catch(Exception e) {}
if (sleepTime > 0) {
try {
Thread.sleep(sleepTime);
} catch (Exception e) {
}
}
if (withShow) {
clearPlot();
@@ -276,79 +276,89 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
if (problem instanceof AbstractOptimizationProblem) {
((AbstractOptimizationProblem)problem).informAboutOptimizer(this);
((AbstractOptimizationProblem) problem).informAboutOptimizer(this);
}
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
public String getStringRepresentation() {
StringBuilder strB=new StringBuilder(200);
StringBuilder strB = new StringBuilder(200);
strB.append("Particle Filter:\nOptimization Problem: ");
strB.append(this.m_Problem.getStringRepresentationForProblem(this));
strB.append("\n");
strB.append(this.m_Population.getStringRepresentation());
return strB.toString();
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a Particle Filter Algorithm.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -356,19 +366,23 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
return "PF";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
@@ -377,22 +391,28 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method will set the selection method that is to be used
/**
* This method will set the selection method that is to be used
*
* @param selection
*/
public void setParentSelection(InterfaceSelection selection) {
this.m_ParentSelection = selection;
}
public InterfaceSelection getParentSelection() {
return this.m_ParentSelection;
}
public String parentSelectionTipText() {
return "Choose a parent selection method.";
}
/**
* @return the withShow
**/
*
*/
public boolean isWithShow() {
return withShow;
}
@@ -402,11 +422,11 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
}
protected void clearPlot() {
if (myPlot!=null) {
if (myPlot != null) {
myPlot.clearAll();
double[][] range = null;
if ((m_Population != null) && (m_Population.size() > 0)) {
range = ((InterfaceDataTypeDouble)this.m_Population.get(0)).getDoubleRange();
range = ((InterfaceDataTypeDouble) this.m_Population.get(0)).getDoubleRange();
}
if (range != null) {
myPlot.setCornerPoints(range, 0);
@@ -416,18 +436,17 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
/**
* @param withShow the withShow to set
**/
*
*/
public void setWithShow(boolean wShow) {
this.withShow = wShow;
if (!withShow) {
myPlot = null;
}
else {
} else {
double[][] range;
if ((m_Population != null) && (m_Population.size() > 0)) {
range = ((InterfaceDataTypeDouble)this.m_Population.get(0)).getDoubleRange();
}
else {
range = ((InterfaceDataTypeDouble) this.m_Population.get(0)).getDoubleRange();
} else {
range = new double[2][];
range[0] = new double[2];
range[0][0] = 0;
@@ -440,28 +459,32 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
/**
* @return the sleepTime
**/
*
*/
public int getSleepTime() {
return sleepTime;
}
/**
* @param sleepTime the sleepTime to set
**/
*
*/
public void setSleepTime(int sleepTime) {
this.sleepTime = sleepTime;
}
/**
* @return the mutationSigma
**/
*
*/
public double getMutationSigma() {
return mutationSigma;
}
/**
* @param mutationSigma the mutationSigma to set
**/
*
*/
public void setMutationSigma(double mutationSigma) {
this.mutationSigma = mutationSigma;
}

8
src/eva2/server/go/strategies/ParticleSwarmOptimization.java
View File

@@ -1731,14 +1731,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
return this.m_Identifier;
}
/**
* This method is required to free the memory on a RMIServer, but there is
* nothing to implement.
*/
@Override
public void freeWilly() {
}
/**
* ********************************************************************************************************************
* These are for GUI

174
src/eva2/server/go/strategies/PopulationBasedIncrementalLearning.java
View File

@@ -13,23 +13,23 @@ import eva2.server.go.problems.AbstractOptimizationProblem;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/** Population based incremental learning in the PSM by Monmarche
* version with also allows to simulate ant systems due to the flexible
* update rule of V. But both are limited to binary genotypes.
* This is a simple implementation of Population Based Incremental Learning.
/**
* Population based incremental learning in the PSM by Monmarche version with
* also allows to simulate ant systems due to the flexible update rule of V. But
* both are limited to binary genotypes. This is a simple implementation of
* Population Based Incremental Learning.
*
* Nicolas Monmarché , Eric Ramat , Guillaume Dromel , Mohamed Slimane , Gilles Venturini:
* On the similarities between AS, BSC and PBIL: toward the birth of a new meta-heuristic.
* TecReport 215. Univ. de Tours, 1999.
* Nicolas Monmarché , Eric Ramat , Guillaume Dromel , Mohamed Slimane , Gilles
* Venturini: On the similarities between AS, BSC and PBIL: toward the birth of
* a new meta-heuristic. TecReport 215. Univ. de Tours, 1999.
*
* Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
* Architecture
*
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase
@@ -43,20 +43,20 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
private double m_MutationRate = 0.5;
private double m_MutateSigma = 0.01;
private int m_NumberOfPositiveSamples = 1;
private double[] m_initialProbabilities = ((PBILPopulation)m_Population).getProbabilityVector();
private double[] m_initialProbabilities = ((PBILPopulation) m_Population).getProbabilityVector();
public PopulationBasedIncrementalLearning() {
}
public PopulationBasedIncrementalLearning(PopulationBasedIncrementalLearning a) {
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.m_LearningRate = a.m_LearningRate;
this.m_MutationRate = a.m_MutationRate;
this.m_MutateSigma = a.m_MutateSigma;
this.m_NumberOfPositiveSamples = a.m_NumberOfPositiveSamples;
this.m_UseElitism = a.m_UseElitism;
this.m_SelectionOperator = (InterfaceSelection)a.m_SelectionOperator.clone();
this.m_SelectionOperator = (InterfaceSelection) a.m_SelectionOperator.clone();
}
@Override
@@ -67,10 +67,10 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
@Override
public void init() {
this.m_Problem.initPopulation(this.m_Population);
if ((m_initialProbabilities!=null) && (m_initialProbabilities.length==((PBILPopulation)m_Population).getProbabilityVector().length)) {
((PBILPopulation)m_Population).setProbabilityVector(m_initialProbabilities);
if ((m_initialProbabilities != null) && (m_initialProbabilities.length == ((PBILPopulation) m_Population).getProbabilityVector().length)) {
((PBILPopulation) m_Population).setProbabilityVector(m_initialProbabilities);
} else {
if (m_initialProbabilities!=null) {
if (m_initialProbabilities != null) {
System.err.println("Warning: initial probability vector doesnt match in length!");
}
}
@@ -78,7 +78,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@@ -88,17 +90,18 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
System.err.println("Error: PBIL only works with GAIndividuals!");
}
this.m_Population = new PBILPopulation();
this.m_Population.addPopulation((Population)pop.clone());
this.m_Population.addPopulation((Population) pop.clone());
if (reset) {
this.m_Population.init();
this.evaluatePopulation(this.m_Population);
}
((PBILPopulation)this.m_Population).buildProbabilityVector();
((PBILPopulation) this.m_Population).buildProbabilityVector();
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will evaluate the current population using the
* given problem.
/**
* This method will evaluate the current population using the given problem.
*
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
@@ -106,11 +109,12 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
population.incrGeneration();
}
/** This method will generate the offspring population from the
* given population of evaluated individuals.
/**
* This method will generate the offspring population from the given
* population of evaluated individuals.
*/
private Population generateChildren() {
PBILPopulation result = (PBILPopulation)this.m_Population.clone();
PBILPopulation result = (PBILPopulation) this.m_Population.clone();
Population examples;
// this.m_NormationOperator.computeSelectionProbability(this.m_Population, "Fitness");
@@ -141,46 +145,55 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
if (m_Problem instanceof AbstractOptimizationProblem) {
if (!(((AbstractOptimizationProblem)m_Problem).getIndividualTemplate() instanceof InterfaceGAIndividual)) {
if (!(((AbstractOptimizationProblem) m_Problem).getIndividualTemplate() instanceof InterfaceGAIndividual)) {
System.err.println("Error: PBIL only works with GAIndividuals!");
}
}
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -188,39 +201,42 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
String result = "";
result += "Population Based Incremental Learning:\n";
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The Population based incremental learning is based on a statistical distribution of bit positions. Please note: This optimizer requires a binary genotype!";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -228,19 +244,23 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
return "PBIL";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Edit the properties of the PBIL population used.";
}
@@ -262,52 +282,66 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
// return "Select the normation method.";
// }
/** This method will set the selection method that is to be used
/**
* This method will set the selection method that is to be used
*
* @param selection
*/
public void setSelectionMethod(InterfaceSelection selection) {
this.m_SelectionOperator = selection;
}
public InterfaceSelection getSelectionMethod() {
return this.m_SelectionOperator;
}
public String selectionMethodTipText() {
return "Choose a selection method.";
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param elitism
*/
public void setElitism (boolean elitism) {
public void setElitism(boolean elitism) {
this.m_UseElitism = elitism;
}
public boolean getElitism() {
return this.m_UseElitism;
}
public String elitismTipText() {
return "Enable/disable elitism.";
}
/** This method will set the learning rate for PBIL
/**
* This method will set the learning rate for PBIL
*
* @param LearningRate
*/
public void setLearningRate (double LearningRate) {
public void setLearningRate(double LearningRate) {
this.m_LearningRate = LearningRate;
if (this.m_LearningRate < 0) {
this.m_LearningRate = 0;
}
}
public double getLearningRate() {
return this.m_LearningRate;
}
public String learningRateTipText() {
return "The learing rate of PBIL.";
}
/** This method will set the mutation rate for PBIL
/**
* This method will set the mutation rate for PBIL
*
* @param m
*/
public void setMutationRate (double m) {
public void setMutationRate(double m) {
this.m_MutationRate = m;
if (this.m_MutationRate < 0) {
this.m_MutationRate = 0;
@@ -316,41 +350,51 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.m_MutationRate = 1;
}
}
public double getMutationRate() {
return this.m_MutationRate;
}
public String mutationRateTipText() {
return "The mutation rate of PBIL.";
}
/** This method will set the mutation sigma for PBIL
/**
* This method will set the mutation sigma for PBIL
*
* @param m
*/
public void setMutateSigma (double m) {
public void setMutateSigma(double m) {
this.m_MutateSigma = m;
if (this.m_MutateSigma < 0) {
this.m_MutateSigma = 0;
}
}
public double getMutateSigma() {
return this.m_MutateSigma;
}
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
/**
* This method will set the number of positive samples for PBIL
*
* @param PositiveSamples
*/
public void setPositiveSamples (int PositiveSamples) {
public void setPositiveSamples(int PositiveSamples) {
this.m_NumberOfPositiveSamples = PositiveSamples;
if (this.m_NumberOfPositiveSamples < 1) {
this.m_NumberOfPositiveSamples = 1;
}
}
public int getPositiveSamples() {
return this.m_NumberOfPositiveSamples;
}
public String positiveSamplesTipText() {
return "The number of positive samples that update the PBIL vector.";
}

231
src/eva2/server/go/strategies/ScatterSearch.java
View File

@@ -25,19 +25,23 @@ import eva2.tools.math.RNG;
import java.util.ArrayList;
/**
* A ScatterSearch implementation taken mainly from [1]. Unfortunately, some parameters as well as
* the local search method are not well defined in [1], so this implementation allows HC and Nelder-Mead
* as local search. If local search is activated, an additional filter is defined, meaning that only those
* A ScatterSearch implementation taken mainly from [1]. Unfortunately, some
* parameters as well as the local search method are not well defined in [1], so
* this implementation allows HC and Nelder-Mead as local search. If local
* search is activated, an additional filter is defined, meaning that only those
* individuals with a high quality fitness are further improved by local search.
* The threshold fitness is either defined relatively to the best/worst fitness values in the reference set
* or as an absolute value (in both cases only the first fitness criterion is regarded).
* The threshold fitness is either defined relatively to the best/worst fitness
* values in the reference set or as an absolute value (in both cases only the
* first fitness criterion is regarded).
*
* @author mkron
*
* [1] M.Rodiguez-Fernandez, J.Egea, J.Banga: Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.
* BMC Bioinformatics 2006, 7:483. BioMed Central 2006.
* [1] M.Rodiguez-Fernandez, J.Egea, J.Banga: Novel metaheuristic for parameter
* estimation in nonlinear dynamic biological systems. BMC Bioinformatics 2006,
* 7:483. BioMed Central 2006.
*/
public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener {
transient private InterfacePopulationChangedEventListener m_Listener = null;
private String m_Identifier = "ScatterSearch";
private AbstractOptimizationProblem problem = new F1Problem();
@@ -58,9 +62,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// simulate an EvA generational cycle
private int generationCycle = 50;
private int fitCrit = -1;
protected boolean checkRange = true;
// private int lastLocalSearch = -1;
// // nr of generations between local searches
// protected int localSearchInterval = 10;
@@ -71,7 +73,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private double nelderMeadInitPerturbation = 0.01;
private double improvementEpsilon = 0.1; // minimal relative fitness improvement for a candidate to be taken over into the refset
private double minDiversityEpsilon = 0.0001; // minimal phenotypic distance for a candidate to be taken over into the refset
private static boolean TRACE = false;
public ScatterSearch() {
@@ -80,10 +81,10 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
public ScatterSearch(ScatterSearch o) {
this.refSet = (Population)o.refSet.clone();
this.problem = (AbstractOptimizationProblem)o.problem.clone();
this.template = (AbstractEAIndividual)o.template.clone();
this.range = ((InterfaceDataTypeDouble)template).getDoubleRange();
this.refSet = (Population) o.refSet.clone();
this.problem = (AbstractOptimizationProblem) o.problem.clone();
this.template = (AbstractEAIndividual) o.template.clone();
this.range = ((InterfaceDataTypeDouble) template).getDoubleRange();
this.refSetSize = o.refSetSize;
this.poolSize = o.poolSize;
this.intervals = o.intervals;
@@ -106,7 +107,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.problem = (AbstractOptimizationProblem)problem;
this.problem = (AbstractOptimizationProblem) problem;
}
@Override
@@ -134,6 +135,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
/**
* Eval an initial population and extract the first refset.
*
* @param pop
*/
private void initRefSet(Population pop) {
@@ -154,29 +156,29 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
* Do default initialization.
*/
private void defaultInit() {
firstTime=true;
firstTime = true;
refSet = null;
combinations = null;
template = problem.getIndividualTemplate();
if (!(template instanceof InterfaceDataTypeDouble)) {
System.err.println("Requiring double data!");
}
else {
} else {
Object dim = BeanInspector.callIfAvailable(problem, "getProblemDimension", null);
if (dim==null) {
if (dim == null) {
System.err.println("Couldnt get problem dimension!");
}
probDim = (Integer)dim;
range = ((InterfaceDataTypeDouble)template).getDoubleRange();
probDim = (Integer) dim;
range = ((InterfaceDataTypeDouble) template).getDoubleRange();
if (TRACE) {
System.out.println("Range is " + BeanInspector.toString(range));
}
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
@@ -188,7 +190,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// problem.evaluate(indy);
// return indy.getFitness(0);
// }
@Override
public void registerPopulationStateChanged(Object source, String name) {
// The events of the interim hill climbing population will be caught here
@@ -197,7 +198,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// System.out.println("bla");
// }
// set funcalls to real value
refSet.SetFunctionCalls(((Population)source).getFunctionCalls());
refSet.SetFunctionCalls(((Population) source).getFunctionCalls());
// System.out.println("FunCallIntervalReached at " + (((Population)source).getFunctionCalls()));
@@ -241,10 +242,10 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
if (TRACE) {
System.out.println("No combinations: " + combinations.size());
}
if (combinations.size()>0) {
if (combinations.size() > 0) {
updateRefSet(refSet, combinations, oldRefSet);
}
} while (refSet.getFunctionCalls()-funCallsStart < generationCycle);
} while (refSet.getFunctionCalls() - funCallsStart < generationCycle);
problem.evaluatePopulationEnd(refSet);
if (TRACE) {
@@ -259,7 +260,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// filter: only check those within 50% of the worst indy relative to the best.
if (relativeFitCriterion) {
double fitRange = refSet.getWorstFitness()[0] - refSet.getBestFitness()[0];
return (cand.getFitness(0) < (refSet.getBestFitness()[0]+(fitRange*localSearchFitnessFilter)));
return (cand.getFitness(0) < (refSet.getBestFitness()[0] + (fitRange * localSearchFitnessFilter)));
} else {
// absolute fitness criterion
return (cand.getFitness(0) < localSearchFitnessFilter);
@@ -271,7 +272,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private Population regenerateRefSet() {
Population diversifiedPop = diversify();
int keep = refSetSize/2;
int keep = refSetSize / 2;
Population newRefSet = refSet.cloneWithoutInds();
if (TRACE) {
System.out.println("regen after " + refSet.getFunctionCalls() + ", best is " + refSet.getBestEAIndividual().getFitness(0));
@@ -283,20 +284,20 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
System.out.println("keeping " + keep + " indies from former ref set, best is " + newRefSet.getBestEAIndividual().getFitness(0));
}
int h=newRefSet.size();
int h = newRefSet.size();
ArrayList<double[]> distVects = new ArrayList<double[]>();
for (int i=1; i<h; i++) {
for (int i = 1; i < h; i++) {
distVects.add(getDiffVect(newRefSet.getEAIndividual(0), newRefSet.getEAIndividual(i)));
}
double maxSP = -1;
int sel = -1;
while (h<refSetSize) {
for (int i=0; i<diversifiedPop.size(); i++) {
while (h < refSetSize) {
for (int i = 0; i < diversifiedPop.size(); i++) {
// the difference of cand and best is multiplied by each earlier difference from refSet indies
double[] vP = calcVectP(diversifiedPop.getEAIndividual(i), newRefSet.getEAIndividual(0), distVects);
double maxTmp = getMaxInVect(vP);
if ((i==0) || (maxTmp < maxSP)) {
if ((i == 0) || (maxTmp < maxSP)) {
maxSP = maxTmp;
sel = i;
}
@@ -321,7 +322,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private double getMaxInVect(double[] vals) {
double dmax = vals[0];
for (int j=1; j<vals.length; j++) {
for (int j = 1; j < vals.length; j++) {
if (vals[j] > dmax) {
dmax = vals[j];
}
@@ -338,23 +339,24 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private double[] multScalarTransposed(double[] diff, ArrayList<double[]> distVects) {
// d[0]*m[0][0], d[1]*m[0][1] etc.
double[] res = new double[distVects.size()];
for (int i=0; i<distVects.size(); i++) {
for (int i = 0; i < distVects.size(); i++) {
res[i] = Mathematics.vvMult(diff, distVects.get(i));
}
return res;
}
private double[] getDiffVect(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
double[] v1 = ((InterfaceDataTypeDouble)indy1).getDoubleData();
double[] v2 = ((InterfaceDataTypeDouble)indy2).getDoubleData();
double[] v1 = ((InterfaceDataTypeDouble) indy1).getDoubleData();
double[] v2 = ((InterfaceDataTypeDouble) indy2).getDoubleData();
return Mathematics.vvSub(v1, v2);
}
/**
* Maybe replace the single worst indy in the refset by the best candidate, which may
* be locally optimized in a local search step.
* The best candidate is removed from the candidate set in any case. The candidate set
* may be cleared if all following individuals would never be taken over to the refset.
* Maybe replace the single worst indy in the refset by the best candidate,
* which may be locally optimized in a local search step. The best candidate
* is removed from the candidate set in any case. The candidate set may be
* cleared if all following individuals would never be taken over to the
* refset.
*
* @param refSet
* @param candidates
@@ -371,7 +373,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
if (isDoLocalSolver(bestCand, refSet)) {
Pair<AbstractEAIndividual, Integer> lsRet = localSolver(bestCand, localSearchSteps);
if ((Math.abs(lsRet.tail() - localSearchSteps)/localSearchSteps) > 0.05) {
if ((Math.abs(lsRet.tail() - localSearchSteps) / localSearchSteps) > 0.05) {
System.err.println("Warning, more than 5% difference in local search step");
}
bestCand = lsRet.head();
@@ -407,18 +409,16 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// so we can just clear the rest of the candidates
if (!doLocalSearch && (bestCand.getFitness().length == 1)) {
candidates.clear();
}
else {
} else {
candidates.remove(bestIndex);
}
}
}
private Pair<AbstractEAIndividual, Integer> localSolver(AbstractEAIndividual cand, int hcSteps) {
if (localSearchMethod.getSelectedTagID()==0) {
if (localSearchMethod.getSelectedTagID() == 0) {
return localSolverHC(cand, hcSteps);
}
else {
} else {
return PostProcess.localSolverNMS(cand, hcSteps, nelderMeadInitPerturbation, problem);
}
}
@@ -438,7 +438,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private int getClosestIndy(AbstractEAIndividual indy, Population refSet) {
double tmpDst, dist = PhenotypeMetric.dist(indy, refSet.getEAIndividual(0));
int sel = 0;
for (int i=1; i<refSet.size(); i++) {
for (int i = 1; i < refSet.size(); i++) {
tmpDst = PhenotypeMetric.dist(indy, refSet.getEAIndividual(i));
if (tmpDst < dist) {
tmpDst = dist;
@@ -449,8 +449,8 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
/**
* Check for both a genotype and phenotype diversity criterion which both must
* be fulfilled for a candidate to be accepted.
* Check for both a genotype and phenotype diversity criterion which both
* must be fulfilled for a candidate to be accepted.
*
* @param cand
* @param popCompGeno
@@ -459,14 +459,14 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
*/
private boolean diversityCriterionFulfilled(AbstractEAIndividual cand, Population popCompGeno, Population popComPheno) {
double minDist = PhenotypeMetric.dist(cand, popCompGeno.getEAIndividual(0));
for (int i=1; i<popCompGeno.size(); i++) {
for (int i = 1; i < popCompGeno.size(); i++) {
minDist = Math.min(minDist, PhenotypeMetric.dist(cand, popCompGeno.getEAIndividual(i)));
}
boolean minDistFulfilled = ((minDiversityEpsilon<=0) || (minDist > minDiversityEpsilon));
boolean minDistFulfilled = ((minDiversityEpsilon <= 0) || (minDist > minDiversityEpsilon));
if (minDistFulfilled && (improvementEpsilon > 0)) {
boolean minImprovementFulfilled = (cand.getFitness(0) < ((1.-improvementEpsilon) * popComPheno.getBestEAIndividual().getFitness(0)));
boolean minImprovementFulfilled = (cand.getFitness(0) < ((1. - improvementEpsilon) * popComPheno.getBestEAIndividual().getFitness(0)));
return minImprovementFulfilled;
} else {
return minDistFulfilled;
@@ -475,6 +475,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
/**
* Recombines the refset to new indies which are also evaluated.
*
* @param refSet
* @return
*/
@@ -482,10 +483,10 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// 3 pair types: better-better, better-worse, worse-worse (half of the pop);
Population combs = new Population();
Population refSorted = refSet.getBestNIndividuals(refSet.size(), fitCrit);
int half = refSet.size()/2;
for (int i=0; i<half-1; i++) { // better-better
int half = refSet.size() / 2;
for (int i = 0; i < half - 1; i++) { // better-better
AbstractEAIndividual indy1 = refSorted.getEAIndividual(i);
for (int j=i+1; j<half; j++) {
for (int j = i + 1; j < half; j++) {
// if (TRACE) System.out.println("combi T bb, " + i+ "/" + j);
AbstractEAIndividual indy2 = refSorted.getEAIndividual(j);
combs.add(combineTypeOne(indy1, indy2));
@@ -495,9 +496,9 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
}
for (int i=0; i<half; i++) { // better-worse
for (int i = 0; i < half; i++) { // better-worse
AbstractEAIndividual indy1 = refSorted.getEAIndividual(i);
for (int j=half; j<refSet.size(); j++) {
for (int j = half; j < refSet.size(); j++) {
// if (TRACE) System.out.println("combi T bw, " + i+ "/" + j);
AbstractEAIndividual indy2 = refSorted.getEAIndividual(j);
combs.add(combineTypeOne(indy1, indy2));
@@ -506,23 +507,22 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
}
for (int i=half; i<refSet.size()-1; i++) { // worse-worse
for (int i = half; i < refSet.size() - 1; i++) { // worse-worse
AbstractEAIndividual indy1 = refSorted.getEAIndividual(i);
for (int j=i+1; j<refSet.size(); j++) {
for (int j = i + 1; j < refSet.size(); j++) {
// if (TRACE) System.out.println("combi T ww, " + i+ "/" + j);
AbstractEAIndividual indy2 = refSorted.getEAIndividual(j);
combs.add(combineTypeTwo(indy1, indy2));
if (RNG.flipCoin(0.5)) {
combs.add(combineTypeOne(indy1, indy2));
}
else {
} else {
combs.add(combineTypeThree(indy1, indy2));
}
}
}
if (TRACE) {
System.out.println("created combinations " + combs.size() + " best is " + combs.getBestEAIndividual().getFitness(0) );
System.out.println("created combinations " + combs.size() + " best is " + combs.getBestEAIndividual().getFitness(0));
}
return combs;
}
@@ -530,28 +530,30 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private AbstractEAIndividual combineTypeOne(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
return combine(indy1, indy2, true, false);
}
private AbstractEAIndividual combineTypeTwo(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
return combine(indy1, indy2, true, true);
}
private AbstractEAIndividual combineTypeThree(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
return combine(indy1, indy2, false, true);
}
private AbstractEAIndividual combine(AbstractEAIndividual indy1, AbstractEAIndividual indy2, boolean bFirst, boolean bAdd) {
AbstractEAIndividual resIndy = (AbstractEAIndividual)indy1.clone();
double[] v1 = ((InterfaceDataTypeDouble)indy1).getDoubleData();
double[] v2 = ((InterfaceDataTypeDouble)indy2).getDoubleData();
AbstractEAIndividual resIndy = (AbstractEAIndividual) indy1.clone();
double[] v1 = ((InterfaceDataTypeDouble) indy1).getDoubleData();
double[] v2 = ((InterfaceDataTypeDouble) indy2).getDoubleData();
double[] dVect = RNG.randomDoubleArray(0, 1, probDim);
for (int i=0; i<probDim; i++) {
dVect[i] *= (v2[i]-v1[i])/2.;
for (int i = 0; i < probDim; i++) {
dVect[i] *= (v2[i] - v1[i]) / 2.;
}
double[] candidate = bFirst ? v1 : v2;
double[] combi = bAdd ? Mathematics.vvAdd(candidate, dVect) : Mathematics.vvSub(candidate, dVect);
if (checkRange) {
Mathematics.projectToRange(combi, range);
}
((InterfaceDataTypeDouble)resIndy).SetDoubleGenotype(combi);
((InterfaceDataTypeDouble) resIndy).SetDoubleGenotype(combi);
problem.evaluate(resIndy);
refSet.incrFunctionCalls();
return resIndy;
@@ -563,25 +565,25 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
private Population getRefSetFitBased(Population curRefSet, Population divPop) {
int h=refSetSize/2;
int h = refSetSize / 2;
curRefSet.addAll(divPop.getBestNIndividuals(h, fitCrit));
Population rest = divPop.getWorstNIndividuals(refSetSize-h, fitCrit);
Population rest = divPop.getWorstNIndividuals(refSetSize - h, fitCrit);
// contains worst indies
double[][] distances = new double[rest.size()][refSetSize];
for (int i=0; i<distances.length; i++) { // compute euc. distances of all rest indies to all refset indies.
for (int j=0; j<h; j++) {
distances[i][j]=PhenotypeMetric.dist(rest.getEAIndividual(i), curRefSet.getEAIndividual(j));
for (int i = 0; i < distances.length; i++) { // compute euc. distances of all rest indies to all refset indies.
for (int j = 0; j < h; j++) {
distances[i][j] = PhenotypeMetric.dist(rest.getEAIndividual(i), curRefSet.getEAIndividual(j));
}
}
while (curRefSet.size()<refSetSize) {
while (curRefSet.size() < refSetSize) {
// "the vector having highest minimum distance will join the refset."
int sel=selectHighestMinDistance(distances, h);
int sel = selectHighestMinDistance(distances, h);
// add the selected diverse indy
curRefSet.add(rest.getEAIndividual(sel));
// compute distances
for (int i=0; i<distances.length; i++) {
distances[i][h]=PhenotypeMetric.dist(rest.getEAIndividual(i), curRefSet.getEAIndividual(h));
for (int i = 0; i < distances.length; i++) {
distances[i][h] = PhenotypeMetric.dist(rest.getEAIndividual(i), curRefSet.getEAIndividual(h));
}
// dont! remove it from the rest indi set
//rest.remove(sel);
@@ -599,7 +601,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
if (dmin < 0) {
return dmin;
} // tweak for trick
for (int j=1; j<maxRow; j++) {
for (int j = 1; j < maxRow; j++) {
if (vals[col][j] < dmin) {
dmin = vals[col][j];
}
@@ -612,8 +614,8 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// select the first index with highest minimum.
double highestMin = getMinInCol(0, maxRow, distances);
int sel = 0;
for (int i=1; i<distances.length; i++) {
double dtmp = getMinInCol(i,maxRow,distances);
for (int i = 1; i < distances.length; i++) {
double dtmp = getMinInCol(i, maxRow, distances);
if (dtmp > highestMin) {
highestMin = dtmp;
sel = i;
@@ -629,10 +631,10 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private Population diversify(Population pop) {
int[][] freq = new int[probDim][intervals];
if (pop.size() > 0) { // count the interval appearances of already given individuals.
for (int k=0; k<pop.size(); k++) {
double[] params = ((InterfaceDataTypeDouble)pop.getEAIndividual(k)).getDoubleData();
for (int j=0; j<probDim; j++) {
for (int iv = 0; iv<intervals; iv++) {
for (int k = 0; k < pop.size(); k++) {
double[] params = ((InterfaceDataTypeDouble) pop.getEAIndividual(k)).getDoubleData();
for (int j = 0; j < probDim; j++) {
for (int iv = 0; iv < intervals; iv++) {
if (isInRangeInterval(params[j], j, iv)) {
freq[j][iv]++;
}
@@ -641,16 +643,16 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
} else {
// or start with diagonals
for (int i=0; i<intervals; i++) {
for (int i = 0; i < intervals; i++) {
pop.add(createDiagIndies(i));
for (int j=0; j<probDim; j++) {
freq[j][i]=1;
for (int j = 0; j < probDim; j++) {
freq[j][i] = 1;
}
}
}
while (pop.size() < poolSize) {
pop.add( createDiverseIndy(freq) );
pop.add(createDiverseIndy(freq));
}
pop.setTargetSize(poolSize);
if (TRACE) {
@@ -660,11 +662,11 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
private AbstractEAIndividual createDiverseIndy(int freq[][]) {
AbstractEAIndividual indy = (AbstractEAIndividual)template.clone();
InterfaceDataTypeDouble dblIndy = (InterfaceDataTypeDouble)indy;
AbstractEAIndividual indy = (AbstractEAIndividual) template.clone();
InterfaceDataTypeDouble dblIndy = (InterfaceDataTypeDouble) indy;
double[] genes = dblIndy.getDoubleData();
for (int i=0; i<probDim; i++) {
for (int i = 0; i < probDim; i++) {
int interv = selectInterv(i, freq);
genes[i] = randInRangeInterval(i, interv);
freq[i][interv]++;
@@ -676,15 +678,15 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private double getFreqDepProb(int dim, int interv, int freq[][]) {
double sum = 0;
for (int k=0; k<intervals; k++) {
sum += (double)freq[dim][k];
for (int k = 0; k < intervals; k++) {
sum += (double) freq[dim][k];
}
return freq[dim][interv]/sum;
return freq[dim][interv] / sum;
}
private int selectInterv(int dim, int freq[][]) {
double[] probs = new double[intervals];
for (int i=0; i<intervals; i++) {
for (int i = 0; i < intervals; i++) {
probs[i] = getFreqDepProb(dim, i, freq);
}
double rnd = RNG.randomDouble();
@@ -692,7 +694,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
double sum = probs[0];
while (sum < rnd) {
sel++;
sum+=probs[sel];
sum += probs[sel];
}
if (sum >= 1.0000001) {
System.err.println("Check this: sum>=1 in selectInterv");
@@ -708,10 +710,10 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
* @return
*/
private AbstractEAIndividual createDiagIndies(int interval) {
AbstractEAIndividual indy = (AbstractEAIndividual)template.clone();
InterfaceDataTypeDouble dblIndy = (InterfaceDataTypeDouble)indy;
AbstractEAIndividual indy = (AbstractEAIndividual) template.clone();
InterfaceDataTypeDouble dblIndy = (InterfaceDataTypeDouble) indy;
double[] genes = dblIndy.getDoubleData();
for (int i=0; i<probDim; i++) {
for (int i = 0; i < probDim; i++) {
genes[i] = randInRangeInterval(i, interval);
}
dblIndy.SetDoubleGenotype(genes);
@@ -719,9 +721,9 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
private boolean isInRangeInterval(double d, int dim, int interval) {
double dimStep = (range[dim][1]-range[dim][0])/intervals;
double lowB = range[dim][0]+(dimStep*interval);
double upB = lowB+dimStep;
double dimStep = (range[dim][1] - range[dim][0]) / intervals;
double lowB = range[dim][0] + (dimStep * interval);
double upB = lowB + dimStep;
return isInRange(d, lowB, upB);
}
@@ -730,14 +732,13 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
private double randInRangeInterval(int dim, int interval) {
double dimStep = (range[dim][1]-range[dim][0])/intervals;
double lowB = range[dim][0]+(dimStep*interval);
double upB = lowB+dimStep;
double dimStep = (range[dim][1] - range[dim][0]) / intervals;
double lowB = range[dim][0] + (dimStep * interval);
double upB = lowB + dimStep;
return RNG.randomDouble(lowB, upB);
}
///////////// Trivials...
@Override
public void setIdentifier(String name) {
m_Identifier = name;
@@ -758,18 +759,17 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
InterfacePopulationChangedEventListener ea) {
m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
@Override
public void freeWilly() {}
@Override
public String getIdentifier() {
@@ -786,7 +786,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
private boolean useLSHC() {
return localSearchMethod.getSelectedTagID()==0;
return localSearchMethod.getSelectedTagID() == 0;
}
/**
@@ -871,7 +871,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
////////////////////////////////////////////7
/**
* This method performs a scatter search runnable.
*/
@@ -998,9 +997,11 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
public double getImprovementEpsilon() {
return improvementEpsilon;
}
public void setImprovementEpsilon(double improvementEpsilon) {
this.improvementEpsilon = improvementEpsilon;
}
public String improvementEpsilonTipText() {
return "Minimal relative fitness improvement for a candidate to enter the refSet - set to zero to deactivate.";
}
@@ -1008,9 +1009,11 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
public double getMinDiversityEpsilon() {
return minDiversityEpsilon;
}
public void setMinDiversityEpsilon(double minDiversityEpsilon) {
this.minDiversityEpsilon = minDiversityEpsilon;
}
public String minDiversityEpsilonTipText() {
return "Minimal distance to other individuals in the refSet for a candidate to enter the refSet - set to zero to deactivate.";
}

144
src/eva2/server/go/strategies/SimulatedAnnealing.java
View File

@@ -10,18 +10,17 @@ import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
/** Simulated Annealing by Nelder and Mead, a simple yet efficient local search
/**
* Simulated Annealing by Nelder and Mead, a simple yet efficient local search
* method. But to become less prone to premature convergence the cooling rate
* has to be tuned to the optimization problem at hand. Again the population size
* gives the number of multi-starts.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 13.05.2004
* Time: 10:30:26
* To change this template use File | Settings | File Templates.
* has to be tuned to the optimization problem at hand. Again the population
* size gives the number of multi-starts. Created by IntelliJ IDEA. User:
* streiche Date: 13.05.2004 Time: 10:30:26 To change this template use File |
* Settings | File Templates.
*/
public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private int m_MultiRuns = 100;
private int m_FitnessCalls = 100;
@@ -29,7 +28,6 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
GAIndividualBinaryData m_Best, m_Test;
public double m_InitialTemperature = 2, m_CurrentTemperature;
public double m_Alpha = 0.9;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -41,8 +39,8 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
}
public SimulatedAnnealing(SimulatedAnnealing a) {
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.m_InitialTemperature = a.m_InitialTemperature;
this.m_CurrentTemperature = a.m_CurrentTemperature;
this.m_Alpha = a.m_Alpha;
@@ -53,7 +51,8 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
return (Object) new SimulatedAnnealing(this);
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
@Override
public void init() {
@@ -63,13 +62,15 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
this.m_Population = (Population)pop.clone();
this.m_Population = (Population) pop.clone();
this.m_CurrentTemperature = this.m_InitialTemperature;
if (reset) {
this.m_Population.init();
@@ -78,12 +79,13 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
}
}
/** This method will optimize
/**
* This method will optimize
*/
@Override
public void optimize() {
AbstractEAIndividual indy;
Population original = (Population)this.m_Population.clone();
Population original = (Population) this.m_Population.clone();
double delta;
for (int i = 0; i < this.m_Population.size(); i++) {
@@ -95,13 +97,13 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
}
this.m_Problem.evaluate(this.m_Population);
for (int i = 0; i < this.m_Population.size(); i++) {
if (((AbstractEAIndividual)original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual)this.m_Population.get(i)))) {
if (((AbstractEAIndividual) original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual) this.m_Population.get(i)))) {
this.m_Population.remove(i);
this.m_Population.add(i, original.get(i));
} else {
delta = this.calculateDelta(((AbstractEAIndividual)original.get(i)), ((AbstractEAIndividual)this.m_Population.get(i)));
delta = this.calculateDelta(((AbstractEAIndividual) original.get(i)), ((AbstractEAIndividual) this.m_Population.get(i)));
//System.out.println("delta: " + delta);
if (Math.exp(-delta/this.m_CurrentTemperature) > RNG.randomInt(0,1)) {
if (Math.exp(-delta / this.m_CurrentTemperature) > RNG.randomInt(0, 1)) {
this.m_Population.remove(i);
this.m_Population.add(i, original.get(i));
}
@@ -112,7 +114,9 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method calculates the difference between the fitness values
/**
* This method calculates the difference between the fitness values
*
* @param org The original
* @param mut The mutant
*/
@@ -127,19 +131,23 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
return result;
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
public void defaultInit() {
this.m_FitnessCallsNeeded = 0;
@@ -147,24 +155,26 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
this.m_Best.defaultInit(m_Problem);
}
/** This method will optimize
/**
* This method will optimize
*/
public void defaultOptimize() {
for (int i = 0; i < m_FitnessCalls; i++) {
this.m_Test = (GAIndividualBinaryData)((this.m_Best).clone());
this.m_Test = (GAIndividualBinaryData) ((this.m_Best).clone());
this.m_Test.defaultMutate();
if (this.m_Test.defaultEvaulateAsMiniBits() < this.m_Best.defaultEvaulateAsMiniBits()) {
this.m_Best = this.m_Test;
}
this.m_FitnessCallsNeeded = i;
if (this.m_Best.defaultEvaulateAsMiniBits() == 0) {
i = this.m_FitnessCalls +1;
i = this.m_FitnessCalls + 1;
}
}
}
/** This main method will start a simple hillclimber.
* No arguments necessary.
/**
* This main method will start a simple hillclimber. No arguments necessary.
*
* @param args
*/
public static void main(String[] args) {
@@ -178,31 +188,35 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
}
TmpMeanCalls /= program.m_MultiRuns;
TmpMeanFitness /= program.m_MultiRuns;
System.out.println("("+program.m_MultiRuns+"/"+program.m_FitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
System.out.println("(" + program.m_MultiRuns + "/" + program.m_FitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
}
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -210,63 +224,70 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
String result = "";
if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
}
else {
} else {
result += "Simulated Annealing:\n";
}
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The simulated annealing uses an additional cooling rate instead of a simple dominate criteria to accpect worse solutions by chance.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
public String getName() {
return "MS-SA";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Change the number of best individuals stored (MS-SA)).";
}
@@ -275,31 +296,40 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** Set the initial temperature
/**
* Set the initial temperature
*
* @return The initial temperature.
*/
public double getInitialTemperature() {
return this.m_InitialTemperature;
}
public void setInitialTemperature(double pop){
public void setInitialTemperature(double pop) {
this.m_InitialTemperature = pop;
}
public String initialTemperatureTipText() {
return "Set the initial temperature.";
}
/** Set alpha, which is used to degrade the temperaure
/**
* Set alpha, which is used to degrade the temperaure
*
* @return The cooling rate.
*/
public double getAlpha() {
return this.m_Alpha;
}
public void setAlpha(double a){
public void setAlpha(double a) {
this.m_Alpha = a;
if (this.m_Alpha > 1) {
this.m_Alpha = 1.0;
}
}
public String alphaTipText() {
return "Set alpha, which is used to degrade the temperaure.";
}

138
src/eva2/server/go/strategies/SteadyStateGA.java
View File

@@ -13,14 +13,12 @@ import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/** A simple implementation of the steady-state GA with variable
* replacement schemes. To reduce the logging effort population.size()
* optimization steps are performed each time optimize() is called.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 19.07.2005
* Time: 14:30:20
* To change this template use File | Settings | File Templates.
/**
* A simple implementation of the steady-state GA with variable replacement
* schemes. To reduce the logging effort population.size() optimization steps
* are performed each time optimize() is called. Created by IntelliJ IDEA. User:
* streiche Date: 19.07.2005 Time: 14:30:20 To change this template use File |
* Settings | File Templates.
*/
public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
@@ -30,7 +28,6 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
private InterfaceSelection m_PartnerSelection = new SelectTournament();
private InterfaceReplacement m_ReplacementSelection = new ReplaceWorst();
private int m_NumberOfPartners = 1;
private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -38,13 +35,13 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
}
public SteadyStateGA(SteadyStateGA a) {
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.m_Identifier = a.m_Identifier;
this.m_NumberOfPartners = a.m_NumberOfPartners;
this.m_ParentSelection = (InterfaceSelection)a.m_ParentSelection.clone();
this.m_PartnerSelection = (InterfaceSelection)a.m_PartnerSelection.clone();
this.m_ReplacementSelection = (InterfaceReplacement)a.m_ReplacementSelection.clone();
this.m_ParentSelection = (InterfaceSelection) a.m_ParentSelection.clone();
this.m_PartnerSelection = (InterfaceSelection) a.m_PartnerSelection.clone();
this.m_ReplacementSelection = (InterfaceReplacement) a.m_ReplacementSelection.clone();
}
@Override
@@ -59,12 +56,14 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param reset If true the population is reset.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
this.m_Population = (Population)pop.clone();
this.m_Population = (Population) pop.clone();
if (reset) {
this.m_Population.init();
this.evaluatePopulation(this.m_Population);
@@ -72,8 +71,9 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
}
}
/** This method will evaluate the current population using the
* given problem.
/**
* This method will evaluate the current population using the given problem.
*
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
@@ -81,8 +81,10 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
population.incrGeneration();
}
/** This method will assign fitness values to all individual in the
* current population.
/**
* This method will assign fitness values to all individual in the current
* population.
*
* @param population The population that is to be evaluated
*/
private void defaultEvaluatePopulation(Population population) {
@@ -95,14 +97,15 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
population.incrGeneration();
}
/** This method will generate the offspring population from the
* given population of evaluated individuals.
/**
* This method will generate the offspring population from the given
* population of evaluated individuals.
*/
private void generateChildren() {
this.m_ParentSelection.prepareSelection(this.m_Population);
this.m_PartnerSelection.prepareSelection(this.m_Population);
Population parents = this.m_ParentSelection.selectFrom(this.m_Population, 1);
AbstractEAIndividual mother = (AbstractEAIndividual)parents.get(0);
AbstractEAIndividual mother = (AbstractEAIndividual) parents.get(0);
parents = this.m_PartnerSelection.findPartnerFor(mother, this.m_Population, this.m_NumberOfPartners);
AbstractEAIndividual[] offSprings = mother.mateWith(parents);
offSprings[0].mutate();
@@ -119,40 +122,48 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
this.m_Population.incrGeneration();
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -160,39 +171,42 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
String result = "";
result += "Genetic Algorithm:\n";
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a Steady-State Genetic Algorithm.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -200,19 +214,23 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
return "SS-GA";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
@@ -221,21 +239,28 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method will set the parent selection method that is to be used
/**
* This method will set the parent selection method that is to be used
*
* @param selection
*/
public void setParentSelection(InterfaceSelection selection) {
this.m_ParentSelection = selection;
}
public InterfaceSelection getParentSelection() {
return this.m_ParentSelection;
}
public String parentSelectionTipText() {
return "Choose a parent selection method.";
}
/** This method will set the number of partners that are needed to create
/**
* This method will set the number of partners that are needed to create
* offsprings by mating
*
* @param partners
*/
public void setNumberOfPartners(int partners) {
@@ -244,35 +269,46 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
}
this.m_NumberOfPartners = partners;
}
public int getNumberOfPartners() {
return this.m_NumberOfPartners;
}
public String numberOfPartnersTipText() {
return "The number of mating partners needed to create offsprings.";
}
/** Choose a selection method for selecting recombination partners for given parents
/**
* Choose a selection method for selecting recombination partners for given
* parents
*
* @param selection
*/
public void setPartnerSelection(InterfaceSelection selection) {
this.m_PartnerSelection = selection;
}
public InterfaceSelection getPartnerSelection() {
return this.m_PartnerSelection;
}
public String partnerSelectionTipText() {
return "Choose a selection method for selecting recombination partners for given parents.";
}
/** Choose a replacement strategy
/**
* Choose a replacement strategy
*
* @param selection
*/
public void setReplacementSelection(InterfaceReplacement selection) {
this.m_ReplacementSelection = selection;
}
public InterfaceReplacement getReplacementSelection() {
return this.m_ReplacementSelection;
}
public String replacementSelectionTipText() {
return "Choose a replacement strategy.";
}

138
src/eva2/server/go/strategies/ThresholdAlgorithm.java
View File

@@ -9,16 +9,14 @@ import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/** Threshold accepting algorithm simliar strategy as the flood
* algorithm, similar problems.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 01.10.2004
* Time: 13:35:49
* To change this template use File | Settings | File Templates.
/**
* Threshold accepting algorithm simliar strategy as the flood algorithm,
* similar problems. Created by IntelliJ IDEA. User: streiche Date: 01.10.2004
* Time: 13:35:49 To change this template use File | Settings | File Templates.
*/
public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private int m_MultiRuns = 100;
private int m_FitnessCalls = 100;
@@ -26,7 +24,6 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
GAIndividualBinaryData m_Best, m_Test;
public double m_InitialT = 2, m_CurrentT;
public double m_Alpha = 0.9;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -38,8 +35,8 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
}
public ThresholdAlgorithm(ThresholdAlgorithm a) {
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.m_InitialT = a.m_InitialT;
this.m_CurrentT = a.m_CurrentT;
this.m_Alpha = a.m_Alpha;
@@ -50,7 +47,8 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
return (Object) new ThresholdAlgorithm(this);
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
@Override
public void init() {
@@ -60,13 +58,15 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
this.m_Population = (Population)pop.clone();
this.m_Population = (Population) pop.clone();
this.m_CurrentT = this.m_InitialT;
if (reset) {
this.m_Population.init();
@@ -75,12 +75,13 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
}
}
/** This method will optimize
/**
* This method will optimize
*/
@Override
public void optimize() {
AbstractEAIndividual indy;
Population original = (Population)this.m_Population.clone();
Population original = (Population) this.m_Population.clone();
double delta;
for (int i = 0; i < this.m_Population.size(); i++) {
@@ -92,7 +93,7 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
}
this.m_Problem.evaluate(this.m_Population);
for (int i = 0; i < this.m_Population.size(); i++) {
delta = this.calculateDelta(((AbstractEAIndividual)original.get(i)), ((AbstractEAIndividual)this.m_Population.get(i)));
delta = this.calculateDelta(((AbstractEAIndividual) original.get(i)), ((AbstractEAIndividual) this.m_Population.get(i)));
if (delta < this.m_CurrentT) {
this.m_Population.remove(i);
this.m_Population.add(i, original.get(i));
@@ -103,7 +104,9 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method calculates the difference between the fitness values
/**
* This method calculates the difference between the fitness values
*
* @param org The original
* @param mut The mutant
*/
@@ -118,19 +121,23 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
return result;
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
public void defaultInit() {
this.m_FitnessCallsNeeded = 0;
@@ -138,24 +145,26 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
this.m_Best.defaultInit(m_Problem);
}
/** This method will optimize
/**
* This method will optimize
*/
public void defaultOptimize() {
for (int i = 0; i < m_FitnessCalls; i++) {
this.m_Test = (GAIndividualBinaryData)((this.m_Best).clone());
this.m_Test = (GAIndividualBinaryData) ((this.m_Best).clone());
this.m_Test.defaultMutate();
if (this.m_Test.defaultEvaulateAsMiniBits() < this.m_Best.defaultEvaulateAsMiniBits()) {
this.m_Best = this.m_Test;
}
this.m_FitnessCallsNeeded = i;
if (this.m_Best.defaultEvaulateAsMiniBits() == 0) {
i = this.m_FitnessCalls +1;
i = this.m_FitnessCalls + 1;
}
}
}
/** This main method will start a simple hillclimber.
* No arguments necessary.
/**
* This main method will start a simple hillclimber. No arguments necessary.
*
* @param args
*/
public static void main(String[] args) {
@@ -169,30 +178,35 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
}
TmpMeanCalls /= program.m_MultiRuns;
TmpMeanFitness /= program.m_MultiRuns;
System.out.println("("+program.m_MultiRuns+"/"+program.m_FitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
System.out.println("(" + program.m_MultiRuns + "/" + program.m_FitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
}
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -200,63 +214,70 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
String result = "";
if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
}
else {
} else {
result += "Threshold Algorithm:\n";
}
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The threshold algorithm uses an declining threshold to accpect new solutions.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
public String getName() {
return "MS-TA";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Change the number of best individuals stored (MS-TA).";
}
@@ -265,31 +286,40 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** Set the initial threshold
/**
* Set the initial threshold
*
* @return The initial temperature.
*/
public double getInitialT() {
return this.m_InitialT;
}
public void setInitialT(double pop){
public void setInitialT(double pop) {
this.m_InitialT = pop;
}
public String initialTTipText() {
return "Set the initial threshold.";
}
/** Set alpha, which is used to degrade the threshold
/**
* Set alpha, which is used to degrade the threshold
*
* @return The initial temperature.
*/
public double getAlpha() {
return this.m_Alpha;
}
public void setAlpha(double a){
public void setAlpha(double a) {
this.m_Alpha = a;
if (this.m_Alpha > 1) {
this.m_Alpha = 1.0;
}
}
public String alphaTipText() {
return "Set alpha, which is used to degrade the threshold.";
}

531
src/eva2/server/go/strategies/Tribes.java
View File

@@ -17,41 +17,41 @@ import eva2.server.go.strategies.tribes.TribesSwarm;
import java.util.Iterator;
import java.util.List;
/**
* This is the TRIBES algorithm, an adaptive, parameter-less PSO implementation.
* I (MK) ported M.Clerc's java version 2006-02 21 and added the original notes below.
* I had to do some modifications for the EvA framework, namely:
* - minor adaptations allover the code to fit into the framework
* - the objective value parameter must now be set within the GUI for each problem by hand (EvA doesnt assume to know an objective beforehand)
* - discrete search spaces are not directly supported any more (no "granularity")
* - the benchmark-collection is gone, it might be included into the EvA benchmark set in the future, though
* - fixed two bugs (SunnySpell link generation, findWorst method)
* - fixed bugs in the CEC 2005 benchmarks (see the corresponding class)
* - I widely kept the original comments, except for places I changed the code so much that they might mislead
* - thats all, I think
* I (MK) ported M.Clerc's java version 2006-02 21 and added the original notes
* below. I had to do some modifications for the EvA framework, namely: - minor
* adaptations allover the code to fit into the framework - the objective value
* parameter must now be set within the GUI for each problem by hand (EvA doesnt
* assume to know an objective beforehand) - discrete search spaces are not
* directly supported any more (no "granularity") - the benchmark-collection is
* gone, it might be included into the EvA benchmark set in the future, though -
* fixed two bugs (SunnySpell link generation, findWorst method) - fixed bugs in
* the CEC 2005 benchmarks (see the corresponding class) - I widely kept the
* original comments, except for places I changed the code so much that they
* might mislead - thats all, I think
*
* I could produce similar results as Clerc's on Rosenbrock and Griewank, (in his book on p. 148),
* I couldnt reproduce the 100% success rate on Ackley, though.
* I could produce similar results as Clerc's on Rosenbrock and Griewank, (in
* his book on p. 148), I couldnt reproduce the 100% success rate on Ackley,
* though.
*
* @author Maurice Clerc, Marcel Kronfeld
* @date 2007-09-13
*
* Original notes:
* @version 2006-02 21
* @author Maurice.Clerc@WriteMe.com
* {@link http://mauriceclerc.net}
* @author Maurice.Clerc@WriteMe.com {@link http://mauriceclerc.net}
* {@link http://clerc.maurice.free.fr/pso/}
*
*/
/* Last updates (M.Clerc)
2006-02-21 Added a repelling option (see variable "repel" in Tribes). Not very convincing
2006-01-03 Fixed a bug in maxIsolated (the memory was not well updated after improvement)
2006-02-21 Added a repelling option (see variable "repel" in Tribes). Not very convincing
2006-01-03 Fixed a bug in maxIsolated (the memory was not well updated after improvement)
Also the same bug in "swarm" and "tribe". Thanks to Yann Cooreen.
Fortunately it was apparently without incidence
2005-12-26 Added items ALL for benchmarks 1 and 2, so that you can launch the whole
2005-12-26 Added items ALL for benchmarks 1 and 2, so that you can launch the whole
benchmark ... and have a cup of tea!
2005-12-26 Fixed a bug. When you launched several optimisations (almost) on the same time
2005-12-26 Fixed a bug. When you launched several optimisations (almost) on the same time
result were not all the same (synchronisation problems)
So you had to launch each problem one at a time.
Now it is done automatically. Even if you launch a problem before than the previous
@@ -129,47 +129,39 @@ import java.util.List;
2005-11-21. Check if it is possible to easily find just the _value_ of the
global minimum (not the position). "Chinese shadow" method?
*/
public class Tribes implements InterfaceOptimizer, java.io.Serializable {
public static final boolean TRACE = false;
protected String m_Identifier = "TRIBES";
transient private InterfacePopulationChangedEventListener m_Listener = null;
protected AbstractOptimizationProblem m_problem;
protected Population population;
public static int maxExplorerNb = 200;
public static int maxMemoryNb = 300;
public static int maxTribeNb = 300;
public static int[] strategies = new int[10]; // Just for information
public static int[] status = new int[9]; // Just for information
public static boolean testBC = false; // TODO project to EvA2
public static int adaptOption = 2;
public static double blind=0; // 0.5 //"Blind" move for very good particles, with a probability Tribes.blind
public static boolean repel=false; // If 1, use a "repelling" strategy (see moveExplorer() )
private boolean checkConstraints=true;
public static double blind = 0; // 0.5 //"Blind" move for very good particles, with a probability Tribes.blind
public static boolean repel = false; // If 1, use a "repelling" strategy (see moveExplorer() )
private boolean checkConstraints = true;
private static final long serialVersionUID = 1L;
TribesSwarm swarm = null;
private int iter;
protected double objectiveFirstDim = 0.;
protected double[][] range, initRange;
protected int notifyGenChangedEvery = 10;
protected int problemDim;
protected int adaptThreshold, adaptMax, adapt;
protected int informOption; /* For the best informant.
-1 => really the best
1 => the best according to a pseudo-gradient method
*/
protected int initExplorerNb=3; // Number of explorers at the very beginning
// use full range (0) or subspace (1) for init options 0 and 1
protected int rangeInitType=1;
protected int initExplorerNb = 3; // Number of explorers at the very beginning
// use full range (0) or subspace (1) for init options 0 and 1
protected int rangeInitType = 1;
private boolean m_Show = false;
transient protected eva2.gui.Plot m_Plot = null;
// private int useAnchors = 0; // use anchors to detect environment changes?
@@ -205,10 +197,10 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
// System.out.println("TRIBES.SetProblem()");
m_problem = (AbstractOptimizationProblem)problem;
m_problem = (AbstractOptimizationProblem) problem;
range = null;
if (problem instanceof InterfaceHasInitRange) {
initRange = (double[][])((InterfaceHasInitRange)problem).getInitRange();
initRange = (double[][]) ((InterfaceHasInitRange) problem).getInitRange();
}
Population pop = new Population(1);
problem.initPopulation(pop);
@@ -244,9 +236,10 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
/**
* As TRIBES manages an own structured set of particles (the list of Tribes containing explorers
* and memories), the setPopulation method is only telling Tribes the range
* of the indiviuals in the beginning of the run, the individuals will be discarded.
* As TRIBES manages an own structured set of particles (the list of Tribes
* containing explorers and memories), the setPopulation method is only
* telling Tribes the range of the indiviuals in the beginning of the run,
* the individuals will be discarded.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
@@ -257,9 +250,9 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
TribesPosition bestMemPos = swarm.getBestMemory().getPos();
AbstractEAIndividual bestExp = population.getBestEAIndividual();
if (bestMemPos.firstIsBetter(bestMemPos.getFitness(), bestExp.getFitness())) {
AbstractEAIndividual indy = (AbstractEAIndividual)bestExp.clone();
AbstractEAIndividual indy = (AbstractEAIndividual) bestExp.clone();
indy.setFitness(bestMemPos.getFitness());
((InterfaceDataTypeDouble)indy).SetDoubleGenotype(bestMemPos.getPos());
((InterfaceDataTypeDouble) indy).SetDoubleGenotype(bestMemPos.getPos());
return indy;
} else {
return bestExp;
@@ -296,8 +289,8 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
// adaptMax=swarmSize;
adaptMax = swarm.linkNb(swarm);
if (adaptThreshold >= adaptMax) {
if (swarm.getBestMemory().getPrevPos().getTotalError() <=
swarm.getBestMemory().getPos().getTotalError()) {
if (swarm.getBestMemory().getPrevPos().getTotalError()
<= swarm.getBestMemory().getPos().getTotalError()) {
adapt = iter; // Memorize at which iteration adaptation occurs
for (int i = 0; i < swarm.getTribeCnt(); i++) {
@@ -333,7 +326,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
if (TRACE) {
System.out.println("loop finished after " + population.getFunctionCalls() + " evaluations");
//for (int i=0; i<population.size(); i++) System.out.println(" * "+((TribesExplorer)population.get(i)).getStringRepresentation());
System.out.println(" best: "+population.getBestEAIndividual().getStringRepresentation() + " - " + population.getBestEAIndividual().getFitness(0));
System.out.println(" best: " + population.getBestEAIndividual().getStringRepresentation() + " - " + population.getBestEAIndividual().getFitness(0));
System.out.println("swarm contains " + swarm.numParticles() + " particles in iteration " + iter);
}
}
@@ -342,9 +335,9 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
// TODO
double pos[], vel[];
for (int i=0; i<pop.size(); i++) {
pos = ((TribesExplorer)pop.getEAIndividual(i)).getDoubleData();
vel = ((TribesExplorer)pop.getEAIndividual(i)).getVelocity();
for (int i = 0; i < pop.size(); i++) {
pos = ((TribesExplorer) pop.getEAIndividual(i)).getDoubleData();
vel = ((TribesExplorer) pop.getEAIndividual(i)).getVelocity();
plotIndy(pos, vel, i);
// hier weiter!
}
@@ -372,7 +365,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
// double[] tmpD = new double[2];
// tmpD[0] = 0;
// tmpD[1] = 0;
this.m_Plot = new eva2.gui.Plot("TRIBES "+ population.getGeneration(), "x1", "x2", range[0], range[1]);
this.m_Plot = new eva2.gui.Plot("TRIBES " + population.getGeneration(), "x1", "x2", range[0], range[1]);
// this.m_Plot.setCornerPoints(range, 0);
}
}
@@ -383,253 +376,161 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
/**
public synchronized void search(param pb, PrintStream runSave, PrintStream synthSave) {
double epsMean, epsMin, epsMax;
double evalMean;
int n;
int run, run1;
int successNb;
// Exploratrices générées
explorer explorer[] = new explorer[
Tribes.maxExplorerNb];
double[] eps = new double[pb.maxRun];
double[] evalNb = new double[pb.maxRun];
double[] temp = new double[3];
successNb = 0;
for (n = 0; n < 9; n++) { // For information
Tribes.strategies[n] = 0;
Tribes.status[n] = 0;
}
;
epsMin = Tribes.infinity;
epsMax = 0;
// Titles
print("\nIter. Eval. Best_fitness", displayPb);
save("\n\n PROBLEM "+pb.function[0],synthSave);
save("\nRun Iter. Eval. Best_fitness Position", synthSave);
save("\n\n PROBLEM "+pb.function[0],runSave);
// **
// * Loop on runs
// *
for (run = 0; run < pb.maxRun; run++) {
run1 = run + 1;
save("\n" + run1 + " ", synthSave);
temp = solve(pb, Tribes.initExplorerNb,runSave,synthSave);
eps[run] = temp[0];
evalNb[run] = temp[1];
successNb = successNb + (int) temp[2];
if (eps[run] < epsMin) {
epsMin = eps[run];
}
if (eps[run] > epsMax) {
epsMax = eps[run];
}
}
// Mean values
epsMean = 0;
evalMean = 0;
for (run = 0; run < pb.maxRun; run++) {
epsMean = epsMean + eps[run];
evalMean = evalMean + evalNb[run];
}
epsMean = epsMean / pb.maxRun;
evalMean = evalMean / pb.maxRun;
print("\nStatuses ", displayPb);
for (n = 1; n < 10; n++) {
print("\n" + n + " " + Tribes.status[n - 1] + " times",
displayPb);
}
print("\nStrategies ", displayPb);
for (n = 1; n < 10; n++) {
print("\n" + n + " " + Tribes.strategies[n - 1] + " times",
displayPb);
}
print("\nMIN BEST TOTAL_ERROR " + epsMin, displayPb);
print("\nMEAN BEST TOTAL_ERROR " + epsMean, displayPb);
print("\nMEAN EVAL. NUMBER " + evalMean, displayPb);
print("\n SUCCESS RATE " + (double) successNb / pb.maxRun,
displayPb);
save("\nMIN BEST TOTAL_ERROR " + epsMin, synthSave);
save("\nMEAN BEST TOTAL_ERROR " + epsMean, synthSave);
save("\nMEAN EVAL. NUMBER " + evalMean, synthSave);
save("\n SUCCESS RATE " + (double) successNb / pb.maxRun, synthSave);
save("\n-1", runSave); // Special value for the end of the file. Used for graphics
} // End of search()
public double[] solve(param pb, int initExplorerNb, PrintStream runSave, PrintStream synthSave) {
int adapt, adaptMax;
int adaptThreshold;
int d, D = pb.H.Dimension;
int evalF;
int iter;
int n;
boolean stop;
double[] temp = new double[3];
int informOption;
// For the best informant.
// -1 => really the best
// 1 => the best according to a pseudo-gradient method
// -----------INIT START
// Generate a swarm
evalF=0;
swarm swarm = new swarm();
evalF=swarm.generateSwarm(pb, initExplorerNb, pb.initType, displayPb,evalF);
// swarm.displaySwarm(swarm,out);
// print("\n Best after init: "+swarm.Best.position.fitness,out);
// Move the swarm as long as the stop criterion is false
iter = 0;
adapt = 0;
stop = false;
informOption = -1;
// Hard coded option
// -1 = absolute best informant
// 1 = relative (pseudo-gradient) best informant. For "niching"
// See also moveExplorer, which can be modified in order to avoid this parameter
//
// -----------INIT END
// -----------OPTIMIZE START
iterations:while (!stop) {
swarm.size = swarm.swarmSize(swarm);
iter++;
// swarm.Best.positionPrev = swarm.Best.position;
evalF=swarm.moveSwarm(pb, swarm, informOption, displayPb, evalF); //*** HERE IT MOVES
// Some display each time there is "enough" improvement
// fduring the process
double enough = 0.005;
if ((1 - enough) * swarm.Best.positionPrev.totalError >
swarm.Best.position.totalError) {
print("\nIter. " + iter, displayPb);
print(" Eval. " + evalF, displayPb);
print(" totalError " + swarm.Best.position.totalError,
displayPb);
print(" " + swarm.size + " particles", displayPb);
}
// Save run info
save("\n" + iter + " " + evalF + " " +
swarm.Best.position.totalError + " " + swarm.size, runSave);
// Evaluate the stop criterion
stop = evalF >= pb.maxEval ||
pb.accuracy > swarm.Best.position.totalError;
if (Tribes.adaptOption == 0) {
continue iterations;
}
if (Tribes.adaptOption == 1) { // Just reinitialize the swarm
adaptThreshold = iter - adapt;
// adaptMax=swarmSize;
adaptMax = swarm.linkNb(swarm);
if (adaptThreshold >= adaptMax) {
if (swarm.Best.positionPrev.totalError <=
swarm.Best.position.totalError) {
adapt = iter; // Memorize at which iteration adaptation occurs
for (n = 0; n < swarm.tribeNb; n++) {
evalF=swarm.tribes[n].reinitTribe(pb,evalF);
}
}
}
continue iterations;
}
// if(swarm.Best.positionPrev.totalError<=swarm.Best.position.totalError)
{
// Structural adaptations
//swarmSize = swarm.swarmSize(swarm);
// print("\nSwarm size (explorers): " + swarmSize,out);
// print(" Tribes: (explorers/memories) ",out);
// for (n = 0; n < swarm.tribeNumber; n++) {
// print(swarm.tribes[n].explorerNb + "/" +
// swarm.tribes[n].memoryNb + " ",out);
// }
//
// On "laisse le temps" à chaque tribu de bouger avant éventuelle adaptation
// La règle est empirique et peut être modifiée
//
adaptThreshold = iter - adapt;
// adaptMax=swarmSize;
adaptMax = swarm.linkNb(swarm);
if (adaptThreshold >= adaptMax) {
adapt = iter; // Memorize at which iteration adaptation occurs
evalF=swarm.adaptSwarm(pb, Tribes.adaptOption, swarm,
displayPb,evalF); // Réalise l'adaptation
// Modifie la recherche de la meilleure informatrice
// normale (la "vraie" meilleure) ou dépendant d'un pseudo-gradient
// (cf. informExplorer)
//
// informOption=-informOption;
}
// print("\n Nb of tribes: " + swarm.tribeNumber +
// "\n Particles/tribe:",out);
// for (n = 0; n < swarm.tribeNumber; n++) {
// print(swarm.tribes[n].explorerNb + " ",out);
// }
//
// print("\n Statuses :");
// for (n = 0; n < swarm.tribeNumber; n++) {
// print(swarm.tribes[n].status + " ",out);
// }
}
// -----------OPTIMIZE END
}
// Result of the run
print("\nBest: eval.= " + evalF + "\n", displayPb);
swarm.Best.displayMemory(displayPb);
save(" " + iter + " " + evalF+ " ", synthSave);
for (d = 0; d < pb.fitnessSize; d++) {
save(swarm.Best.position.fitness[d] + " ", synthSave);
}
for (d = 0; d < D; d++) {
save(" " + swarm.Best.position.x[d], synthSave);
}
// Prepare return
temp[0] = swarm.Best.position.totalError;
temp[1] = evalF;
if (evalF < pb.maxEval) {
temp[2] = 1;
} else {
temp[2] = 0;
}
return temp;
}
**/
*
* public synchronized void search(param pb, PrintStream runSave,
* PrintStream synthSave) {
*
* double epsMean, epsMin, epsMax; double evalMean; int n; int run, run1;
* int successNb;
*
* // Exploratrices générées explorer explorer[] = new explorer[
* Tribes.maxExplorerNb];
*
* double[] eps = new double[pb.maxRun]; double[] evalNb = new
* double[pb.maxRun]; double[] temp = new double[3]; successNb = 0; for (n =
* 0; n < 9; n++) { // For information Tribes.strategies[n] = 0;
* Tribes.status[n] = 0; } ; epsMin = Tribes.infinity; epsMax = 0;
*
* // Titles
*
* print("\nIter. Eval. Best_fitness", displayPb); save("\n\n PROBLEM
* "+pb.function[0],synthSave); save("\nRun Iter. Eval. Best_fitness
* Position", synthSave); save("\n\n PROBLEM "+pb.function[0],runSave);
*
* // ** // * Loop on runs // * for (run = 0; run < pb.maxRun; run++) { run1
* = run + 1; save("\n" + run1 + " ", synthSave); temp = solve(pb,
* Tribes.initExplorerNb,runSave,synthSave);
*
* eps[run] = temp[0]; evalNb[run] = temp[1]; successNb = successNb + (int)
* temp[2]; if (eps[run] < epsMin) { epsMin = eps[run]; } if (eps[run] >
* epsMax) { epsMax = eps[run]; } }
*
* // Mean values epsMean = 0; evalMean = 0; for (run = 0; run < pb.maxRun;
* run++) { epsMean = epsMean + eps[run]; evalMean = evalMean + evalNb[run];
* }
*
* epsMean = epsMean / pb.maxRun; evalMean = evalMean / pb.maxRun;
* print("\nStatuses ", displayPb); for (n = 1; n < 10; n++) { print("\n" +
* n + " " + Tribes.status[n - 1] + " times", displayPb); }
* print("\nStrategies ", displayPb); for (n = 1; n < 10; n++) { print("\n"
* + n + " " + Tribes.strategies[n - 1] + " times", displayPb); }
* print("\nMIN BEST TOTAL_ERROR " + epsMin, displayPb); print("\nMEAN BEST
* TOTAL_ERROR " + epsMean, displayPb); print("\nMEAN EVAL. NUMBER " +
* evalMean, displayPb); print("\n SUCCESS RATE " + (double) successNb /
* pb.maxRun, displayPb);
*
* save("\nMIN BEST TOTAL_ERROR " + epsMin, synthSave); save("\nMEAN BEST
* TOTAL_ERROR " + epsMean, synthSave); save("\nMEAN EVAL. NUMBER " +
* evalMean, synthSave); save("\n SUCCESS RATE " + (double) successNb /
* pb.maxRun, synthSave);
*
* save("\n-1", runSave); // Special value for the end of the file. Used for
* graphics
*
* } // End of search()
*
* public double[] solve(param pb, int initExplorerNb, PrintStream runSave,
* PrintStream synthSave) { int adapt, adaptMax; int adaptThreshold; int d,
* D = pb.H.Dimension; int evalF; int iter; int n; boolean stop; double[]
* temp = new double[3]; int informOption; // For the best informant. // -1
* => really the best // 1 => the best according to a pseudo-gradient method
*
* // -----------INIT START // Generate a swarm evalF=0; swarm swarm = new
* swarm(); evalF=swarm.generateSwarm(pb, initExplorerNb, pb.initType,
* displayPb,evalF);
*
* // swarm.displaySwarm(swarm,out); // print("\n Best after init:
* "+swarm.Best.position.fitness,out);
*
* // Move the swarm as long as the stop criterion is false iter = 0; adapt
* = 0; stop = false; informOption = -1; // Hard coded option // -1 =
* absolute best informant // 1 = relative (pseudo-gradient) best informant.
* For "niching" // See also moveExplorer, which can be modified in order to
* avoid this parameter // // -----------INIT END
*
*
* // -----------OPTIMIZE START iterations:while (!stop) {
*
* swarm.size = swarm.swarmSize(swarm); iter++; // swarm.Best.positionPrev =
* swarm.Best.position;
*
* evalF=swarm.moveSwarm(pb, swarm, informOption, displayPb, evalF); //***
* HERE IT MOVES
*
* // Some display each time there is "enough" improvement // fduring the
* process
*
* double enough = 0.005; if ((1 - enough) *
* swarm.Best.positionPrev.totalError > swarm.Best.position.totalError) {
* print("\nIter. " + iter, displayPb); print(" Eval. " + evalF, displayPb);
* print(" totalError " + swarm.Best.position.totalError, displayPb);
* print(" " + swarm.size + " particles", displayPb); } // Save run info
* save("\n" + iter + " " + evalF + " " + swarm.Best.position.totalError + "
* " + swarm.size, runSave);
*
* // Evaluate the stop criterion stop = evalF >= pb.maxEval || pb.accuracy
* > swarm.Best.position.totalError;
*
* if (Tribes.adaptOption == 0) { continue iterations; }
*
* if (Tribes.adaptOption == 1) { // Just reinitialize the swarm
* adaptThreshold = iter - adapt; // adaptMax=swarmSize; adaptMax =
* swarm.linkNb(swarm); if (adaptThreshold >= adaptMax) { if
* (swarm.Best.positionPrev.totalError <= swarm.Best.position.totalError) {
* adapt = iter; // Memorize at which iteration adaptation occurs
*
* for (n = 0; n < swarm.tribeNb; n++) {
* evalF=swarm.tribes[n].reinitTribe(pb,evalF); } } } continue iterations; }
*
* // if(swarm.Best.positionPrev.totalError<=swarm.Best.position.totalError)
* { // Structural adaptations
*
* //swarmSize = swarm.swarmSize(swarm);
*
* // print("\nSwarm size (explorers): " + swarmSize,out); // print("
* Tribes: (explorers/memories) ",out); // for (n = 0; n <
* swarm.tribeNumber; n++) { // print(swarm.tribes[n].explorerNb + "/" + //
* swarm.tribes[n].memoryNb + " ",out); // } // // On "laisse le temps" à
* chaque tribu de bouger avant éventuelle adaptation // La règle est
* empirique et peut être modifiée //
*
* adaptThreshold = iter - adapt; // adaptMax=swarmSize; adaptMax =
* swarm.linkNb(swarm);
*
* if (adaptThreshold >= adaptMax) { adapt = iter; // Memorize at which
* iteration adaptation occurs evalF=swarm.adaptSwarm(pb,
* Tribes.adaptOption, swarm, displayPb,evalF); // Réalise l'adaptation
*
* // Modifie la recherche de la meilleure informatrice // normale (la
* "vraie" meilleure) ou dépendant d'un pseudo-gradient // (cf.
* informExplorer) // // informOption=-informOption; } // print("\n Nb of
* tribes: " + swarm.tribeNumber + // "\n Particles/tribe:",out); // for (n
* = 0; n < swarm.tribeNumber; n++) { // print(swarm.tribes[n].explorerNb +
* " ",out); // } // // print("\n Statuses :"); // for (n = 0; n <
* swarm.tribeNumber; n++) { // print(swarm.tribes[n].status + " ",out); //
* }
*
*
* }
* // -----------OPTIMIZE END }
*
* // Result of the run print("\nBest: eval.= " + evalF + "\n", displayPb);
* swarm.Best.displayMemory(displayPb);
*
* save(" " + iter + " " + evalF+ " ", synthSave);
*
* for (d = 0; d < pb.fitnessSize; d++) {
* save(swarm.Best.position.fitness[d] + " ", synthSave); }
*
* for (d = 0; d < D; d++) { save(" " + swarm.Best.position.x[d],
* synthSave); }
*
* // Prepare return temp[0] = swarm.Best.position.totalError; temp[1] =
* evalF; if (evalF < pb.maxEval) { temp[2] = 1; } else { temp[2] = 0; }
*
* return temp; }
*
*/
/**
* Population will be hidden.
*/
@@ -638,9 +539,10 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
/**
* As TRIBES manages an own structured set of particles (the list of Tribes containing explorers
* and memories), the setPopulation method is only telling Tribes the range
* of the indiviuals in the beginning of the run, the individuals will be discarded.
* As TRIBES manages an own structured set of particles (the list of Tribes
* containing explorers and memories), the setPopulation method is only
* telling Tribes the range of the indiviuals in the beginning of the run,
* the individuals will be discarded.
*/
@Override
public void setPopulation(Population pop) {
@@ -649,7 +551,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
population = pop;
if (population.get(0) instanceof InterfaceDataTypeDouble) {
range = ((InterfaceDataTypeDouble)population.get(0)).getDoubleRange();
range = ((InterfaceDataTypeDouble) population.get(0)).getDoubleRange();
setDimension(range.length);
} else {
System.err.println("warning, TRIBES requires InterfaceESIndidivual instead of " + population.get(0).getClass() + ". Couldnt correctly init the problem range.");
@@ -666,10 +568,11 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
/**
* Be aware that TRIBES uses two kinds of particles: explorers and memories. As memories
* are inactive in that they dont search the problem space directly, they are not included
* in the returned population. This, however, means that the best found solution might not
* be inluded as well at several if not most stages of the search.
* Be aware that TRIBES uses two kinds of particles: explorers and memories.
* As memories are inactive in that they dont search the problem space
* directly, they are not included in the returned population. This,
* however, means that the best found solution might not be inluded as well
* at several if not most stages of the search.
*/
@Override
public Population getPopulation() {
@@ -677,15 +580,16 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
/**
* Return a SolutionSet of TribesExplorers (AbstractEAIndividuals) of which some where
* memory particles, thus the returned population is larger than the current population.
* Return a SolutionSet of TribesExplorers (AbstractEAIndividuals) of which
* some where memory particles, thus the returned population is larger than
* the current population.
*
* @return a population of possible solutions.
*/
@Override
public InterfaceSolutionSet getAllSolutions() {
// return population and memories?
Population all = (Population)population.clone();
Population all = (Population) population.clone();
List<TribesPosition> mems = swarm.collectMem();
for (Iterator<TribesPosition> iterator = mems.iterator(); iterator.hasNext();) {
TribesPosition tp = iterator.next();
@@ -698,7 +602,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
protected TribesExplorer positionToExplorer(TribesPosition pos) {
TribesExplorer tmp = (TribesExplorer)population.get(0);
TribesExplorer tmp = (TribesExplorer) population.get(0);
if (tmp == null) {
System.err.println("Error in Tribes::positionToExplorer!");
}
@@ -709,23 +613,27 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
return indy;
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
@@ -736,13 +644,11 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
return (evals % notifyGenChangedEvery) == 0;
}
@Override
public void freeWilly() {}
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return m_Identifier;
@@ -821,7 +727,6 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
// public void setUseAnchor(boolean useAnchor) {
// this.useAnchor = useAnchor;
// }
/**
* @return the m_Show
*/

168
src/eva2/server/go/strategies/WingedMultiObjectiveEA.java
View File

@@ -11,17 +11,14 @@ import eva2.server.go.problems.AbstractMultiObjectiveOptimizationProblem;
import eva2.server.go.problems.FM0Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/** The winged MOEA was a nice idea, which didn't really work out.
* Here a standard MOEA is assisted by n additional local searchers, each
* optimizing just one objective. The idea was that these local optimizers
* would span the search space and would allow the MOEA to converge faster.
* But in the end the performance of this algorithm strongly depends on the
* optimization problem.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 16.02.2005
* Time: 16:34:22
* To change this template use File | Settings | File Templates.
/**
* The winged MOEA was a nice idea, which didn't really work out. Here a
* standard MOEA is assisted by n additional local searchers, each optimizing
* just one objective. The idea was that these local optimizers would span the
* search space and would allow the MOEA to converge faster. But in the end the
* performance of this algorithm strongly depends on the optimization problem.
* Created by IntelliJ IDEA. User: streiche Date: 16.02.2005 Time: 16:34:22 To
* change this template use File | Settings | File Templates.
*/
public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable {
@@ -40,17 +37,17 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
}
public WingedMultiObjectiveEA(WingedMultiObjectiveEA a) {
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_MOOptimizer = (InterfaceOptimizer)a.m_MOOptimizer.clone();
this.m_SOOptimizer = (InterfaceOptimizer)a.m_SOOptimizer.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.m_MOOptimizer = (InterfaceOptimizer) a.m_MOOptimizer.clone();
this.m_SOOptimizer = (InterfaceOptimizer) a.m_SOOptimizer.clone();
if (a.m_SOOptimizers != null) {
this.m_SOOptimizers = new InterfaceOptimizer[a.m_SOOptimizers.length];
for (int i = 0; i < this.m_SOOptimizers.length; i++) {
this.m_SOOptimizers[i] = (InterfaceOptimizer)a.m_SOOptimizers[i].clone();
this.m_SOOptimizers[i] = (InterfaceOptimizer) a.m_SOOptimizers[i].clone();
}
}
this.m_MigrationRate = a.m_MigrationRate;
this.m_Population = (Population)a.m_Population.clone();
this.m_Population = (Population) a.m_Population.clone();
}
@Override
@@ -61,18 +58,18 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
@Override
public void init() {
if (this.m_Problem instanceof AbstractMultiObjectiveOptimizationProblem) {
AbstractMultiObjectiveOptimizationProblem tmpProb = (AbstractMultiObjectiveOptimizationProblem)this.m_Problem;
AbstractMultiObjectiveOptimizationProblem tmpProb = (AbstractMultiObjectiveOptimizationProblem) this.m_Problem;
AbstractMultiObjectiveOptimizationProblem tmpP;
MOSOWeightedFitness tmpWF;
PropertyDoubleArray tmpDA;
int dim = this.m_OutputDimension;
double[] weights;
// dim = tmpProb.getOutputDimension();
this.m_MOOptimizer.setProblem((InterfaceOptimizationProblem)this.m_Problem.clone());
this.m_MOOptimizer.setProblem((InterfaceOptimizationProblem) this.m_Problem.clone());
this.m_MOOptimizer.init();
this.m_SOOptimizers = new InterfaceOptimizer[dim];
for (int i = 0; i < dim; i++) {
tmpP = (AbstractMultiObjectiveOptimizationProblem)this.m_Problem.clone();
tmpP = (AbstractMultiObjectiveOptimizationProblem) this.m_Problem.clone();
weights = new double[dim];
for (int j = 0; j < dim; j++) {
weights[j] = 0;
@@ -82,7 +79,7 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
tmpWF = new MOSOWeightedFitness();
tmpWF.setWeights(tmpDA);
tmpP.setMOSOConverter(tmpWF);
this.m_SOOptimizers[i] = (InterfaceOptimizer)this.m_SOOptimizer.clone();
this.m_SOOptimizers[i] = (InterfaceOptimizer) this.m_SOOptimizer.clone();
this.m_SOOptimizers[i].setProblem(tmpP);
this.m_SOOptimizers[i].init();
}
@@ -94,26 +91,27 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
if (this.m_Problem instanceof AbstractMultiObjectiveOptimizationProblem) {
AbstractMultiObjectiveOptimizationProblem tmpProb = (AbstractMultiObjectiveOptimizationProblem)this.m_Problem;
AbstractMultiObjectiveOptimizationProblem tmpProb = (AbstractMultiObjectiveOptimizationProblem) this.m_Problem;
AbstractMultiObjectiveOptimizationProblem tmpP;
MOSOWeightedFitness tmpWF;
PropertyDoubleArray tmpDA;
int dim = 2;
double[] weights;
// dim = tmpProb.getOutputDimension();
this.m_MOOptimizer.setProblem((InterfaceOptimizationProblem)this.m_Problem.clone());
this.m_MOOptimizer.setProblem((InterfaceOptimizationProblem) this.m_Problem.clone());
this.m_MOOptimizer.initByPopulation(pop, reset);
this.m_SOOptimizers = new InterfaceOptimizer[dim];
for (int i = 0; i < dim; i++) {
tmpP = (AbstractMultiObjectiveOptimizationProblem)this.m_Problem.clone();
tmpP = (AbstractMultiObjectiveOptimizationProblem) this.m_Problem.clone();
weights = new double[dim];
for (int j = 0; j < dim; j++) {
weights[j] = 0;
@@ -123,7 +121,7 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
tmpWF = new MOSOWeightedFitness();
tmpWF.setWeights(tmpDA);
tmpP.setMOSOConverter(tmpWF);
this.m_SOOptimizers[i] = (InterfaceOptimizer)this.m_SOOptimizer.clone();
this.m_SOOptimizers[i] = (InterfaceOptimizer) this.m_SOOptimizer.clone();
this.m_SOOptimizers[i].setProblem(tmpP);
this.m_SOOptimizers[i].initByPopulation(pop, reset);
}
@@ -135,7 +133,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** The optimize method will compute a 'improved' and evaluated population
/**
* The optimize method will compute a 'improved' and evaluated population
*/
@Override
public void optimize() {
@@ -154,8 +153,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will manage comunication between the
* islands
/**
* This method will manage comunication between the islands
*/
private void communicate() {
int oldFunctionCalls;
@@ -163,11 +162,11 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.m_Population.SetFunctionCalls(0);
Population pop;
// first collect all the data
pop = (Population)this.m_MOOptimizer.getPopulation().clone();
pop = (Population) this.m_MOOptimizer.getPopulation().clone();
this.m_Population.addPopulation(pop);
this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
for (int i = 0; i < this.m_SOOptimizers.length; i++) {
pop = (Population)this.m_SOOptimizers[i].getPopulation().clone();
pop = (Population) this.m_SOOptimizers[i].getPopulation().clone();
this.m_Population.addPopulation(pop);
this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
}
@@ -180,7 +179,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.migrate();
}
/** This method implements the migration between the optimzers
/**
* This method implements the migration between the optimzers
*
*/
private void migrate() {
@@ -188,12 +188,12 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
double tmpF1, tmpF2;
// for each dimension find the best
for (int i = 0; i < this.m_OutputDimension; i++) {
bestIndys[i] = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Population.get(0)).clone();
bestIndys[i] = (AbstractEAIndividual) ((AbstractEAIndividual) this.m_Population.get(0)).clone();
tmpF1 = bestIndys[i].getFitness(i);
// for each individual find the best
for (int j = 0; j < this.m_Population.size(); j++) {
if (((AbstractEAIndividual)this.m_Population.get(j)).getFitness(i) < tmpF1) {
bestIndys[i] = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Population.get(j)).clone();
if (((AbstractEAIndividual) this.m_Population.get(j)).getFitness(i) < tmpF1) {
bestIndys[i] = (AbstractEAIndividual) ((AbstractEAIndividual) this.m_Population.get(j)).clone();
tmpF1 = bestIndys[i].getFitness(i);
}
}
@@ -201,54 +201,64 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
// now perform the migration
AbstractEAIndividual tmpIndy;
for (int i = 0; i < this.m_OutputDimension; i++) {
tmpIndy = (AbstractEAIndividual)bestIndys[i].clone();
tmpIndy = (AbstractEAIndividual) bestIndys[i].clone();
this.m_MOOptimizer.getProblem().evaluate(tmpIndy);
this.m_MOOptimizer.getPopulation().set(i, tmpIndy);
tmpIndy = (AbstractEAIndividual)bestIndys[i].clone();
tmpIndy = (AbstractEAIndividual) bestIndys[i].clone();
this.m_SOOptimizers[i].getProblem().evaluate(tmpIndy);
this.m_SOOptimizers[i].getPopulation().set(0, bestIndys[i]);
}
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener==ea) {
m_Listener=null;
if (m_Listener == ea) {
m_Listener = null;
return true;
} else {
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent (String name) {
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem (InterfaceOptimizationProblem problem) {
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem () {
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -256,39 +266,42 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
String result = "";
result += "EMO:\n";
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is Evolutionary Multi-Criteria Optimization Algorithm hybridized with Local Searchers to span the Pareto-Front.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -296,19 +309,23 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
return "EMO-LS";
}
/** 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.
/**
* 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.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop){
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "(Defunct)";
}
@@ -317,41 +334,54 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method allows you to set/get the optimizing technique to use.
/**
* This method allows you to set/get the optimizing technique to use.
*
* @return The current optimizing method
*/
public InterfaceOptimizer getMOOptimizer() {
return this.m_MOOptimizer;
}
public void setMOOptimizer(InterfaceOptimizer b){
public void setMOOptimizer(InterfaceOptimizer b) {
this.m_MOOptimizer = b;
}
public String mOOptimizerTipText() {
return "Choose a population based optimizing technique to use.";
}
/** This method allows you to set/get the optimizing technique to use.
/**
* This method allows you to set/get the optimizing technique to use.
*
* @return The current optimizing method
*/
public InterfaceOptimizer getSOOptimizer() {
return this.m_SOOptimizer;
}
public void setSOOptimizer(InterfaceOptimizer b){
public void setSOOptimizer(InterfaceOptimizer b) {
this.m_SOOptimizer = b;
}
public String sOOptimizerTipText() {
return "Choose a population based optimizing technique to use.";
}
/** This method allows you to set/get the archiving strategy to use.
/**
* This method allows you to set/get the archiving strategy to use.
*
* @return The current optimizing method
*/
public int getMigrationRate() {
return this.m_MigrationRate;
}
public void setMigrationRate(int b){
public void setMigrationRate(int b) {
this.m_MigrationRate = b;
}
public String migrationRateTipText() {
return "Choose a proper migration rate.";
}