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

4
src/eva2/server/go/InterfaceGOStandalone.java
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@@ -8,6 +8,6 @@ package eva2.server.go;
* To change this template use Options | File Templates. * To change this template use Options | File Templates.
*/ */
public interface InterfaceGOStandalone { public interface InterfaceGOStandalone {
public void startExperiment(); void startExperiment();
public void setShow(boolean t); 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 source The source of the event.
* @param name Could be used to indicate the nature 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);
} }

27
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()); private static final Logger LOGGER = Logger.getLogger(BOA.class.getName());
transient private InterfacePopulationChangedEventListener m_Listener = null; transient private InterfacePopulationChangedEventListener m_Listener = null;
private String m_Identifier = "BOA"; private String m_Identifier = "BOA";
private int probDim = 8; private int probDim = 8;
private int fitCrit = -1; private int fitCrit = -1;
private int PopSize = 50; private int PopSize = 50;
@@ -63,7 +62,6 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
private int count = 0; private int count = 0;
private String netFolder = "BOAOutput"; private String netFolder = "BOAOutput";
private int[][] edgeRate = null; private int[][] edgeRate = null;
private BOAScoringMethods scoringMethod = BOAScoringMethods.BDM; private BOAScoringMethods scoringMethod = BOAScoringMethods.BDM;
private boolean printNetworks = false; private boolean printNetworks = false;
private boolean printEdgeRate = false; private boolean printEdgeRate = false;
@@ -72,7 +70,6 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
// private boolean printExtraOutput = false; // private boolean printExtraOutput = false;
public BOA() { public BOA() {
} }
public BOA(int numberOfParents, int popSize, BOAScoringMethods method, public BOA(int numberOfParents, int popSize, BOAScoringMethods method,
@@ -185,11 +182,9 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
private static BitSet getBinaryData(AbstractEAIndividual indy) { private static BitSet getBinaryData(AbstractEAIndividual indy) {
if (indy instanceof InterfaceGAIndividual) { if (indy instanceof InterfaceGAIndividual) {
return ((InterfaceGAIndividual) indy).getBGenotype(); return ((InterfaceGAIndividual) indy).getBGenotype();
} } else if (indy instanceof InterfaceDataTypeBinary) {
else if (indy instanceof InterfaceDataTypeBinary) {
return ((InterfaceDataTypeBinary) indy).getBinaryData(); return ((InterfaceDataTypeBinary) indy).getBinaryData();
} } else {
else {
throw new RuntimeException( throw new RuntimeException(
"Unable to get binary representation for " "Unable to get binary representation for "
+ indy.getClass()); + indy.getClass());
@@ -200,8 +195,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
* evaluate the given Individual and increments the counter. if the * evaluate the given Individual and increments the counter. if the
* individual is null, only the counter is incremented * individual is null, only the counter is incremented
* *
* @param indy * @param indy the individual you want to evaluate
* 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 // 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 * Generate a Bayesian network with the individuals of the population as a
* reference Point * reference Point
* *
* @param pop * @param pop the individuals the network is based on
* the individuals the network is based on
*/ */
private void constructNetwork(Population pop) { private void constructNetwork(Population pop) {
generateGreedy(pop); generateGreedy(pop);
@@ -394,6 +387,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
/** /**
* remove the individuals in pop from the population * remove the individuals in pop from the population
*
* @param pop * @param pop
*/ */
public void remove(Population pop) { public void remove(Population pop) {
@@ -627,15 +621,9 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
return "Bayesian Network"; return "Bayesian Network";
} }
@Override
public void freeWilly() {
}
// ------------------------------- // -------------------------------
// -------------GUI--------------- // -------------GUI---------------
// ------------------------------- // -------------------------------
public int getNumberOfParents() { public int getNumberOfParents() {
return this.numberOfParents; return this.numberOfParents;
} }
@@ -731,7 +719,6 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
// public boolean isPrintExtraOutput() { // public boolean isPrintExtraOutput() {
// return this.printExtraOutput; // return this.printExtraOutput;
// } // }
// public void setPrintExtraOutput(boolean b) { // public void setPrintExtraOutput(boolean b) {
// this.printExtraOutput = b; // this.printExtraOutput = b;
// GenericObjectEditor.setHideProperty(getClass(), "printNetworks", // GenericObjectEditor.setHideProperty(getClass(), "printNetworks",
@@ -743,11 +730,9 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
// GenericObjectEditor.setHideProperty(getClass(), "printTimestamps", // GenericObjectEditor.setHideProperty(getClass(), "printTimestamps",
// !printExtraOutput); // !printExtraOutput);
// } // }
// public String printExtraOutputTipText() { // public String printExtraOutputTipText() {
// return "do you want to print extra output files"; // return "do you want to print extra output files";
// } // }
public boolean isPrintNetworks() { public boolean isPrintNetworks() {
return this.printNetworks; 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"; return "Print the time starting time and a timestamp after each generation";
} }
public static void main(String[] args) { public static void main(String[] args) {
Population pop = new Population(); Population pop = new Population();
GAIndividualBinaryData indy1 = new GAIndividualBinaryData(); GAIndividualBinaryData indy1 = new GAIndividualBinaryData();
@@ -980,5 +964,4 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
// b.print(); // b.print();
// b.printNetworkToFile("test"); // b.printNetworkToFile("test");
} }
} }

100
src/eva2/server/go/strategies/BinaryScatterSearch.java
View File

@@ -30,15 +30,15 @@ import java.util.BitSet;
* *
* @author Alex * @author Alex
* *
* F. Gortazar, A. Duarte, M. Laguna and R. Marti: Black Box Scatter Search for General Classes of Binary Optimization Problems * F. Gortazar, A. Duarte, M. Laguna and R. Marti: Black Box Scatter Search for
* Computers and Operations research, vol. 37, no. 11, pp. 1977-1986 (2010) * 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 { public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener {
private static boolean TRACE = false; private static boolean TRACE = false;
transient private InterfacePopulationChangedEventListener m_Listener = null; transient private InterfacePopulationChangedEventListener m_Listener = null;
private String m_Identifier = "BinaryScatterSearch"; private String m_Identifier = "BinaryScatterSearch";
private int MaxImpIter = 5; private int MaxImpIter = 5;
private int poolSize = 100; private int poolSize = 100;
private int refSetSize = 10; private int refSetSize = 10;
@@ -63,7 +63,9 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
} }
/** /**
* 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 * @param b
*/ */
public BinaryScatterSearch(BinaryScatterSearch b) { public BinaryScatterSearch(BinaryScatterSearch b) {
@@ -89,12 +91,15 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/** /**
* Create a new BinaryScatterSearch with the given Parameters * Create a new BinaryScatterSearch with the given Parameters
*
* @param refSetS the refSetSize * @param refSetS the refSetSize
* @param poolS the poolSize * @param poolS the poolSize
* @param lowerThreshold the lower Boundary for the local Search * @param lowerThreshold the lower Boundary for the local Search
* @param upperThreshold the upper 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 perCentFirstIndGenerator how many individuals (in prospect of the
* @param perCentSecondIndGenerator how many individuals (in prospect of the poolSize) are generated through the second Generator * 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 prob the Problem
*/ */
public BinaryScatterSearch( public BinaryScatterSearch(
@@ -111,12 +116,15 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/** /**
* Create a new BinaryScatterSearch with the given Parameters * Create a new BinaryScatterSearch with the given Parameters
*
* @param refSetS the refSetSize * @param refSetS the refSetSize
* @param poolS the poolSize * @param poolS the poolSize
* @param lowerThreshold the lower Boundary for the local Search * @param lowerThreshold the lower Boundary for the local Search
* @param upperThreshold the upper 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 perCentFirstIndGenerator how many individuals (in prospect of the
* @param perCentSecondIndGenerator how many individuals (in prospect of the poolSize) are generated through the second Generator * 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 prob the Problem
* @param cross the Crossover-Operators * @param cross the Crossover-Operators
*/ */
@@ -165,7 +173,9 @@ 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 * @param indy the individual you want to evaluate
*/ */
private void evaluate(AbstractEAIndividual indy) { private void evaluate(AbstractEAIndividual indy) {
@@ -226,7 +236,8 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/** /**
* *
* @param pop the initial 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(); int numToInit = this.poolSize - pop.size();
@@ -248,9 +259,10 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
} }
/** /**
* Generate a new Population with diverse Individuals starting with 000...000, * Generate a new Population with diverse Individuals starting with
* then 010101...01, 101010...10, 001001001...001, 110110110...110 and so on * 000...000, then 010101...01, 101010...10, 001001001...001,
* The returned population is evaluated. * 110110110...110 and so on The returned population is evaluated.
*
* @param pop the initial Population * @param pop the initial Population
* @return the new Population * @return the new Population
*/ */
@@ -263,8 +275,10 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
} }
/** /**
* Generate a new Population with diverse Individuals starting with 000...000, then 010101...01, * Generate a new Population with diverse Individuals starting with
* 101010...10, 001001001...001, 110110110...110 and so on * 000...000, then 010101...01, 101010...10, 001001001...001,
* 110110110...110 and so on
*
* @param pop the initial Population * @param pop the initial Population
* @return the new Population * @return the new Population
*/ */
@@ -303,7 +317,9 @@ 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 * @param pop the population
* @return the new Population * @return the new Population
*/ */
@@ -329,7 +345,9 @@ 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 * @param pop the population
* @return the new Population * @return the new Population
*/ */
@@ -355,7 +373,9 @@ 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 i the position
* @param pop the population * @param pop the population
* @return The number of individuals that have a '1' on the i-th position * @return The number of individuals that have a '1' on the i-th position
@@ -373,7 +393,9 @@ 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 i the position
* @param pop the population * @param pop the population
* @return The number of individuals that have a '0' on the i-th position * @return The number of individuals that have a '0' on the i-th position
@@ -393,17 +415,17 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
private static BitSet getBinaryData(AbstractEAIndividual indy) { private static BitSet getBinaryData(AbstractEAIndividual indy) {
if (indy instanceof InterfaceGAIndividual) { if (indy instanceof InterfaceGAIndividual) {
return ((InterfaceGAIndividual) indy).getBGenotype(); return ((InterfaceGAIndividual) indy).getBGenotype();
} } else if (indy instanceof InterfaceDataTypeBinary) {
else if (indy instanceof InterfaceDataTypeBinary) {
return ((InterfaceDataTypeBinary) indy).getBinaryData(); return ((InterfaceDataTypeBinary) indy).getBinaryData();
} } else {
else {
throw new RuntimeException("Unable to get binary representation for " + indy.getClass()); 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 i the position
* @param pop the population * @param pop the population
* @return the sum * @return the sum
@@ -421,7 +443,9 @@ 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 i the position
* @param pop the population * @param pop the population
* @return the sum * @return the sum
@@ -439,9 +463,11 @@ 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. * calculates a score that gives a reference Point if the Bit on the i-th
* If the bit is set to '1' and you get a high score then the Bit is probably set correct. * position is right. If the bit is set to '1' and you get a high score then
* If the bit is set to '0' and you get a low score then the Bit is probably set correct. * 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 i the position
* @param pop the population * @param pop the population
* @return the score * @return the score
@@ -455,9 +481,9 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
return v; return v;
} }
/** /**
* calculate the first RefSet with the given Population as a reference Point * calculate the first RefSet with the given Population as a reference Point
*
* @param pop the generated Pool * @param pop the generated Pool
*/ */
private void initRefSet(Population pop) { private void initRefSet(Population pop) {
@@ -479,6 +505,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/** /**
* Update the reference Set * Update the reference Set
*
* @param replaceWorstHalf replaces the worst half of the RefSet if set * @param replaceWorstHalf replaces the worst half of the RefSet if set
* @return has the Population changed * @return has the Population changed
*/ */
@@ -531,6 +558,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/** /**
* Order the given List according to the score of the given values * Order the given List according to the score of the given values
*
* @param list the initial List * @param list the initial List
* @return the ordered List * @return the ordered List
*/ */
@@ -557,6 +585,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/** /**
* Do a local search * Do a local search
*
* @param indy the individual that will be improved * @param indy the individual that will be improved
* @return the new improved individual * @return the new improved individual
*/ */
@@ -621,6 +650,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/** /**
* Combine all the individuals in the reference Set (always 2) * Combine all the individuals in the reference Set (always 2)
*
* @return the List with all the combinations * @return the List with all the combinations
*/ */
public ArrayList<Population> generateSubsets() { public ArrayList<Population> generateSubsets() {
@@ -638,6 +668,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/** /**
* combine the first individual with the second one * combine the first individual with the second one
*
* @param pop the Population * @param pop the Population
* @return the new Individual * @return the new Individual
*/ */
@@ -664,6 +695,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
/** /**
* look if the individual is already in the population * look if the individual is already in the population
*
* @param indy the Individual to be tested * @param indy the Individual to be tested
* @param pop the population in where to search * @param pop the population in where to search
* @return is the individual already in the Population * @return is the individual already in the Population
@@ -771,12 +803,6 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
return "BinaryScatterSearch"; 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) { if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name); this.m_Listener.registerPopulationStateChanged(this, name);
@@ -797,7 +823,6 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
} }
//----------GUI---------- //----------GUI----------
public int getPoolSize() { public int getPoolSize() {
return this.poolSize; return this.poolSize;
} }
@@ -850,7 +875,6 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
return this.cross; return this.cross;
} }
public void setCrossoverMethods(AdaptiveCrossoverEAMixer c) { public void setCrossoverMethods(AdaptiveCrossoverEAMixer c) {
this.cross = c; this.cross = c;
} }

114
src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java
View File

@@ -1,6 +1,5 @@
package eva2.server.go.strategies; package eva2.server.go.strategies;
import eva2.server.go.InterfacePopulationChangedEventListener; import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual; import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.InterfaceGAIndividual; import eva2.server.go.individuals.InterfaceGAIndividual;
@@ -15,22 +14,22 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG; import eva2.tools.math.RNG;
import java.util.BitSet; 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 * This is an implementation of the CHC Adaptive Search Algorithm by Eshelman.
* sure whether i've implemented this correctly, but i definitely wasn't able to make * It is limited to binary data and is based on massively disruptive crossover.
* it competitive to a standard GA.. *sigh* * I'm not sure whether i've implemented this correctly, but i definitely wasn't
* This is a implementation of the CHC Adaptive Search Algorithm (Cross generational * 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). * elitist selection, Heterogeneous recombination and Cataclysmic mutation).
* Citation: * Citation:
* *
* Copyright: Copyright (c) 2003 * Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
* Company: University of Tuebingen, Computer Architecture * Architecture
*
* @author Felix Streichert * @author Felix Streichert
* @version: $Revision: 307 $ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $ * 2007) $ $Author: mkron $
* $Author: mkron $
*/ */
public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.Serializable { public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.Serializable {
private double m_InitialDifferenceThreshold = 0.25; 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 InterfaceOptimizationProblem m_Problem = new B1Problem();
private InterfaceSelection m_RecombSelectionOperator = new SelectRandom(); private InterfaceSelection m_RecombSelectionOperator = new SelectRandom();
private InterfaceSelection m_PopulSelectionOperator = new SelectBestSingle(); private InterfaceSelection m_PopulSelectionOperator = new SelectBestSingle();
transient private String m_Identifier = ""; transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
@@ -80,7 +78,9 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 pop The initial population
* @param reset If true the population is reset. * @param reset If true the population is reset.
*/ */
@@ -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 * @param population The population that is to be evaluated
*/ */
private void evaluatePopulation(Population population) { private void evaluatePopulation(Population population) {
@@ -112,8 +113,9 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
population.incrGeneration(); 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() { private Population generateChildren() {
Population result = this.m_Population.cloneWithoutInds(), parents, partners; Population result = this.m_Population.cloneWithoutInds(), parents, partners;
@@ -149,7 +151,9 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return result; return result;
} }
/** This method computes the Hamming Distance between n-Individuals /**
* This method computes the Hamming Distance between n-Individuals
*
* @param dad * @param dad
* @param partners * @param partners
* @return The maximal Hamming Distance between dad and the partners * @return The maximal Hamming Distance between dad and the partners
@@ -172,8 +176,10 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return result; 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() { private void diverge() {
AbstractEAIndividual best = this.m_Population.getBestEAIndividual(); AbstractEAIndividual best = this.m_Population.getBestEAIndividual();
@@ -189,8 +195,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
if (RNG.flipCoin(this.m_DivergenceRate)) { if (RNG.flipCoin(this.m_DivergenceRate)) {
if (tmpBitSet.get(j)) { if (tmpBitSet.get(j)) {
tmpBitSet.clear(j); tmpBitSet.clear(j);
} } else {
else {
tmpBitSet.set(j); tmpBitSet.set(j);
} }
} }
@@ -237,6 +242,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -248,7 +254,8 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
} }
} }
/** Something has changed /**
* Something has changed
*/ */
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
@@ -256,20 +263,25 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
} }
} }
/** This method will set the problem that is to be optimized /**
* This method will set the problem that is to be optimized
*
* @param problem * @param problem
*/ */
@Override @Override
public void setProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; 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 * @return A descriptive string
*/ */
@Override @Override
@@ -282,36 +294,37 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return result; 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { public static String globalInfo() {
return "This is an implementation of the CHC Adaptive Search Algorithm by Eselman."; 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 * @return The name of the algorithm
*/ */
@Override @Override
@@ -319,19 +332,23 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return "CHC"; 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 * Assuming that all optimizer will store thier data in a population we will
* 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. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "Edit the properties of the population used."; return "Edit the properties of the population used.";
} }
@@ -353,21 +370,26 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
// public String normationMethodTipText() { // public String normationMethodTipText() {
// return "Select the normation method."; // return "Select the normation method.";
// } // }
/**
/** Enable/disable elitism. * Enable/disable elitism.
*
* @param elitism * @param elitism
*/ */
public void setElitism(boolean elitism) { public void setElitism(boolean elitism) {
this.m_UseElitism = elitism; this.m_UseElitism = elitism;
} }
public boolean getElitism() { public boolean getElitism() {
return this.m_UseElitism; return this.m_UseElitism;
} }
public String elitismTipText() { public String elitismTipText() {
return "Enable/disable elitism."; return "Enable/disable elitism.";
} }
/** The number of mating partners needed to create offsprings. /**
* The number of mating partners needed to create offsprings.
*
* @param partners * @param partners
*/ */
public void setNumberOfPartners(int partners) { public void setNumberOfPartners(int partners) {
@@ -376,9 +398,11 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
} }
this.m_NumberOfPartners = partners; this.m_NumberOfPartners = partners;
} }
public int getNumberOfPartners() { public int getNumberOfPartners() {
return this.m_NumberOfPartners; return this.m_NumberOfPartners;
} }
public String numberOfPartnersTipText() { public String numberOfPartnersTipText() {
return "The number of mating partners needed to create offsprings."; return "The number of mating partners needed to create offsprings.";
} }

205
src/eva2/server/go/strategies/ClusterBasedNichingEA.java
View File

@@ -41,47 +41,44 @@ import java.util.LinkedList;
import java.util.List; import java.util.List;
import java.util.PriorityQueue; import java.util.PriorityQueue;
/** The infamous clustering based niching EA, still under construction. /**
* It should be able to identify and track multiple global/local optima * The infamous clustering based niching EA, still under construction. It should
* at the same time. * be able to identify and track multiple global/local optima at the same time.
* *
* Notes: For std. GA, the mutation rate may have to reduced, because the * Notes: For std. GA, the mutation rate may have to reduced, because the
* initial step size tends to be rel. large and easily disperse clustered * initial step size tends to be rel. large and easily disperse clustered
* species (so that they fall below the minimum swarm size and the local * species (so that they fall below the minimum swarm size and the local optimum
* optimum is lost). * is lost).
* *
* For the CBN-PSO remember to use the IndividualDataMetric so that the * For the CBN-PSO remember to use the IndividualDataMetric so that the
* remembered positions are used for clustering (which are rel. stable - * remembered positions are used for clustering (which are rel. stable - so that
* so that species clustering actually makes sense). * species clustering actually makes sense).
*
* Copyright: Copyright (c) 2010 Company: University of Tuebingen, Computer
* Architecture
* *
* Copyright: Copyright (c) 2010
* Company: University of Tuebingen, Computer Architecture
* @author Felix Streichert, Marcel Kronfeld * @author Felix Streichert, Marcel Kronfeld
*/ */
public class ClusterBasedNichingEA implements InterfacePopulationChangedEventListener, InterfaceAdditionalPopulationInformer, InterfaceOptimizer, java.io.Serializable { public class ClusterBasedNichingEA implements InterfacePopulationChangedEventListener, InterfaceAdditionalPopulationInformer, InterfaceOptimizer, java.io.Serializable {
private static final long serialVersionUID = -3143069327594708609L; private static final long serialVersionUID = -3143069327594708609L;
private Population m_Population = new Population(); private Population m_Population = new Population();
private transient Population m_Archive = new Population(); private transient Population m_Archive = new Population();
private ArrayList<Population> m_Species = new ArrayList<Population>(); private ArrayList<Population> m_Species = new ArrayList<Population>();
private Population m_Undifferentiated = new Population(); private Population m_Undifferentiated = new Population();
private transient Population m_doomedPop = new Population(); private transient Population m_doomedPop = new Population();
private InterfaceOptimizationProblem m_Problem = new B1Problem(); private InterfaceOptimizationProblem m_Problem = new B1Problem();
private InterfaceOptimizer m_Optimizer = new GeneticAlgorithm(); private InterfaceOptimizer m_Optimizer = new GeneticAlgorithm();
private InterfaceClustering m_CAForSpeciesDifferentation = new ClusteringDensityBased(); private InterfaceClustering m_CAForSpeciesDifferentation = new ClusteringDensityBased();
private InterfaceClustering m_CAForSpeciesMerging = new ClusteringDensityBased(); private InterfaceClustering m_CAForSpeciesMerging = new ClusteringDensityBased();
private double clusterDiffDist = 0.05; private double clusterDiffDist = 0.05;
// private double clusterMergeDist = 0.0001; // private double clusterMergeDist = 0.0001;
// private Distraction distraction = null; // private Distraction distraction = null;
private boolean useDistraction = false; private boolean useDistraction = false;
// private double distrDefaultStrength = .7; // private double distrDefaultStrength = .7;
private double epsilonBound = 1e-10; private double epsilonBound = 1e-10;
transient private String m_Identifier = ""; transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
private int m_SpeciesCycle = 1; private int m_SpeciesCycle = 1;
// from which size on is a species considered active // from which size on is a species considered active
// private int m_actSpecSize = 2; // private int m_actSpecSize = 2;
@@ -93,7 +90,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
private int m_PopulationSize = 50; private int m_PopulationSize = 50;
private int convergedCnt = 0; private int convergedCnt = 0;
private int collisions = 0; private int collisions = 0;
private static boolean TRACE = false, TRACE_STATE = false, TRACE_EVTS = false; private static boolean TRACE = false, TRACE_STATE = false, TRACE_EVTS = false;
private int m_ShowCycle = 0; private int m_ShowCycle = 0;
transient private TopoPlot m_Topology; transient private TopoPlot m_Topology;
@@ -103,7 +99,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
private int m_maxSpeciesSize = 15; private int m_maxSpeciesSize = 15;
private AbstractEAIndividualComparator reduceSizeComparator = new AbstractEAIndividualComparator(); private AbstractEAIndividualComparator reduceSizeComparator = new AbstractEAIndividualComparator();
private AbstractEAIndividualComparator histComparator = new AbstractEAIndividualComparator("", -1, true); private AbstractEAIndividualComparator histComparator = new AbstractEAIndividualComparator("", -1, true);
protected ParameterControlManager paramControl = new ParameterControlManager(); protected ParameterControlManager paramControl = new ParameterControlManager();
private double avgDistForConvergence = 0.1; // Upper bound for average indy distance in a species in the test for convergence private double avgDistForConvergence = 0.1; // Upper bound for average indy distance in a species in the test for convergence
@@ -113,10 +108,10 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// if (useDistraction) distraction = new Distraction(distrDefaultStrength, Distraction.METH_BEST); // if (useDistraction) distraction = new Distraction(distrDefaultStrength, Distraction.METH_BEST);
} }
/********************************************************************************************************************** /**
* ********************************************************************************************************************
* These are for InterfaceParamControllable * These are for InterfaceParamControllable
*/ */
public Object[] getParamControl() { public Object[] getParamControl() {
List<Object> ctrlbls = ParameterControlManager.listOfControllables(this); List<Object> ctrlbls = ParameterControlManager.listOfControllables(this);
ctrlbls.add(paramControl); ctrlbls.add(paramControl);
@@ -127,18 +122,20 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
/** /**
* This method is necessary to allow access from the Processor. * This method is necessary to allow access from the Processor.
*
* @return * @return
*/ */
// public ParameterControlManager getParamControl() { // public ParameterControlManager getParamControl() {
// return paramControl; // return paramControl;
// } // }
public ParamAdaption[] getParameterControl() { public ParamAdaption[] getParameterControl() {
return paramControl.getSingleAdapters(); return paramControl.getSingleAdapters();
} }
public void setParameterControl(ParamAdaption[] paramControl) { public void setParameterControl(ParamAdaption[] paramControl) {
this.paramControl.setSingleAdapters(paramControl); this.paramControl.setSingleAdapters(paramControl);
} }
public String parameterControlTipText() { public String parameterControlTipText() {
return "You may define dynamic paramter control strategies using the parameter name."; return "You may define dynamic paramter control strategies using the parameter name.";
} }
@@ -184,8 +181,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
public void init() { public void init() {
if (m_Undifferentiated == null) { if (m_Undifferentiated == null) {
this.m_Undifferentiated = new Population(m_PopulationSize); this.m_Undifferentiated = new Population(m_PopulationSize);
} } else {
else {
m_Undifferentiated.resetProperties(); m_Undifferentiated.resetProperties();
m_Undifferentiated.setTargetSize(m_PopulationSize); m_Undifferentiated.setTargetSize(m_PopulationSize);
} }
@@ -236,7 +232,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
this.firePropertyChangedEvent("FirstGenerationPerformed"); this.firePropertyChangedEvent("FirstGenerationPerformed");
} }
/** 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 pop The initial population
* @param reset If true the population is reset. * @param reset If true the population is reset.
*/ */
@@ -249,8 +247,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
initDefaults(reset); initDefaults(reset);
} }
/** 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 * @param population The population that is to be evaluated
*/ */
private void evaluatePopulation(Population population) { private void evaluatePopulation(Population population) {
@@ -352,14 +351,12 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
double[] pos1, pos2; double[] pos1, pos2;
if (indy1 instanceof InterfaceDataTypeDouble) { if (indy1 instanceof InterfaceDataTypeDouble) {
pos1 = ((InterfaceDataTypeDouble) indy1).getDoubleData(); pos1 = ((InterfaceDataTypeDouble) indy1).getDoubleData();
} } else {
else {
pos1 = (indy1).getDoublePosition(); pos1 = (indy1).getDoublePosition();
} }
if (indy2 instanceof InterfaceDataTypeDouble) { if (indy2 instanceof InterfaceDataTypeDouble) {
pos2 = ((InterfaceDataTypeDouble) indy2).getDoubleData(); pos2 = ((InterfaceDataTypeDouble) indy2).getDoubleData();
} } else {
else {
pos2 = (indy2).getDoublePosition(); pos2 = (indy2).getDoublePosition();
} }
tp.getFunctionArea().drawLine(pos1, pos2); tp.getFunctionArea().drawLine(pos1, pos2);
@@ -380,7 +377,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
p.getFunctionArea().addDElement(popRep); p.getFunctionArea().addDElement(popRep);
} }
/** This method is called to generate n freshly initialized individuals /**
* This method is called to generate n freshly initialized individuals
*
* @param n Number of new individuals * @param n Number of new individuals
* @return A population of new individuals * @return A population of new individuals
*/ */
@@ -397,8 +396,8 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
} }
/** /**
* This method checks whether a species is converged, i.e. the best fitness has not improved * This method checks whether a species is converged, i.e. the best fitness
* for a number of generations. * has not improved for a number of generations.
* *
* @param pop The species to test * @param pop The species to test
* @return True if converged. * @return True if converged.
@@ -459,14 +458,16 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
} }
/** /**
* Define the criterion by which individual improvement is judged. The original version defined * Define the criterion by which individual improvement is judged. The
* improvement strictly, but for some EA this should be done more laxly. E.g. DE will hardly ever * original version defined improvement strictly, but for some EA this
* stop improving slightly, so optionally use an epsilon-bound: improvement only counts if it is * should be done more laxly. E.g. DE will hardly ever stop improving
* larger than epsilon in case useEpsilonBound is true. * slightly, so optionally use an epsilon-bound: improvement only counts if
* it is larger than epsilon in case useEpsilonBound is true.
* *
* @param firstIndy * @param firstIndy
* @param secIndy * @param secIndy
* @return true if the second individual has improved in relation to the first one * @return true if the second individual has improved in relation to the
* first one
*/ */
private boolean testSecondForImprovement(AbstractEAIndividual firstIndy, AbstractEAIndividual secIndy) { private boolean testSecondForImprovement(AbstractEAIndividual firstIndy, AbstractEAIndividual secIndy) {
if (epsilonBound > 0) { if (epsilonBound > 0) {
@@ -938,7 +939,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// pop.synchSize(); // pop.synchSize();
// return pop; // return pop;
// } // }
/** /**
* Replace the undifferentiated population with the given one. * Replace the undifferentiated population with the given one.
* *
@@ -971,8 +971,8 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
} }
/** /**
* Merge two species by adding the second to the first. Keep the longer history. The second * Merge two species by adding the second to the first. Keep the longer
* species should be deactivated after merging. * history. The second species should be deactivated after merging.
* *
* @param pop1 * @param pop1
* @param pop2 * @param pop2
@@ -997,7 +997,8 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
} }
/** /**
* A split event will reset the new species model so as to have a fresh start. * A split event will reset the new species model so as to have a fresh
* start.
* *
* @param parentSp * @param parentSp
* @param newSp * @param newSp
@@ -1028,7 +1029,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// protected boolean isActive(Population pop) { // protected boolean isActive(Population pop) {
// return (pop.size() >= m_actSpecSize); // return (pop.size() >= m_actSpecSize);
// } // }
// /** // /**
// * Deactivate a given species by removing all individuals and inserting // * Deactivate a given species by removing all individuals and inserting
// * only the given survivor, sets the population size to one. // * only the given survivor, sets the population size to one.
@@ -1042,7 +1042,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// spec.add(survivor); // spec.add(survivor);
// spec.setPopulationSize(1); // spec.setPopulationSize(1);
// } // }
// public int countActiveSpec() { // public int countActiveSpec() {
// int k = 0; // int k = 0;
// for (int i=0; i<m_Species.size(); i++) { // for (int i=0; i<m_Species.size(); i++) {
@@ -1050,8 +1049,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// } // }
// return k; // return k;
// } // }
/**
/** This method allows an optimizer to register a change in the optimizer. * This method allows an optimizer to register a change in the optimizer.
*
* @param source The source of the event. * @param source The source of the event.
* @param name Could be used to indicate the nature of the event. * @param name Could be used to indicate the nature of the event.
*/ */
@@ -1064,6 +1064,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -1074,13 +1075,16 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
return false; return false;
} }
} }
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name); 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 * @param problem
*/ */
@Override @Override
@@ -1088,13 +1092,16 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
this.m_Problem = problem; this.m_Problem = problem;
this.m_Optimizer.setProblem(this.m_Problem); this.m_Optimizer.setProblem(this.m_Problem);
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; 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 * @return A descriptive string
*/ */
@Override @Override
@@ -1107,36 +1114,37 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
return result; 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { public static String globalInfo() {
return "This is a versatile species based niching EA method."; return "This is a versatile species based niching EA method.";
} }
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm * @return The name of the algorithm
*/ */
@Override @Override
@@ -1209,28 +1217,34 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// public String applyClearingTipText() { // public String applyClearingTipText() {
// return "Clearing removes all but the best individuals from an identified species."; // return "Clearing removes all but the best individuals from an identified species.";
// } // }
/**
/** This method allows you to set/get the switch that toggles the use * This method allows you to set/get the switch that toggles the use of
* of species convergence. * species convergence.
*
* @return The current status of this flag * @return The current status of this flag
*/ */
public boolean isUseMerging() { public boolean isUseMerging() {
return this.m_mergeSpecies; return this.m_mergeSpecies;
} }
public void setUseMerging(boolean b) { public void setUseMerging(boolean b) {
this.m_mergeSpecies = b; this.m_mergeSpecies = b;
GenericObjectEditor.setHideProperty(this.getClass(), "mergingCA", !m_mergeSpecies); GenericObjectEditor.setHideProperty(this.getClass(), "mergingCA", !m_mergeSpecies);
} }
public String useMergingTipText() { public String useMergingTipText() {
return "Toggle the use of species merging."; return "Toggle the use of species merging.";
} }
/** Choose a population based optimizing technique to use /**
* Choose a population based optimizing technique to use
*
* @return The current optimizing method * @return The current optimizing method
*/ */
public InterfaceOptimizer getOptimizer() { public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer; return this.m_Optimizer;
} }
public void setOptimizer(InterfaceOptimizer b) { public void setOptimizer(InterfaceOptimizer b) {
this.m_Optimizer = b; this.m_Optimizer = b;
if (b instanceof EvolutionStrategies) { if (b instanceof EvolutionStrategies) {
@@ -1238,32 +1252,41 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
setMuLambdaRatio(es.getMu() / (double) es.getLambda()); setMuLambdaRatio(es.getMu() / (double) es.getLambda());
} }
} }
public String optimizerTipText() { public String optimizerTipText() {
return "Choose a population based optimizing technique to use."; return "Choose a population based optimizing technique to use.";
} }
/** The cluster algorithm on which the species differentiation is based /**
* The cluster algorithm on which the species differentiation is based
*
* @return The current clustering method * @return The current clustering method
*/ */
public InterfaceClustering getDifferentiationCA() { public InterfaceClustering getDifferentiationCA() {
return this.m_CAForSpeciesDifferentation; return this.m_CAForSpeciesDifferentation;
} }
public void setDifferentiationCA(InterfaceClustering b) { public void setDifferentiationCA(InterfaceClustering b) {
this.m_CAForSpeciesDifferentation = b; this.m_CAForSpeciesDifferentation = b;
} }
public String differentiationCATipText() { public String differentiationCATipText() {
return "The cluster algorithm on which the species differentation is based."; return "The cluster algorithm on which the species differentation is based.";
} }
/** The Cluster Algorithm on which the species convergence is based. /**
* The Cluster Algorithm on which the species convergence is based.
*
* @return The current clustering method * @return The current clustering method
*/ */
public InterfaceClustering getMergingCA() { public InterfaceClustering getMergingCA() {
return this.m_CAForSpeciesMerging; return this.m_CAForSpeciesMerging;
} }
public void setMergingCA(InterfaceClustering b) { public void setMergingCA(InterfaceClustering b) {
this.m_CAForSpeciesMerging = b; this.m_CAForSpeciesMerging = b;
} }
public String mergingCATipText() { public String mergingCATipText() {
return "The cluster algorithm on which the species merging is based."; return "The cluster algorithm on which the species merging is based.";
} }
@@ -1277,44 +1300,58 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// public String useArchiveTipText() { // public String useArchiveTipText() {
// return "Toggle usage of an archive where converged species are saved and the individuals reinitialized."; // return "Toggle usage of an archive where converged species are saved and the individuals reinitialized.";
// } // }
/**
/** Determines how often species differentation/convergence is performed. * Determines how often species differentation/convergence is performed.
* @return This number gives the generations when specification is performed. *
* @return This number gives the generations when specification is
* performed.
*/ */
public int getSpeciesCycle() { public int getSpeciesCycle() {
return this.m_SpeciesCycle; return this.m_SpeciesCycle;
} }
public void setSpeciesCycle(int b) { public void setSpeciesCycle(int b) {
this.m_SpeciesCycle = b; this.m_SpeciesCycle = b;
} }
public String speciesCycleTipText() { public String speciesCycleTipText() {
return "Determines how often species differentation/convergence is performed."; return "Determines how often species differentation/convergence is performed.";
} }
/** TDetermines how often show is performed. /**
* @return This number gives the generations when specification is performed. * TDetermines how often show is performed.
*
* @return This number gives the generations when specification is
* performed.
*/ */
public int getShowCycle() { public int getShowCycle() {
return this.m_ShowCycle; return this.m_ShowCycle;
} }
public void setShowCycle(int b) { public void setShowCycle(int b) {
this.m_ShowCycle = b; this.m_ShowCycle = b;
if (b <= 0) { if (b <= 0) {
m_Topology = null; m_Topology = null;
} }
} }
public String showCycleTipText() { public String showCycleTipText() {
return "Determines how often show is performed (generations); set to zero to deactivate."; return "Determines how often show is performed (generations); set to zero to deactivate.";
} }
/** Determines the size of the initial population.
/**
* Determines the size of the initial population.
*
* @return This number gives initial population size. * @return This number gives initial population size.
*/ */
public int getPopulationSize() { public int getPopulationSize() {
return this.m_PopulationSize; return this.m_PopulationSize;
} }
public void setPopulationSize(int b) { public void setPopulationSize(int b) {
this.m_PopulationSize = b; this.m_PopulationSize = b;
} }
public String populationSizeTipText() { public String populationSizeTipText() {
return "Determines the size of the initial population."; return "Determines the size of the initial population.";
} }
@@ -1329,9 +1366,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// public double getMuLambdaRatio() { // public double getMuLambdaRatio() {
// return muLambdaRatio; // return muLambdaRatio;
// } // }
/** /**
* This is now set if an ES is set as optimizer. * This is now set if an ES is set as optimizer.
*
* @param muLambdaRatio the muLambdaRatio to set * @param muLambdaRatio the muLambdaRatio to set
*/ */
public void setMuLambdaRatio(double muLambdaRatio) { public void setMuLambdaRatio(double muLambdaRatio) {
@@ -1369,7 +1406,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// public void setDistractionActive(boolean useDistraction) { // public void setDistractionActive(boolean useDistraction) {
// this.useDistraction = useDistraction; // this.useDistraction = useDistraction;
// } // }
// /** // /**
// * @return the distrDefaultStrength // * @return the distrDefaultStrength
// */ // */
@@ -1384,19 +1420,20 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// this.distrDefaultStrength = distrDefaultStrength; // this.distrDefaultStrength = distrDefaultStrength;
// distraction.setDefaultStrength(distrDefaultStrength); // distraction.setDefaultStrength(distrDefaultStrength);
// } // }
/** /**
* @return the sleepTime * @return the sleepTime
*/ */
public int getSleepTime() { public int getSleepTime() {
return sleepTime; return sleepTime;
} }
/** /**
* @param sleepTime the sleepTime to set * @param sleepTime the sleepTime to set
*/ */
public void setSleepTime(int sleepTime) { public void setSleepTime(int sleepTime) {
this.sleepTime = sleepTime; this.sleepTime = sleepTime;
} }
public String sleepTimeTipText() { public String sleepTimeTipText() {
return "Let the thread sleep between iterations (nice when visualizing)"; return "Let the thread sleep between iterations (nice when visualizing)";
} }
@@ -1407,12 +1444,14 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
public double getEpsilonBound() { public double getEpsilonBound() {
return epsilonBound; return epsilonBound;
} }
/** /**
* @param epsilonBound the epsilonBound to set * @param epsilonBound the epsilonBound to set
*/ */
public void setEpsilonBound(double epsilonBound) { public void setEpsilonBound(double epsilonBound) {
this.epsilonBound = epsilonBound; this.epsilonBound = epsilonBound;
} }
public String epsilonBoundTipText() { public String epsilonBoundTipText() {
return "If fitness improves less than this value within the halting window, convergence is assumed. May be set to zero."; return "If fitness improves less than this value within the halting window, convergence is assumed. May be set to zero.";
} }
@@ -1453,14 +1492,15 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
} }
/** /**
* Calculate average of Population measures (mean, minimal and maximal distance within a species) * Calculate average of Population measures (mean, minimal and maximal
* distance within a species)
*
* @return average population measures * @return average population measures
*/ */
protected double[] getAvgSpeciesMeasures() { protected double[] getAvgSpeciesMeasures() {
if (m_Species == null || (m_Species.size() == 0)) { if (m_Species == null || (m_Species.size() == 0)) {
return new double[]{0}; return new double[]{0};
} } else {
else {
double[] measures = m_Species.get(0).getPopulationMeasures(); double[] measures = m_Species.get(0).getPopulationMeasures();
for (int i = 1; i < m_Species.size(); i++) { for (int i = 1; i < m_Species.size(); i++) {
Mathematics.vvAdd(measures, m_Species.get(i).getPopulationMeasures(), measures); Mathematics.vvAdd(measures, m_Species.get(i).getPopulationMeasures(), measures);
@@ -1475,10 +1515,12 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
public int getMaxSpeciesSize() { public int getMaxSpeciesSize() {
return m_maxSpeciesSize; return m_maxSpeciesSize;
} }
public void setMaxSpeciesSize(int mMaxSpeciesSize) { public void setMaxSpeciesSize(int mMaxSpeciesSize) {
m_maxSpeciesSize = mMaxSpeciesSize; m_maxSpeciesSize = mMaxSpeciesSize;
GenericObjectEditor.setShowProperty(this.getClass(), "reduceSizeComparator", (m_maxSpeciesSize >= m_minGroupSize)); GenericObjectEditor.setShowProperty(this.getClass(), "reduceSizeComparator", (m_maxSpeciesSize >= m_minGroupSize));
} }
public String maxSpeciesSizeTipText() { public String maxSpeciesSizeTipText() {
return "If >= " + m_minGroupSize + ", larger species are reduced to the given size by reinitializing the worst individuals."; return "If >= " + m_minGroupSize + ", larger species are reduced to the given size by reinitializing the worst individuals.";
} }
@@ -1486,9 +1528,11 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
public String reduceSizeComparatorTipText() { public String reduceSizeComparatorTipText() {
return "Set the comparator used to define the 'worst' individuals when reducing species size."; return "Set the comparator used to define the 'worst' individuals when reducing species size.";
} }
public AbstractEAIndividualComparator getReduceSizeComparator() { public AbstractEAIndividualComparator getReduceSizeComparator() {
return reduceSizeComparator; return reduceSizeComparator;
} }
public void setReduceSizeComparator( public void setReduceSizeComparator(
AbstractEAIndividualComparator reduceSizeComparator) { AbstractEAIndividualComparator reduceSizeComparator) {
this.reduceSizeComparator = reduceSizeComparator; this.reduceSizeComparator = reduceSizeComparator;
@@ -1523,10 +1567,11 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
/** /**
* Calculate the clustering parameter in such a way that about one q-th part * Calculate the clustering parameter in such a way that about one q-th part
* of the range of the given problem is within one hyper sphere of the clustering parameter. * of the range of the given problem is within one hyper sphere of the
* clustering parameter.
* *
* For certain types of parameter adaption schemes, this automatically sets the upper limit * For certain types of parameter adaption schemes, this automatically sets
* if the clustering parameter is controlled. * the upper limit if the clustering parameter is controlled.
* *
* @param prob * @param prob
* @param q * @param q

65
src/eva2/server/go/strategies/ClusteringHillClimbing.java
View File

@@ -16,27 +16,29 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.Pair; import eva2.tools.Pair;
import java.io.Serializable; import java.io.Serializable;
/** /**
* The clustering hill climber is similar to a multi-start hill climber. In addition so optimizing * The clustering hill climber is similar to a multi-start hill climber. In
* a set of individuals in parallel using a (1+1) strategy, the population is clustered in regular * addition so optimizing a set of individuals in parallel using a (1+1)
* intervals. If several individuals have gathered together in the sense that they are interpreted * strategy, the population is clustered in regular intervals. If several
* as a cluster, only a subset of representatives of the cluster is taken over to the next HC step * individuals have gathered together in the sense that they are interpreted as
* while the rest is discarded. This means that the population size may be reduced. * 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. * As soon as the improvement by HC lies below a threshold, the mutation step
* If the step size is decreased below a certain threshold, the current population is stored to * size is decreased. If the step size is decreased below a certain threshold,
* an archive and reinitialized. Thus, the number of optima that may be found and returned by * the current population is stored to an archive and reinitialized. Thus, the
* getAllSolutions is higher than the population size. * number of optima that may be found and returned by getAllSolutions is higher
* than the population size.
* *
* @author mkron * @author mkron
* *
*/ */
public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener, public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener,
InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer { InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
public static final boolean TRACE = false; public static final boolean TRACE = false;
transient private String m_Identifier = ""; transient private String m_Identifier = "";
private Population m_Population = new Population(); private Population m_Population = new Population();
private transient Population archive = new Population(); private transient Population archive = new Population();
@@ -101,18 +103,22 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * @param problem
*/ */
@Override @Override
public void setProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; return this.m_Problem;
@@ -122,6 +128,7 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -132,6 +139,7 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
return false; return false;
} }
} }
@Override @Override
public void init() { public void init() {
loopCnt = 0; loopCnt = 0;
@@ -145,7 +153,9 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 pop The initial population
* @param reset If true the population is reset. * @param reset If true the population is reset.
*/ */
@@ -161,7 +171,8 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
} }
} }
/** Something has changed /**
* Something has changed
*/ */
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
@@ -181,8 +192,7 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
int evalsNow, lastOverhead = (m_Population.getFunctionCalls() % hcEvalCycle); int evalsNow, lastOverhead = (m_Population.getFunctionCalls() % hcEvalCycle);
if (lastOverhead > 0) { if (lastOverhead > 0) {
evalsNow = (2 * hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle)); evalsNow = (2 * hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle));
} } else {
else {
evalsNow = hcEvalCycle; evalsNow = hcEvalCycle;
} }
do { do {
@@ -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 * Assuming that all optimizer will store thier data in a population we will
* 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. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "Change the number of starting individuals stored (Cluster-HC)."; return "Change the number of starting individuals stored (Cluster-HC).";
} }
@@ -295,8 +309,10 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
return new SolutionSet(m_Population, tmp); 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 * @return A descriptive string
*/ */
@Override @Override
@@ -310,17 +326,14 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
return sbuf.toString(); return sbuf.toString();
} }
@Override
public void freeWilly() {}
@Override @Override
public String getName() { public String getName() {
return "ClustHC-" + initialPopSize + "-" + localSearchMethod; return "ClustHC-" + initialPopSize + "-" + localSearchMethod;
} }
public static String globalInfo() { public static String globalInfo() {
return "Similar to multi-start HC, but clusters the population during optimization to remove redundant individuals for efficiency." + 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."; + "If the local search step does not achieve a minimum improvement, the population may be reinitialized.";
} }
/** /**

196
src/eva2/server/go/strategies/DifferentialEvolution.java
View File

@@ -21,12 +21,12 @@ import eva2.tools.math.RNG;
import java.util.Vector; import java.util.Vector;
/** /**
* Differential evolution implementing DE1 and DE2 following the paper of Storm and * Differential evolution implementing DE1 and DE2 following the paper of Storm
* Price and the Trigonometric DE published recently. * and Price and the Trigonometric DE published recently. Please note that DE
* Please note that DE will only work on real-valued genotypes and will ignore * will only work on real-valued genotypes and will ignore all mutation and
* all mutation and crossover operators selected. * crossover operators selected. Added aging mechanism to provide for
* Added aging mechanism to provide for dynamically changing problems. If an individual * dynamically changing problems. If an individual reaches the age limit, it is
* reaches the age limit, it is doomed and replaced by the next challenge vector, even if its worse. * doomed and replaced by the next challenge vector, even if its worse.
* *
*/ */
public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serializable { public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serializable {
@@ -44,7 +44,6 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// to log the parents of a newly created indy. // to log the parents of a newly created indy.
public boolean doLogParents = false; // deactivate for better performance public boolean doLogParents = false; // deactivate for better performance
private transient Vector<AbstractEAIndividual> parents = null; private transient Vector<AbstractEAIndividual> parents = null;
private boolean randomizeFKLambda = false; private boolean randomizeFKLambda = false;
private boolean generational = true; private boolean generational = true;
private String m_Identifier = ""; private String m_Identifier = "";
@@ -110,7 +109,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
setDEType(getDEType()); 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 pop The initial population
* @param reset If true the population is reset. * @param reset If true the population is reset.
*/ */
@@ -126,8 +127,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// else children = new Population(m_Population.size()); // 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 * @param population The population that is to be evaluated
*/ */
private void evaluatePopulation(Population population) { 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 returns a difference vector between two random individuals
* This method should make sure that delta is not zero. * from the population. This method should make sure that delta is not zero.
* *
* @param pop The population to choose from * @param pop The population to choose from
* @return The delta vector * @return The delta vector
@@ -178,8 +180,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
for (int i = 0; i < x1.length; i++) { for (int i = 0; i < x1.length; i++) {
if (RNG.flipCoin(1 / (double) x1.length)) { if (RNG.flipCoin(1 / (double) x1.length)) {
result[i] = 0.01 * RNG.gaussianDouble(0.1); result[i] = 0.01 * RNG.gaussianDouble(0.1);
} } else {
else {
result[i] = 0; result[i] = 0;
} }
isEmpty = (isEmpty && (result[i] == 0)); isEmpty = (isEmpty && (result[i] == 0));
@@ -191,8 +192,8 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
} }
/** /**
* This method returns a difference vector between two random individuals from the population. * This method returns a difference vector between two random individuals
* This method should make sure that delta is not zero. * from the population. This method should make sure that delta is not zero.
* *
* @param pop The population to choose from * @param pop The population to choose from
* @return The delta vector * @return The delta vector
@@ -234,8 +235,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
for (int i = 0; i < x1.length; i++) { for (int i = 0; i < x1.length; i++) {
if (RNG.flipCoin(1 / (double) x1.length)) { if (RNG.flipCoin(1 / (double) x1.length)) {
result[i] = 0.01 * RNG.gaussianDouble(0.1); result[i] = 0.01 * RNG.gaussianDouble(0.1);
} } else {
else {
result[i] = 0; result[i] = 0;
} }
isEmpty = (isEmpty && (result[i] == 0)); isEmpty = (isEmpty && (result[i] == 0));
@@ -283,7 +283,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return result; 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 * @param pop The population to choose from
* @return the delta vector * @return the delta vector
*/ */
@@ -301,8 +303,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// result[1] = indy2.getDGenotype(); // result[1] = indy2.getDGenotype();
// return result; // 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 * @param pop The current population
* @return AbstractEAIndividual * @return AbstractEAIndividual
*/ */
@@ -313,8 +316,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
if (doLogParents) { if (doLogParents) {
parents = new Vector<AbstractEAIndividual>(); parents = new Vector<AbstractEAIndividual>();
} } else {
else {
parents = null; parents = null;
} }
try { try {
@@ -453,8 +455,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
private double getCurrentK() { private double getCurrentK() {
if (randomizeFKLambda) { if (randomizeFKLambda) {
return RNG.randomDouble(m_k * 0.8, m_k * 1.2); return RNG.randomDouble(m_k * 0.8, m_k * 1.2);
} } else {
else {
return m_k; return m_k;
} }
} }
@@ -462,8 +463,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
private double getCurrentLambda() { private double getCurrentLambda() {
if (randomizeFKLambda) { if (randomizeFKLambda) {
return RNG.randomDouble(m_Lambda * 0.8, m_Lambda * 1.2); return RNG.randomDouble(m_Lambda * 0.8, m_Lambda * 1.2);
} } else {
else {
return m_Lambda; return m_Lambda;
} }
} }
@@ -471,8 +471,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
private double getCurrentF() { private double getCurrentF() {
if (randomizeFKLambda) { if (randomizeFKLambda) {
return RNG.randomDouble(m_F * 0.8, m_F * 1.2); return RNG.randomDouble(m_F * 0.8, m_F * 1.2);
} } else {
else {
return m_F; return m_F;
} }
} }
@@ -504,16 +503,16 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
public void optimize() { public void optimize() {
if (generational) { if (generational) {
optimizeGenerational(); optimizeGenerational();
} } else {
else {
optimizeSteadyState(); optimizeSteadyState();
} }
} }
/** /**
* This generational DE variant calls the method AbstractOptimizationProblem.evaluate(Population). * This generational DE variant calls the method
* Its performance may be slightly worse for schemes that rely on current best individuals, * AbstractOptimizationProblem.evaluate(Population). Its performance may be
* because improvements are not immediately incorporated as in the steady state DE. * 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. * However it may be easier to parallelize.
* *
*/ */
@@ -524,15 +523,13 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// m_Problem.evaluatePopulationStart(m_Population); // m_Problem.evaluatePopulationStart(m_Population);
if (children == null) { if (children == null) {
children = new Population(m_Population.size()); children = new Population(m_Population.size());
} } else {
else {
children.clear(); children.clear();
} }
for (int i = 0; i < this.m_Population.size(); i++) { for (int i = 0; i < this.m_Population.size(); i++) {
if (cyclePop) { if (cyclePop) {
parentIndex = i; parentIndex = i;
} } else {
else {
parentIndex = RNG.randomInt(0, this.m_Population.size() - 1); parentIndex = RNG.randomInt(0, this.m_Population.size() - 1);
} }
AbstractEAIndividual indy = generateNewIndividual(m_Population, parentIndex); AbstractEAIndividual indy = generateNewIndividual(m_Population, parentIndex);
@@ -543,7 +540,8 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
m_Problem.evaluate(children); m_Problem.evaluate(children);
/** /**
* MdP: added a reevalutation mechanism for dynamically changing problems * MdP: added a reevalutation mechanism for dynamically changing
* problems
*/ */
if (isReEvaluate()) { if (isReEvaluate()) {
for (int i = 0; i < this.m_Population.size(); i++) { for (int i = 0; i < this.m_Population.size(); i++) {
@@ -561,8 +559,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
AbstractEAIndividual indy = children.getEAIndividual(i); AbstractEAIndividual indy = children.getEAIndividual(i);
if (cyclePop) { if (cyclePop) {
parentIndex = i; parentIndex = i;
} } else {
else {
parentIndex = RNG.randomInt(0, this.m_Population.size() - 1); parentIndex = RNG.randomInt(0, this.m_Population.size() - 1);
} }
if (nextDoomed >= 0) { // this one is lucky, may replace an 'old' one if (nextDoomed >= 0) { // this one is lucky, may replace an 'old' one
@@ -600,7 +597,8 @@ 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()) { if (isReEvaluate()) {
nextDoomed = -1; nextDoomed = -1;
@@ -618,8 +616,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
for (int i = 0; i < this.m_Population.size(); i++) { for (int i = 0; i < this.m_Population.size(); i++) {
if (cyclePop) { if (cyclePop) {
index = i; index = i;
} } else {
else {
index = RNG.randomInt(0, this.m_Population.size() - 1); index = RNG.randomInt(0, this.m_Population.size() - 1);
} }
indy = generateNewIndividual(m_Population, index); indy = generateNewIndividual(m_Population, index);
@@ -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. * Search for the first individual which is older than the age limit and
* If there is no age limit or all individuals are younger, -1 is returned. The start index * return its index. If there is no age limit or all individuals are
* of the search may be provided to make iterative search efficient. * younger, -1 is returned. The start index of the search may be provided to
* make iterative search efficient.
* *
* @param pop Population to search * @param pop Population to search
* @param startIndex index to start the search from * @param startIndex index to start the search from
@@ -718,7 +716,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return -1; 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 * @param ea
*/ */
@Override @Override
@@ -728,6 +728,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
} }
this.m_Listener.add(ea); this.m_Listener.add(ea);
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -738,7 +739,10 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return false; return false;
} }
} }
/** Something has changed
/**
* Something has changed
*
* @param name * @param name
*/ */
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
@@ -749,20 +753,25 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
} }
} }
/** This method will set the problem that is to be optimized /**
* This method will set the problem that is to be optimized
*
* @param problem * @param problem
*/ */
@Override @Override
public void setProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = (AbstractOptimizationProblem) problem; this.m_Problem = (AbstractOptimizationProblem) problem;
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return (InterfaceOptimizationProblem) this.m_Problem; return (InterfaceOptimizationProblem) 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 * @return A descriptive string
*/ */
@Override @Override
@@ -774,35 +783,38 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
result += this.m_Population.getStringRepresentation(); result += this.m_Population.getStringRepresentation();
return result; 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 * @param name The identifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { public static String globalInfo() {
return "Differential Evolution using a steady-state population scheme."; 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 * @return The name of the algorithm
*/ */
@Override @Override
@@ -810,19 +822,23 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return "DE"; 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 * Assuming that all optimizer will store their data in a population we will
* of the optimizer. * allow access to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "Edit the properties of the population used."; 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); 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 * @param f
*/ */
public void setF(double f) { public void setF(double f) {
this.m_F = f; this.m_F = f;
} }
public double getF() { public double getF() {
return this.m_F; return this.m_F;
} }
public String fTipText() { public String fTipText() {
return "F is a real and constant factor which controls the amplification of the differential variation."; 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 * @param k
*/ */
public void setK(double k) { public void setK(double k) {
@@ -858,27 +882,36 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
} }
this.m_k = k; this.m_k = k;
} }
public double getK() { public double getK() {
return this.m_k; return this.m_k;
} }
public String kTipText() { public String kTipText() {
return "Probability of alteration through DE (a.k.a. CR, similar to discrete uniform crossover)."; 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 * @param l
*/ */
public void setLambda(double l) { public void setLambda(double l) {
this.m_Lambda = l; this.m_Lambda = l;
} }
public double getLambda() { public double getLambda() {
return this.m_Lambda; return this.m_Lambda;
} }
public String lambdaTipText() { public String lambdaTipText() {
return "Enhance greediness through amplification of the differential vector to the best individual for DE2."; 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 * @param l
*/ */
public void setMt(double l) { public void setMt(double l) {
@@ -890,14 +923,18 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
this.m_Mt = 1; this.m_Mt = 1;
} }
} }
public double getMt() { public double getMt() {
return this.m_Mt; return this.m_Mt;
} }
public String mtTipText() { public String mtTipText() {
return "In case of trigonometric mutation DE, the TMO is applied with probability Mt."; 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. * @param s The type.
*/ */
public void setDEType(DETypeEnum s) { public void setDEType(DETypeEnum s) {
@@ -906,23 +943,27 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
GenericObjectEditor.setShowProperty(this.getClass(), "lambda", s == DETypeEnum.DE2_CurrentToBest); GenericObjectEditor.setShowProperty(this.getClass(), "lambda", s == DETypeEnum.DE2_CurrentToBest);
GenericObjectEditor.setShowProperty(this.getClass(), "mt", s == DETypeEnum.TrigonometricDE); GenericObjectEditor.setShowProperty(this.getClass(), "mt", s == DETypeEnum.TrigonometricDE);
} }
public DETypeEnum getDEType() { public DETypeEnum getDEType() {
return this.m_DEType; return this.m_DEType;
} }
public String dETypeTipText() { public String dETypeTipText() {
return "Choose the type of Differential Evolution."; return "Choose the type of Differential Evolution.";
} }
/** /**
* @return the maximumAge * @return the maximumAge
**/ *
*/
public int getMaximumAge() { public int getMaximumAge() {
return maximumAge; return maximumAge;
} }
/** /**
* @param maximumAge the maximumAge to set * @param maximumAge the maximumAge to set
**/ *
*/
public void setMaximumAge(int maximumAge) { public void setMaximumAge(int maximumAge) {
this.maximumAge = maximumAge; this.maximumAge = maximumAge;
} }
@@ -933,6 +974,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
/** /**
* Check whether the problem range will be enforced. * Check whether the problem range will be enforced.
*
* @return the forceRange * @return the forceRange
*/ */
public boolean isCheckRange() { public boolean isCheckRange() {
@@ -973,7 +1015,6 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// public String cyclePopTipText() { // public String cyclePopTipText() {
// return "Use all individuals as parents in cyclic sequence instead of randomly."; // return "Use all individuals as parents in cyclic sequence instead of randomly.";
// } // }
public boolean isCompareToParent() { public boolean isCompareToParent() {
return compareToParent; return compareToParent;
} }
@@ -1012,14 +1053,16 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
/** /**
* @return the maximumAge * @return the maximumAge
**/ *
*/
public boolean isReEvaluate() { public boolean isReEvaluate() {
return reEvaluate; return reEvaluate;
} }
/** /**
* @param maximumAge the maximumAge to set * @param maximumAge the maximumAge to set
**/ *
*/
public void setReEvaluate(boolean reEvaluate) { public void setReEvaluate(boolean reEvaluate) {
this.reEvaluate = reEvaluate; this.reEvaluate = reEvaluate;
} }
@@ -1027,5 +1070,4 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
public String reEvaluateTipText() { public String reEvaluateTipText() {
return "Reeavulates individuals which are older than maximum age instead of discarding them"; 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 * with the given parameters. If windowLen <= 0, the deactivation mechanism
* is disabled. This provides for semi-sequential niching with DPI-ES * is disabled. This provides for semi-sequential niching with DPI-ES
* *
*
*
* *
* @param threshold * @param threshold
@@ -948,7 +950,8 @@ public class EsDpiNiching implements InterfaceOptimizer, Serializable, Interface
/** /**
* Calculate the dynamic population size, which is the number of individuals * Calculate the dynamic population size, which is the number of individuals
* that are currently "alive" in the peak set. This must be implemented in * 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 * @return
*/ */
@@ -1048,10 +1051,6 @@ public class EsDpiNiching implements InterfaceOptimizer, Serializable, Interface
} }
} }
@Override
public void freeWilly() {
}
@Override @Override
public InterfaceSolutionSet getAllSolutions() { public InterfaceSolutionSet getAllSolutions() {
Population peaks = new Population(peakOpts.length); 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.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem; 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 * 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, * only stategy that is able to utilize the 1/5 success rule mutation.
* there is a minor problem with the interpretation of the population size in constrast * Unfortunately, there is a minor problem with the interpretation of the
* to the parameters mu and lambda used by Rechenberg and Schwefel. Therefore, i'm * population size in constrast to the parameters mu and lambda used by
* afraid that the interpretation of the population size may be subject to future * Rechenberg and Schwefel. Therefore, i'm afraid that the interpretation of the
* changes. * population size may be subject to future changes. This is a implementation of
* This is a implementation of Evolution Strategies. * Evolution Strategies. Copyright: Copyright (c) 2003 Company: University of
* Copyright: Copyright (c) 2003 * Tuebingen, Computer Architecture
* Company: University of Tuebingen, Computer Architecture *
* @author Felix Streichert * @author Felix Streichert
* @version: $Revision: 307 $ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $ * 2007) $ $Author: mkron $
* $Author: mkron $
*/ */
public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializable { public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializable {
@@ -339,18 +339,9 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
return this.identifier; 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 * These are for GUI
*/ */
/** /**
* This method returns a global info string * This method returns a global info string
* *

97
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.F1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem; 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 * Evolutionary programming by Fogel. Works fine but is actually a quite greedy
* set to 1.0. * local search strategy solely based on mutation. To prevent any confusion, the
* Potential citation: the PhD thesis of David B. Fogel (1992). * 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 * @author Felix Streichert
* @version: $Revision: 307 $ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $ * 2007) $ $Author: mkron $
* $Author: mkron $
*/ */
public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Serializable { public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Serializable {
private int m_PopulationSize = 0; private int m_PopulationSize = 0;
private Population m_Population = new Population(); private Population m_Population = new Population();
private InterfaceOptimizationProblem m_Problem = new F1Problem(); private InterfaceOptimizationProblem m_Problem = new F1Problem();
private InterfaceSelection m_EnvironmentSelection = new SelectEPTournaments(); private InterfaceSelection m_EnvironmentSelection = new SelectEPTournaments();
private String m_Identifier = ""; private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
@@ -56,7 +55,9 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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. * @param reset If true the population is reset.
*/ */
@Override @Override
@@ -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 * @param population The population that is to be evaluated
*/ */
private void evaluatePopulation(Population population) { private void evaluatePopulation(Population population) {
@@ -78,8 +80,9 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
population.incrGeneration(); 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() { private Population generateChildren() {
Population result = (Population) this.m_Population.cloneWithoutInds(); Population result = (Population) this.m_Population.cloneWithoutInds();
@@ -113,13 +116,16 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 * @param ea
*/ */
@Override @Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -130,7 +136,9 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
return false; return false;
} }
} }
/** Something has changed
/**
* Something has changed
*/ */
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
@@ -138,20 +146,25 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
} }
} }
/** This method will set the problem that is to be optimized /**
* This method will set the problem that is to be optimized
*
* @param problem * @param problem
*/ */
@Override @Override
public void setProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; 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 * @return A descriptive string
*/ */
@Override @Override
@@ -163,35 +176,38 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
result += this.m_Population.getStringRepresentation(); result += this.m_Population.getStringRepresentation();
return result; 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { public static String globalInfo() {
return "This is a basic Evolutionary Programming scheme."; 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 * @return The name of the algorithm
*/ */
@Override @Override
@@ -199,19 +215,23 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
return "EP"; 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 * Assuming that all optimizer will store thier data in a population we will
* 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. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "Edit the properties of the population used."; return "Edit the properties of the population used.";
} }
@@ -220,15 +240,20 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
public InterfaceSolutionSet getAllSolutions() { public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation()); return new SolutionSet(getPopulation());
} }
/** Choose a method for selecting the reduced population.
/**
* Choose a method for selecting the reduced population.
*
* @param selection * @param selection
*/ */
public void setEnvironmentSelection(InterfaceSelection selection) { public void setEnvironmentSelection(InterfaceSelection selection) {
this.m_EnvironmentSelection = selection; this.m_EnvironmentSelection = selection;
} }
public InterfaceSelection getEnvironmentSelection() { public InterfaceSelection getEnvironmentSelection() {
return this.m_EnvironmentSelection; return this.m_EnvironmentSelection;
} }
public String environmentSelectionTipText() { public String environmentSelectionTipText() {
return "Choose a method for selecting the reduced population."; return "Choose a method for selecting the reduced population.";
} }

125
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.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem; 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 flood algorithm, and alternative to the threshold algorithms. No really
* the initial flood peak and the drain rate such that it fits the current optimization * good but commonly known and sometimes even used. Here the problem is to
* problem. But again this is a greedy local search strategy. Similar to the * choose the initial flood peak and the drain rate such that it fits the
* evolutionary programming strategy this strategy sets the mutation rate temporarily * current optimization problem. But again this is a greedy local search
* to 1.0. * strategy. Similar to the evolutionary programming strategy this strategy sets
* The algorithm regards only one-dimensional fitness. * the mutation rate temporarily to 1.0. The algorithm regards only
* Created by IntelliJ IDEA. * one-dimensional fitness. Created by IntelliJ IDEA. User: streiche Date:
* User: streiche * 01.10.2004 Time: 13:46:02 To change this template use File | Settings | File
* Date: 01.10.2004 * Templates.
* Time: 13:46:02
* To change this template use File | Settings | File Templates.
*/ */
public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable { public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase // These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem(); private InterfaceOptimizationProblem m_Problem = new B1Problem();
private int m_MultiRuns = 100; private int m_MultiRuns = 100;
private int m_FitnessCalls = 100; private int m_FitnessCalls = 100;
@@ -31,7 +30,6 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
GAIndividualBinaryData m_Best, m_Test; GAIndividualBinaryData m_Best, m_Test;
public double m_InitialFloodPeak = 2000.0, m_CurrentFloodPeak; public double m_InitialFloodPeak = 2000.0, m_CurrentFloodPeak;
public double m_DrainRate = 1.0; public double m_DrainRate = 1.0;
// These variables are necessary for the more complex LectureGUI enviroment // These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = ""; transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
@@ -54,7 +52,8 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
return (Object) new FloodAlgorithm(this); return (Object) new FloodAlgorithm(this);
} }
/** This method will init the HillClimber /**
* This method will init the HillClimber
*/ */
@Override @Override
public void init() { public void init() {
@@ -64,7 +63,9 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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. * @param reset If true the population is reset.
*/ */
@Override @Override
@@ -78,7 +79,8 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
this.m_CurrentFloodPeak = this.m_InitialFloodPeak; this.m_CurrentFloodPeak = this.m_InitialFloodPeak;
} }
/** This method will optimize /**
* This method will optimize
*/ */
@Override @Override
public void optimize() { public void optimize() {
@@ -106,7 +108,9 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 org The original
* @param mut The mutant * @param mut The mutant
*/ */
@@ -121,19 +125,23 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
return result; 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 * @param problem
*/ */
@Override @Override
public void setProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; return this.m_Problem;
} }
/** This method will init the HillClimber /**
* This method will init the HillClimber
*/ */
public void defaultInit() { public void defaultInit() {
this.m_FitnessCallsNeeded = 0; this.m_FitnessCallsNeeded = 0;
@@ -141,7 +149,8 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
this.m_Best.defaultInit(m_Problem); this.m_Best.defaultInit(m_Problem);
} }
/** This method will optimize /**
* This method will optimize
*/ */
public void defaultOptimize() { public void defaultOptimize() {
for (int i = 0; i < m_FitnessCalls; i++) { for (int i = 0; i < m_FitnessCalls; i++) {
@@ -157,8 +166,9 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
} }
} }
/** This main method will start a simple hillclimber. /**
* No arguments necessary. * This main method will start a simple hillclimber. No arguments necessary.
*
* @param args * @param args
*/ */
public static void main(String[] args) { public static void main(String[] args) {
@@ -175,13 +185,16 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
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 * @param ea
*/ */
@Override @Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -192,7 +205,9 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
return false; return false;
} }
} }
/** Something has changed
/**
* Something has changed
*/ */
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
@@ -200,8 +215,10 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
} }
} }
/** 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 * @return A descriptive string
*/ */
@Override @Override
@@ -209,8 +226,7 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
String result = ""; String result = "";
if (this.m_Population.size() > 1) { if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n"; result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
} } else {
else {
result += "Simulated Annealing:\n"; result += "Simulated Annealing:\n";
} }
result += "Optimization Problem: "; result += "Optimization Problem: ";
@@ -218,35 +234,38 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
result += this.m_Population.getStringRepresentation(); result += this.m_Population.getStringRepresentation();
return result; 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { 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!)."; 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 * @return The name of the algorithm
*/ */
@Override @Override
@@ -254,55 +273,67 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
return "MS-FA"; 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 * Assuming that all optimizer will store thier data in a population we will
* 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. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "Change the number of best individuals stored (MS-FA)."; return "Change the number of best individuals stored (MS-FA).";
} }
@Override @Override
public InterfaceSolutionSet getAllSolutions() { public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation()); 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. * @return The initial flood level.
*/ */
public double getInitialFloodPeak() { public double getInitialFloodPeak() {
return this.m_InitialFloodPeak; return this.m_InitialFloodPeak;
} }
public void setInitialFloodPeak(double pop) { public void setInitialFloodPeak(double pop) {
this.m_InitialFloodPeak = pop; this.m_InitialFloodPeak = pop;
} }
public String initialFloodPeakTipText() { public String initialFloodPeakTipText() {
return "Set the initial flood peak."; 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. * @return The drain rate.
*/ */
public double getDrainRate() { public double getDrainRate() {
return this.m_DrainRate; return this.m_DrainRate;
} }
public void setDrainRate(double a) { public void setDrainRate(double a) {
this.m_DrainRate = a; this.m_DrainRate = a;
if (this.m_DrainRate < 0) { if (this.m_DrainRate < 0) {
this.m_DrainRate = 0.0; this.m_DrainRate = 0.0;
} }
} }
public String drainRateTipText() { public String drainRateTipText() {
return "Set the drain rate that reduces the current flood level each generation."; 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; 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 * These are for GUI

53
src/eva2/server/go/strategies/GradientDescentAlgorithm.java
View File

@@ -22,11 +22,9 @@ import eva2.tools.ReflectPackage;
* @author not attributable * @author not attributable
* @version 1.0 * @version 1.0
*/ */
public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Serializable { public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Serializable {
private InterfaceOptimizationProblem m_Problem; private InterfaceOptimizationProblem m_Problem;
InterfaceDataTypeDouble m_Best, m_Test; InterfaceDataTypeDouble m_Best, m_Test;
private int iterations = 1; private int iterations = 1;
private double wDecreaseStepSize = 0.5; private double wDecreaseStepSize = 0.5;
@@ -46,13 +44,10 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
public double maximumabsolutechange = 0.2; public double maximumabsolutechange = 0.2;
// Hashtable indyhash; // Hashtable indyhash;
// These variables are necessary for the more complex LectureGUI enviroment // These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = ""; transient private String m_Identifier = "";
private Population m_Population; private Population m_Population;
private static boolean TRACE = false; private static boolean TRACE = false;
private static final String lockKey = "gdaLockDataKey"; private static final String lockKey = "gdaLockDataKey";
private static final String lastFitnessKey = "gdaLastFitDataKey"; private static final String lastFitnessKey = "gdaLastFitDataKey";
private static final String stepSizeKey = "gdaStepSizeDataKey"; private static final String stepSizeKey = "gdaStepSizeDataKey";
@@ -98,7 +93,9 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
@Override @Override
public Object clone() { public Object clone() {
/**@todo Implement InterfaceOptimizer method*/ /**
* @todo Implement InterfaceOptimizer method
*/
throw new java.lang.UnsupportedOperationException("Method clone() not yet implemented."); throw new java.lang.UnsupportedOperationException("Method clone() not yet implemented.");
} }
@@ -311,7 +308,6 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
this.firePropertyChangedEvent(Population.nextGenerationPerformed); this.firePropertyChangedEvent(Population.nextGenerationPerformed);
} }
private double momentumweigth = 0.1; private double momentumweigth = 0.1;
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
@@ -340,8 +336,9 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
this.m_Population = pop; 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 * @param name The indenifier
*/ */
@Override @Override
@@ -374,6 +371,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -384,6 +382,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
return false; return false;
} }
} }
public static void main(String[] args) { public static void main(String[] args) {
GradientDescentAlgorithm program = new GradientDescentAlgorithm(); GradientDescentAlgorithm program = new GradientDescentAlgorithm();
InterfaceOptimizationProblem problem = new F1Problem(); InterfaceOptimizationProblem problem = new F1Problem();
@@ -399,22 +398,22 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
} }
} }
@Override
public void freeWilly() { }
public static String globalInfo() { 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 //////////////// for global adaption
public boolean isAdaptStepSizeGlobally() { public boolean isAdaptStepSizeGlobally() {
return globalStepSizeAdaption; return globalStepSizeAdaption;
} }
public void setAdaptStepSizeGlobally(boolean globalstepsizeadaption) { public void setAdaptStepSizeGlobally(boolean globalstepsizeadaption) {
this.globalStepSizeAdaption = globalstepsizeadaption; this.globalStepSizeAdaption = globalstepsizeadaption;
if (globalstepsizeadaption && localStepSizeAdaption) { if (globalstepsizeadaption && localStepSizeAdaption) {
setAdaptStepSizeLocally(false); setAdaptStepSizeLocally(false);
} }
} }
public String adaptStepSizeGloballyTipText() { public String adaptStepSizeGloballyTipText() {
return "Use a single step size per individual - (priority over local step size)."; 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() { public double getGlobalMaxStepSize() {
return globalmaxstepsize; return globalmaxstepsize;
} }
public void setGlobalMaxStepSize(double p) { public void setGlobalMaxStepSize(double p) {
globalmaxstepsize = p; globalmaxstepsize = p;
} }
public String globalMaxStepSizeTipText() { public String globalMaxStepSizeTipText() {
return "Maximum step size for global adaption."; return "Maximum step size for global adaption.";
} }
@@ -432,9 +433,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getGlobalMinStepSize() { public double getGlobalMinStepSize() {
return globalminstepsize; return globalminstepsize;
} }
public void setGlobalMinStepSize(double p) { public void setGlobalMinStepSize(double p) {
globalminstepsize = p; globalminstepsize = p;
} }
public String globalMindStepSizeTipText() { public String globalMindStepSizeTipText() {
return "Minimum step size for global adaption."; return "Minimum step size for global adaption.";
} }
@@ -442,9 +445,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getGlobalInitStepSize() { public double getGlobalInitStepSize() {
return globalinitstepsize; return globalinitstepsize;
} }
public void setGlobalInitStepSize(double initstepsize) { public void setGlobalInitStepSize(double initstepsize) {
this.globalinitstepsize = initstepsize; this.globalinitstepsize = initstepsize;
} }
public String globalInitStepSizeTipText() { public String globalInitStepSizeTipText() {
return "Initial step size for global adaption."; return "Initial step size for global adaption.";
} }
@@ -453,12 +458,14 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public boolean isAdaptStepSizeLocally() { public boolean isAdaptStepSizeLocally() {
return localStepSizeAdaption; return localStepSizeAdaption;
} }
public void setAdaptStepSizeLocally(boolean stepsizeadaption) { public void setAdaptStepSizeLocally(boolean stepsizeadaption) {
this.localStepSizeAdaption = stepsizeadaption; this.localStepSizeAdaption = stepsizeadaption;
if (globalStepSizeAdaption && localStepSizeAdaption) { if (globalStepSizeAdaption && localStepSizeAdaption) {
setAdaptStepSizeGlobally(false); setAdaptStepSizeGlobally(false);
} }
} }
public String adaptStepSizeLocallyTipText() { public String adaptStepSizeLocallyTipText() {
return "Use a step size parameter in any dimension."; return "Use a step size parameter in any dimension.";
} }
@@ -466,6 +473,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getLocalMinStepSize() { public double getLocalMinStepSize() {
return localminstepsize; return localminstepsize;
} }
public void setLocalMinStepSize(double localminstepsize) { public void setLocalMinStepSize(double localminstepsize) {
this.localminstepsize = localminstepsize; this.localminstepsize = localminstepsize;
} }
@@ -473,6 +481,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getLocalMaxStepSize() { public double getLocalMaxStepSize() {
return localmaxstepsize; return localmaxstepsize;
} }
public void setLocalMaxStepSize(double localmaxstepsize) { public void setLocalMaxStepSize(double localmaxstepsize) {
this.localmaxstepsize = localmaxstepsize; this.localmaxstepsize = localmaxstepsize;
} }
@@ -480,9 +489,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public void setStepSizeIncreaseFact(double nplus) { public void setStepSizeIncreaseFact(double nplus) {
this.wIncreaseStepSize = nplus; this.wIncreaseStepSize = nplus;
} }
public double getStepSizeIncreaseFact() { public double getStepSizeIncreaseFact() {
return wIncreaseStepSize; return wIncreaseStepSize;
} }
public String stepSizeIncreaseFactTipText() { public String stepSizeIncreaseFactTipText() {
return "Factor for increasing the step size in adaption."; 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) { public void setStepSizeDecreaseFact(double nminus) {
this.wDecreaseStepSize = nminus; this.wDecreaseStepSize = nminus;
} }
public double getStepSizeDecreaseFact() { public double getStepSizeDecreaseFact() {
return wDecreaseStepSize; return wDecreaseStepSize;
} }
public String stepSizeDecreaseFactTipText() { public String stepSizeDecreaseFactTipText() {
return "Factor for decreasing the step size in adaption."; return "Factor for decreasing the step size in adaption.";
} }
@@ -501,40 +514,47 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public boolean isRecovery() { public boolean isRecovery() {
return recovery; return recovery;
} }
public void setRecovery(boolean recovery) { public void setRecovery(boolean recovery) {
this.recovery = recovery; this.recovery = recovery;
} }
public int getRecoveryLocksteps() { public int getRecoveryLocksteps() {
return recoverylocksteps; return recoverylocksteps;
} }
public void setRecoveryLocksteps(int locksteps) { public void setRecoveryLocksteps(int locksteps) {
this.recoverylocksteps = locksteps; this.recoverylocksteps = locksteps;
} }
public double getRecoveryThreshold() { public double getRecoveryThreshold() {
return recoverythreshold; return recoverythreshold;
} }
public void setRecoveryThreshold(double recoverythreshold) { public void setRecoveryThreshold(double recoverythreshold) {
this.recoverythreshold = recoverythreshold; this.recoverythreshold = recoverythreshold;
} }
public String recoveryThresholdTipText() { public String recoveryThresholdTipText() {
return "If the fitness exceeds this threshold, an unstable area is assumed and one step recovered."; return "If the fitness exceeds this threshold, an unstable area is assumed and one step recovered.";
} }
public int getIterations() { public int getIterations() {
return iterations; return iterations;
} }
public void setIterations(int iterations) { public void setIterations(int iterations) {
this.iterations = iterations; this.iterations = iterations;
} }
public String iterationsTipText() { public String iterationsTipText() {
return "The number of GD-iterations per generation."; return "The number of GD-iterations per generation.";
} }
public boolean isManhattan() { public boolean isManhattan() {
return manhattan; return manhattan;
} }
public void setManhattan(boolean manhattan) { public void setManhattan(boolean manhattan) {
this.manhattan = manhattan; this.manhattan = manhattan;
} }
@@ -542,6 +562,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public boolean isMomentumTerm() { public boolean isMomentumTerm() {
return momentumterm; return momentumterm;
} }
public void setMomentumTerm(boolean momentum) { public void setMomentumTerm(boolean momentum) {
this.momentumterm = momentum; this.momentumterm = momentum;
} }
@@ -549,6 +570,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getMomentumWeigth() { public double getMomentumWeigth() {
return momentumweigth; return momentumweigth;
} }
public void setMomentumWeigth(double momentumweigth) { public void setMomentumWeigth(double momentumweigth) {
this.momentumweigth = momentumweigth; this.momentumweigth = momentumweigth;
} }
@@ -556,11 +578,12 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getMaximumAbsoluteChange() { public double getMaximumAbsoluteChange() {
return maximumabsolutechange; return maximumabsolutechange;
} }
public void setMaximumAbsoluteChange(double maximumabsolutechange) { public void setMaximumAbsoluteChange(double maximumabsolutechange) {
this.maximumabsolutechange = maximumabsolutechange; this.maximumabsolutechange = maximumabsolutechange;
} }
public String maximumAbsoluteChangeTipText() { public String maximumAbsoluteChangeTipText() {
return "The maximum change along a coordinate in one step."; return "The maximum change along a coordinate in one step.";
} }
} }

87
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.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem; import eva2.server.go.problems.InterfaceOptimizationProblem;
/**
/** This is a Multi-Start Hill-Climber, here the population size gives the number of * This is a Multi-Start Hill-Climber, here the population size gives the number
* multi-starts. Similar to the evolutionary programming strategy this strategy sets the * of multi-starts. Similar to the evolutionary programming strategy this
* mutation rate temporarily to 1.0. * strategy sets the mutation rate temporarily to 1.0. Copyright: Copyright (c)
* Copyright: Copyright (c) 2003 * 2003 Company: University of Tuebingen, Computer Architecture
* Company: University of Tuebingen, Computer Architecture *
* @author Felix Streichert * @author Felix Streichert
* @version: $Revision: 307 $ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $ * 2007) $ $Author: mkron $
* $Author: mkron $
*/ */
public class HillClimbing implements InterfaceOptimizer, java.io.Serializable { public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase // These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem(); private InterfaceOptimizationProblem m_Problem = new B1Problem();
private InterfaceMutation mutator = null; private InterfaceMutation mutator = null;
// private int m_MultiRuns = 100; // private int m_MultiRuns = 100;
// private int m_FitnessCalls = 100; // private int m_FitnessCalls = 100;
// private int m_FitnessCallsNeeded = 0; // private int m_FitnessCallsNeeded = 0;
// GAIndividualBinaryData m_Best, m_Test; // GAIndividualBinaryData m_Best, m_Test;
// These variables are necessary for the more complex LectureGUI enviroment // These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = ""; transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
@@ -50,7 +48,8 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
return (Object) new HillClimbing(this); return (Object) new HillClimbing(this);
} }
/** This method will init the HillClimber /**
* This method will init the HillClimber
*/ */
@Override @Override
public void init() { public void init() {
@@ -69,7 +68,8 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
} }
} }
/** This method will optimize /**
* This method will optimize
*/ */
@Override @Override
public void optimize() { public void optimize() {
@@ -84,8 +84,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
indy.setMutationProbability(1.0); indy.setMutationProbability(1.0);
if (mutator == null) { if (mutator == null) {
indy.mutate(); indy.mutate();
} } else {
else {
mutator.mutate(indy); mutator.mutate(indy);
} }
indy.setMutationProbability(tmpD); indy.setMutationProbability(tmpD);
@@ -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. * Allows to set a desired mutator by hand, which is used instead of the one
* Set it to null to use the one in the individuals, which is the default. * in the individuals. Set it to null to use the one in the individuals,
* which is the default.
* *
* @param mute * @param mute
*/ */
@@ -132,13 +132,16 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
mutator = mute; 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 * @param problem
*/ */
@Override @Override
public void setProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; 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; // if (this.m_Best.defaultEvaulateAsMiniBits() == 0) i = this.m_FitnessCalls +1;
// } // }
// } // }
// /** This main method will start a simple hillclimber. // /** This main method will start a simple hillclimber.
// * No arguments necessary. // * No arguments necessary.
// * @param args // * @param args
@@ -181,14 +183,16 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
// TmpMeanFitness = TmpMeanFitness/program.m_MultiRuns; // TmpMeanFitness = 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 * @param ea
*/ */
@Override @Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -199,7 +203,9 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
return false; return false;
} }
} }
/** Something has changed
/**
* Something has changed
*/ */
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
@@ -207,8 +213,10 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
} }
} }
/** 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 * @return A descriptive string
*/ */
@Override @Override
@@ -216,8 +224,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
String result = ""; String result = "";
if (this.m_Population.size() > 1) { if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n"; result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
} } else {
else {
result += "Hill Climbing:\n"; result += "Hill Climbing:\n";
} }
result += "Optimization Problem: "; result += "Optimization Problem: ";
@@ -225,45 +232,50 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
result += this.m_Population.getStringRepresentation(); result += this.m_Population.getStringRepresentation();
return result; 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { 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."; 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 * @return The name of the algorithm
*/ */
@Override @Override
public String getName() { public String getName() {
return "MS-HC" + getIdentifier(); return "MS-HC" + getIdentifier();
} }
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
@@ -273,6 +285,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
public InterfaceSolutionSet getAllSolutions() { public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation()); return new SolutionSet(getPopulation());
} }
public String populationTipText() { public String populationTipText() {
return "Change the number of best individuals stored (MS-HC)."; 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 /** This method will return deep clone of the optimizer
* @return The clone * @return The clone
*/ */
public Object clone(); Object clone();
/** This method will return a naming String /** This method will return a naming String
* @return The name of the algorithm * @return The name of the algorithm
*/ */
public String getName(); String getName();
/** /**
* This method allows you to add a listener to the Optimizer. * This method allows you to add a listener to the Optimizer.
* @param ea * @param ea
*/ */
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea); void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea);
/** /**
* This method removes a listener from the Optimizer. It returns true on success, * This method removes a listener from the Optimizer. It returns true on success,
* false if the listener could not be found. * false if the listener could not be found.
* @param ea * @param ea
*/ */
public boolean removePopulationChangedEventListener(InterfacePopulationChangedEventListener ea); boolean removePopulationChangedEventListener(InterfacePopulationChangedEventListener ea);
/** This method will init the optimizer /** This method will init the optimizer
*/ */
public void init(); void init();
/** /**
* This method will init the optimizer with a given population. * This method will init the optimizer with a given population.
@@ -52,22 +52,22 @@ public interface InterfaceOptimizer {
* @param pop The initial population * @param pop The initial population
* @param reset If true the population is reinitialized and reevaluated. * @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 /** This method will optimize for a single iteration, after this step
* the population should be as big as possible (ie. the size of lambda * 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 * and not mu) and all individual should be evaluated. This allows more
* usefull statistics on the population. * usefull statistics on the population.
*/ */
public void optimize(); void optimize();
/** Assuming that all optimizer will store their data in a population /** Assuming that all optimizer will store their data in a population
* we will allow access to this population to query to current state * we will allow access to this population to query to current state
* of the optimizer. * of the optimizer.
* @return The population of current solutions to a given problem. * @return The population of current solutions to a given problem.
*/ */
public Population getPopulation(); Population getPopulation();
public void setPopulation(Population pop); void setPopulation(Population pop);
/** /**
* Return all found solutions (local optima) if they are not contained in the current population. Be * 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. * @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 * This method allows you to set an identifier for the algorithm
* @param name The identifier * @param name The identifier
*/ */
public void setIdentifier(String name); void setIdentifier(String name);
public String getIdentifier(); String getIdentifier();
/** /**
* This method will set the problem that is to be optimized. The problem * This method will set the problem that is to be optimized. The problem
@@ -93,17 +93,12 @@ public interface InterfaceOptimizer {
* *
* @param problem * @param problem
*/ */
public void setProblem (InterfaceOptimizationProblem problem); void setProblem (InterfaceOptimizationProblem problem);
public InterfaceOptimizationProblem getProblem (); InterfaceOptimizationProblem getProblem ();
/** This method will return a string describing all properties of the optimizer /** This method will return a string describing all properties of the optimizer
* and the applied methods. * and the applied methods.
* @return A descriptive string * @return A descriptive string
*/ */
public String getStringRepresentation(); String getStringRepresentation();
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
public void freeWilly();
} }

168
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.InterfaceOptimizationProblem;
import eva2.server.go.problems.TF1Problem; 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 * The one and only island model for parallelization. Since parallelization
* evaluation and on the other hand the GUI based distribution scheme is rather prone * based on the RMIProxyRemoteThread is on the one hand much slower than
* to config errors (the correct ssh version is required, the jar needs to be in * benchmark function evaluation and on the other hand the GUI based
* the working dir and possible problem data must be on the servers to) an implicit * distribution scheme is rather prone to config errors (the correct ssh version
* island-model has been implemented too to allow fast and reliable computation. * is required, the jar needs to be in the working dir and possible problem data
* This is still usefull, since it is less prone to premature convergence and also * must be on the servers to) an implicit island-model has been implemented too
* an heterogenuous island model can be used. * 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 * A population of the same size is sent to all nodes and evaluated there
* for a cycle (more precisely: for MigrationRate generations) after which a communication * independently for a cycle (more precisely: for MigrationRate generations)
* step is performed according to the migration model. Only after migration is a main * after which a communication step is performed according to the migration
* cycle complete, the statistics updated etc. * model. Only after migration is a main cycle complete, the statistics updated
* etc.
* *
* Created by IntelliJ IDEA. * Created by IntelliJ IDEA. User: streiche Date: 12.09.2004 Time: 14:48:20 To
* User: streiche * change this template use File | Settings | File Templates.
* 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 { public class IslandModelEA implements InterfacePopulationChangedEventListener, InterfaceOptimizer, java.io.Serializable {
private Population m_Population = new Population(); private Population m_Population = new Population();
@@ -47,22 +46,18 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
// private String[] m_NodesList; // private String[] m_NodesList;
private int m_MigrationRate = 10; private int m_MigrationRate = 10;
private boolean m_HeterogenuousProblems = false; private boolean m_HeterogenuousProblems = false;
// These are the processor to run on // These are the processor to run on
private int m_numLocalCPUs = 1; private int m_numLocalCPUs = 1;
private boolean m_localOnly = false; private boolean m_localOnly = false;
transient private InterfaceOptimizer[] m_Islands; transient private InterfaceOptimizer[] m_Islands;
// This is for debugging // This is for debugging
private boolean m_LogLocalChanges = true; private boolean m_LogLocalChanges = true;
private boolean m_Show = false; private boolean m_Show = false;
transient private Plot m_Plot = null; transient private Plot m_Plot = null;
transient private String m_Identifier = ""; transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
transient private final boolean TRACE = false; transient private final boolean TRACE = false;
public IslandModelEA() { public IslandModelEA() {
} }
@@ -152,7 +147,9 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.firePropertyChangedEvent(Population.nextGenerationPerformed, this.m_Optimizer.getPopulation()); 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. * @param reset If true the population is reset.
*/ */
@Override @Override
@@ -223,7 +220,8 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.firePropertyChangedEvent(Population.nextGenerationPerformed, this.m_Optimizer.getPopulation()); 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 @Override
public void optimize() { public void optimize() {
@@ -253,8 +251,8 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
System.gc(); System.gc();
} }
/** This method will manage communication between the /**
* islands * This method will manage communication between the islands
*/ */
private void communicate() { private void communicate() {
// Here i'll have to wait until all islands are finished // Here i'll have to wait until all islands are finished
@@ -296,13 +294,16 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Migration.migrate(this.m_Islands); 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 * @param ea
*/ */
@Override @Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -313,7 +314,9 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
return false; 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) { if (this.m_Listener != null) {
@@ -321,7 +324,9 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
} }
} }
/** This method will set the problem that is to be optimized /**
* This method will set the problem that is to be optimized
*
* @param problem * @param problem
*/ */
@Override @Override
@@ -329,13 +334,16 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Problem = problem; this.m_Problem = problem;
this.m_Optimizer.setProblem(problem); this.m_Optimizer.setProblem(problem);
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; 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 * @return A descriptive string
*/ */
@Override @Override
@@ -362,7 +370,8 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
return result; return result;
} }
/** This method is to test the parallelization scheme /**
* This method is to test the parallelization scheme
* *
* @param args * @param args
*/ */
@@ -410,47 +419,48 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
//System.exit(0); //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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; return this.m_Identifier;
} }
/** This method will return the Optimizers /**
* This method will return the Optimizers
*
* @return An array of optimizers * @return An array of optimizers
*/ */
public InterfaceOptimizer[] getOptimizers() { public InterfaceOptimizer[] getOptimizers() {
return this.m_Islands; 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 * @param t
*/ */
public void setHeterogenuousProblems(boolean t) { public void setHeterogenuousProblems(boolean t) {
this.m_HeterogenuousProblems = 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 * 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 source The source of the event.
* @param name Could be used to indicate the nature of the event. * @param name Could be used to indicate the nature of the event.
*/ */
@@ -469,16 +479,22 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
//System.out.println(sourceID + " is at generation "+ opt.getPopulation().getGeneration() +" i'm at " +this.m_Generation); //System.out.println(sourceID + " is at generation "+ opt.getPopulation().getGeneration() +" i'm at " +this.m_Generation);
} }
/********************************************************************************************************************** /**
* ********************************************************************************************************************
* These are for GUI * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { public static String globalInfo() {
return "This is an island model EA distributing the individuals across several (remote) CPUs for optimization."; 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 * @return The name of the algorithm
*/ */
@Override @Override
@@ -486,8 +502,10 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
return "IslandEA"; 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 * @return The current optimization mode
*/ */
// TODO Deactivated from GUI because the current implementation does not really parallelize on a multicore. // TODO Deactivated from GUI because the current implementation does not really parallelize on a multicore.
@@ -498,59 +516,76 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
public void setLocalOnly(boolean b) { public void setLocalOnly(boolean b) {
this.m_localOnly = b; this.m_localOnly = b;
} }
public String localOnlyTipText() { public String localOnlyTipText() {
return "Toggle between usage of local CPUs and remote servers."; 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 * @return The current optimzation mode
*/ */
public boolean getShow() { public boolean getShow() {
return this.m_Show; return this.m_Show;
} }
public void setShow(boolean b) { public void setShow(boolean b) {
this.m_Show = b; this.m_Show = b;
this.m_LogLocalChanges = b; this.m_LogLocalChanges = b;
} }
public String showTipText() { public String showTipText() {
return "This will show the local performance."; 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 * @return The current optimizing method
*/ */
public InterfaceOptimizer getOptimizer() { public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer; return this.m_Optimizer;
} }
public void setOptimizer(InterfaceOptimizer b) { public void setOptimizer(InterfaceOptimizer b) {
this.m_Optimizer = b; this.m_Optimizer = b;
} }
public String optimizerTipText() { public String optimizerTipText() {
return "Choose a population based optimizing technique to use."; 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 * @return The current migration strategy
*/ */
public InterfaceMigration getMigrationStrategy() { public InterfaceMigration getMigrationStrategy() {
return this.m_Migration; return this.m_Migration;
} }
public void setMigrationStrategy(InterfaceMigration b) { public void setMigrationStrategy(InterfaceMigration b) {
this.m_Migration = b; this.m_Migration = b;
} }
public String migrationStrategyTipText() { public String migrationStrategyTipText() {
return "Choose a migration strategy to use."; 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 * @return The current migration rate
*/ */
public int getMigrationRate() { public int getMigrationRate() {
return this.m_MigrationRate; return this.m_MigrationRate;
} }
public void setMigrationRate(int b) { public void setMigrationRate(int b) {
this.m_MigrationRate = b; this.m_MigrationRate = b;
} }
public String migrationRateTipText() { public String migrationRateTipText() {
return "Set the migration rate for communication between islands."; 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)."; 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 * Assuming that all optimizer will store thier data in a population we will
* 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. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
// @todo Jetzt m<>sste ich die pop auch auf die Rechner verteilen... // @todo Jetzt m<>sste ich die pop auch auf die Rechner verteilen...
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "(Defunct)"; return "(Defunct)";
} }
@@ -581,14 +620,16 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
public InterfaceSolutionSet getAllSolutions() { public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation()); 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. * @param n Number of processors.
*/ */
public void setNumberLocalCPUs(int n) { public void setNumberLocalCPUs(int n) {
if (n >= 1) { if (n >= 1) {
this.m_numLocalCPUs = n; this.m_numLocalCPUs = n;
} } else {
else {
System.err.println("Number of CPUs must be at least 1!"); System.err.println("Number of CPUs must be at least 1!");
} }
} }
@@ -597,6 +638,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
// public int getNumberLocalCPUs() { // public int getNumberLocalCPUs() {
// return this.m_LocalCPUs; // return this.m_LocalCPUs;
// } // }
public String numberLocalCPUsTipText() { public String numberLocalCPUsTipText() {
return "Set the number of local CPUS (>=1, only used in local mode)."; 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"; return "Linkage Tree GA";
} }
@Override
public void freeWilly() {
}
@Override @Override
public void registerPopulationStateChanged(Object source, String name) { public void registerPopulationStateChanged(Object source, String name) {
// The events of the interim hill climbing population will be caught here // The events of the interim hill climbing population will be caught here

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

@@ -343,10 +343,6 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac
return "Linkage Tree GA"; return "Linkage Tree GA";
} }
@Override
public void freeWilly() {
}
@Override @Override
public void registerPopulationStateChanged(Object source, String name) { public void registerPopulationStateChanged(Object source, String name) {
// The events of the interim hill climbing population will be caught here // The events of the interim hill climbing population will be caught here

71
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 eva2.server.go.problems.InterfaceOptimizationProblem;
import java.util.Hashtable; import java.util.Hashtable;
/** /**
* A memetic algorithm by hannes planatscher. The local search strategy can only * A memetic algorithm by hannes planatscher. The local search strategy can only
* be applied to problems which implement the InterfaceLocalSearchable else the * be applied to problems which implement the InterfaceLocalSearchable else the
* local search will not be activated at all. * local search will not be activated at all. <p> Title: EvA2 </p> <p>
* <p> * Description: </p> <p> Copyright: Copyright (c) 2003 </p> <p> Company: </p>
* Title: EvA2
* </p>
* <p>
* Description:
* </p>
* <p>
* Copyright: Copyright (c) 2003
* </p>
* <p>
* Company:
* </p>
* *
* @author not attributable * @author not attributable
* @version 1.0 * @version 1.0
*/ */
public class MemeticAlgorithm implements InterfaceOptimizer, public class MemeticAlgorithm implements InterfaceOptimizer,
java.io.Serializable { java.io.Serializable {
@@ -41,32 +28,20 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
* serial version uid. * serial version uid.
*/ */
private static final long serialVersionUID = -1730086430763348568L; private static final long serialVersionUID = -1730086430763348568L;
private int localSearchSteps = 1; private int localSearchSteps = 1;
private int subsetsize = 5; private int subsetsize = 5;
private int globalSearchIterations = 1; private int globalSearchIterations = 1;
private boolean lamarckism = true; private boolean lamarckism = true;
// int counter = 0; !? // int counter = 0; !?
// int maxfunctioncalls = 1000; !? // int maxfunctioncalls = 1000; !?
private boolean TRACE = false; private boolean TRACE = false;
private String m_Identifier = ""; private String m_Identifier = "";
private InterfaceOptimizationProblem m_Problem = new F1Problem(); private InterfaceOptimizationProblem m_Problem = new F1Problem();
private InterfaceOptimizer m_GlobalOptimizer = new GeneticAlgorithm(); private InterfaceOptimizer m_GlobalOptimizer = new GeneticAlgorithm();
private InterfaceSelection selectorPlug = new SelectBestIndividuals(); private InterfaceSelection selectorPlug = new SelectBestIndividuals();
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
public MemeticAlgorithm() { public MemeticAlgorithm() {
} }
public MemeticAlgorithm(MemeticAlgorithm a) { public MemeticAlgorithm(MemeticAlgorithm a) {
@@ -108,8 +83,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
/** /**
* This method will evaluate the current population using the given problem. * This method will evaluate the current population using the given problem.
* *
* @param population * @param population The population that is to be evaluated
* The population that is to be evaluated
*/ */
private void evaluatePopulation(Population population) { private void evaluatePopulation(Population population) {
this.m_Problem.evaluate(population); this.m_Problem.evaluate(population);
@@ -212,6 +186,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -222,6 +197,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
return false; return false;
} }
} }
/** /**
* Something has changed * Something has changed
*/ */
@@ -251,8 +227,8 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
} }
/** /**
* This method will return a string describing all properties of the optimizer * This method will return a string describing all properties of the
* and the applied methods. * optimizer and the applied methods.
* *
* @return A descriptive string * @return A descriptive string
*/ */
@@ -269,8 +245,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
/** /**
* This method allows you to set an identifier for the algorithm * This method allows you to set an identifier for the algorithm
* *
* @param name * @param name The indenifier
* The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
@@ -282,14 +257,6 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
return this.m_Identifier; 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 * @return description
*/ */
public static String globalInfo() { public static String globalInfo() {
return "This is a basic generational Memetic Algorithm. Local search steps are performed on a selected subset " + 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 " + + "of individuals after certain numbers of global search iterations. Note "
"that the problem class must implement InterfaceLocalSearchable."; + "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 * 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. * @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 * @param localSearchSteps
*/ */
public void setLocalSearchSteps(int localSearchSteps) { public void setLocalSearchSteps(int localSearchSteps) {
this.localSearchSteps = localSearchSteps; this.localSearchSteps = localSearchSteps;
} }
public int getLocalSearchSteps() { public int getLocalSearchSteps() {
return localSearchSteps; return localSearchSteps;
} }
public String localSearchStepsTipText() { public String localSearchStepsTipText() {
return "Choose the number of local search steps to perform per selected individual."; 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) { public void setGlobalSearchIterations(int globalSearchSteps) {
this.globalSearchIterations = globalSearchSteps; this.globalSearchIterations = globalSearchSteps;
} }
public int getGlobalSearchIterations() { public int getGlobalSearchIterations() {
return globalSearchIterations; return globalSearchIterations;
} }
public String globalSearchIterationsTipText() { public String globalSearchIterationsTipText() {
return "Choose the interval between the application of the local search."; 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) { public void setSubsetsize(int subsetsize) {
this.subsetsize = subsetsize; this.subsetsize = subsetsize;
} }
public int getSubsetsize() { public int getSubsetsize() {
return subsetsize; return subsetsize;
} }
public String subsetsizeTipText() { public String subsetsizeTipText() {
return "Choose the number of individuals to be locally optimized."; return "Choose the number of individuals to be locally optimized.";
} }
@@ -413,9 +388,11 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
public void setLamarckism(boolean lamarckism) { public void setLamarckism(boolean lamarckism) {
this.lamarckism = lamarckism; this.lamarckism = lamarckism;
} }
public String lamarckismTipText() { public String lamarckismTipText() {
return "Toggle between Lamarckism and the Baldwin Effect."; return "Toggle between Lamarckism and the Baldwin Effect.";
} }
public boolean isLamarckism() { public boolean isLamarckism() {
return lamarckism; return lamarckism;
} }
@@ -423,9 +400,11 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
public InterfaceSelection getSubSetSelector() { public InterfaceSelection getSubSetSelector() {
return selectorPlug; return selectorPlug;
} }
public void setSubSetSelector(InterfaceSelection selectorPlug) { public void setSubSetSelector(InterfaceSelection selectorPlug) {
this.selectorPlug = selectorPlug; this.selectorPlug = selectorPlug;
} }
public String subSetSelectorTipText() { public String subSetSelectorTipText() {
return "Selection method to select the subset on which local search is to be performed."; return "Selection method to select the subset on which local search is to be performed.";
} }

100
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; import eva2.server.go.problems.InterfaceOptimizationProblem;
/** /**
* The simple random or Monte-Carlo search, simple but useful * The simple random or Monte-Carlo search, simple but useful to evaluate the
* to evaluate the complexity of the search space. * complexity of the search space. This implements a Random Walk Search using
* This implements a Random Walk Search using the initialization * the initialization method of the problem instance, meaning that the random
* method of the problem instance, meaning that the random characteristics * characteristics may be problem dependent.
* 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 * @author Felix Streichert
* @version: $Revision: 307 $ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $ * 2007) $ $Author: mkron $
* $Author: mkron $
*/ */
public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializable { public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializable {
/** /**
* Generated serial version id. * Generated serial version id.
*/ */
@@ -35,7 +35,6 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
private int m_FitnessCallsNeeded = 0; private int m_FitnessCallsNeeded = 0;
private Population m_Population; private Population m_Population;
private GAIndividualBinaryData m_Best, m_Test; private GAIndividualBinaryData m_Best, m_Test;
// These variables are necessary for the more complex LectureGUI enviroment // These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = ""; transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
@@ -55,7 +54,8 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
return (Object) new MonteCarloSearch(this); return (Object) new MonteCarloSearch(this);
} }
/** This method will init the MonteCarloSearch /**
* This method will init the MonteCarloSearch
*/ */
@Override @Override
public void init() { public void init() {
@@ -64,7 +64,9 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 pop The initial population
* @param reset If true the population is reset. * @param reset If true the population is reset.
*/ */
@@ -79,8 +81,8 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
} }
/** /**
* This method will optimize without specific operators, by just calling the individual method * This method will optimize without specific operators, by just calling the
* for initialization. * individual method for initialization.
*/ */
@Override @Override
public void optimize() { public void optimize() {
@@ -103,20 +105,23 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 * @param problem
*/ */
@Override @Override
public void setProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; return this.m_Problem;
} }
/** This method will init the HillClimber /**
* This method will init the HillClimber
*/ */
public void defaultInit() { public void defaultInit() {
this.m_FitnessCallsNeeded = 0; this.m_FitnessCallsNeeded = 0;
@@ -124,7 +129,8 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
this.m_Best.defaultInit(m_Problem); this.m_Best.defaultInit(m_Problem);
} }
/** This method will optimize /**
* This method will optimize
*/ */
public void defaultOptimize() { public void defaultOptimize() {
for (int i = 0; i < m_FitnessCalls; i++) { for (int i = 0; i < m_FitnessCalls; i++) {
@@ -140,8 +146,9 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
} }
} }
/** This main method will start a simple hillclimber. /**
* No arguments necessary. * This main method will start a simple hillclimber. No arguments necessary.
*
* @param args * @param args
*/ */
public static void main(String[] args) { public static void main(String[] args) {
@@ -158,13 +165,16 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
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 * @param ea
*/ */
@Override @Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -175,7 +185,9 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
return false; return false;
} }
} }
/** Something has changed
/**
* Something has changed
*/ */
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
@@ -183,8 +195,10 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
} }
} }
/** 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 * @return A descriptive string
*/ */
@Override @Override
@@ -196,54 +210,62 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
result += this.m_Population.getStringRepresentation(); result += this.m_Population.getStringRepresentation();
return result; 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { public static String globalInfo() {
return "The Monte Carlo Search repeatively creates random individuals and stores the best individuals found."; 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 * @return The name of the algorithm
*/ */
@Override @Override
public String getName() { public String getName() {
return "MCS"; 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. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "Change the number of best individuals stored."; 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 { class CounterClass {
public CounterClass(int i) { public CounterClass(int i) {
value = i; value = i;
} }
public int value; public int value;
public boolean seen = false; public boolean seen = false;
} }
private String m_Identifier = "MOCMAES"; private String m_Identifier = "MOCMAES";
private Population m_Population; private Population m_Population;
private AbstractOptimizationProblem m_Problem; private AbstractOptimizationProblem m_Problem;
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
private int m_lambda = 1; private int m_lambda = 1;
private int m_lambdamo = 1; private int m_lambdamo = 1;
@@ -105,15 +101,6 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable {
this.m_Listener = ea; this.m_Listener = ea;
} }
/*
* (non-Javadoc)
*
* @see eva2.server.go.strategies.InterfaceOptimizer#freeWilly()
*/
@Override
public void freeWilly() {
}
/* /*
* (non-Javadoc) * (non-Javadoc)
* *
@@ -221,8 +208,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, 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 * @param population The population that is to be evaluated
* The population that is to be evaluated
*/ */
private void evaluatePopulation(Population population) { private void evaluatePopulation(Population population) {
this.m_Problem.evaluate(population); this.m_Problem.evaluate(population);
@@ -417,5 +403,4 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable {
* *
* public void setLambdaMo(int mLambda) { m_lambdamo = mLambda; } * public void setLambdaMo(int mLambda) { m_lambdamo = mLambda; }
*/ */
} }

112
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; import eva2.server.go.problems.InterfaceOptimizationProblem;
/** /**
* A generic framework for multi-objecitve optimization, you need * A generic framework for multi-objecitve optimization, you need to specify an
* to specify an optimization strategy (like GA), an archiver and * optimization strategy (like GA), an archiver and an information retrival
* an information retrival strategy. With this scheme you can realized: * strategy. With this scheme you can realized: Vector Evaluated GA Random
* Vector Evaluated GA * Weight GA Multiple Objective GA NSGA NSGA-II SPEA SPEA 2 PESA PESA-II In case
* Random Weight GA * you address a multi-objective optimization problem with a single- objective
* Multiple Objective GA * optimizer instead of this MOEA, such an optimizer would randomly toggle
* NSGA * between the objective for each selection and thus explore at least the
* NSGA-II * extreme points of the objective space, but simpler methods like random search
* SPEA * or hill-climbing might even fail on that. Created by IntelliJ IDEA. User:
* SPEA 2 * streiche Date: 05.06.2003 Time: 11:03:50 To change this template use Options
* PESA * | File Templates.
* 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 { public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable {
@@ -82,8 +71,9 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 pop The initial population
* @param reset If true the population is reset. * @param reset If true the population is reset.
*/ */
@@ -94,7 +84,8 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 @Override
public void optimize() { public void optimize() {
@@ -186,7 +177,9 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
} }
} }
/** This method will set the problem that is to be optimized /**
* This method will set the problem that is to be optimized
*
* @param problem * @param problem
*/ */
@Override @Override
@@ -194,13 +187,16 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
this.m_Problem = problem; this.m_Problem = problem;
this.m_Optimizer.setProblem(problem); this.m_Optimizer.setProblem(problem);
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; 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 * @return A descriptive string
*/ */
@Override @Override
@@ -219,35 +215,37 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
return result; 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { public static String globalInfo() {
return "This is a general Multi-objective Evolutionary Optimization Framework."; 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 * @return The name of the algorithm
*/ */
@Override @Override
@@ -255,19 +253,23 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
return "MOEA"; 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 * Assuming that all optimizer will store thier data in a population we will
* 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. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Optimizer.getPopulation(); return this.m_Optimizer.getPopulation();
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Optimizer.setPopulation(pop); this.m_Optimizer.setPopulation(pop);
} }
public String populationTipText() { public String populationTipText() {
return "Edit the properties of the Population used."; 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))); 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 * @return The current optimizing method
*/ */
public InterfaceOptimizer getOptimizer() { public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer; return this.m_Optimizer;
} }
public void setOptimizer(InterfaceOptimizer b) { public void setOptimizer(InterfaceOptimizer b) {
this.m_Optimizer = b; this.m_Optimizer = b;
} }
public String optimizerTipText() { public String optimizerTipText() {
return "Choose a population based optimizing technique to use."; 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 * @return The current optimizing method
*/ */
public InterfaceArchiving getArchivingStrategy() { public InterfaceArchiving getArchivingStrategy() {
return this.m_Archiver; return this.m_Archiver;
} }
public void setArchivingStrategy(InterfaceArchiving b) { public void setArchivingStrategy(InterfaceArchiving b) {
this.m_Archiver = b; this.m_Archiver = b;
} }
public String archivingStrategyTipText() { public String archivingStrategyTipText() {
return "Choose the archiving strategy."; 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 * @return The current optimizing method
*/ */
public InterfaceInformationRetrieval getInformationRetrieval() { public InterfaceInformationRetrieval getInformationRetrieval() {
return this.m_InformationRetrieval; return this.m_InformationRetrieval;
} }
public void setInformationRetrieval(InterfaceInformationRetrieval b) { public void setInformationRetrieval(InterfaceInformationRetrieval b) {
this.m_InformationRetrieval = b; this.m_InformationRetrieval = b;
} }
public String informationRetrievalTipText() { public String informationRetrievalTipText() {
return "Choose the Information Retrieval strategy."; 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 * @return The current optimizing method
*/ */
public int getArchiveSize() { public int getArchiveSize() {
@@ -328,6 +344,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
} }
return archive.getTargetSize(); return archive.getTargetSize();
} }
public void setArchiveSize(int b) { public void setArchiveSize(int b) {
Population archive = this.m_Optimizer.getPopulation().getArchive(); Population archive = this.m_Optimizer.getPopulation().getArchive();
if (archive == null) { if (archive == null) {
@@ -336,6 +353,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
} }
archive.setTargetSize(b); archive.setTargetSize(b);
} }
public String archiveSizeTipText() { public String archiveSizeTipText() {
return "Choose the size of the archive."; return "Choose the size of the archive.";
} }

44
src/eva2/server/go/strategies/NelderMeadSimplex.java
View File

@@ -15,9 +15,9 @@ import java.io.Serializable;
import java.util.Vector; import java.util.Vector;
/** /**
* Nelder-Mead-Simplex does not guarantee an equal number of evaluations within each optimize call * Nelder-Mead-Simplex does not guarantee an equal number of evaluations within
* because of the different step types. * each optimize call because of the different step types. Range check is now
* Range check is now available by projection at the bounds. * available by projection at the bounds.
* *
* @author mkron * @author mkron
* *
@@ -27,9 +27,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
private int populationSize = 100; private int populationSize = 100;
// simulating the generational cycle. Set rather small (eg 5) for use as local search, higher for global search (eg 50) // 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 generationCycle = 50;
private int fitIndex = 0; // choose criterion for multi objective functions private int fitIndex = 0; // choose criterion for multi objective functions
private Population m_Population; private Population m_Population;
private AbstractOptimizationProblem m_Problem; private AbstractOptimizationProblem m_Problem;
private transient Vector<InterfacePopulationChangedEventListener> m_Listener; private transient Vector<InterfacePopulationChangedEventListener> m_Listener;
@@ -101,15 +99,11 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
if (m_Listener == null) { if (m_Listener == null) {
return false; return false;
} } else {
else {
return m_Listener.remove(ea); return m_Listener.remove(ea);
} }
} }
@Override
public void freeWilly() {}
protected double[] calcChallengeVect(double[] centroid, double[] refX) { protected double[] calcChallengeVect(double[] centroid, double[] refX) {
double[] r = new double[centroid.length]; double[] r = new double[centroid.length];
for (int i = 0; i < r.length; i++) { for (int i = 0; i < r.length; i++) {
@@ -370,10 +364,8 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
/** /**
* This method creates a Nelder-Mead instance. * This method creates a Nelder-Mead instance.
* *
* @param pop * @param pop The size of the population
* The size of the population * @param problem The problem to be optimized
* @param problem
* The problem to be optimized
* @param listener * @param listener
* @return An optimization procedure that performs nelder mead optimization. * @return An optimization procedure that performs nelder mead optimization.
*/ */
@@ -398,15 +390,15 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
/** /**
* This method creates a Nelder-Mead instance with an initial population * 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 * around a given candidate solution. The population is created as a simplex
* perturbation ratio or randomly across the search range if the perturbation ratio is * with given perturbation ratio or randomly across the search range if the
* zero or below zero. * perturbation ratio is zero or below zero.
* *
* *
* @param problem * @param problem The problem to be optimized
* The problem to be optimized
* @param candidate starting point of the search * @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 * @param listener
* @return An optimization procedure that performs nelder mead optimization. * @return An optimization procedure that performs nelder mead optimization.
*/ */
@@ -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 * From a given candidate solution, create n solutions around the candidate,
* new candidate differs in i dimensions by a distance of perturbRatio relative to the range in * where every i-th new candidate differs in i dimensions by a distance of
* that dimension (respecting the range). * perturbRatio relative to the range in that dimension (respecting the
* The new solutions are returned as a population, which, if includeCand is true, * range). The new solutions are returned as a population, which, if
* also contains the initial candidate. However, the new candidates have not been evaluated. * includeCand is true, also contains the initial candidate. However, the
* new candidates have not been evaluated.
* *
* @param candidate * @param candidate
* @param perturbRelative * @param perturbRelative
@@ -515,5 +508,4 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
public String critIndexTipText() { public String critIndexTipText() {
return "For multi-criterial problems, set the index of the fitness to be used in 0..n-1. Default is 0"; return "For multi-criterial problems, set the index of the fitness to be used in 0..n-1. Default is 0";
} }
} }

494
src/eva2/server/go/strategies/NichePSO.java
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119
src/eva2/server/go/strategies/ParticleFilterOptimization.java
View File

@@ -1,6 +1,5 @@
package eva2.server.go.strategies; package eva2.server.go.strategies;
import eva2.gui.BeanInspector; import eva2.gui.BeanInspector;
import eva2.gui.GenericObjectEditor; import eva2.gui.GenericObjectEditor;
import eva2.gui.Plot; 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 * This is a Particle Filter implemented by Frank Senke, only some documentation
* here and not completely checked whether this works on arbitrary problem * 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. * This is a implementation of Genetic Algorithms. Copyright: Copyright (c) 2003
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture * Company: University of Tuebingen, Computer Architecture
*
* @author Felix Streichert * @author Felix Streichert
* @version: $Revision: 307 $ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $ * 2007) $ $Author: mkron $
* $Author: mkron $
*/ */
public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.Serializable { 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 static final long serialVersionUID = 1L;
private Population m_Population = new Population(); private Population m_Population = new Population();
private InterfaceOptimizationProblem m_Problem = new F1Problem(); private InterfaceOptimizationProblem m_Problem = new F1Problem();
private InterfaceSelection m_ParentSelection = new SelectParticleWheel(0.5); private InterfaceSelection m_ParentSelection = new SelectParticleWheel(0.5);
//private boolean m_UseElitism = true; //private boolean m_UseElitism = true;
private String m_Identifier = ""; private String m_Identifier = "";
private boolean withShow = false; private boolean withShow = false;
private double mutationSigma = 0.01; private double mutationSigma = 0.01;
@@ -51,9 +49,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
private double initialVelocity = 0.02; private double initialVelocity = 0.02;
private double rotationDeg = 20.; private double rotationDeg = 20.;
private int popSize = 300; private int popSize = 300;
private int sleepTime = 0; private int sleepTime = 0;
transient private int indCount = 0; transient private int indCount = 0;
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
transient Plot myPlot = null; transient Plot myPlot = null;
@@ -117,7 +113,9 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 pop The initial population
* @param reset If true the population is reset. * @param reset If true the population is reset.
*/ */
@@ -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 * @param population The population that is to be evaluated
*/ */
private Population evaluatePopulation(Population population) { private Population evaluatePopulation(Population population) {
@@ -157,8 +156,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
if (randomImmigrationQuota > 0) { if (randomImmigrationQuota > 0) {
if (randomImmigrationQuota > 1.) { if (randomImmigrationQuota > 1.) {
System.err.println("Error, invalid immigration quota!"); System.err.println("Error, invalid immigration quota!");
} } else {
else {
targetSize = (int) (this.m_Population.getTargetSize() * (1. - randomImmigrationQuota)); targetSize = (int) (this.m_Population.getTargetSize() * (1. - randomImmigrationQuota));
targetSize = Math.max(1, targetSize); // guarantee at least one to be selected targetSize = Math.max(1, targetSize); // guarantee at least one to be selected
if (targetSize < this.m_Population.getTargetSize()) { if (targetSize < this.m_Population.getTargetSize()) {
@@ -218,8 +216,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
if (useCircles) { if (useCircles) {
myPlot.getFunctionArea().drawCircle("", curPosition, graphLabel); myPlot.getFunctionArea().drawCircle("", curPosition, graphLabel);
} } else {
else {
myPlot.setUnconnectedPoint(curPosition[0], curPosition[1], graphLabel); myPlot.setUnconnectedPoint(curPosition[0], curPosition[1], graphLabel);
} }
// myPlot.setConnectedPoint(curPosition[0], curPosition[1], graphLabel); // myPlot.setConnectedPoint(curPosition[0], curPosition[1], graphLabel);
@@ -252,7 +249,10 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
nextGeneration = resample(m_Population); nextGeneration = resample(m_Population);
if (sleepTime > 0) { if (sleepTime > 0) {
try { Thread.sleep(sleepTime); } catch(Exception e) {} try {
Thread.sleep(sleepTime);
} catch (Exception e) {
}
} }
if (withShow) { if (withShow) {
clearPlot(); clearPlot();
@@ -276,6 +276,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -286,13 +287,16 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
return false; return false;
} }
} }
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name); 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 * @param problem
*/ */
@Override @Override
@@ -302,13 +306,16 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
((AbstractOptimizationProblem) problem).informAboutOptimizer(this); ((AbstractOptimizationProblem) problem).informAboutOptimizer(this);
} }
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; 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 * @return A descriptive string
*/ */
@Override @Override
@@ -320,35 +327,38 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
strB.append(this.m_Population.getStringRepresentation()); strB.append(this.m_Population.getStringRepresentation());
return strB.toString(); 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { public static String globalInfo() {
return "This is a Particle Filter Algorithm."; 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 * @return The name of the algorithm
*/ */
@Override @Override
@@ -356,19 +366,23 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
return "PF"; 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 * Assuming that all optimizer will store thier data in a population we will
* 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. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "Edit the properties of the population used."; return "Edit the properties of the population used.";
} }
@@ -377,22 +391,28 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
public InterfaceSolutionSet getAllSolutions() { public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation()); 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 * @param selection
*/ */
public void setParentSelection(InterfaceSelection selection) { public void setParentSelection(InterfaceSelection selection) {
this.m_ParentSelection = selection; this.m_ParentSelection = selection;
} }
public InterfaceSelection getParentSelection() { public InterfaceSelection getParentSelection() {
return this.m_ParentSelection; return this.m_ParentSelection;
} }
public String parentSelectionTipText() { public String parentSelectionTipText() {
return "Choose a parent selection method."; return "Choose a parent selection method.";
} }
/** /**
* @return the withShow * @return the withShow
**/ *
*/
public boolean isWithShow() { public boolean isWithShow() {
return withShow; return withShow;
} }
@@ -416,18 +436,17 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
/** /**
* @param withShow the withShow to set * @param withShow the withShow to set
**/ *
*/
public void setWithShow(boolean wShow) { public void setWithShow(boolean wShow) {
this.withShow = wShow; this.withShow = wShow;
if (!withShow) { if (!withShow) {
myPlot = null; myPlot = null;
} } else {
else {
double[][] range; double[][] range;
if ((m_Population != null) && (m_Population.size() > 0)) { if ((m_Population != null) && (m_Population.size() > 0)) {
range = ((InterfaceDataTypeDouble) this.m_Population.get(0)).getDoubleRange(); range = ((InterfaceDataTypeDouble) this.m_Population.get(0)).getDoubleRange();
} } else {
else {
range = new double[2][]; range = new double[2][];
range[0] = new double[2]; range[0] = new double[2];
range[0][0] = 0; range[0][0] = 0;
@@ -440,28 +459,32 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
/** /**
* @return the sleepTime * @return the sleepTime
**/ *
*/
public int getSleepTime() { public int getSleepTime() {
return sleepTime; return sleepTime;
} }
/** /**
* @param sleepTime the sleepTime to set * @param sleepTime the sleepTime to set
**/ *
*/
public void setSleepTime(int sleepTime) { public void setSleepTime(int sleepTime) {
this.sleepTime = sleepTime; this.sleepTime = sleepTime;
} }
/** /**
* @return the mutationSigma * @return the mutationSigma
**/ *
*/
public double getMutationSigma() { public double getMutationSigma() {
return mutationSigma; return mutationSigma;
} }
/** /**
* @param mutationSigma the mutationSigma to set * @param mutationSigma the mutationSigma to set
**/ *
*/
public void setMutationSigma(double mutationSigma) { public void setMutationSigma(double mutationSigma) {
this.mutationSigma = 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; 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 * These are for GUI

128
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.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem; 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 * Population based incremental learning in the PSM by Monmarche version with
* update rule of V. But both are limited to binary genotypes. * also allows to simulate ant systems due to the flexible update rule of V. But
* This is a simple implementation of Population Based Incremental Learning. * 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: * Nicolas Monmarché , Eric Ramat , Guillaume Dromel , Mohamed Slimane , Gilles
* On the similarities between AS, BSC and PBIL: toward the birth of a new meta-heuristic. * Venturini: On the similarities between AS, BSC and PBIL: toward the birth of
* TecReport 215. Univ. de Tours, 1999. * 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 * @author Felix Streichert
* @version: $Revision: 307 $ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $ * 2007) $ $Author: mkron $
* $Author: mkron $
*/ */
public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, java.io.Serializable { public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase // These variables are necessary for the simple testcase
@@ -78,7 +78,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 pop The initial population
* @param reset If true the population is reset. * @param reset If true the population is reset.
*/ */
@@ -97,8 +99,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 * @param population The population that is to be evaluated
*/ */
private void evaluatePopulation(Population population) { private void evaluatePopulation(Population population) {
@@ -106,8 +109,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
population.incrGeneration(); 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() { private Population generateChildren() {
PBILPopulation result = (PBILPopulation) this.m_Population.clone(); PBILPopulation result = (PBILPopulation) this.m_Population.clone();
@@ -141,7 +145,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 * @param problem
*/ */
@Override @Override
@@ -153,14 +159,17 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
} }
} }
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; return this.m_Problem;
} }
@Override @Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -171,7 +180,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
return false; return false;
} }
} }
/** Something has changed
/**
* Something has changed
*/ */
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
@@ -179,8 +190,10 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
} }
} }
/** 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 * @return A descriptive string
*/ */
@Override @Override
@@ -192,35 +205,38 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
result += this.m_Population.getStringRepresentation(); result += this.m_Population.getStringRepresentation();
return result; 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { 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!"; 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 * @return The name of the algorithm
*/ */
@Override @Override
@@ -228,19 +244,23 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
return "PBIL"; 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 * Assuming that all optimizer will store thier data in a population we will
* 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. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "Edit the properties of the PBIL population used."; return "Edit the properties of the PBIL population used.";
} }
@@ -262,33 +282,43 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
// return "Select the normation method."; // 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 * @param selection
*/ */
public void setSelectionMethod(InterfaceSelection selection) { public void setSelectionMethod(InterfaceSelection selection) {
this.m_SelectionOperator = selection; this.m_SelectionOperator = selection;
} }
public InterfaceSelection getSelectionMethod() { public InterfaceSelection getSelectionMethod() {
return this.m_SelectionOperator; return this.m_SelectionOperator;
} }
public String selectionMethodTipText() { public String selectionMethodTipText() {
return "Choose a selection method."; 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 * @param elitism
*/ */
public void setElitism(boolean elitism) { public void setElitism(boolean elitism) {
this.m_UseElitism = elitism; this.m_UseElitism = elitism;
} }
public boolean getElitism() { public boolean getElitism() {
return this.m_UseElitism; return this.m_UseElitism;
} }
public String elitismTipText() { public String elitismTipText() {
return "Enable/disable elitism."; return "Enable/disable elitism.";
} }
/** This method will set the learning rate for PBIL /**
* This method will set the learning rate for PBIL
*
* @param LearningRate * @param LearningRate
*/ */
public void setLearningRate(double LearningRate) { public void setLearningRate(double LearningRate) {
@@ -297,14 +327,18 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.m_LearningRate = 0; this.m_LearningRate = 0;
} }
} }
public double getLearningRate() { public double getLearningRate() {
return this.m_LearningRate; return this.m_LearningRate;
} }
public String learningRateTipText() { public String learningRateTipText() {
return "The learing rate of PBIL."; 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 * @param m
*/ */
public void setMutationRate(double m) { public void setMutationRate(double m) {
@@ -316,14 +350,18 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.m_MutationRate = 1; this.m_MutationRate = 1;
} }
} }
public double getMutationRate() { public double getMutationRate() {
return this.m_MutationRate; return this.m_MutationRate;
} }
public String mutationRateTipText() { public String mutationRateTipText() {
return "The mutation rate of PBIL."; 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 * @param m
*/ */
public void setMutateSigma(double m) { public void setMutateSigma(double m) {
@@ -332,14 +370,18 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.m_MutateSigma = 0; this.m_MutateSigma = 0;
} }
} }
public double getMutateSigma() { public double getMutateSigma() {
return this.m_MutateSigma; return this.m_MutateSigma;
} }
public String mutateSigmaTipText() { public String mutateSigmaTipText() {
return "Set the sigma for the mutation of the probability vector."; 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 * @param PositiveSamples
*/ */
public void setPositiveSamples(int PositiveSamples) { public void setPositiveSamples(int PositiveSamples) {
@@ -348,9 +390,11 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.m_NumberOfPositiveSamples = 1; this.m_NumberOfPositiveSamples = 1;
} }
} }
public int getPositiveSamples() { public int getPositiveSamples() {
return this.m_NumberOfPositiveSamples; return this.m_NumberOfPositiveSamples;
} }
public String positiveSamplesTipText() { public String positiveSamplesTipText() {
return "The number of positive samples that update the PBIL vector."; return "The number of positive samples that update the PBIL vector.";
} }

63
src/eva2/server/go/strategies/ScatterSearch.java
View File

@@ -25,19 +25,23 @@ import eva2.tools.math.RNG;
import java.util.ArrayList; import java.util.ArrayList;
/** /**
* A ScatterSearch implementation taken mainly from [1]. Unfortunately, some parameters as well as * A ScatterSearch implementation taken mainly from [1]. Unfortunately, some
* the local search method are not well defined in [1], so this implementation allows HC and Nelder-Mead * parameters as well as the local search method are not well defined in [1], so
* as local search. If local search is activated, an additional filter is defined, meaning that only those * 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. * 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 * The threshold fitness is either defined relatively to the best/worst fitness
* or as an absolute value (in both cases only the first fitness criterion is regarded). * values in the reference set or as an absolute value (in both cases only the
* first fitness criterion is regarded).
* *
* @author mkron * @author mkron
* *
* [1] M.Rodiguez-Fernandez, J.Egea, J.Banga: Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems. * [1] M.Rodiguez-Fernandez, J.Egea, J.Banga: Novel metaheuristic for parameter
* BMC Bioinformatics 2006, 7:483. BioMed Central 2006. * estimation in nonlinear dynamic biological systems. BMC Bioinformatics 2006,
* 7:483. BioMed Central 2006.
*/ */
public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener { public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener {
transient private InterfacePopulationChangedEventListener m_Listener = null; transient private InterfacePopulationChangedEventListener m_Listener = null;
private String m_Identifier = "ScatterSearch"; private String m_Identifier = "ScatterSearch";
private AbstractOptimizationProblem problem = new F1Problem(); private AbstractOptimizationProblem problem = new F1Problem();
@@ -58,9 +62,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// simulate an EvA generational cycle // simulate an EvA generational cycle
private int generationCycle = 50; private int generationCycle = 50;
private int fitCrit = -1; private int fitCrit = -1;
protected boolean checkRange = true; protected boolean checkRange = true;
// private int lastLocalSearch = -1; // private int lastLocalSearch = -1;
// // nr of generations between local searches // // nr of generations between local searches
// protected int localSearchInterval = 10; // protected int localSearchInterval = 10;
@@ -71,7 +73,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private double nelderMeadInitPerturbation = 0.01; 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 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 double minDiversityEpsilon = 0.0001; // minimal phenotypic distance for a candidate to be taken over into the refset
private static boolean TRACE = false; private static boolean TRACE = false;
public ScatterSearch() { public ScatterSearch() {
@@ -134,6 +135,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
/** /**
* Eval an initial population and extract the first refset. * Eval an initial population and extract the first refset.
*
* @param pop * @param pop
*/ */
private void initRefSet(Population pop) { private void initRefSet(Population pop) {
@@ -160,8 +162,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
template = problem.getIndividualTemplate(); template = problem.getIndividualTemplate();
if (!(template instanceof InterfaceDataTypeDouble)) { if (!(template instanceof InterfaceDataTypeDouble)) {
System.err.println("Requiring double data!"); System.err.println("Requiring double data!");
} } else {
else {
Object dim = BeanInspector.callIfAvailable(problem, "getProblemDimension", null); Object dim = BeanInspector.callIfAvailable(problem, "getProblemDimension", null);
if (dim == null) { if (dim == null) {
System.err.println("Couldnt get problem dimension!"); System.err.println("Couldnt get problem dimension!");
@@ -174,7 +175,8 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
} }
} }
/** Something has changed /**
* Something has changed
*/ */
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
@@ -188,7 +190,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// problem.evaluate(indy); // problem.evaluate(indy);
// return indy.getFitness(0); // return indy.getFitness(0);
// } // }
@Override @Override
public void registerPopulationStateChanged(Object source, String name) { public void registerPopulationStateChanged(Object source, String name) {
// The events of the interim hill climbing population will be caught here // The events of the interim hill climbing population will be caught here
@@ -351,10 +352,11 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
} }
/** /**
* Maybe replace the single worst indy in the refset by the best candidate, which may * Maybe replace the single worst indy in the refset by the best candidate,
* be locally optimized in a local search step. * which may be locally optimized in a local search step. The best candidate
* The best candidate is removed from the candidate set in any case. The candidate set * is removed from the candidate set in any case. The candidate set may be
* may be cleared if all following individuals would never be taken over to the refset. * cleared if all following individuals would never be taken over to the
* refset.
* *
* @param refSet * @param refSet
* @param candidates * @param candidates
@@ -407,8 +409,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// so we can just clear the rest of the candidates // so we can just clear the rest of the candidates
if (!doLocalSearch && (bestCand.getFitness().length == 1)) { if (!doLocalSearch && (bestCand.getFitness().length == 1)) {
candidates.clear(); candidates.clear();
} } else {
else {
candidates.remove(bestIndex); candidates.remove(bestIndex);
} }
} }
@@ -417,8 +418,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private Pair<AbstractEAIndividual, Integer> localSolver(AbstractEAIndividual cand, int hcSteps) { private Pair<AbstractEAIndividual, Integer> localSolver(AbstractEAIndividual cand, int hcSteps) {
if (localSearchMethod.getSelectedTagID() == 0) { if (localSearchMethod.getSelectedTagID() == 0) {
return localSolverHC(cand, hcSteps); return localSolverHC(cand, hcSteps);
} } else {
else {
return PostProcess.localSolverNMS(cand, hcSteps, nelderMeadInitPerturbation, problem); return PostProcess.localSolverNMS(cand, hcSteps, nelderMeadInitPerturbation, problem);
} }
} }
@@ -449,8 +449,8 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
} }
/** /**
* Check for both a genotype and phenotype diversity criterion which both must * Check for both a genotype and phenotype diversity criterion which both
* be fulfilled for a candidate to be accepted. * must be fulfilled for a candidate to be accepted.
* *
* @param cand * @param cand
* @param popCompGeno * @param popCompGeno
@@ -475,6 +475,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
/** /**
* Recombines the refset to new indies which are also evaluated. * Recombines the refset to new indies which are also evaluated.
*
* @param refSet * @param refSet
* @return * @return
*/ */
@@ -514,8 +515,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
combs.add(combineTypeTwo(indy1, indy2)); combs.add(combineTypeTwo(indy1, indy2));
if (RNG.flipCoin(0.5)) { if (RNG.flipCoin(0.5)) {
combs.add(combineTypeOne(indy1, indy2)); combs.add(combineTypeOne(indy1, indy2));
} } else {
else {
combs.add(combineTypeThree(indy1, indy2)); combs.add(combineTypeThree(indy1, indy2));
} }
} }
@@ -530,9 +530,11 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private AbstractEAIndividual combineTypeOne(AbstractEAIndividual indy1, AbstractEAIndividual indy2) { private AbstractEAIndividual combineTypeOne(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
return combine(indy1, indy2, true, false); return combine(indy1, indy2, true, false);
} }
private AbstractEAIndividual combineTypeTwo(AbstractEAIndividual indy1, AbstractEAIndividual indy2) { private AbstractEAIndividual combineTypeTwo(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
return combine(indy1, indy2, true, true); return combine(indy1, indy2, true, true);
} }
private AbstractEAIndividual combineTypeThree(AbstractEAIndividual indy1, AbstractEAIndividual indy2) { private AbstractEAIndividual combineTypeThree(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
return combine(indy1, indy2, false, true); return combine(indy1, indy2, false, true);
} }
@@ -737,7 +739,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
} }
///////////// Trivials... ///////////// Trivials...
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
m_Identifier = name; m_Identifier = name;
@@ -758,6 +759,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
m_Listener = ea; m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -768,8 +770,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
return false; return false;
} }
} }
@Override
public void freeWilly() {}
@Override @Override
public String getIdentifier() { public String getIdentifier() {
@@ -871,7 +871,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
} }
////////////////////////////////////////////7 ////////////////////////////////////////////7
/** /**
* This method performs a scatter search runnable. * This method performs a scatter search runnable.
*/ */
@@ -998,9 +997,11 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
public double getImprovementEpsilon() { public double getImprovementEpsilon() {
return improvementEpsilon; return improvementEpsilon;
} }
public void setImprovementEpsilon(double improvementEpsilon) { public void setImprovementEpsilon(double improvementEpsilon) {
this.improvementEpsilon = improvementEpsilon; this.improvementEpsilon = improvementEpsilon;
} }
public String improvementEpsilonTipText() { public String improvementEpsilonTipText() {
return "Minimal relative fitness improvement for a candidate to enter the refSet - set to zero to deactivate."; 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() { public double getMinDiversityEpsilon() {
return minDiversityEpsilon; return minDiversityEpsilon;
} }
public void setMinDiversityEpsilon(double minDiversityEpsilon) { public void setMinDiversityEpsilon(double minDiversityEpsilon) {
this.minDiversityEpsilon = minDiversityEpsilon; this.minDiversityEpsilon = minDiversityEpsilon;
} }
public String minDiversityEpsilonTipText() { public String minDiversityEpsilonTipText() {
return "Minimal distance to other individuals in the refSet for a candidate to enter the refSet - set to zero to deactivate."; return "Minimal distance to other individuals in the refSet for a candidate to enter the refSet - set to zero to deactivate.";
} }

106
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.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG; 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 * 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 * has to be tuned to the optimization problem at hand. Again the population
* gives the number of multi-starts. * size gives the number of multi-starts. Created by IntelliJ IDEA. User:
* Created by IntelliJ IDEA. * streiche Date: 13.05.2004 Time: 10:30:26 To change this template use File |
* User: streiche * Settings | File Templates.
* 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 { public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase // These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem(); private InterfaceOptimizationProblem m_Problem = new B1Problem();
private int m_MultiRuns = 100; private int m_MultiRuns = 100;
private int m_FitnessCalls = 100; private int m_FitnessCalls = 100;
@@ -29,7 +28,6 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
GAIndividualBinaryData m_Best, m_Test; GAIndividualBinaryData m_Best, m_Test;
public double m_InitialTemperature = 2, m_CurrentTemperature; public double m_InitialTemperature = 2, m_CurrentTemperature;
public double m_Alpha = 0.9; public double m_Alpha = 0.9;
// These variables are necessary for the more complex LectureGUI enviroment // These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = ""; transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
@@ -53,7 +51,8 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
return (Object) new SimulatedAnnealing(this); return (Object) new SimulatedAnnealing(this);
} }
/** This method will init the HillClimber /**
* This method will init the HillClimber
*/ */
@Override @Override
public void init() { public void init() {
@@ -63,7 +62,9 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 pop The initial population
* @param reset If true the population is reset. * @param reset If true the population is reset.
*/ */
@@ -78,7 +79,8 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
} }
} }
/** This method will optimize /**
* This method will optimize
*/ */
@Override @Override
public void optimize() { public void optimize() {
@@ -112,7 +114,9 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 org The original
* @param mut The mutant * @param mut The mutant
*/ */
@@ -127,19 +131,23 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
return result; 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 * @param problem
*/ */
@Override @Override
public void setProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; return this.m_Problem;
} }
/** This method will init the HillClimber /**
* This method will init the HillClimber
*/ */
public void defaultInit() { public void defaultInit() {
this.m_FitnessCallsNeeded = 0; this.m_FitnessCallsNeeded = 0;
@@ -147,7 +155,8 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
this.m_Best.defaultInit(m_Problem); this.m_Best.defaultInit(m_Problem);
} }
/** This method will optimize /**
* This method will optimize
*/ */
public void defaultOptimize() { public void defaultOptimize() {
for (int i = 0; i < m_FitnessCalls; i++) { for (int i = 0; i < m_FitnessCalls; i++) {
@@ -163,8 +172,9 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
} }
} }
/** This main method will start a simple hillclimber. /**
* No arguments necessary. * This main method will start a simple hillclimber. No arguments necessary.
*
* @param args * @param args
*/ */
public static void main(String[] args) { public static void main(String[] args) {
@@ -185,6 +195,7 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -195,14 +206,17 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
return false; return false;
} }
} }
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name); 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 * @return A descriptive string
*/ */
@Override @Override
@@ -210,8 +224,7 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
String result = ""; String result = "";
if (this.m_Population.size() > 1) { if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n"; result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
} } else {
else {
result += "Simulated Annealing:\n"; result += "Simulated Annealing:\n";
} }
result += "Optimization Problem: "; result += "Optimization Problem: ";
@@ -219,54 +232,62 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
result += this.m_Population.getStringRepresentation(); result += this.m_Population.getStringRepresentation();
return result; 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { 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."; 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 * @return The name of the algorithm
*/ */
@Override @Override
public String getName() { public String getName() {
return "MS-SA"; 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. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "Change the number of best individuals stored (MS-SA))."; 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() { public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation()); return new SolutionSet(getPopulation());
} }
/** Set the initial temperature
/**
* Set the initial temperature
*
* @return The initial temperature. * @return The initial temperature.
*/ */
public double getInitialTemperature() { public double getInitialTemperature() {
return this.m_InitialTemperature; return this.m_InitialTemperature;
} }
public void setInitialTemperature(double pop) { public void setInitialTemperature(double pop) {
this.m_InitialTemperature = pop; this.m_InitialTemperature = pop;
} }
public String initialTemperatureTipText() { public String initialTemperatureTipText() {
return "Set the initial temperature."; 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. * @return The cooling rate.
*/ */
public double getAlpha() { public double getAlpha() {
return this.m_Alpha; return this.m_Alpha;
} }
public void setAlpha(double a) { public void setAlpha(double a) {
this.m_Alpha = a; this.m_Alpha = a;
if (this.m_Alpha > 1) { if (this.m_Alpha > 1) {
this.m_Alpha = 1.0; this.m_Alpha = 1.0;
} }
} }
public String alphaTipText() { public String alphaTipText() {
return "Set alpha, which is used to degrade the temperaure."; return "Set alpha, which is used to degrade the temperaure.";
} }

110
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.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem; 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() * A simple implementation of the steady-state GA with variable replacement
* optimization steps are performed each time optimize() is called. * schemes. To reduce the logging effort population.size() optimization steps
* Created by IntelliJ IDEA. * are performed each time optimize() is called. Created by IntelliJ IDEA. User:
* User: streiche * streiche Date: 19.07.2005 Time: 14:30:20 To change this template use File |
* Date: 19.07.2005 * Settings | File Templates.
* Time: 14:30:20
* To change this template use File | Settings | File Templates.
*/ */
public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable { 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 InterfaceSelection m_PartnerSelection = new SelectTournament();
private InterfaceReplacement m_ReplacementSelection = new ReplaceWorst(); private InterfaceReplacement m_ReplacementSelection = new ReplaceWorst();
private int m_NumberOfPartners = 1; private int m_NumberOfPartners = 1;
private String m_Identifier = ""; private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
@@ -59,7 +56,9 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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. * @param reset If true the population is reset.
*/ */
@Override @Override
@@ -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 * @param population The population that is to be evaluated
*/ */
private void evaluatePopulation(Population population) { private void evaluatePopulation(Population population) {
@@ -81,8 +81,10 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
population.incrGeneration(); 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 * @param population The population that is to be evaluated
*/ */
private void defaultEvaluatePopulation(Population population) { private void defaultEvaluatePopulation(Population population) {
@@ -95,8 +97,9 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
population.incrGeneration(); 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() { private void generateChildren() {
this.m_ParentSelection.prepareSelection(this.m_Population); this.m_ParentSelection.prepareSelection(this.m_Population);
@@ -119,10 +122,12 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
this.m_Population.incrGeneration(); this.m_Population.incrGeneration();
this.firePropertyChangedEvent(Population.nextGenerationPerformed); this.firePropertyChangedEvent(Population.nextGenerationPerformed);
} }
@Override @Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -133,26 +138,32 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
return false; return false;
} }
} }
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name); 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 * @param problem
*/ */
@Override @Override
public void setProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; 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 * @return A descriptive string
*/ */
@Override @Override
@@ -164,35 +175,38 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
result += this.m_Population.getStringRepresentation(); result += this.m_Population.getStringRepresentation();
return result; 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { public static String globalInfo() {
return "This is a Steady-State Genetic Algorithm."; 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 * @return The name of the algorithm
*/ */
@Override @Override
@@ -200,19 +214,23 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
return "SS-GA"; 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 * Assuming that all optimizer will store thier data in a population we will
* 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. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "Edit the properties of the population used."; return "Edit the properties of the population used.";
} }
@@ -221,21 +239,28 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
public InterfaceSolutionSet getAllSolutions() { public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation()); 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 * @param selection
*/ */
public void setParentSelection(InterfaceSelection selection) { public void setParentSelection(InterfaceSelection selection) {
this.m_ParentSelection = selection; this.m_ParentSelection = selection;
} }
public InterfaceSelection getParentSelection() { public InterfaceSelection getParentSelection() {
return this.m_ParentSelection; return this.m_ParentSelection;
} }
public String parentSelectionTipText() { public String parentSelectionTipText() {
return "Choose a parent selection method."; 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 * offsprings by mating
*
* @param partners * @param partners
*/ */
public void setNumberOfPartners(int partners) { public void setNumberOfPartners(int partners) {
@@ -244,35 +269,46 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
} }
this.m_NumberOfPartners = partners; this.m_NumberOfPartners = partners;
} }
public int getNumberOfPartners() { public int getNumberOfPartners() {
return this.m_NumberOfPartners; return this.m_NumberOfPartners;
} }
public String numberOfPartnersTipText() { public String numberOfPartnersTipText() {
return "The number of mating partners needed to create offsprings."; 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 * @param selection
*/ */
public void setPartnerSelection(InterfaceSelection selection) { public void setPartnerSelection(InterfaceSelection selection) {
this.m_PartnerSelection = selection; this.m_PartnerSelection = selection;
} }
public InterfaceSelection getPartnerSelection() { public InterfaceSelection getPartnerSelection() {
return this.m_PartnerSelection; return this.m_PartnerSelection;
} }
public String partnerSelectionTipText() { public String partnerSelectionTipText() {
return "Choose a selection method for selecting recombination partners for given parents."; return "Choose a selection method for selecting recombination partners for given parents.";
} }
/** Choose a replacement strategy /**
* Choose a replacement strategy
*
* @param selection * @param selection
*/ */
public void setReplacementSelection(InterfaceReplacement selection) { public void setReplacementSelection(InterfaceReplacement selection) {
this.m_ReplacementSelection = selection; this.m_ReplacementSelection = selection;
} }
public InterfaceReplacement getReplacementSelection() { public InterfaceReplacement getReplacementSelection() {
return this.m_ReplacementSelection; return this.m_ReplacementSelection;
} }
public String replacementSelectionTipText() { public String replacementSelectionTipText() {
return "Choose a replacement strategy."; return "Choose a replacement strategy.";
} }

104
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.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem; import eva2.server.go.problems.InterfaceOptimizationProblem;
/** Threshold accepting algorithm simliar strategy as the flood /**
* algorithm, similar problems. * Threshold accepting algorithm simliar strategy as the flood algorithm,
* Created by IntelliJ IDEA. * similar problems. Created by IntelliJ IDEA. User: streiche Date: 01.10.2004
* User: streiche * Time: 13:35:49 To change this template use File | Settings | File Templates.
* 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 { public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase // These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem(); private InterfaceOptimizationProblem m_Problem = new B1Problem();
private int m_MultiRuns = 100; private int m_MultiRuns = 100;
private int m_FitnessCalls = 100; private int m_FitnessCalls = 100;
@@ -26,7 +24,6 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
GAIndividualBinaryData m_Best, m_Test; GAIndividualBinaryData m_Best, m_Test;
public double m_InitialT = 2, m_CurrentT; public double m_InitialT = 2, m_CurrentT;
public double m_Alpha = 0.9; public double m_Alpha = 0.9;
// These variables are necessary for the more complex LectureGUI enviroment // These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = ""; transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener; transient private InterfacePopulationChangedEventListener m_Listener;
@@ -50,7 +47,8 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
return (Object) new ThresholdAlgorithm(this); return (Object) new ThresholdAlgorithm(this);
} }
/** This method will init the HillClimber /**
* This method will init the HillClimber
*/ */
@Override @Override
public void init() { public void init() {
@@ -60,7 +58,9 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 pop The initial population
* @param reset If true the population is reset. * @param reset If true the population is reset.
*/ */
@@ -75,7 +75,8 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
} }
} }
/** This method will optimize /**
* This method will optimize
*/ */
@Override @Override
public void optimize() { public void optimize() {
@@ -103,7 +104,9 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 org The original
* @param mut The mutant * @param mut The mutant
*/ */
@@ -118,19 +121,23 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
return result; 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 * @param problem
*/ */
@Override @Override
public void setProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; return this.m_Problem;
} }
/** This method will init the HillClimber /**
* This method will init the HillClimber
*/ */
public void defaultInit() { public void defaultInit() {
this.m_FitnessCallsNeeded = 0; this.m_FitnessCallsNeeded = 0;
@@ -138,7 +145,8 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
this.m_Best.defaultInit(m_Problem); this.m_Best.defaultInit(m_Problem);
} }
/** This method will optimize /**
* This method will optimize
*/ */
public void defaultOptimize() { public void defaultOptimize() {
for (int i = 0; i < m_FitnessCalls; i++) { for (int i = 0; i < m_FitnessCalls; i++) {
@@ -154,8 +162,9 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
} }
} }
/** This main method will start a simple hillclimber. /**
* No arguments necessary. * This main method will start a simple hillclimber. No arguments necessary.
*
* @param args * @param args
*/ */
public static void main(String[] args) { public static void main(String[] args) {
@@ -171,10 +180,12 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
TmpMeanFitness /= 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 @Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -185,14 +196,17 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
return false; return false;
} }
} }
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name); 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 * @return A descriptive string
*/ */
@Override @Override
@@ -200,8 +214,7 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
String result = ""; String result = "";
if (this.m_Population.size() > 1) { if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n"; result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
} } else {
else {
result += "Threshold Algorithm:\n"; result += "Threshold Algorithm:\n";
} }
result += "Optimization Problem: "; result += "Optimization Problem: ";
@@ -209,54 +222,62 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
result += this.m_Population.getStringRepresentation(); result += this.m_Population.getStringRepresentation();
return result; 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { public static String globalInfo() {
return "The threshold algorithm uses an declining threshold to accpect new solutions."; 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 * @return The name of the algorithm
*/ */
@Override @Override
public String getName() { public String getName() {
return "MS-TA"; 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. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "Change the number of best individuals stored (MS-TA)."; 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() { public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation()); return new SolutionSet(getPopulation());
} }
/** Set the initial threshold
/**
* Set the initial threshold
*
* @return The initial temperature. * @return The initial temperature.
*/ */
public double getInitialT() { public double getInitialT() {
return this.m_InitialT; return this.m_InitialT;
} }
public void setInitialT(double pop) { public void setInitialT(double pop) {
this.m_InitialT = pop; this.m_InitialT = pop;
} }
public String initialTTipText() { public String initialTTipText() {
return "Set the initial threshold."; 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. * @return The initial temperature.
*/ */
public double getAlpha() { public double getAlpha() {
return this.m_Alpha; return this.m_Alpha;
} }
public void setAlpha(double a) { public void setAlpha(double a) {
this.m_Alpha = a; this.m_Alpha = a;
if (this.m_Alpha > 1) { if (this.m_Alpha > 1) {
this.m_Alpha = 1.0; this.m_Alpha = 1.0;
} }
} }
public String alphaTipText() { public String alphaTipText() {
return "Set alpha, which is used to degrade the threshold."; return "Set alpha, which is used to degrade the threshold.";
} }

485
src/eva2/server/go/strategies/Tribes.java
View File

@@ -17,30 +17,30 @@ import eva2.server.go.strategies.tribes.TribesSwarm;
import java.util.Iterator; import java.util.Iterator;
import java.util.List; import java.util.List;
/** /**
* This is the TRIBES algorithm, an adaptive, parameter-less PSO implementation. * 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 (MK) ported M.Clerc's java version 2006-02 21 and added the original notes
* I had to do some modifications for the EvA framework, namely: * below. I had to do some modifications for the EvA framework, namely: - minor
* - minor adaptations allover the code to fit into the framework * adaptations allover the code to fit into the framework - the objective value
* - the objective value parameter must now be set within the GUI for each problem by hand (EvA doesnt assume to know an objective beforehand) * parameter must now be set within the GUI for each problem by hand (EvA doesnt
* - discrete search spaces are not directly supported any more (no "granularity") * assume to know an objective beforehand) - discrete search spaces are not
* - the benchmark-collection is gone, it might be included into the EvA benchmark set in the future, though * directly supported any more (no "granularity") - the benchmark-collection is
* - fixed two bugs (SunnySpell link generation, findWorst method) * gone, it might be included into the EvA benchmark set in the future, though -
* - fixed bugs in the CEC 2005 benchmarks (see the corresponding class) * fixed two bugs (SunnySpell link generation, findWorst method) - fixed bugs in
* - I widely kept the original comments, except for places I changed the code so much that they might mislead * the CEC 2005 benchmarks (see the corresponding class) - I widely kept the
* - thats all, I think * 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 could produce similar results as Clerc's on Rosenbrock and Griewank, (in
* I couldnt reproduce the 100% success rate on Ackley, though. * his book on p. 148), I couldnt reproduce the 100% success rate on Ackley,
* though.
* *
* @author Maurice Clerc, Marcel Kronfeld * @author Maurice Clerc, Marcel Kronfeld
* @date 2007-09-13 * @date 2007-09-13
* *
* Original notes: * Original notes:
* @version 2006-02 21 * @version 2006-02 21
* @author Maurice.Clerc@WriteMe.com * @author Maurice.Clerc@WriteMe.com {@link http://mauriceclerc.net}
* {@link http://mauriceclerc.net}
* {@link http://clerc.maurice.free.fr/pso/} * {@link http://clerc.maurice.free.fr/pso/}
* *
*/ */
@@ -129,47 +129,39 @@ import java.util.List;
2005-11-21. Check if it is possible to easily find just the _value_ of the 2005-11-21. Check if it is possible to easily find just the _value_ of the
global minimum (not the position). "Chinese shadow" method? global minimum (not the position). "Chinese shadow" method?
*/ */
public class Tribes implements InterfaceOptimizer, java.io.Serializable { public class Tribes implements InterfaceOptimizer, java.io.Serializable {
public static final boolean TRACE = false; public static final boolean TRACE = false;
protected String m_Identifier = "TRIBES"; protected String m_Identifier = "TRIBES";
transient private InterfacePopulationChangedEventListener m_Listener = null; transient private InterfacePopulationChangedEventListener m_Listener = null;
protected AbstractOptimizationProblem m_problem; protected AbstractOptimizationProblem m_problem;
protected Population population; protected Population population;
public static int maxExplorerNb = 200; public static int maxExplorerNb = 200;
public static int maxMemoryNb = 300; public static int maxMemoryNb = 300;
public static int maxTribeNb = 300; public static int maxTribeNb = 300;
public static int[] strategies = new int[10]; // Just for information public static int[] strategies = new int[10]; // Just for information
public static int[] status = new int[9]; // Just for information public static int[] status = new int[9]; // Just for information
public static boolean testBC = false; // TODO project to EvA2 public static boolean testBC = false; // TODO project to EvA2
public static int adaptOption = 2; 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 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() ) public static boolean repel = false; // If 1, use a "repelling" strategy (see moveExplorer() )
private boolean checkConstraints = true; private boolean checkConstraints = true;
private static final long serialVersionUID = 1L; private static final long serialVersionUID = 1L;
TribesSwarm swarm = null; TribesSwarm swarm = null;
private int iter; private int iter;
protected double objectiveFirstDim = 0.; protected double objectiveFirstDim = 0.;
protected double[][] range, initRange; protected double[][] range, initRange;
protected int notifyGenChangedEvery = 10; protected int notifyGenChangedEvery = 10;
protected int problemDim; protected int problemDim;
protected int adaptThreshold, adaptMax, adapt; protected int adaptThreshold, adaptMax, adapt;
protected int informOption; /* For the best informant. protected int informOption; /* For the best informant.
-1 => really the best -1 => really the best
1 => the best according to a pseudo-gradient method 1 => the best according to a pseudo-gradient method
*/ */
protected int initExplorerNb = 3; // Number of explorers at the very beginning protected int initExplorerNb = 3; // Number of explorers at the very beginning
// use full range (0) or subspace (1) for init options 0 and 1 // use full range (0) or subspace (1) for init options 0 and 1
protected int rangeInitType = 1; protected int rangeInitType = 1;
private boolean m_Show = false; private boolean m_Show = false;
transient protected eva2.gui.Plot m_Plot = null; transient protected eva2.gui.Plot m_Plot = null;
// private int useAnchors = 0; // use anchors to detect environment changes? // private int useAnchors = 0; // use anchors to detect environment changes?
@@ -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 * As TRIBES manages an own structured set of particles (the list of Tribes
* and memories), the setPopulation method is only telling Tribes the range * containing explorers and memories), the setPopulation method is only
* of the indiviuals in the beginning of the run, the individuals will be discarded. * telling Tribes the range of the indiviuals in the beginning of the run,
* the individuals will be discarded.
*/ */
@Override @Override
public void initByPopulation(Population pop, boolean reset) { public void initByPopulation(Population pop, boolean reset) {
@@ -296,8 +289,8 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
// adaptMax=swarmSize; // adaptMax=swarmSize;
adaptMax = swarm.linkNb(swarm); adaptMax = swarm.linkNb(swarm);
if (adaptThreshold >= adaptMax) { if (adaptThreshold >= adaptMax) {
if (swarm.getBestMemory().getPrevPos().getTotalError() <= if (swarm.getBestMemory().getPrevPos().getTotalError()
swarm.getBestMemory().getPos().getTotalError()) { <= swarm.getBestMemory().getPos().getTotalError()) {
adapt = iter; // Memorize at which iteration adaptation occurs adapt = iter; // Memorize at which iteration adaptation occurs
for (int i = 0; i < swarm.getTribeCnt(); i++) { for (int i = 0; i < swarm.getTribeCnt(); i++) {
@@ -383,253 +376,161 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
} }
/** /**
*
public synchronized void search(param pb, PrintStream runSave, PrintStream synthSave) { * public synchronized void search(param pb, PrintStream runSave,
* PrintStream synthSave) {
double epsMean, epsMin, epsMax; *
double evalMean; * double epsMean, epsMin, epsMax; double evalMean; int n; int run, run1;
int n; * int successNb;
int run, run1; *
int successNb; * // Exploratrices générées explorer explorer[] = new explorer[
* Tribes.maxExplorerNb];
// Exploratrices générées *
explorer explorer[] = new explorer[ * double[] eps = new double[pb.maxRun]; double[] evalNb = new
Tribes.maxExplorerNb]; * double[pb.maxRun]; double[] temp = new double[3]; successNb = 0; for (n =
* 0; n < 9; n++) { // For information Tribes.strategies[n] = 0;
double[] eps = new double[pb.maxRun]; * Tribes.status[n] = 0; } ; epsMin = Tribes.infinity; epsMax = 0;
double[] evalNb = new double[pb.maxRun]; *
double[] temp = new double[3]; * // Titles
successNb = 0; *
for (n = 0; n < 9; n++) { // For information * print("\nIter. Eval. Best_fitness", displayPb); save("\n\n PROBLEM
Tribes.strategies[n] = 0; * "+pb.function[0],synthSave); save("\nRun Iter. Eval. Best_fitness
Tribes.status[n] = 0; * Position", synthSave); save("\n\n PROBLEM "+pb.function[0],runSave);
} *
; * // ** // * Loop on runs // * for (run = 0; run < pb.maxRun; run++) { run1
epsMin = Tribes.infinity; * = run + 1; save("\n" + run1 + " ", synthSave); temp = solve(pb,
epsMax = 0; * Tribes.initExplorerNb,runSave,synthSave);
*
// Titles * eps[run] = temp[0]; evalNb[run] = temp[1]; successNb = successNb + (int)
* temp[2]; if (eps[run] < epsMin) { epsMin = eps[run]; } if (eps[run] >
print("\nIter. Eval. Best_fitness", displayPb); * epsMax) { epsMax = eps[run]; } }
save("\n\n PROBLEM "+pb.function[0],synthSave); *
save("\nRun Iter. Eval. Best_fitness Position", synthSave); * // Mean values epsMean = 0; evalMean = 0; for (run = 0; run < pb.maxRun;
save("\n\n PROBLEM "+pb.function[0],runSave); * run++) { epsMean = epsMean + eps[run]; evalMean = evalMean + evalNb[run];
* }
// ** *
// * Loop on runs * epsMean = epsMean / pb.maxRun; evalMean = evalMean / pb.maxRun;
// * * print("\nStatuses ", displayPb); for (n = 1; n < 10; n++) { print("\n" +
for (run = 0; run < pb.maxRun; run++) { * n + " " + Tribes.status[n - 1] + " times", displayPb); }
run1 = run + 1; * print("\nStrategies ", displayPb); for (n = 1; n < 10; n++) { print("\n"
save("\n" + run1 + " ", synthSave); * + n + " " + Tribes.strategies[n - 1] + " times", displayPb); }
temp = solve(pb, Tribes.initExplorerNb,runSave,synthSave); * print("\nMIN BEST TOTAL_ERROR " + epsMin, displayPb); print("\nMEAN BEST
* TOTAL_ERROR " + epsMean, displayPb); print("\nMEAN EVAL. NUMBER " +
eps[run] = temp[0]; * evalMean, displayPb); print("\n SUCCESS RATE " + (double) successNb /
evalNb[run] = temp[1]; * pb.maxRun, displayPb);
successNb = successNb + (int) temp[2]; *
if (eps[run] < epsMin) { * save("\nMIN BEST TOTAL_ERROR " + epsMin, synthSave); save("\nMEAN BEST
epsMin = eps[run]; * TOTAL_ERROR " + epsMean, synthSave); save("\nMEAN EVAL. NUMBER " +
} * evalMean, synthSave); save("\n SUCCESS RATE " + (double) successNb /
if (eps[run] > epsMax) { * pb.maxRun, synthSave);
epsMax = eps[run]; *
} * save("\n-1", runSave); // Special value for the end of the file. Used for
} * graphics
*
// Mean values * } // End of search()
epsMean = 0; *
evalMean = 0; * public double[] solve(param pb, int initExplorerNb, PrintStream runSave,
for (run = 0; run < pb.maxRun; run++) { * PrintStream synthSave) { int adapt, adaptMax; int adaptThreshold; int d,
epsMean = epsMean + eps[run]; * D = pb.H.Dimension; int evalF; int iter; int n; boolean stop; double[]
evalMean = evalMean + evalNb[run]; * temp = new double[3]; int informOption; // For the best informant. // -1
} * => really the best // 1 => the best according to a pseudo-gradient method
*
epsMean = epsMean / pb.maxRun; * // -----------INIT START // Generate a swarm evalF=0; swarm swarm = new
evalMean = evalMean / pb.maxRun; * swarm(); evalF=swarm.generateSwarm(pb, initExplorerNb, pb.initType,
print("\nStatuses ", displayPb); * displayPb,evalF);
for (n = 1; n < 10; n++) { *
print("\n" + n + " " + Tribes.status[n - 1] + " times", * // swarm.displaySwarm(swarm,out); // print("\n Best after init:
displayPb); * "+swarm.Best.position.fitness,out);
} *
print("\nStrategies ", displayPb); * // Move the swarm as long as the stop criterion is false iter = 0; adapt
for (n = 1; n < 10; n++) { * = 0; stop = false; informOption = -1; // Hard coded option // -1 =
print("\n" + n + " " + Tribes.strategies[n - 1] + " times", * absolute best informant // 1 = relative (pseudo-gradient) best informant.
displayPb); * For "niching" // See also moveExplorer, which can be modified in order to
} * avoid this parameter // // -----------INIT END
print("\nMIN BEST TOTAL_ERROR " + epsMin, displayPb); *
print("\nMEAN BEST TOTAL_ERROR " + epsMean, displayPb); *
print("\nMEAN EVAL. NUMBER " + evalMean, displayPb); * // -----------OPTIMIZE START iterations:while (!stop) {
print("\n SUCCESS RATE " + (double) successNb / pb.maxRun, *
displayPb); * swarm.size = swarm.swarmSize(swarm); iter++; // swarm.Best.positionPrev =
* swarm.Best.position;
save("\nMIN BEST TOTAL_ERROR " + epsMin, synthSave); *
save("\nMEAN BEST TOTAL_ERROR " + epsMean, synthSave); * evalF=swarm.moveSwarm(pb, swarm, informOption, displayPb, evalF); //***
save("\nMEAN EVAL. NUMBER " + evalMean, synthSave); * HERE IT MOVES
save("\n SUCCESS RATE " + (double) successNb / pb.maxRun, synthSave); *
* // Some display each time there is "enough" improvement // fduring the
save("\n-1", runSave); // Special value for the end of the file. Used for graphics * process
*
} // End of search() * double enough = 0.005; if ((1 - enough) *
* swarm.Best.positionPrev.totalError > swarm.Best.position.totalError) {
public double[] solve(param pb, int initExplorerNb, PrintStream runSave, PrintStream synthSave) { * print("\nIter. " + iter, displayPb); print(" Eval. " + evalF, displayPb);
int adapt, adaptMax; * print(" totalError " + swarm.Best.position.totalError, displayPb);
int adaptThreshold; * print(" " + swarm.size + " particles", displayPb); } // Save run info
int d, D = pb.H.Dimension; * save("\n" + iter + " " + evalF + " " + swarm.Best.position.totalError + "
int evalF; * " + swarm.size, runSave);
int iter; *
int n; * // Evaluate the stop criterion stop = evalF >= pb.maxEval || pb.accuracy
boolean stop; * > swarm.Best.position.totalError;
double[] temp = new double[3]; *
int informOption; * if (Tribes.adaptOption == 0) { continue iterations; }
// For the best informant. *
// -1 => really the best * if (Tribes.adaptOption == 1) { // Just reinitialize the swarm
// 1 => the best according to a pseudo-gradient method * adaptThreshold = iter - adapt; // adaptMax=swarmSize; adaptMax =
* swarm.linkNb(swarm); if (adaptThreshold >= adaptMax) { if
// -----------INIT START * (swarm.Best.positionPrev.totalError <= swarm.Best.position.totalError) {
// Generate a swarm * adapt = iter; // Memorize at which iteration adaptation occurs
evalF=0; *
swarm swarm = new swarm(); * for (n = 0; n < swarm.tribeNb; n++) {
evalF=swarm.generateSwarm(pb, initExplorerNb, pb.initType, displayPb,evalF); * evalF=swarm.tribes[n].reinitTribe(pb,evalF); } } } continue iterations; }
*
// swarm.displaySwarm(swarm,out); * // if(swarm.Best.positionPrev.totalError<=swarm.Best.position.totalError)
// print("\n Best after init: "+swarm.Best.position.fitness,out); * { // Structural adaptations
*
// Move the swarm as long as the stop criterion is false * //swarmSize = swarm.swarmSize(swarm);
iter = 0; *
adapt = 0; * // print("\nSwarm size (explorers): " + swarmSize,out); // print("
stop = false; * Tribes: (explorers/memories) ",out); // for (n = 0; n <
informOption = -1; * swarm.tribeNumber; n++) { // print(swarm.tribes[n].explorerNb + "/" + //
// Hard coded option * swarm.tribes[n].memoryNb + " ",out); // } // // On "laisse le temps" à
// -1 = absolute best informant * chaque tribu de bouger avant éventuelle adaptation // La règle est
// 1 = relative (pseudo-gradient) best informant. For "niching" * empirique et peut être modifiée //
// See also moveExplorer, which can be modified in order to avoid this parameter *
// * adaptThreshold = iter - adapt; // adaptMax=swarmSize; adaptMax =
// -----------INIT END * swarm.linkNb(swarm);
*
* if (adaptThreshold >= adaptMax) { adapt = iter; // Memorize at which
// -----------OPTIMIZE START * iteration adaptation occurs evalF=swarm.adaptSwarm(pb,
iterations:while (!stop) { * Tribes.adaptOption, swarm, displayPb,evalF); // Réalise l'adaptation
*
swarm.size = swarm.swarmSize(swarm); * // Modifie la recherche de la meilleure informatrice // normale (la
iter++; * "vraie" meilleure) ou dépendant d'un pseudo-gradient // (cf.
// swarm.Best.positionPrev = swarm.Best.position; * informExplorer) // // informOption=-informOption; } // print("\n Nb of
* tribes: " + swarm.tribeNumber + // "\n Particles/tribe:",out); // for (n
evalF=swarm.moveSwarm(pb, swarm, informOption, displayPb, evalF); //*** HERE IT MOVES * = 0; n < swarm.tribeNumber; n++) { // print(swarm.tribes[n].explorerNb +
* " ",out); // } // // print("\n Statuses :"); // for (n = 0; n <
// Some display each time there is "enough" improvement * swarm.tribeNumber; n++) { // print(swarm.tribes[n].status + " ",out); //
// fduring the process * }
*
double enough = 0.005; *
if ((1 - enough) * swarm.Best.positionPrev.totalError > * }
swarm.Best.position.totalError) { * // -----------OPTIMIZE END }
print("\nIter. " + iter, displayPb); *
print(" Eval. " + evalF, displayPb); * // Result of the run print("\nBest: eval.= " + evalF + "\n", displayPb);
print(" totalError " + swarm.Best.position.totalError, * swarm.Best.displayMemory(displayPb);
displayPb); *
print(" " + swarm.size + " particles", displayPb); * save(" " + iter + " " + evalF+ " ", synthSave);
} *
// Save run info * for (d = 0; d < pb.fitnessSize; d++) {
save("\n" + iter + " " + evalF + " " + * save(swarm.Best.position.fitness[d] + " ", synthSave); }
swarm.Best.position.totalError + " " + swarm.size, runSave); *
* for (d = 0; d < D; d++) { save(" " + swarm.Best.position.x[d],
// Evaluate the stop criterion * synthSave); }
stop = evalF >= pb.maxEval || *
pb.accuracy > swarm.Best.position.totalError; * // Prepare return temp[0] = swarm.Best.position.totalError; temp[1] =
* evalF; if (evalF < pb.maxEval) { temp[2] = 1; } else { temp[2] = 0; }
if (Tribes.adaptOption == 0) { *
continue iterations; * return temp; }
} *
*/
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. * 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 * As TRIBES manages an own structured set of particles (the list of Tribes
* and memories), the setPopulation method is only telling Tribes the range * containing explorers and memories), the setPopulation method is only
* of the indiviuals in the beginning of the run, the individuals will be discarded. * telling Tribes the range of the indiviuals in the beginning of the run,
* the individuals will be discarded.
*/ */
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
@@ -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 * Be aware that TRIBES uses two kinds of particles: explorers and memories.
* are inactive in that they dont search the problem space directly, they are not included * As memories are inactive in that they dont search the problem space
* in the returned population. This, however, means that the best found solution might not * directly, they are not included in the returned population. This,
* be inluded as well at several if not most stages of the search. * however, means that the best found solution might not be inluded as well
* at several if not most stages of the search.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
@@ -677,8 +580,9 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
} }
/** /**
* Return a SolutionSet of TribesExplorers (AbstractEAIndividuals) of which some where * Return a SolutionSet of TribesExplorers (AbstractEAIndividuals) of which
* memory particles, thus the returned population is larger than the current population. * some where memory particles, thus the returned population is larger than
* the current population.
* *
* @return a population of possible solutions. * @return a population of possible solutions.
*/ */
@@ -709,13 +613,16 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
return indy; 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 * @param ea
*/ */
@Override @Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -726,6 +633,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
return false; return false;
} }
} }
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name); this.m_Listener.registerPopulationStateChanged(this, name);
@@ -736,13 +644,11 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
return (evals % notifyGenChangedEvery) == 0; return (evals % notifyGenChangedEvery) == 0;
} }
@Override
public void freeWilly() {}
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return m_Identifier; return m_Identifier;
@@ -821,7 +727,6 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
// public void setUseAnchor(boolean useAnchor) { // public void setUseAnchor(boolean useAnchor) {
// this.useAnchor = useAnchor; // this.useAnchor = useAnchor;
// } // }
/** /**
* @return the m_Show * @return the m_Show
*/ */

108
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.FM0Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem; 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 * The winged MOEA was a nice idea, which didn't really work out. Here a
* optimizing just one objective. The idea was that these local optimizers * standard MOEA is assisted by n additional local searchers, each optimizing
* would span the search space and would allow the MOEA to converge faster. * just one objective. The idea was that these local optimizers would span the
* But in the end the performance of this algorithm strongly depends on the * search space and would allow the MOEA to converge faster. But in the end the
* optimization problem. * performance of this algorithm strongly depends on the optimization problem.
* Created by IntelliJ IDEA. * Created by IntelliJ IDEA. User: streiche Date: 16.02.2005 Time: 16:34:22 To
* User: streiche * change this template use File | Settings | File Templates.
* 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 { public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable {
@@ -94,8 +91,9 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 pop The initial population
* @param reset If true the population is reset. * @param reset If true the population is reset.
*/ */
@@ -135,7 +133,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.firePropertyChangedEvent(Population.nextGenerationPerformed); 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 @Override
public void optimize() { public void optimize() {
@@ -154,8 +153,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.firePropertyChangedEvent(Population.nextGenerationPerformed); this.firePropertyChangedEvent(Population.nextGenerationPerformed);
} }
/** This method will manage comunication between the /**
* islands * This method will manage comunication between the islands
*/ */
private void communicate() { private void communicate() {
int oldFunctionCalls; int oldFunctionCalls;
@@ -180,7 +179,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.migrate(); this.migrate();
} }
/** This method implements the migration between the optimzers /**
* This method implements the migration between the optimzers
* *
*/ */
private void migrate() { private void migrate() {
@@ -210,13 +210,16 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
} }
} }
/** 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 * @param ea
*/ */
@Override @Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea; this.m_Listener = ea;
} }
@Override @Override
public boolean removePopulationChangedEventListener( public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) { InterfacePopulationChangedEventListener ea) {
@@ -227,7 +230,9 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
return false; return false;
} }
} }
/** Something has changed
/**
* Something has changed
*/ */
protected void firePropertyChangedEvent(String name) { protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) { if (this.m_Listener != null) {
@@ -235,20 +240,25 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
} }
} }
/** This method will set the problem that is to be optimized /**
* This method will set the problem that is to be optimized
*
* @param problem * @param problem
*/ */
@Override @Override
public void setProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
@Override @Override
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
return this.m_Problem; 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 * @return A descriptive string
*/ */
@Override @Override
@@ -260,35 +270,38 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
result += this.m_Population.getStringRepresentation(); result += this.m_Population.getStringRepresentation();
return result; 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 * @param name The indenifier
*/ */
@Override @Override
public void setIdentifier(String name) { public void setIdentifier(String name) {
this.m_Identifier = name; this.m_Identifier = name;
} }
@Override @Override
public String getIdentifier() { public String getIdentifier() {
return this.m_Identifier; 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 * These are for GUI
*/ */
/** This method returns a global info string /**
* This method returns a global info string
*
* @return description * @return description
*/ */
public static String globalInfo() { public static String globalInfo() {
return "This is Evolutionary Multi-Criteria Optimization Algorithm hybridized with Local Searchers to span the Pareto-Front."; 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 * @return The name of the algorithm
*/ */
@Override @Override
@@ -296,19 +309,23 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
return "EMO-LS"; 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 * Assuming that all optimizer will store their data in a population we will
* of the optimizer. * allow access to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem. * @return The population of current solutions to a given problem.
*/ */
@Override @Override
public Population getPopulation() { public Population getPopulation() {
return this.m_Population; return this.m_Population;
} }
@Override @Override
public void setPopulation(Population pop) { public void setPopulation(Population pop) {
this.m_Population = pop; this.m_Population = pop;
} }
public String populationTipText() { public String populationTipText() {
return "(Defunct)"; return "(Defunct)";
} }
@@ -317,41 +334,54 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
public InterfaceSolutionSet getAllSolutions() { public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation()); 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 * @return The current optimizing method
*/ */
public InterfaceOptimizer getMOOptimizer() { public InterfaceOptimizer getMOOptimizer() {
return this.m_MOOptimizer; return this.m_MOOptimizer;
} }
public void setMOOptimizer(InterfaceOptimizer b) { public void setMOOptimizer(InterfaceOptimizer b) {
this.m_MOOptimizer = b; this.m_MOOptimizer = b;
} }
public String mOOptimizerTipText() { public String mOOptimizerTipText() {
return "Choose a population based optimizing technique to use."; 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 * @return The current optimizing method
*/ */
public InterfaceOptimizer getSOOptimizer() { public InterfaceOptimizer getSOOptimizer() {
return this.m_SOOptimizer; return this.m_SOOptimizer;
} }
public void setSOOptimizer(InterfaceOptimizer b) { public void setSOOptimizer(InterfaceOptimizer b) {
this.m_SOOptimizer = b; this.m_SOOptimizer = b;
} }
public String sOOptimizerTipText() { public String sOOptimizerTipText() {
return "Choose a population based optimizing technique to use."; 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 * @return The current optimizing method
*/ */
public int getMigrationRate() { public int getMigrationRate() {
return this.m_MigrationRate; return this.m_MigrationRate;
} }
public void setMigrationRate(int b) { public void setMigrationRate(int b) {
this.m_MigrationRate = b; this.m_MigrationRate = b;
} }
public String migrationRateTipText() { public String migrationRateTipText() {
return "Choose a proper migration rate."; return "Choose a proper migration rate.";
} }