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refs #24 and #25

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

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

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

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

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

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

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

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());
transient private InterfacePopulationChangedEventListener m_Listener = null;
private String m_Identifier = "BOA";
private int probDim = 8;
private int fitCrit = -1;
private int PopSize = 50;
@@ -63,7 +62,6 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
private int count = 0;
private String netFolder = "BOAOutput";
private int[][] edgeRate = null;
private BOAScoringMethods scoringMethod = BOAScoringMethods.BDM;
private boolean printNetworks = false;
private boolean printEdgeRate = false;
@@ -72,7 +70,6 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
// private boolean printExtraOutput = false;
public BOA() {
}
public BOA(int numberOfParents, int popSize, BOAScoringMethods method,
@@ -185,11 +182,9 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
private static BitSet getBinaryData(AbstractEAIndividual indy) {
if (indy instanceof InterfaceGAIndividual) {
return ((InterfaceGAIndividual) indy).getBGenotype();
}
else if (indy instanceof InterfaceDataTypeBinary) {
} else if (indy instanceof InterfaceDataTypeBinary) {
return ((InterfaceDataTypeBinary) indy).getBinaryData();
}
else {
} else {
throw new RuntimeException(
"Unable to get binary representation for "
+ indy.getClass());
@@ -200,8 +195,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
* evaluate the given Individual and increments the counter. if the
* individual is null, only the counter is incremented
*
* @param indy
* the individual you want to evaluate
* @param indy the individual you want to evaluate
*/
private void evaluate(AbstractEAIndividual indy) {
// evaluate the given individual if it is not null
@@ -343,8 +337,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
* Generate a Bayesian network with the individuals of the population as a
* reference Point
*
* @param pop
* the individuals the network is based on
* @param pop the individuals the network is based on
*/
private void constructNetwork(Population pop) {
generateGreedy(pop);
@@ -394,6 +387,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
/**
* remove the individuals in pop from the population
*
* @param pop
*/
public void remove(Population pop) {
@@ -627,15 +621,9 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
return "Bayesian Network";
}
@Override
public void freeWilly() {
}
// -------------------------------
// -------------GUI---------------
// -------------------------------
public int getNumberOfParents() {
return this.numberOfParents;
}
@@ -731,7 +719,6 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
// public boolean isPrintExtraOutput() {
// return this.printExtraOutput;
// }
// public void setPrintExtraOutput(boolean b) {
// this.printExtraOutput = b;
// GenericObjectEditor.setHideProperty(getClass(), "printNetworks",
@@ -743,11 +730,9 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
// GenericObjectEditor.setHideProperty(getClass(), "printTimestamps",
// !printExtraOutput);
// }
// public String printExtraOutputTipText() {
// return "do you want to print extra output files";
// }
public boolean isPrintNetworks() {
return this.printNetworks;
}
@@ -796,7 +781,6 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
return "Print the time starting time and a timestamp after each generation";
}
public static void main(String[] args) {
Population pop = new Population();
GAIndividualBinaryData indy1 = new GAIndividualBinaryData();
@@ -980,5 +964,4 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
// b.print();
// b.printNetworkToFile("test");
}
}

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

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

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

@@ -1,6 +1,5 @@
package eva2.server.go.strategies;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.InterfaceGAIndividual;
@@ -15,22 +14,22 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import java.util.BitSet;
/** This is an implementation of the CHC Adaptive Search Algorithm by Eshelman. It is
* limited to binary data and is based on massively disruptive crossover. I'm not
* sure whether i've implemented this correctly, but i definitely wasn't able to make
* it competitive to a standard GA.. *sigh*
* This is a implementation of the CHC Adaptive Search Algorithm (Cross generational
/**
* This is an implementation of the CHC Adaptive Search Algorithm by Eshelman.
* It is limited to binary data and is based on massively disruptive crossover.
* I'm not sure whether i've implemented this correctly, but i definitely wasn't
* able to make it competitive to a standard GA.. *sigh* This is a
* implementation of the CHC Adaptive Search Algorithm (Cross generational
* elitist selection, Heterogeneous recombination and Cataclysmic mutation).
* Citation:
*
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
* Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
* Architecture
*
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.Serializable {
private double m_InitialDifferenceThreshold = 0.25;
@@ -42,7 +41,6 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private InterfaceSelection m_RecombSelectionOperator = new SelectRandom();
private InterfaceSelection m_PopulSelectionOperator = new SelectBestSingle();
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -80,7 +78,9 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@@ -103,8 +103,9 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
}
}
/** This method will evaluate the current population using the
* given problem.
/**
* This method will evaluate the current population using the given problem.
*
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
@@ -112,8 +113,9 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
population.incrGeneration();
}
/** This method will generate the offspring population from the
* given population of evaluated individuals.
/**
* This method will generate the offspring population from the given
* population of evaluated individuals.
*/
private Population generateChildren() {
Population result = this.m_Population.cloneWithoutInds(), parents, partners;
@@ -149,7 +151,9 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return result;
}
/** This method computes the Hamming Distance between n-Individuals
/**
* This method computes the Hamming Distance between n-Individuals
*
* @param dad
* @param partners
* @return The maximal Hamming Distance between dad and the partners
@@ -172,8 +176,10 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return result;
}
/** This method method replaces the current population with copies of the current
* best individual but all but one are randomized with a very high mutation rate.
/**
* This method method replaces the current population with copies of the
* current best individual but all but one are randomized with a very high
* mutation rate.
*/
private void diverge() {
AbstractEAIndividual best = this.m_Population.getBestEAIndividual();
@@ -189,8 +195,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
if (RNG.flipCoin(this.m_DivergenceRate)) {
if (tmpBitSet.get(j)) {
tmpBitSet.clear(j);
}
else {
} else {
tmpBitSet.set(j);
}
}
@@ -237,6 +242,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
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) {
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
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -282,36 +294,37 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is an implementation of the CHC Adaptive Search Algorithm by Eselman.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -319,19 +332,23 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return "CHC";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
@@ -353,21 +370,26 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
// public String normationMethodTipText() {
// return "Select the normation method.";
// }
/** Enable/disable elitism.
/**
* Enable/disable elitism.
*
* @param elitism
*/
public void setElitism(boolean elitism) {
this.m_UseElitism = elitism;
}
public boolean getElitism() {
return this.m_UseElitism;
}
public String elitismTipText() {
return "Enable/disable elitism.";
}
/** The number of mating partners needed to create offsprings.
/**
* The number of mating partners needed to create offsprings.
*
* @param partners
*/
public void setNumberOfPartners(int partners) {
@@ -376,9 +398,11 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
}
this.m_NumberOfPartners = partners;
}
public int getNumberOfPartners() {
return this.m_NumberOfPartners;
}
public String numberOfPartnersTipText() {
return "The number of mating partners needed to create offsprings.";
}

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.PriorityQueue;
/** The infamous clustering based niching EA, still under construction.
* It should be able to identify and track multiple global/local optima
* at the same time.
/**
* The infamous clustering based niching EA, still under construction. It should
* 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
* 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
* optimum is lost).
* species (so that they fall below the minimum swarm size and the local optimum
* is lost).
*
* For the CBN-PSO remember to use the IndividualDataMetric so that the
* remembered positions are used for clustering (which are rel. stable -
* so that species clustering actually makes sense).
* remembered positions are used for clustering (which are rel. stable - so that
* 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
*/
public class ClusterBasedNichingEA implements InterfacePopulationChangedEventListener, InterfaceAdditionalPopulationInformer, InterfaceOptimizer, java.io.Serializable {
private static final long serialVersionUID = -3143069327594708609L;
private Population m_Population = new Population();
private transient Population m_Archive = new Population();
private ArrayList<Population> m_Species = new ArrayList<Population>();
private Population m_Undifferentiated = new Population();
private transient Population m_doomedPop = new Population();
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private InterfaceOptimizer m_Optimizer = new GeneticAlgorithm();
private InterfaceClustering m_CAForSpeciesDifferentation = new ClusteringDensityBased();
private InterfaceClustering m_CAForSpeciesMerging = new ClusteringDensityBased();
private double clusterDiffDist = 0.05;
// private double clusterMergeDist = 0.0001;
// private Distraction distraction = null;
private boolean useDistraction = false;
// private double distrDefaultStrength = .7;
private double epsilonBound = 1e-10;
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
private int m_SpeciesCycle = 1;
// from which size on is a species considered active
// private int m_actSpecSize = 2;
@@ -93,7 +90,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
private int m_PopulationSize = 50;
private int convergedCnt = 0;
private int collisions = 0;
private static boolean TRACE = false, TRACE_STATE = false, TRACE_EVTS = false;
private int m_ShowCycle = 0;
transient private TopoPlot m_Topology;
@@ -103,7 +99,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
private int m_maxSpeciesSize = 15;
private AbstractEAIndividualComparator reduceSizeComparator = new AbstractEAIndividualComparator();
private AbstractEAIndividualComparator histComparator = new AbstractEAIndividualComparator("", -1, true);
protected ParameterControlManager paramControl = new ParameterControlManager();
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);
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for InterfaceParamControllable
*/
public Object[] getParamControl() {
List<Object> ctrlbls = ParameterControlManager.listOfControllables(this);
ctrlbls.add(paramControl);
@@ -127,18 +122,20 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
/**
* This method is necessary to allow access from the Processor.
*
* @return
*/
// public ParameterControlManager getParamControl() {
// return paramControl;
// }
public ParamAdaption[] getParameterControl() {
return paramControl.getSingleAdapters();
}
public void setParameterControl(ParamAdaption[] paramControl) {
this.paramControl.setSingleAdapters(paramControl);
}
public String parameterControlTipText() {
return "You may define dynamic paramter control strategies using the parameter name.";
}
@@ -184,8 +181,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
public void init() {
if (m_Undifferentiated == null) {
this.m_Undifferentiated = new Population(m_PopulationSize);
}
else {
} else {
m_Undifferentiated.resetProperties();
m_Undifferentiated.setTargetSize(m_PopulationSize);
}
@@ -236,7 +232,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
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 reset If true the population is reset.
*/
@@ -249,8 +247,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
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
*/
private void evaluatePopulation(Population population) {
@@ -352,14 +351,12 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
double[] pos1, pos2;
if (indy1 instanceof InterfaceDataTypeDouble) {
pos1 = ((InterfaceDataTypeDouble) indy1).getDoubleData();
}
else {
} else {
pos1 = (indy1).getDoublePosition();
}
if (indy2 instanceof InterfaceDataTypeDouble) {
pos2 = ((InterfaceDataTypeDouble) indy2).getDoubleData();
}
else {
} else {
pos2 = (indy2).getDoublePosition();
}
tp.getFunctionArea().drawLine(pos1, pos2);
@@ -380,7 +377,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
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
* @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
* for a number of generations.
* This method checks whether a species is converged, i.e. the best fitness
* has not improved for a number of generations.
*
* @param pop The species to test
* @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
* improvement strictly, but for some EA this should be done more laxly. E.g. DE will hardly ever
* stop improving slightly, so optionally use an epsilon-bound: improvement only counts if it is
* larger than epsilon in case useEpsilonBound is true.
* Define the criterion by which individual improvement is judged. The
* original version defined improvement strictly, but for some EA this
* should be done more laxly. E.g. DE will hardly ever stop improving
* slightly, so optionally use an epsilon-bound: improvement only counts if
* it is larger than epsilon in case useEpsilonBound is true.
*
* @param firstIndy
* @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) {
if (epsilonBound > 0) {
@@ -938,7 +939,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// pop.synchSize();
// return pop;
// }
/**
* 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
* species should be deactivated after merging.
* Merge two species by adding the second to the first. Keep the longer
* history. The second species should be deactivated after merging.
*
* @param pop1
* @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 newSp
@@ -1028,7 +1029,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// protected boolean isActive(Population pop) {
// return (pop.size() >= m_actSpecSize);
// }
// /**
// * Deactivate a given species by removing all individuals and inserting
// * only the given survivor, sets the population size to one.
@@ -1042,7 +1042,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// spec.add(survivor);
// spec.setPopulationSize(1);
// }
// public int countActiveSpec() {
// int k = 0;
// for (int i=0; i<m_Species.size(); i++) {
@@ -1050,8 +1049,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// }
// 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 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) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -1074,13 +1075,16 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
return false;
}
}
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
@@ -1088,13 +1092,16 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
this.m_Problem = problem;
this.m_Optimizer.setProblem(this.m_Problem);
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -1107,36 +1114,37 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a 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
*/
@Override
@@ -1209,28 +1217,34 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// public String applyClearingTipText() {
// 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
* of species convergence.
/**
* This method allows you to set/get the switch that toggles the use of
* species convergence.
*
* @return The current status of this flag
*/
public boolean isUseMerging() {
return this.m_mergeSpecies;
}
public void setUseMerging(boolean b) {
this.m_mergeSpecies = b;
GenericObjectEditor.setHideProperty(this.getClass(), "mergingCA", !m_mergeSpecies);
}
public String useMergingTipText() {
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
*/
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
public void setOptimizer(InterfaceOptimizer b) {
this.m_Optimizer = b;
if (b instanceof EvolutionStrategies) {
@@ -1238,32 +1252,41 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
setMuLambdaRatio(es.getMu() / (double) es.getLambda());
}
}
public String optimizerTipText() {
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
*/
public InterfaceClustering getDifferentiationCA() {
return this.m_CAForSpeciesDifferentation;
}
public void setDifferentiationCA(InterfaceClustering b) {
this.m_CAForSpeciesDifferentation = b;
}
public String differentiationCATipText() {
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
*/
public InterfaceClustering getMergingCA() {
return this.m_CAForSpeciesMerging;
}
public void setMergingCA(InterfaceClustering b) {
this.m_CAForSpeciesMerging = b;
}
public String mergingCATipText() {
return "The cluster algorithm on which the species merging is based.";
}
@@ -1277,44 +1300,58 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// public String useArchiveTipText() {
// return "Toggle usage of an archive where converged species are saved and the individuals reinitialized.";
// }
/** Determines how often species differentation/convergence is performed.
* @return This number gives the generations when specification is performed.
/**
* Determines how often species differentation/convergence is performed.
*
* @return This number gives the generations when specification is
* performed.
*/
public int getSpeciesCycle() {
return this.m_SpeciesCycle;
}
public void setSpeciesCycle(int b) {
this.m_SpeciesCycle = b;
}
public String speciesCycleTipText() {
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() {
return this.m_ShowCycle;
}
public void setShowCycle(int b) {
this.m_ShowCycle = b;
if (b <= 0) {
m_Topology = null;
}
}
public String showCycleTipText() {
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.
*/
public int getPopulationSize() {
return this.m_PopulationSize;
}
public void setPopulationSize(int b) {
this.m_PopulationSize = b;
}
public String populationSizeTipText() {
return "Determines the size of the initial population.";
}
@@ -1329,9 +1366,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// public double getMuLambdaRatio() {
// return muLambdaRatio;
// }
/**
* This is now set if an ES is set as optimizer.
*
* @param muLambdaRatio the muLambdaRatio to set
*/
public void setMuLambdaRatio(double muLambdaRatio) {
@@ -1369,7 +1406,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// public void setDistractionActive(boolean useDistraction) {
// this.useDistraction = useDistraction;
// }
// /**
// * @return the distrDefaultStrength
// */
@@ -1384,19 +1420,20 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// this.distrDefaultStrength = distrDefaultStrength;
// distraction.setDefaultStrength(distrDefaultStrength);
// }
/**
* @return the sleepTime
*/
public int getSleepTime() {
return sleepTime;
}
/**
* @param sleepTime the sleepTime to set
*/
public void setSleepTime(int sleepTime) {
this.sleepTime = sleepTime;
}
public String sleepTimeTipText() {
return "Let the thread sleep between iterations (nice when visualizing)";
}
@@ -1407,12 +1444,14 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
public double getEpsilonBound() {
return epsilonBound;
}
/**
* @param epsilonBound the epsilonBound to set
*/
public void setEpsilonBound(double epsilonBound) {
this.epsilonBound = epsilonBound;
}
public String epsilonBoundTipText() {
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
*/
protected double[] getAvgSpeciesMeasures() {
if (m_Species == null || (m_Species.size() == 0)) {
return new double[]{0};
}
else {
} else {
double[] measures = m_Species.get(0).getPopulationMeasures();
for (int i = 1; i < m_Species.size(); i++) {
Mathematics.vvAdd(measures, m_Species.get(i).getPopulationMeasures(), measures);
@@ -1475,10 +1515,12 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
public int getMaxSpeciesSize() {
return m_maxSpeciesSize;
}
public void setMaxSpeciesSize(int mMaxSpeciesSize) {
m_maxSpeciesSize = mMaxSpeciesSize;
GenericObjectEditor.setShowProperty(this.getClass(), "reduceSizeComparator", (m_maxSpeciesSize >= m_minGroupSize));
}
public String maxSpeciesSizeTipText() {
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() {
return "Set the comparator used to define the 'worst' individuals when reducing species size.";
}
public AbstractEAIndividualComparator getReduceSizeComparator() {
return reduceSizeComparator;
}
public void setReduceSizeComparator(
AbstractEAIndividualComparator 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
* 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
* if the clustering parameter is controlled.
* For certain types of parameter adaption schemes, this automatically sets
* the upper limit if the clustering parameter is controlled.
*
* @param prob
* @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 java.io.Serializable;
/**
* The clustering hill climber is similar to a multi-start hill climber. In addition so optimizing
* a set of individuals in parallel using a (1+1) strategy, the population is clustered in regular
* intervals. If several individuals have gathered together in the sense that they are interpreted
* as a cluster, only a subset of representatives of the cluster is taken over to the next HC step
* while the rest is discarded. This means that the population size may be reduced.
* The clustering hill climber is similar to a multi-start hill climber. In
* addition so optimizing a set of individuals in parallel using a (1+1)
* strategy, the population is clustered in regular intervals. If several
* individuals have gathered together in the sense that they are interpreted as
* a cluster, only a subset of representatives of the cluster is taken over to
* the next HC step while the rest is discarded. This means that the population
* size may be reduced.
*
* As soon as the improvement by HC lies below a threshold, the mutation step size is decreased.
* If the step size is decreased below a certain threshold, the current population is stored to
* an archive and reinitialized. Thus, the number of optima that may be found and returned by
* getAllSolutions is higher than the population size.
* As soon as the improvement by HC lies below a threshold, the mutation step
* size is decreased. If the step size is decreased below a certain threshold,
* the current population is stored to an archive and reinitialized. Thus, the
* number of optima that may be found and returned by getAllSolutions is higher
* than the population size.
*
* @author mkron
*
*/
public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener,
InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
transient private InterfacePopulationChangedEventListener m_Listener;
public static final boolean TRACE = false;
transient private String m_Identifier = "";
private Population m_Population = new Population();
private transient Population archive = new Population();
@@ -101,18 +103,22 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
@@ -122,6 +128,7 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -132,6 +139,7 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
return false;
}
}
@Override
public void init() {
loopCnt = 0;
@@ -145,7 +153,9 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@@ -161,7 +171,8 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
@@ -181,8 +192,7 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
int evalsNow, lastOverhead = (m_Population.getFunctionCalls() % hcEvalCycle);
if (lastOverhead > 0) {
evalsNow = (2 * hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle));
}
else {
} else {
evalsNow = hcEvalCycle;
}
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
* of the optimizer.
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Change the number of starting individuals stored (Cluster-HC).";
}
@@ -295,8 +309,10 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
return new SolutionSet(m_Population, tmp);
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -310,17 +326,14 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
return sbuf.toString();
}
@Override
public void freeWilly() {}
@Override
public String getName() {
return "ClustHC-" + initialPopSize + "-" + localSearchMethod;
}
public static String globalInfo() {
return "Similar to multi-start HC, but clusters the population during optimization to remove redundant individuals for efficiency." +
"If the local search step does not achieve a minimum improvement, the population may be reinitialized.";
return "Similar to multi-start HC, but clusters the population during optimization to remove redundant individuals for efficiency."
+ "If the local search step does not achieve a minimum improvement, the population may be reinitialized.";
}
/**

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

@@ -21,12 +21,12 @@ import eva2.tools.math.RNG;
import java.util.Vector;
/**
* Differential evolution implementing DE1 and DE2 following the paper of Storm and
* Price and the Trigonometric DE published recently.
* Please note that DE will only work on real-valued genotypes and will ignore
* all mutation and crossover operators selected.
* Added aging mechanism to provide for dynamically changing problems. If an individual
* reaches the age limit, it is doomed and replaced by the next challenge vector, even if its worse.
* Differential evolution implementing DE1 and DE2 following the paper of Storm
* and Price and the Trigonometric DE published recently. Please note that DE
* will only work on real-valued genotypes and will ignore all mutation and
* crossover operators selected. Added aging mechanism to provide for
* dynamically changing problems. If an individual reaches the age limit, it is
* doomed and replaced by the next challenge vector, even if its worse.
*
*/
public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serializable {
@@ -44,7 +44,6 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// to log the parents of a newly created indy.
public boolean doLogParents = false; // deactivate for better performance
private transient Vector<AbstractEAIndividual> parents = null;
private boolean randomizeFKLambda = false;
private boolean generational = true;
private String m_Identifier = "";
@@ -110,7 +109,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
setDEType(getDEType());
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@@ -126,8 +127,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// else children = new Population(m_Population.size());
}
/** This method will evaluate the current population using the
* given problem.
/**
* This method will evaluate the current population using the given problem.
*
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
@@ -136,8 +138,8 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
}
/**
* This method returns a difference vector between two random individuals from the population.
* This method should make sure that delta is not zero.
* This method returns a difference vector between two random individuals
* from the population. This method should make sure that delta is not zero.
*
* @param pop The population to choose from
* @return The delta vector
@@ -178,8 +180,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
for (int i = 0; i < x1.length; i++) {
if (RNG.flipCoin(1 / (double) x1.length)) {
result[i] = 0.01 * RNG.gaussianDouble(0.1);
}
else {
} else {
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 should make sure that delta is not zero.
* This method returns a difference vector between two random individuals
* from the population. This method should make sure that delta is not zero.
*
* @param pop The population to choose from
* @return The delta vector
@@ -234,8 +235,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
for (int i = 0; i < x1.length; i++) {
if (RNG.flipCoin(1 / (double) x1.length)) {
result[i] = 0.01 * RNG.gaussianDouble(0.1);
}
else {
} else {
result[i] = 0;
}
isEmpty = (isEmpty && (result[i] == 0));
@@ -283,7 +283,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return result;
}
/** This method returns two parents to the original individual
/**
* This method returns two parents to the original individual
*
* @param pop The population to choose from
* @return the delta vector
*/
@@ -301,8 +303,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// result[1] = indy2.getDGenotype();
// return result;
// }
/** This method will generate one new individual from the given population
/**
* This method will generate one new individual from the given population
*
* @param pop The current population
* @return AbstractEAIndividual
*/
@@ -313,8 +316,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
if (doLogParents) {
parents = new Vector<AbstractEAIndividual>();
}
else {
} else {
parents = null;
}
try {
@@ -453,8 +455,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
private double getCurrentK() {
if (randomizeFKLambda) {
return RNG.randomDouble(m_k * 0.8, m_k * 1.2);
}
else {
} else {
return m_k;
}
}
@@ -462,8 +463,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
private double getCurrentLambda() {
if (randomizeFKLambda) {
return RNG.randomDouble(m_Lambda * 0.8, m_Lambda * 1.2);
}
else {
} else {
return m_Lambda;
}
}
@@ -471,8 +471,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
private double getCurrentF() {
if (randomizeFKLambda) {
return RNG.randomDouble(m_F * 0.8, m_F * 1.2);
}
else {
} else {
return m_F;
}
}
@@ -504,16 +503,16 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
public void optimize() {
if (generational) {
optimizeGenerational();
}
else {
} else {
optimizeSteadyState();
}
}
/**
* This generational DE variant calls the method AbstractOptimizationProblem.evaluate(Population).
* Its performance may be slightly worse for schemes that rely on current best individuals,
* because improvements are not immediately incorporated as in the steady state DE.
* This generational DE variant calls the method
* AbstractOptimizationProblem.evaluate(Population). Its performance may be
* slightly worse for schemes that rely on current best individuals, because
* improvements are not immediately incorporated as in the steady state DE.
* However it may be easier to parallelize.
*
*/
@@ -524,15 +523,13 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// m_Problem.evaluatePopulationStart(m_Population);
if (children == null) {
children = new Population(m_Population.size());
}
else {
} else {
children.clear();
}
for (int i = 0; i < this.m_Population.size(); i++) {
if (cyclePop) {
parentIndex = i;
}
else {
} else {
parentIndex = RNG.randomInt(0, this.m_Population.size() - 1);
}
AbstractEAIndividual indy = generateNewIndividual(m_Population, parentIndex);
@@ -543,7 +540,8 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
m_Problem.evaluate(children);
/**
* MdP: added a reevalutation mechanism for dynamically changing problems
* MdP: added a reevalutation mechanism for dynamically changing
* problems
*/
if (isReEvaluate()) {
for (int i = 0; i < this.m_Population.size(); i++) {
@@ -561,8 +559,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
AbstractEAIndividual indy = children.getEAIndividual(i);
if (cyclePop) {
parentIndex = i;
}
else {
} else {
parentIndex = RNG.randomInt(0, this.m_Population.size() - 1);
}
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()) {
nextDoomed = -1;
@@ -618,8 +616,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
for (int i = 0; i < this.m_Population.size(); i++) {
if (cyclePop) {
index = i;
}
else {
} else {
index = RNG.randomInt(0, this.m_Population.size() - 1);
}
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.
* If there is no age limit or all individuals are younger, -1 is returned. The start index
* of the search may be provided to make iterative search efficient.
* Search for the first individual which is older than the age limit and
* return its index. If there is no age limit or all individuals are
* younger, -1 is returned. The start index of the search may be provided to
* make iterative search efficient.
*
* @param pop Population to search
* @param startIndex index to start the search from
@@ -718,7 +716,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return -1;
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
@@ -728,6 +728,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
}
this.m_Listener.add(ea);
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -738,7 +739,10 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return false;
}
}
/** Something has changed
/**
* Something has changed
*
* @param 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
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = (AbstractOptimizationProblem) problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return (InterfaceOptimizationProblem) this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -774,35 +783,38 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The identifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "Differential Evolution using a steady-state population scheme.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -810,19 +822,23 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return "DE";
}
/** Assuming that all optimizer will store their data in a population
* we will allow access to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store their data in a population we will
* allow access to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
@@ -833,20 +849,28 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return new SolutionSet(pop, pop);
}
/** F is a real and constant factor which controls the amplification of the differential variation
/**
* F is a real and constant factor which controls the amplification of the
* differential variation
*
* @param f
*/
public void setF(double f) {
this.m_F = f;
}
public double getF() {
return this.m_F;
}
public String fTipText() {
return "F is a real and constant factor which controls the amplification of the differential variation.";
}
/** Probability of alteration through DE (something like a discrete uniform crossover is performed here)
/**
* Probability of alteration through DE (something like a discrete uniform
* crossover is performed here)
*
* @param k
*/
public void setK(double k) {
@@ -858,27 +882,36 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
}
this.m_k = k;
}
public double getK() {
return this.m_k;
}
public String kTipText() {
return "Probability of alteration through DE (a.k.a. CR, similar to discrete uniform crossover).";
}
/** Enhance greediness through amplification of the differential vector to the best individual for DE2
/**
* Enhance greediness through amplification of the differential vector to
* the best individual for DE2
*
* @param l
*/
public void setLambda(double l) {
this.m_Lambda = l;
}
public double getLambda() {
return this.m_Lambda;
}
public String lambdaTipText() {
return "Enhance greediness through amplification of the differential vector to the best individual for DE2.";
}
/** In case of trig. mutation DE, the TMO is applied wit probability Mt
/**
* In case of trig. mutation DE, the TMO is applied wit probability Mt
*
* @param l
*/
public void setMt(double l) {
@@ -890,14 +923,18 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
this.m_Mt = 1;
}
}
public double getMt() {
return this.m_Mt;
}
public String mtTipText() {
return "In case of trigonometric mutation DE, the TMO is applied with probability Mt.";
}
/** This method allows you to choose the type of Differential Evolution.
/**
* This method allows you to choose the type of Differential Evolution.
*
* @param s The type.
*/
public void setDEType(DETypeEnum s) {
@@ -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(), "mt", s == DETypeEnum.TrigonometricDE);
}
public DETypeEnum getDEType() {
return this.m_DEType;
}
public String dETypeTipText() {
return "Choose the type of Differential Evolution.";
}
/**
* @return the maximumAge
**/
*
*/
public int getMaximumAge() {
return maximumAge;
}
/**
* @param maximumAge the maximumAge to set
**/
*
*/
public void setMaximumAge(int maximumAge) {
this.maximumAge = maximumAge;
}
@@ -933,6 +974,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
/**
* Check whether the problem range will be enforced.
*
* @return the forceRange
*/
public boolean isCheckRange() {
@@ -973,7 +1015,6 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
// public String cyclePopTipText() {
// return "Use all individuals as parents in cyclic sequence instead of randomly.";
// }
public boolean isCompareToParent() {
return compareToParent;
}
@@ -1012,14 +1053,16 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
/**
* @return the maximumAge
**/
*
*/
public boolean isReEvaluate() {
return reEvaluate;
}
/**
* @param maximumAge the maximumAge to set
**/
*
*/
public void setReEvaluate(boolean reEvaluate) {
this.reEvaluate = reEvaluate;
}
@@ -1027,5 +1070,4 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
public String reEvaluateTipText() {
return "Reeavulates individuals which are older than maximum age instead of discarding them";
}
}

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

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

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

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

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.InterfaceOptimizationProblem;
/** Evolutionary programming by Fogel. Works fine but is actually a quite greedy local search
* strategy solely based on mutation. To prevent any confusion, the mutation rate is temporaily
* set to 1.0.
* Potential citation: the PhD thesis of David B. Fogel (1992).
/**
* Evolutionary programming by Fogel. Works fine but is actually a quite greedy
* local search strategy solely based on mutation. To prevent any confusion, the
* mutation rate is temporaily set to 1.0. Potential citation: the PhD thesis of
* David B. Fogel (1992).
*
* Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
* Architecture
*
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Serializable {
private int m_PopulationSize = 0;
private Population m_Population = new Population();
private InterfaceOptimizationProblem m_Problem = new F1Problem();
private InterfaceSelection m_EnvironmentSelection = new SelectEPTournaments();
private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -56,7 +55,9 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param reset If true the population is reset.
*/
@Override
@@ -69,8 +70,9 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
}
}
/** This method will evaluate the current population using the
* given problem.
/**
* This method will evaluate the current population using the given problem.
*
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
@@ -78,8 +80,9 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
population.incrGeneration();
}
/** This method will generate the offspring population from the
* given population of evaluated individuals.
/**
* This method will generate the offspring population from the given
* population of evaluated individuals.
*/
private Population generateChildren() {
Population result = (Population) this.m_Population.cloneWithoutInds();
@@ -113,13 +116,16 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -130,7 +136,9 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent(String name) {
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
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -163,35 +176,38 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a basic Evolutionary Programming scheme.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -199,19 +215,23 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
return "EP";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
@@ -220,15 +240,20 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** Choose a method for selecting the reduced population.
/**
* Choose a method for selecting the reduced population.
*
* @param selection
*/
public void setEnvironmentSelection(InterfaceSelection selection) {
this.m_EnvironmentSelection = selection;
}
public InterfaceSelection getEnvironmentSelection() {
return this.m_EnvironmentSelection;
}
public String environmentSelectionTipText() {
return "Choose a method for selecting the reduced population.";
}

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.InterfaceOptimizationProblem;
/** The flood algorithm, and alternative to the threshold algorithms. No really
* good but commonly known and sometimes even used. Here the problem is to choose
* the initial flood peak and the drain rate such that it fits the current optimization
* problem. But again this is a greedy local search strategy. Similar to the
* evolutionary programming strategy this strategy sets the mutation rate temporarily
* to 1.0.
* The algorithm regards only one-dimensional fitness.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 01.10.2004
* Time: 13:46:02
* To change this template use File | Settings | File Templates.
/**
* The flood algorithm, and alternative to the threshold algorithms. No really
* good but commonly known and sometimes even used. Here the problem is to
* choose the initial flood peak and the drain rate such that it fits the
* current optimization problem. But again this is a greedy local search
* strategy. Similar to the evolutionary programming strategy this strategy sets
* the mutation rate temporarily to 1.0. The algorithm regards only
* one-dimensional fitness. Created by IntelliJ IDEA. User: streiche Date:
* 01.10.2004 Time: 13:46:02 To change this template use File | Settings | File
* Templates.
*/
public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private int m_MultiRuns = 100;
private int m_FitnessCalls = 100;
@@ -31,7 +30,6 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
GAIndividualBinaryData m_Best, m_Test;
public double m_InitialFloodPeak = 2000.0, m_CurrentFloodPeak;
public double m_DrainRate = 1.0;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -54,7 +52,8 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
return (Object) new FloodAlgorithm(this);
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
@Override
public void init() {
@@ -64,7 +63,9 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param reset If true the population is reset.
*/
@Override
@@ -78,7 +79,8 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
this.m_CurrentFloodPeak = this.m_InitialFloodPeak;
}
/** This method will optimize
/**
* This method will optimize
*/
@Override
public void optimize() {
@@ -106,7 +108,9 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method calculates the difference between the fitness values
/**
* This method calculates the difference between the fitness values
*
* @param org The original
* @param mut The mutant
*/
@@ -121,19 +125,23 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
return result;
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
public void defaultInit() {
this.m_FitnessCallsNeeded = 0;
@@ -141,7 +149,8 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
this.m_Best.defaultInit(m_Problem);
}
/** This method will optimize
/**
* This method will optimize
*/
public void defaultOptimize() {
for (int i = 0; i < m_FitnessCalls; i++) {
@@ -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
*/
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);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -192,7 +205,9 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent(String name) {
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
*/
@Override
@@ -209,8 +226,7 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
String result = "";
if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
}
else {
} else {
result += "Simulated Annealing:\n";
}
result += "Optimization Problem: ";
@@ -218,35 +234,38 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The flood algorithm uses an declining flood peak to accpect new solutions (*shudder* check inital flood peak and drain very carefully!).";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -254,55 +273,67 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
return "MS-FA";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Change the number of best individuals stored (MS-FA).";
}
@Override
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This methods allow you to set/get the temperatur of the flood
* algorithm procedure
/**
* This methods allow you to set/get the temperatur of the flood algorithm
* procedure
*
* @return The initial flood level.
*/
public double getInitialFloodPeak() {
return this.m_InitialFloodPeak;
}
public void setInitialFloodPeak(double pop) {
this.m_InitialFloodPeak = pop;
}
public String initialFloodPeakTipText() {
return "Set the initial flood peak.";
}
/** This methods allow you to set/get the drain rate of the flood
* algorithm procedure
/**
* This methods allow you to set/get the drain rate of the flood algorithm
* procedure
*
* @return The drain rate.
*/
public double getDrainRate() {
return this.m_DrainRate;
}
public void setDrainRate(double a) {
this.m_DrainRate = a;
if (this.m_DrainRate < 0) {
this.m_DrainRate = 0.0;
}
}
public String drainRateTipText() {
return "Set the drain rate that reduces the current flood level each generation.";
}

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

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

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

@@ -22,11 +22,9 @@ import eva2.tools.ReflectPackage;
* @author not attributable
* @version 1.0
*/
public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Serializable {
private InterfaceOptimizationProblem m_Problem;
InterfaceDataTypeDouble m_Best, m_Test;
private int iterations = 1;
private double wDecreaseStepSize = 0.5;
@@ -46,13 +44,10 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
transient private InterfacePopulationChangedEventListener m_Listener;
public double maximumabsolutechange = 0.2;
// Hashtable indyhash;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
private Population m_Population;
private static boolean TRACE = false;
private static final String lockKey = "gdaLockDataKey";
private static final String lastFitnessKey = "gdaLastFitDataKey";
private static final String stepSizeKey = "gdaStepSizeDataKey";
@@ -98,7 +93,9 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
@Override
public Object clone() {
/**@todo Implement InterfaceOptimizer method*/
/**
* @todo Implement InterfaceOptimizer method
*/
throw new java.lang.UnsupportedOperationException("Method clone() not yet implemented.");
}
@@ -311,7 +308,6 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
private double momentumweigth = 0.1;
protected void firePropertyChangedEvent(String name) {
@@ -340,8 +336,9 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
this.m_Population = pop;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
@@ -374,6 +371,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -384,6 +382,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
return false;
}
}
public static void main(String[] args) {
GradientDescentAlgorithm program = new GradientDescentAlgorithm();
InterfaceOptimizationProblem problem = new F1Problem();
@@ -399,22 +398,22 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
}
}
@Override
public void freeWilly() { }
public static String globalInfo() {
return "Gradient Descent can be applied to derivable functions (" + InterfaceFirstOrderDerivableProblem.class.getSimpleName() + ").";
}
//////////////// for global adaption
public boolean isAdaptStepSizeGlobally() {
return globalStepSizeAdaption;
}
public void setAdaptStepSizeGlobally(boolean globalstepsizeadaption) {
this.globalStepSizeAdaption = globalstepsizeadaption;
if (globalstepsizeadaption && localStepSizeAdaption) {
setAdaptStepSizeLocally(false);
}
}
public String adaptStepSizeGloballyTipText() {
return "Use a single step size per individual - (priority over local step size).";
}
@@ -422,9 +421,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getGlobalMaxStepSize() {
return globalmaxstepsize;
}
public void setGlobalMaxStepSize(double p) {
globalmaxstepsize = p;
}
public String globalMaxStepSizeTipText() {
return "Maximum step size for global adaption.";
}
@@ -432,9 +433,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getGlobalMinStepSize() {
return globalminstepsize;
}
public void setGlobalMinStepSize(double p) {
globalminstepsize = p;
}
public String globalMindStepSizeTipText() {
return "Minimum step size for global adaption.";
}
@@ -442,9 +445,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getGlobalInitStepSize() {
return globalinitstepsize;
}
public void setGlobalInitStepSize(double initstepsize) {
this.globalinitstepsize = initstepsize;
}
public String globalInitStepSizeTipText() {
return "Initial step size for global adaption.";
}
@@ -453,12 +458,14 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public boolean isAdaptStepSizeLocally() {
return localStepSizeAdaption;
}
public void setAdaptStepSizeLocally(boolean stepsizeadaption) {
this.localStepSizeAdaption = stepsizeadaption;
if (globalStepSizeAdaption && localStepSizeAdaption) {
setAdaptStepSizeGlobally(false);
}
}
public String adaptStepSizeLocallyTipText() {
return "Use a step size parameter in any dimension.";
}
@@ -466,6 +473,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getLocalMinStepSize() {
return localminstepsize;
}
public void setLocalMinStepSize(double localminstepsize) {
this.localminstepsize = localminstepsize;
}
@@ -473,6 +481,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getLocalMaxStepSize() {
return localmaxstepsize;
}
public void setLocalMaxStepSize(double localmaxstepsize) {
this.localmaxstepsize = localmaxstepsize;
}
@@ -480,9 +489,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public void setStepSizeIncreaseFact(double nplus) {
this.wIncreaseStepSize = nplus;
}
public double getStepSizeIncreaseFact() {
return wIncreaseStepSize;
}
public String stepSizeIncreaseFactTipText() {
return "Factor for increasing the step size in adaption.";
}
@@ -490,9 +501,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public void setStepSizeDecreaseFact(double nminus) {
this.wDecreaseStepSize = nminus;
}
public double getStepSizeDecreaseFact() {
return wDecreaseStepSize;
}
public String stepSizeDecreaseFactTipText() {
return "Factor for decreasing the step size in adaption.";
}
@@ -501,40 +514,47 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public boolean isRecovery() {
return recovery;
}
public void setRecovery(boolean recovery) {
this.recovery = recovery;
}
public int getRecoveryLocksteps() {
return recoverylocksteps;
}
public void setRecoveryLocksteps(int locksteps) {
this.recoverylocksteps = locksteps;
}
public double getRecoveryThreshold() {
return recoverythreshold;
}
public void setRecoveryThreshold(double recoverythreshold) {
this.recoverythreshold = recoverythreshold;
}
public String recoveryThresholdTipText() {
return "If the fitness exceeds this threshold, an unstable area is assumed and one step recovered.";
}
public int getIterations() {
return iterations;
}
public void setIterations(int iterations) {
this.iterations = iterations;
}
public String iterationsTipText() {
return "The number of GD-iterations per generation.";
}
public boolean isManhattan() {
return manhattan;
}
public void setManhattan(boolean manhattan) {
this.manhattan = manhattan;
}
@@ -542,6 +562,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public boolean isMomentumTerm() {
return momentumterm;
}
public void setMomentumTerm(boolean momentum) {
this.momentumterm = momentum;
}
@@ -549,6 +570,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getMomentumWeigth() {
return momentumweigth;
}
public void setMomentumWeigth(double momentumweigth) {
this.momentumweigth = momentumweigth;
}
@@ -556,11 +578,12 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public double getMaximumAbsoluteChange() {
return maximumabsolutechange;
}
public void setMaximumAbsoluteChange(double maximumabsolutechange) {
this.maximumabsolutechange = maximumabsolutechange;
}
public String maximumAbsoluteChangeTipText() {
return "The maximum change along a coordinate in one step.";
}
}

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.InterfaceOptimizationProblem;
/** This is a Multi-Start Hill-Climber, here the population size gives the number of
* multi-starts. Similar to the evolutionary programming strategy this strategy sets the
* mutation rate temporarily to 1.0.
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
/**
* This is a Multi-Start Hill-Climber, here the population size gives the number
* of multi-starts. Similar to the evolutionary programming strategy this
* strategy sets the mutation rate temporarily to 1.0. Copyright: Copyright (c)
* 2003 Company: University of Tuebingen, Computer Architecture
*
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private InterfaceMutation mutator = null;
// private int m_MultiRuns = 100;
// private int m_FitnessCalls = 100;
// private int m_FitnessCallsNeeded = 0;
// GAIndividualBinaryData m_Best, m_Test;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -50,7 +48,8 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
return (Object) new HillClimbing(this);
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
@Override
public void init() {
@@ -69,7 +68,8 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
}
}
/** This method will optimize
/**
* This method will optimize
*/
@Override
public void optimize() {
@@ -84,8 +84,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
indy.setMutationProbability(1.0);
if (mutator == null) {
indy.mutate();
}
else {
} else {
mutator.mutate(indy);
}
indy.setMutationProbability(tmpD);
@@ -123,8 +122,9 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
}
/**
* Allows to set a desired mutator by hand, which is used instead of the one in the individuals.
* Set it to null to use the one in the individuals, which is the default.
* Allows to set a desired mutator by hand, which is used instead of the one
* in the individuals. Set it to null to use the one in the individuals,
* which is the default.
*
* @param mute
*/
@@ -132,13 +132,16 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
mutator = mute;
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
@@ -163,7 +166,6 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
// if (this.m_Best.defaultEvaulateAsMiniBits() == 0) i = this.m_FitnessCalls +1;
// }
// }
// /** This main method will start a simple hillclimber.
// * No arguments necessary.
// * @param args
@@ -181,14 +183,16 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
// TmpMeanFitness = TmpMeanFitness/program.m_MultiRuns;
// System.out.println("("+program.m_MultiRuns+"/"+program.m_FitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
// }
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -199,7 +203,9 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent(String name) {
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
*/
@Override
@@ -216,8 +224,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
String result = "";
if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
}
else {
} else {
result += "Hill Climbing:\n";
}
result += "Optimization Problem: ";
@@ -225,45 +232,50 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The Hill Climber uses the default EA mutation and initializing operators. If the population size is bigger than one a multi-start Hill Climber is performed.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
public String getName() {
return "MS-HC" + getIdentifier();
}
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
@@ -273,6 +285,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
public String populationTipText() {
return "Change the number of best individuals stored (MS-HC).";
}

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

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

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.TF1Problem;
/** The one and only island model for parallelization. Since parallelization based
* on the RMIProxyRemoteThread is on the one hand much slower than benchmark function
* evaluation and on the other hand the GUI based distribution scheme is rather prone
* to config errors (the correct ssh version is required, the jar needs to be in
* the working dir and possible problem data must be on the servers to) an implicit
* island-model has been implemented too to allow fast and reliable computation.
* This is still usefull, since it is less prone to premature convergence and also
* an heterogenuous island model can be used.
/**
* The one and only island model for parallelization. Since parallelization
* based on the RMIProxyRemoteThread is on the one hand much slower than
* benchmark function evaluation and on the other hand the GUI based
* distribution scheme is rather prone to config errors (the correct ssh version
* is required, the jar needs to be in the working dir and possible problem data
* must be on the servers to) an implicit island-model has been implemented too
* to allow fast and reliable computation. This is still usefull, since it is
* less prone to premature convergence and also an heterogenuous island model
* can be used.
*
* A population of the same size is sent to all nodes and evaluated there independently
* for a cycle (more precisely: for MigrationRate generations) after which a communication
* step is performed according to the migration model. Only after migration is a main
* cycle complete, the statistics updated etc.
* A population of the same size is sent to all nodes and evaluated there
* independently for a cycle (more precisely: for MigrationRate generations)
* after which a communication step is performed according to the migration
* model. Only after migration is a main cycle complete, the statistics updated
* etc.
*
* Created by IntelliJ IDEA.
* User: streiche
* Date: 12.09.2004
* Time: 14:48:20
* To change this template use File | Settings | File Templates.
* Created by IntelliJ IDEA. User: streiche Date: 12.09.2004 Time: 14:48:20 To
* change this template use File | Settings | File Templates.
*/
public class IslandModelEA implements InterfacePopulationChangedEventListener, InterfaceOptimizer, java.io.Serializable {
private Population m_Population = new Population();
@@ -47,22 +46,18 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
// private String[] m_NodesList;
private int m_MigrationRate = 10;
private boolean m_HeterogenuousProblems = false;
// These are the processor to run on
private int m_numLocalCPUs = 1;
private boolean m_localOnly = false;
transient private InterfaceOptimizer[] m_Islands;
// This is for debugging
private boolean m_LogLocalChanges = true;
private boolean m_Show = false;
transient private Plot m_Plot = null;
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
transient private final boolean TRACE = false;
public IslandModelEA() {
}
@@ -152,7 +147,9 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.firePropertyChangedEvent(Population.nextGenerationPerformed, this.m_Optimizer.getPopulation());
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param reset If true the population is reset.
*/
@Override
@@ -223,7 +220,8 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.firePropertyChangedEvent(Population.nextGenerationPerformed, this.m_Optimizer.getPopulation());
}
/** The optimize method will compute an 'improved' and evaluated population
/**
* The optimize method will compute an 'improved' and evaluated population
*/
@Override
public void optimize() {
@@ -253,8 +251,8 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
System.gc();
}
/** This method will manage communication between the
* islands
/**
* This method will manage communication between the islands
*/
private void communicate() {
// Here i'll have to wait until all islands are finished
@@ -296,13 +294,16 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Migration.migrate(this.m_Islands);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -313,7 +314,9 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent(String name, Population population) {
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
*/
@Override
@@ -329,13 +334,16 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Problem = problem;
this.m_Optimizer.setProblem(problem);
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -362,7 +370,8 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
return result;
}
/** This method is to test the parallelization scheme
/**
* This method is to test the parallelization scheme
*
* @param args
*/
@@ -410,47 +419,48 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
//System.exit(0);
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method will return the Optimizers
/**
* This method will return the Optimizers
*
* @return An array of optimizers
*/
public InterfaceOptimizer[] getOptimizers() {
return this.m_Islands;
}
/** This method will allow you to toggel between homogenuous and heterogenuous problems.
* In case of heterogenuous problems the individuals need to be reevaluated after migration.
/**
* This method will allow you to toggel between homogenuous and
* heterogenuous problems. In case of heterogenuous problems the individuals
* need to be reevaluated after migration.
*
* @param t
*/
public void setHeterogenuousProblems(boolean t) {
this.m_HeterogenuousProblems = t;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
for (int i = 0; i < this.m_Islands.length; i++) {
this.m_Islands[i].freeWilly();
}
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for InterfacePopulationChangedEventListener
*/
/** This method allows an optimizer to register a change in the EA-lecture
/**
* This method allows an optimizer to register a change in the EA-lecture
*
* @param source The source of the event.
* @param name Could be used to indicate the nature of the event.
*/
@@ -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);
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is an island model EA distributing the individuals across several (remote) CPUs for optimization.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -486,8 +502,10 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
return "IslandEA";
}
/** This method allows you to toggle between a truly parallel
* and a serial implementation.
/**
* This method allows you to toggle between a truly parallel and a serial
* implementation.
*
* @return The current optimization mode
*/
// TODO Deactivated from GUI because the current implementation does not really parallelize on a multicore.
@@ -498,59 +516,76 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
public void setLocalOnly(boolean b) {
this.m_localOnly = b;
}
public String localOnlyTipText() {
return "Toggle between usage of local CPUs and remote servers.";
}
/** This will show the local performance
/**
* This will show the local performance
*
* @return The current optimzation mode
*/
public boolean getShow() {
return this.m_Show;
}
public void setShow(boolean b) {
this.m_Show = b;
this.m_LogLocalChanges = b;
}
public String showTipText() {
return "This will show the local performance.";
}
/** This method allows you to set/get the optimizing technique to use.
/**
* This method allows you to set/get the optimizing technique to use.
*
* @return The current optimizing method
*/
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
public void setOptimizer(InterfaceOptimizer b) {
this.m_Optimizer = b;
}
public String optimizerTipText() {
return "Choose a population based optimizing technique to use.";
}
/** This method allows you to set/get the migration strategy to use.
/**
* This method allows you to set/get the migration strategy to use.
*
* @return The current migration strategy
*/
public InterfaceMigration getMigrationStrategy() {
return this.m_Migration;
}
public void setMigrationStrategy(InterfaceMigration b) {
this.m_Migration = b;
}
public String migrationStrategyTipText() {
return "Choose a migration strategy to use.";
}
/** This method allows you to set/get the migration rate to use.
/**
* This method allows you to set/get the migration rate to use.
*
* @return The current migration rate
*/
public int getMigrationRate() {
return this.m_MigrationRate;
}
public void setMigrationRate(int b) {
this.m_MigrationRate = b;
}
public String migrationRateTipText() {
return "Set the migration rate for communication between islands.";
}
@@ -559,20 +594,24 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
return "Choose and manage the servers (only active in parallelized mode).";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
// @todo Jetzt m<>sste ich die pop auch auf die Rechner verteilen...
this.m_Population = pop;
}
public String populationTipText() {
return "(Defunct)";
}
@@ -581,14 +620,16 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method allows you to set the number of processors in local mode
/**
* This method allows you to set the number of processors in local mode
*
* @param n Number of processors.
*/
public void setNumberLocalCPUs(int n) {
if (n >= 1) {
this.m_numLocalCPUs = n;
}
else {
} else {
System.err.println("Number of CPUs must be at least 1!");
}
}
@@ -597,6 +638,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
// public int getNumberLocalCPUs() {
// return this.m_LocalCPUs;
// }
public String numberLocalCPUsTipText() {
return "Set the number of local CPUS (>=1, only used in local mode).";
}

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

@@ -364,10 +364,6 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface
return "Linkage Tree GA";
}
@Override
public void freeWilly() {
}
@Override
public void registerPopulationStateChanged(Object source, String name) {
// The events of the interim hill climbing population will be caught here

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";
}
@Override
public void freeWilly() {
}
@Override
public void registerPopulationStateChanged(Object source, String name) {
// 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 java.util.Hashtable;
/**
* A memetic algorithm by hannes planatscher. The local search strategy can only
* be applied to problems which implement the InterfaceLocalSearchable else the
* local search will not be activated at all.
* <p>
* Title: EvA2
* </p>
* <p>
* Description:
* </p>
* <p>
* Copyright: Copyright (c) 2003
* </p>
* <p>
* Company:
* </p>
* local search will not be activated at all. <p> Title: EvA2 </p> <p>
* Description: </p> <p> Copyright: Copyright (c) 2003 </p> <p> Company: </p>
*
* @author not attributable
* @version 1.0
*/
public class MemeticAlgorithm implements InterfaceOptimizer,
java.io.Serializable {
@@ -41,32 +28,20 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
* serial version uid.
*/
private static final long serialVersionUID = -1730086430763348568L;
private int localSearchSteps = 1;
private int subsetsize = 5;
private int globalSearchIterations = 1;
private boolean lamarckism = true;
// int counter = 0; !?
// int maxfunctioncalls = 1000; !?
private boolean TRACE = false;
private String m_Identifier = "";
private InterfaceOptimizationProblem m_Problem = new F1Problem();
private InterfaceOptimizer m_GlobalOptimizer = new GeneticAlgorithm();
private InterfaceSelection selectorPlug = new SelectBestIndividuals();
transient private InterfacePopulationChangedEventListener m_Listener;
public MemeticAlgorithm() {
}
public MemeticAlgorithm(MemeticAlgorithm a) {
@@ -108,8 +83,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
/**
* This method will evaluate the current population using the given problem.
*
* @param population
* The population that is to be evaluated
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
this.m_Problem.evaluate(population);
@@ -212,6 +186,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -222,6 +197,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
return false;
}
}
/**
* Something has changed
*/
@@ -251,8 +227,8 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
}
/**
* This method will return a string describing all properties of the optimizer
* and the applied methods.
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@@ -269,8 +245,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
/**
* This method allows you to set an identifier for the algorithm
*
* @param name
* The indenifier
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
@@ -282,14 +257,6 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
return this.m_Identifier;
}
/**
* This method is required to free the memory on a RMIServer, but there is
* nothing to implement.
*/
@Override
public void freeWilly() {
}
/*
* ========================================================================================
@@ -301,9 +268,9 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
* @return description
*/
public static String globalInfo() {
return "This is a basic generational Memetic Algorithm. Local search steps are performed on a selected subset " +
"of individuals after certain numbers of global search iterations. Note " +
"that the problem class must implement InterfaceLocalSearchable.";
return "This is a basic generational Memetic Algorithm. Local search steps are performed on a selected subset "
+ "of individuals after certain numbers of global search iterations. Note "
+ "that the problem class must implement InterfaceLocalSearchable.";
}
/**
@@ -318,7 +285,8 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the optimizer.
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@@ -356,16 +324,19 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
}
/**
* Choose the number of local search steps to perform per selected individual.
* Choose the number of local search steps to perform per selected
* individual.
*
* @param localSearchSteps
*/
public void setLocalSearchSteps(int localSearchSteps) {
this.localSearchSteps = localSearchSteps;
}
public int getLocalSearchSteps() {
return localSearchSteps;
}
public String localSearchStepsTipText() {
return "Choose the number of local search steps to perform per selected individual.";
}
@@ -378,9 +349,11 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
public void setGlobalSearchIterations(int globalSearchSteps) {
this.globalSearchIterations = globalSearchSteps;
}
public int getGlobalSearchIterations() {
return globalSearchIterations;
}
public String globalSearchIterationsTipText() {
return "Choose the interval between the application of the local search.";
}
@@ -398,9 +371,11 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
public void setSubsetsize(int subsetsize) {
this.subsetsize = subsetsize;
}
public int getSubsetsize() {
return subsetsize;
}
public String subsetsizeTipText() {
return "Choose the number of individuals to be locally optimized.";
}
@@ -413,9 +388,11 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
public void setLamarckism(boolean lamarckism) {
this.lamarckism = lamarckism;
}
public String lamarckismTipText() {
return "Toggle between Lamarckism and the Baldwin Effect.";
}
public boolean isLamarckism() {
return lamarckism;
}
@@ -423,9 +400,11 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
public InterfaceSelection getSubSetSelector() {
return selectorPlug;
}
public void setSubSetSelector(InterfaceSelection selectorPlug) {
this.selectorPlug = selectorPlug;
}
public String subSetSelectorTipText() {
return "Selection method to select the subset on which local search is to be performed.";
}

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;
/**
* The simple random or Monte-Carlo search, simple but useful
* to evaluate the complexity of the search space.
* This implements a Random Walk Search using the initialization
* method of the problem instance, meaning that the random characteristics
* may be problem dependent.
* The simple random or Monte-Carlo search, simple but useful to evaluate the
* complexity of the search space. This implements a Random Walk Search using
* the initialization method of the problem instance, meaning that the random
* characteristics may be problem dependent.
*
* Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
* Architecture
*
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializable {
/**
* Generated serial version id.
*/
@@ -35,7 +35,6 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
private int m_FitnessCallsNeeded = 0;
private Population m_Population;
private GAIndividualBinaryData m_Best, m_Test;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -55,7 +54,8 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
return (Object) new MonteCarloSearch(this);
}
/** This method will init the MonteCarloSearch
/**
* This method will init the MonteCarloSearch
*/
@Override
public void init() {
@@ -64,7 +64,9 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@@ -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
* for initialization.
* This method will optimize without specific operators, by just calling the
* individual method for initialization.
*/
@Override
public void optimize() {
@@ -103,20 +105,23 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
public void defaultInit() {
this.m_FitnessCallsNeeded = 0;
@@ -124,7 +129,8 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
this.m_Best.defaultInit(m_Problem);
}
/** This method will optimize
/**
* This method will optimize
*/
public void defaultOptimize() {
for (int i = 0; i < m_FitnessCalls; i++) {
@@ -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
*/
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);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -175,7 +185,9 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent(String name) {
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
*/
@Override
@@ -196,54 +210,62 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The Monte Carlo Search repeatively creates random individuals and stores the best individuals found.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
public String getName() {
return "MCS";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Change the number of best individuals stored.";
}

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

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

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;
/**
* A generic framework for multi-objecitve optimization, you need
* to specify an optimization strategy (like GA), an archiver and
* an information retrival strategy. With this scheme you can realized:
* Vector Evaluated GA
* Random Weight GA
* Multiple Objective GA
* NSGA
* NSGA-II
* SPEA
* SPEA 2
* PESA
* PESA-II
* In case you address a multi-objective optimization problem with a single-
* objective optimizer instead of this MOEA, such an optimizer would randomly
* toggle between the objective for each selection and thus explore at least
* the extreme points of the objective space, but simpler methods like
* random search or hill-climbing might even fail on that.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 05.06.2003
* Time: 11:03:50
* To change this template use Options | File Templates.
* A generic framework for multi-objecitve optimization, you need to specify an
* optimization strategy (like GA), an archiver and an information retrival
* strategy. With this scheme you can realized: Vector Evaluated GA Random
* Weight GA Multiple Objective GA NSGA NSGA-II SPEA SPEA 2 PESA PESA-II In case
* you address a multi-objective optimization problem with a single- objective
* optimizer instead of this MOEA, such an optimizer would randomly toggle
* between the objective for each selection and thus explore at least the
* extreme points of the objective space, but simpler methods like random search
* or hill-climbing might even fail on that. Created by IntelliJ IDEA. User:
* streiche Date: 05.06.2003 Time: 11:03:50 To change this template use Options
* | File Templates.
*/
public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable {
@@ -82,8 +71,9 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@@ -94,7 +84,8 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** The optimize method will compute a 'improved' and evaluated population
/**
* The optimize method will compute a 'improved' and evaluated population
*/
@Override
public void optimize() {
@@ -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
*/
@Override
@@ -194,13 +187,16 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
this.m_Problem = problem;
this.m_Optimizer.setProblem(problem);
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -219,35 +215,37 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a general Multi-objective Evolutionary Optimization Framework.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -255,19 +253,23 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
return "MOEA";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Optimizer.getPopulation();
}
@Override
public void setPopulation(Population pop) {
this.m_Optimizer.setPopulation(pop);
}
public String populationTipText() {
return "Edit the properties of the Population used.";
}
@@ -277,47 +279,61 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
return new SolutionSet(getPopulation(), ArchivingNSGAII.getNonDominatedSortedFront(getPopulation().getArchive()).getSortedPop(new AbstractEAIndividualComparator(0)));
}
/** This method allows you to set/get the optimizing technique to use.
/**
* This method allows you to set/get the optimizing technique to use.
*
* @return The current optimizing method
*/
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
public void setOptimizer(InterfaceOptimizer b) {
this.m_Optimizer = b;
}
public String optimizerTipText() {
return "Choose a population based optimizing technique to use.";
}
/** This method allows you to set/get the archiving strategy to use.
/**
* This method allows you to set/get the archiving strategy to use.
*
* @return The current optimizing method
*/
public InterfaceArchiving getArchivingStrategy() {
return this.m_Archiver;
}
public void setArchivingStrategy(InterfaceArchiving b) {
this.m_Archiver = b;
}
public String archivingStrategyTipText() {
return "Choose the archiving strategy.";
}
/** This method allows you to set/get the Information Retrieval strategy to use.
/**
* This method allows you to set/get the Information Retrieval strategy to
* use.
*
* @return The current optimizing method
*/
public InterfaceInformationRetrieval getInformationRetrieval() {
return this.m_InformationRetrieval;
}
public void setInformationRetrieval(InterfaceInformationRetrieval b) {
this.m_InformationRetrieval = b;
}
public String informationRetrievalTipText() {
return "Choose the Information Retrieval strategy.";
}
/** This method allows you to set/get the size of the archive.
/**
* This method allows you to set/get the size of the archive.
*
* @return The current optimizing method
*/
public int getArchiveSize() {
@@ -328,6 +344,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
}
return archive.getTargetSize();
}
public void setArchiveSize(int b) {
Population archive = this.m_Optimizer.getPopulation().getArchive();
if (archive == null) {
@@ -336,6 +353,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
}
archive.setTargetSize(b);
}
public String archiveSizeTipText() {
return "Choose the size of the archive.";
}

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

@@ -15,9 +15,9 @@ import java.io.Serializable;
import java.util.Vector;
/**
* Nelder-Mead-Simplex does not guarantee an equal number of evaluations within each optimize call
* because of the different step types.
* Range check is now available by projection at the bounds.
* Nelder-Mead-Simplex does not guarantee an equal number of evaluations within
* each optimize call because of the different step types. Range check is now
* available by projection at the bounds.
*
* @author mkron
*
@@ -27,9 +27,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
private int populationSize = 100;
// simulating the generational cycle. Set rather small (eg 5) for use as local search, higher for global search (eg 50)
private int generationCycle = 50;
private int fitIndex = 0; // choose criterion for multi objective functions
private Population m_Population;
private AbstractOptimizationProblem m_Problem;
private transient Vector<InterfacePopulationChangedEventListener> m_Listener;
@@ -101,15 +99,11 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
InterfacePopulationChangedEventListener ea) {
if (m_Listener == null) {
return false;
}
else {
} else {
return m_Listener.remove(ea);
}
}
@Override
public void freeWilly() {}
protected double[] calcChallengeVect(double[] centroid, double[] refX) {
double[] r = new double[centroid.length];
for (int i = 0; i < r.length; i++) {
@@ -370,10 +364,8 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
/**
* This method creates a Nelder-Mead instance.
*
* @param pop
* The size of the population
* @param problem
* The problem to be optimized
* @param pop The size of the population
* @param problem The problem to be optimized
* @param listener
* @return An optimization procedure that performs nelder mead optimization.
*/
@@ -398,15 +390,15 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
/**
* This method creates a Nelder-Mead instance with an initial population
* around a given candidate solution. The population is created as a simplex with given
* perturbation ratio or randomly across the search range if the perturbation ratio is
* zero or below zero.
* around a given candidate solution. The population is created as a simplex
* with given perturbation ratio or randomly across the search range if the
* perturbation ratio is zero or below zero.
*
*
* @param problem
* The problem to be optimized
* @param problem The problem to be optimized
* @param candidate starting point of the search
* @param perturbationRatio perturbation ratio relative to the problem range for the initial simplex creation
* @param perturbationRatio perturbation ratio relative to the problem range
* for the initial simplex creation
* @param listener
* @return An optimization procedure that performs nelder mead optimization.
*/
@@ -441,11 +433,12 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
}
/**
* From a given candidate solution, create n solutions around the candidate, where every i-th
* new candidate differs in i dimensions by a distance of perturbRatio relative to the range in
* that dimension (respecting the range).
* The new solutions are returned as a population, which, if includeCand is true,
* also contains the initial candidate. However, the new candidates have not been evaluated.
* From a given candidate solution, create n solutions around the candidate,
* where every i-th new candidate differs in i dimensions by a distance of
* perturbRatio relative to the range in that dimension (respecting the
* range). The new solutions are returned as a population, which, if
* includeCand is true, also contains the initial candidate. However, the
* new candidates have not been evaluated.
*
* @param candidate
* @param perturbRelative
@@ -515,5 +508,4 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
public String critIndexTipText() {
return "For multi-criterial problems, set the index of the fitness to be used in 0..n-1. Default is 0";
}
}

494
src/eva2/server/go/strategies/NichePSO.java
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File diff suppressed because it is too large Load Diff

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

@@ -1,6 +1,5 @@
package eva2.server.go.strategies;
import eva2.gui.BeanInspector;
import eva2.gui.GenericObjectEditor;
import eva2.gui.Plot;
@@ -22,28 +21,27 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
/**
* This is a Particle Filter implemented by Frank Senke, only some documentation
* here and not completely checked whether this works on arbitrary problem
* instances. MK did some adaptations, this should work on real valued problems now.
* instances. MK did some adaptations, this should work on real valued problems
* now.
*
* This is a implementation of Genetic Algorithms.
* Copyright: Copyright (c) 2003
* This is a implementation of Genetic Algorithms. Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
*
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.Serializable {
/**
* Comment for <code>serialVersionUID</code>
* Comment for
* <code>serialVersionUID</code>
*/
private static final long serialVersionUID = 1L;
private Population m_Population = new Population();
private InterfaceOptimizationProblem m_Problem = new F1Problem();
private InterfaceSelection m_ParentSelection = new SelectParticleWheel(0.5);
//private boolean m_UseElitism = true;
private String m_Identifier = "";
private boolean withShow = false;
private double mutationSigma = 0.01;
@@ -51,9 +49,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
private double initialVelocity = 0.02;
private double rotationDeg = 20.;
private int popSize = 300;
private int sleepTime = 0;
transient private int indCount = 0;
transient private InterfacePopulationChangedEventListener m_Listener;
transient Plot myPlot = null;
@@ -117,7 +113,9 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@@ -131,8 +129,9 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
}
}
/** This method will evaluate the current population using the
* given problem.
/**
* This method will evaluate the current population using the given problem.
*
* @param population The population that is to be evaluated
*/
private Population evaluatePopulation(Population population) {
@@ -157,8 +156,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
if (randomImmigrationQuota > 0) {
if (randomImmigrationQuota > 1.) {
System.err.println("Error, invalid immigration quota!");
}
else {
} else {
targetSize = (int) (this.m_Population.getTargetSize() * (1. - randomImmigrationQuota));
targetSize = Math.max(1, targetSize); // guarantee at least one to be selected
if (targetSize < this.m_Population.getTargetSize()) {
@@ -218,8 +216,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
if (useCircles) {
myPlot.getFunctionArea().drawCircle("", curPosition, graphLabel);
}
else {
} else {
myPlot.setUnconnectedPoint(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);
if (sleepTime > 0) {
try { Thread.sleep(sleepTime); } catch(Exception e) {}
try {
Thread.sleep(sleepTime);
} catch (Exception e) {
}
}
if (withShow) {
clearPlot();
@@ -276,6 +276,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -286,13 +287,16 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
return false;
}
}
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
@@ -302,13 +306,16 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
((AbstractOptimizationProblem) problem).informAboutOptimizer(this);
}
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -320,35 +327,38 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
strB.append(this.m_Population.getStringRepresentation());
return strB.toString();
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a Particle Filter Algorithm.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -356,19 +366,23 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
return "PF";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
@@ -377,22 +391,28 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method will set the selection method that is to be used
/**
* This method will set the selection method that is to be used
*
* @param selection
*/
public void setParentSelection(InterfaceSelection selection) {
this.m_ParentSelection = selection;
}
public InterfaceSelection getParentSelection() {
return this.m_ParentSelection;
}
public String parentSelectionTipText() {
return "Choose a parent selection method.";
}
/**
* @return the withShow
**/
*
*/
public boolean isWithShow() {
return withShow;
}
@@ -416,18 +436,17 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
/**
* @param withShow the withShow to set
**/
*
*/
public void setWithShow(boolean wShow) {
this.withShow = wShow;
if (!withShow) {
myPlot = null;
}
else {
} else {
double[][] range;
if ((m_Population != null) && (m_Population.size() > 0)) {
range = ((InterfaceDataTypeDouble) this.m_Population.get(0)).getDoubleRange();
}
else {
} else {
range = new double[2][];
range[0] = new double[2];
range[0][0] = 0;
@@ -440,28 +459,32 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
/**
* @return the sleepTime
**/
*
*/
public int getSleepTime() {
return sleepTime;
}
/**
* @param sleepTime the sleepTime to set
**/
*
*/
public void setSleepTime(int sleepTime) {
this.sleepTime = sleepTime;
}
/**
* @return the mutationSigma
**/
*
*/
public double getMutationSigma() {
return mutationSigma;
}
/**
* @param mutationSigma the mutationSigma to set
**/
*
*/
public void setMutationSigma(double mutationSigma) {
this.mutationSigma = mutationSigma;
}

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

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

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.InterfaceOptimizationProblem;
/** Population based incremental learning in the PSM by Monmarche
* version with also allows to simulate ant systems due to the flexible
* update rule of V. But both are limited to binary genotypes.
* This is a simple implementation of Population Based Incremental Learning.
/**
* Population based incremental learning in the PSM by Monmarche version with
* also allows to simulate ant systems due to the flexible update rule of V. But
* both are limited to binary genotypes. This is a simple implementation of
* Population Based Incremental Learning.
*
* Nicolas Monmarché , Eric Ramat , Guillaume Dromel , Mohamed Slimane , Gilles Venturini:
* On the similarities between AS, BSC and PBIL: toward the birth of a new meta-heuristic.
* TecReport 215. Univ. de Tours, 1999.
* Nicolas Monmarché , Eric Ramat , Guillaume Dromel , Mohamed Slimane , Gilles
* Venturini: On the similarities between AS, BSC and PBIL: toward the birth of
* a new meta-heuristic. TecReport 215. Univ. de Tours, 1999.
*
* Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
* Architecture
*
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
* @author Felix Streichert
* @version: $Revision: 307 $
* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
* @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
* 2007) $ $Author: mkron $
*/
public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase
@@ -78,7 +78,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@@ -97,8 +99,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will evaluate the current population using the
* given problem.
/**
* This method will evaluate the current population using the given problem.
*
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
@@ -106,8 +109,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
population.incrGeneration();
}
/** This method will generate the offspring population from the
* given population of evaluated individuals.
/**
* This method will generate the offspring population from the given
* population of evaluated individuals.
*/
private Population generateChildren() {
PBILPopulation result = (PBILPopulation) this.m_Population.clone();
@@ -141,7 +145,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
@@ -153,14 +159,17 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
}
}
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -171,7 +180,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent(String name) {
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
*/
@Override
@@ -192,35 +205,38 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The Population based incremental learning is based on a statistical distribution of bit positions. Please note: This optimizer requires a binary genotype!";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -228,19 +244,23 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
return "PBIL";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Edit the properties of the PBIL population used.";
}
@@ -262,33 +282,43 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
// return "Select the normation method.";
// }
/** This method will set the selection method that is to be used
/**
* This method will set the selection method that is to be used
*
* @param selection
*/
public void setSelectionMethod(InterfaceSelection selection) {
this.m_SelectionOperator = selection;
}
public InterfaceSelection getSelectionMethod() {
return this.m_SelectionOperator;
}
public String selectionMethodTipText() {
return "Choose a selection method.";
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param elitism
*/
public void setElitism(boolean elitism) {
this.m_UseElitism = elitism;
}
public boolean getElitism() {
return this.m_UseElitism;
}
public String elitismTipText() {
return "Enable/disable elitism.";
}
/** This method will set the learning rate for PBIL
/**
* This method will set the learning rate for PBIL
*
* @param LearningRate
*/
public void setLearningRate(double LearningRate) {
@@ -297,14 +327,18 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.m_LearningRate = 0;
}
}
public double getLearningRate() {
return this.m_LearningRate;
}
public String learningRateTipText() {
return "The learing rate of PBIL.";
}
/** This method will set the mutation rate for PBIL
/**
* This method will set the mutation rate for PBIL
*
* @param m
*/
public void setMutationRate(double m) {
@@ -316,14 +350,18 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.m_MutationRate = 1;
}
}
public double getMutationRate() {
return this.m_MutationRate;
}
public String mutationRateTipText() {
return "The mutation rate of PBIL.";
}
/** This method will set the mutation sigma for PBIL
/**
* This method will set the mutation sigma for PBIL
*
* @param m
*/
public void setMutateSigma(double m) {
@@ -332,14 +370,18 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.m_MutateSigma = 0;
}
}
public double getMutateSigma() {
return this.m_MutateSigma;
}
public String mutateSigmaTipText() {
return "Set the sigma for the mutation of the probability vector.";
}
/** This method will set the number of positive samples for PBIL
/**
* This method will set the number of positive samples for PBIL
*
* @param PositiveSamples
*/
public void setPositiveSamples(int PositiveSamples) {
@@ -348,9 +390,11 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
this.m_NumberOfPositiveSamples = 1;
}
}
public int getPositiveSamples() {
return this.m_NumberOfPositiveSamples;
}
public String positiveSamplesTipText() {
return "The number of positive samples that update the PBIL vector.";
}

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

@@ -25,19 +25,23 @@ import eva2.tools.math.RNG;
import java.util.ArrayList;
/**
* A ScatterSearch implementation taken mainly from [1]. Unfortunately, some parameters as well as
* the local search method are not well defined in [1], so this implementation allows HC and Nelder-Mead
* as local search. If local search is activated, an additional filter is defined, meaning that only those
* A ScatterSearch implementation taken mainly from [1]. Unfortunately, some
* parameters as well as the local search method are not well defined in [1], so
* this implementation allows HC and Nelder-Mead as local search. If local
* search is activated, an additional filter is defined, meaning that only those
* individuals with a high quality fitness are further improved by local search.
* The threshold fitness is either defined relatively to the best/worst fitness values in the reference set
* or as an absolute value (in both cases only the first fitness criterion is regarded).
* The threshold fitness is either defined relatively to the best/worst fitness
* values in the reference set or as an absolute value (in both cases only the
* first fitness criterion is regarded).
*
* @author mkron
*
* [1] M.Rodiguez-Fernandez, J.Egea, J.Banga: Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.
* BMC Bioinformatics 2006, 7:483. BioMed Central 2006.
* [1] M.Rodiguez-Fernandez, J.Egea, J.Banga: Novel metaheuristic for parameter
* estimation in nonlinear dynamic biological systems. BMC Bioinformatics 2006,
* 7:483. BioMed Central 2006.
*/
public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener {
transient private InterfacePopulationChangedEventListener m_Listener = null;
private String m_Identifier = "ScatterSearch";
private AbstractOptimizationProblem problem = new F1Problem();
@@ -58,9 +62,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// simulate an EvA generational cycle
private int generationCycle = 50;
private int fitCrit = -1;
protected boolean checkRange = true;
// private int lastLocalSearch = -1;
// // nr of generations between local searches
// protected int localSearchInterval = 10;
@@ -71,7 +73,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private double nelderMeadInitPerturbation = 0.01;
private double improvementEpsilon = 0.1; // minimal relative fitness improvement for a candidate to be taken over into the refset
private double minDiversityEpsilon = 0.0001; // minimal phenotypic distance for a candidate to be taken over into the refset
private static boolean TRACE = false;
public ScatterSearch() {
@@ -134,6 +135,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
/**
* Eval an initial population and extract the first refset.
*
* @param pop
*/
private void initRefSet(Population pop) {
@@ -160,8 +162,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
template = problem.getIndividualTemplate();
if (!(template instanceof InterfaceDataTypeDouble)) {
System.err.println("Requiring double data!");
}
else {
} else {
Object dim = BeanInspector.callIfAvailable(problem, "getProblemDimension", null);
if (dim == null) {
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) {
if (this.m_Listener != null) {
@@ -188,7 +190,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// problem.evaluate(indy);
// return indy.getFitness(0);
// }
@Override
public void registerPopulationStateChanged(Object source, String name) {
// The events of the interim hill climbing population will be caught here
@@ -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
* be locally optimized in a local search step.
* The best candidate is removed from the candidate set in any case. The candidate set
* may be cleared if all following individuals would never be taken over to the refset.
* Maybe replace the single worst indy in the refset by the best candidate,
* which may be locally optimized in a local search step. The best candidate
* is removed from the candidate set in any case. The candidate set may be
* cleared if all following individuals would never be taken over to the
* refset.
*
* @param refSet
* @param candidates
@@ -407,8 +409,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// so we can just clear the rest of the candidates
if (!doLocalSearch && (bestCand.getFitness().length == 1)) {
candidates.clear();
}
else {
} else {
candidates.remove(bestIndex);
}
}
@@ -417,8 +418,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private Pair<AbstractEAIndividual, Integer> localSolver(AbstractEAIndividual cand, int hcSteps) {
if (localSearchMethod.getSelectedTagID() == 0) {
return localSolverHC(cand, hcSteps);
}
else {
} else {
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
* be fulfilled for a candidate to be accepted.
* Check for both a genotype and phenotype diversity criterion which both
* must be fulfilled for a candidate to be accepted.
*
* @param cand
* @param popCompGeno
@@ -475,6 +475,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
/**
* Recombines the refset to new indies which are also evaluated.
*
* @param refSet
* @return
*/
@@ -514,8 +515,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
combs.add(combineTypeTwo(indy1, indy2));
if (RNG.flipCoin(0.5)) {
combs.add(combineTypeOne(indy1, indy2));
}
else {
} else {
combs.add(combineTypeThree(indy1, indy2));
}
}
@@ -530,9 +530,11 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
private AbstractEAIndividual combineTypeOne(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
return combine(indy1, indy2, true, false);
}
private AbstractEAIndividual combineTypeTwo(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
return combine(indy1, indy2, true, true);
}
private AbstractEAIndividual combineTypeThree(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
return combine(indy1, indy2, false, true);
}
@@ -737,7 +739,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
///////////// Trivials...
@Override
public void setIdentifier(String name) {
m_Identifier = name;
@@ -758,6 +759,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
InterfacePopulationChangedEventListener ea) {
m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -768,8 +770,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
return false;
}
}
@Override
public void freeWilly() {}
@Override
public String getIdentifier() {
@@ -871,7 +871,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
}
////////////////////////////////////////////7
/**
* This method performs a scatter search runnable.
*/
@@ -998,9 +997,11 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
public double getImprovementEpsilon() {
return improvementEpsilon;
}
public void setImprovementEpsilon(double improvementEpsilon) {
this.improvementEpsilon = improvementEpsilon;
}
public String improvementEpsilonTipText() {
return "Minimal relative fitness improvement for a candidate to enter the refSet - set to zero to deactivate.";
}
@@ -1008,9 +1009,11 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
public double getMinDiversityEpsilon() {
return minDiversityEpsilon;
}
public void setMinDiversityEpsilon(double minDiversityEpsilon) {
this.minDiversityEpsilon = minDiversityEpsilon;
}
public String minDiversityEpsilonTipText() {
return "Minimal distance to other individuals in the refSet for a candidate to enter the refSet - set to zero to deactivate.";
}

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.tools.math.RNG;
/** Simulated Annealing by Nelder and Mead, a simple yet efficient local search
/**
* Simulated Annealing by Nelder and Mead, a simple yet efficient local search
* method. But to become less prone to premature convergence the cooling rate
* has to be tuned to the optimization problem at hand. Again the population size
* gives the number of multi-starts.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 13.05.2004
* Time: 10:30:26
* To change this template use File | Settings | File Templates.
* has to be tuned to the optimization problem at hand. Again the population
* size gives the number of multi-starts. Created by IntelliJ IDEA. User:
* streiche Date: 13.05.2004 Time: 10:30:26 To change this template use File |
* Settings | File Templates.
*/
public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private int m_MultiRuns = 100;
private int m_FitnessCalls = 100;
@@ -29,7 +28,6 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
GAIndividualBinaryData m_Best, m_Test;
public double m_InitialTemperature = 2, m_CurrentTemperature;
public double m_Alpha = 0.9;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -53,7 +51,8 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
return (Object) new SimulatedAnnealing(this);
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
@Override
public void init() {
@@ -63,7 +62,9 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@@ -78,7 +79,8 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
}
}
/** This method will optimize
/**
* This method will optimize
*/
@Override
public void optimize() {
@@ -112,7 +114,9 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method calculates the difference between the fitness values
/**
* This method calculates the difference between the fitness values
*
* @param org The original
* @param mut The mutant
*/
@@ -127,19 +131,23 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
return result;
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
public void defaultInit() {
this.m_FitnessCallsNeeded = 0;
@@ -147,7 +155,8 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
this.m_Best.defaultInit(m_Problem);
}
/** This method will optimize
/**
* This method will optimize
*/
public void defaultOptimize() {
for (int i = 0; i < m_FitnessCalls; i++) {
@@ -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
*/
public static void main(String[] args) {
@@ -185,6 +195,7 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -195,14 +206,17 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
return false;
}
}
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -210,8 +224,7 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
String result = "";
if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
}
else {
} else {
result += "Simulated Annealing:\n";
}
result += "Optimization Problem: ";
@@ -219,54 +232,62 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The simulated annealing uses an additional cooling rate instead of a simple dominate criteria to accpect worse solutions by chance.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
public String getName() {
return "MS-SA";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Change the number of best individuals stored (MS-SA)).";
}
@@ -275,31 +296,40 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** Set the initial temperature
/**
* Set the initial temperature
*
* @return The initial temperature.
*/
public double getInitialTemperature() {
return this.m_InitialTemperature;
}
public void setInitialTemperature(double pop) {
this.m_InitialTemperature = pop;
}
public String initialTemperatureTipText() {
return "Set the initial temperature.";
}
/** Set alpha, which is used to degrade the temperaure
/**
* Set alpha, which is used to degrade the temperaure
*
* @return The cooling rate.
*/
public double getAlpha() {
return this.m_Alpha;
}
public void setAlpha(double a) {
this.m_Alpha = a;
if (this.m_Alpha > 1) {
this.m_Alpha = 1.0;
}
}
public String alphaTipText() {
return "Set alpha, which is used to degrade the temperaure.";
}

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.InterfaceOptimizationProblem;
/** A simple implementation of the steady-state GA with variable
* replacement schemes. To reduce the logging effort population.size()
* optimization steps are performed each time optimize() is called.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 19.07.2005
* Time: 14:30:20
* To change this template use File | Settings | File Templates.
/**
* A simple implementation of the steady-state GA with variable replacement
* schemes. To reduce the logging effort population.size() optimization steps
* are performed each time optimize() is called. Created by IntelliJ IDEA. User:
* streiche Date: 19.07.2005 Time: 14:30:20 To change this template use File |
* Settings | File Templates.
*/
public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
@@ -30,7 +28,6 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
private InterfaceSelection m_PartnerSelection = new SelectTournament();
private InterfaceReplacement m_ReplacementSelection = new ReplaceWorst();
private int m_NumberOfPartners = 1;
private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -59,7 +56,9 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param reset If true the population is reset.
*/
@Override
@@ -72,8 +71,9 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
}
}
/** This method will evaluate the current population using the
* given problem.
/**
* This method will evaluate the current population using the given problem.
*
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
@@ -81,8 +81,10 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
population.incrGeneration();
}
/** This method will assign fitness values to all individual in the
* current population.
/**
* This method will assign fitness values to all individual in the current
* population.
*
* @param population The population that is to be evaluated
*/
private void defaultEvaluatePopulation(Population population) {
@@ -95,8 +97,9 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
population.incrGeneration();
}
/** This method will generate the offspring population from the
* given population of evaluated individuals.
/**
* This method will generate the offspring population from the given
* population of evaluated individuals.
*/
private void generateChildren() {
this.m_ParentSelection.prepareSelection(this.m_Population);
@@ -119,10 +122,12 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
this.m_Population.incrGeneration();
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -133,26 +138,32 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
return false;
}
}
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -164,35 +175,38 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a Steady-State Genetic Algorithm.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -200,19 +214,23 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
return "SS-GA";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
@@ -221,21 +239,28 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method will set the parent selection method that is to be used
/**
* This method will set the parent selection method that is to be used
*
* @param selection
*/
public void setParentSelection(InterfaceSelection selection) {
this.m_ParentSelection = selection;
}
public InterfaceSelection getParentSelection() {
return this.m_ParentSelection;
}
public String parentSelectionTipText() {
return "Choose a parent selection method.";
}
/** This method will set the number of partners that are needed to create
/**
* This method will set the number of partners that are needed to create
* offsprings by mating
*
* @param partners
*/
public void setNumberOfPartners(int partners) {
@@ -244,35 +269,46 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
}
this.m_NumberOfPartners = partners;
}
public int getNumberOfPartners() {
return this.m_NumberOfPartners;
}
public String numberOfPartnersTipText() {
return "The number of mating partners needed to create offsprings.";
}
/** Choose a selection method for selecting recombination partners for given parents
/**
* Choose a selection method for selecting recombination partners for given
* parents
*
* @param selection
*/
public void setPartnerSelection(InterfaceSelection selection) {
this.m_PartnerSelection = selection;
}
public InterfaceSelection getPartnerSelection() {
return this.m_PartnerSelection;
}
public String partnerSelectionTipText() {
return "Choose a selection method for selecting recombination partners for given parents.";
}
/** Choose a replacement strategy
/**
* Choose a replacement strategy
*
* @param selection
*/
public void setReplacementSelection(InterfaceReplacement selection) {
this.m_ReplacementSelection = selection;
}
public InterfaceReplacement getReplacementSelection() {
return this.m_ReplacementSelection;
}
public String replacementSelectionTipText() {
return "Choose a replacement strategy.";
}

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.InterfaceOptimizationProblem;
/** Threshold accepting algorithm simliar strategy as the flood
* algorithm, similar problems.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 01.10.2004
* Time: 13:35:49
* To change this template use File | Settings | File Templates.
/**
* Threshold accepting algorithm simliar strategy as the flood algorithm,
* similar problems. Created by IntelliJ IDEA. User: streiche Date: 01.10.2004
* Time: 13:35:49 To change this template use File | Settings | File Templates.
*/
public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private int m_MultiRuns = 100;
private int m_FitnessCalls = 100;
@@ -26,7 +24,6 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
GAIndividualBinaryData m_Best, m_Test;
public double m_InitialT = 2, m_CurrentT;
public double m_Alpha = 0.9;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
@@ -50,7 +47,8 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
return (Object) new ThresholdAlgorithm(this);
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
@Override
public void init() {
@@ -60,7 +58,9 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@@ -75,7 +75,8 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
}
}
/** This method will optimize
/**
* This method will optimize
*/
@Override
public void optimize() {
@@ -103,7 +104,9 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method calculates the difference between the fitness values
/**
* This method calculates the difference between the fitness values
*
* @param org The original
* @param mut The mutant
*/
@@ -118,19 +121,23 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
return result;
}
/** This method will set the problem that is to be optimized
/**
* This method will set the problem that is to be optimized
*
* @param problem
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will init the HillClimber
/**
* This method will init the HillClimber
*/
public void defaultInit() {
this.m_FitnessCallsNeeded = 0;
@@ -138,7 +145,8 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
this.m_Best.defaultInit(m_Problem);
}
/** This method will optimize
/**
* This method will optimize
*/
public void defaultOptimize() {
for (int i = 0; i < m_FitnessCalls; i++) {
@@ -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
*/
public static void main(String[] args) {
@@ -171,10 +180,12 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
TmpMeanFitness /= program.m_MultiRuns;
System.out.println("(" + program.m_MultiRuns + "/" + program.m_FitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
}
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -185,14 +196,17 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
return false;
}
}
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
}
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -200,8 +214,7 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
String result = "";
if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
}
else {
} else {
result += "Threshold Algorithm:\n";
}
result += "Optimization Problem: ";
@@ -209,54 +222,62 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The threshold algorithm uses an declining threshold to accpect new solutions.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
public String getName() {
return "MS-TA";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store thier data in a population we will
* allow acess to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "Change the number of best individuals stored (MS-TA).";
}
@@ -265,31 +286,40 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** Set the initial threshold
/**
* Set the initial threshold
*
* @return The initial temperature.
*/
public double getInitialT() {
return this.m_InitialT;
}
public void setInitialT(double pop) {
this.m_InitialT = pop;
}
public String initialTTipText() {
return "Set the initial threshold.";
}
/** Set alpha, which is used to degrade the threshold
/**
* Set alpha, which is used to degrade the threshold
*
* @return The initial temperature.
*/
public double getAlpha() {
return this.m_Alpha;
}
public void setAlpha(double a) {
this.m_Alpha = a;
if (this.m_Alpha > 1) {
this.m_Alpha = 1.0;
}
}
public String alphaTipText() {
return "Set alpha, which is used to degrade the threshold.";
}

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.List;
/**
* This is the TRIBES algorithm, an adaptive, parameter-less PSO implementation.
* I (MK) ported M.Clerc's java version 2006-02 21 and added the original notes below.
* I had to do some modifications for the EvA framework, namely:
* - minor adaptations allover the code to fit into the framework
* - the objective value parameter must now be set within the GUI for each problem by hand (EvA doesnt assume to know an objective beforehand)
* - discrete search spaces are not directly supported any more (no "granularity")
* - the benchmark-collection is gone, it might be included into the EvA benchmark set in the future, though
* - fixed two bugs (SunnySpell link generation, findWorst method)
* - fixed bugs in the CEC 2005 benchmarks (see the corresponding class)
* - I widely kept the original comments, except for places I changed the code so much that they might mislead
* - thats all, I think
* I (MK) ported M.Clerc's java version 2006-02 21 and added the original notes
* below. I had to do some modifications for the EvA framework, namely: - minor
* adaptations allover the code to fit into the framework - the objective value
* parameter must now be set within the GUI for each problem by hand (EvA doesnt
* assume to know an objective beforehand) - discrete search spaces are not
* directly supported any more (no "granularity") - the benchmark-collection is
* gone, it might be included into the EvA benchmark set in the future, though -
* fixed two bugs (SunnySpell link generation, findWorst method) - fixed bugs in
* the CEC 2005 benchmarks (see the corresponding class) - I widely kept the
* original comments, except for places I changed the code so much that they
* might mislead - thats all, I think
*
* I could produce similar results as Clerc's on Rosenbrock and Griewank, (in his book on p. 148),
* I couldnt reproduce the 100% success rate on Ackley, though.
* I could produce similar results as Clerc's on Rosenbrock and Griewank, (in
* his book on p. 148), I couldnt reproduce the 100% success rate on Ackley,
* though.
*
* @author Maurice Clerc, Marcel Kronfeld
* @date 2007-09-13
*
* Original notes:
* @version 2006-02 21
* @author Maurice.Clerc@WriteMe.com
* {@link http://mauriceclerc.net}
* @author Maurice.Clerc@WriteMe.com {@link http://mauriceclerc.net}
* {@link http://clerc.maurice.free.fr/pso/}
*
*/
@@ -129,47 +129,39 @@ import java.util.List;
2005-11-21. Check if it is possible to easily find just the _value_ of the
global minimum (not the position). "Chinese shadow" method?
*/
public class Tribes implements InterfaceOptimizer, java.io.Serializable {
public static final boolean TRACE = false;
protected String m_Identifier = "TRIBES";
transient private InterfacePopulationChangedEventListener m_Listener = null;
protected AbstractOptimizationProblem m_problem;
protected Population population;
public static int maxExplorerNb = 200;
public static int maxMemoryNb = 300;
public static int maxTribeNb = 300;
public static int[] strategies = new int[10]; // Just for information
public static int[] status = new int[9]; // Just for information
public static boolean testBC = false; // TODO project to EvA2
public static int adaptOption = 2;
public static double blind = 0; // 0.5 //"Blind" move for very good particles, with a probability Tribes.blind
public static boolean repel = false; // If 1, use a "repelling" strategy (see moveExplorer() )
private boolean checkConstraints = true;
private static final long serialVersionUID = 1L;
TribesSwarm swarm = null;
private int iter;
protected double objectiveFirstDim = 0.;
protected double[][] range, initRange;
protected int notifyGenChangedEvery = 10;
protected int problemDim;
protected int adaptThreshold, adaptMax, adapt;
protected int informOption; /* For the best informant.
-1 => really the best
1 => the best according to a pseudo-gradient method
*/
protected int initExplorerNb = 3; // Number of explorers at the very beginning
// use full range (0) or subspace (1) for init options 0 and 1
protected int rangeInitType = 1;
private boolean m_Show = false;
transient protected eva2.gui.Plot m_Plot = null;
// 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
* and memories), the setPopulation method is only telling Tribes the range
* of the indiviuals in the beginning of the run, the individuals will be discarded.
* As TRIBES manages an own structured set of particles (the list of Tribes
* containing explorers and memories), the setPopulation method is only
* telling Tribes the range of the indiviuals in the beginning of the run,
* the individuals will be discarded.
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
@@ -296,8 +289,8 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
// adaptMax=swarmSize;
adaptMax = swarm.linkNb(swarm);
if (adaptThreshold >= adaptMax) {
if (swarm.getBestMemory().getPrevPos().getTotalError() <=
swarm.getBestMemory().getPos().getTotalError()) {
if (swarm.getBestMemory().getPrevPos().getTotalError()
<= swarm.getBestMemory().getPos().getTotalError()) {
adapt = iter; // Memorize at which iteration adaptation occurs
for (int i = 0; i < swarm.getTribeCnt(); i++) {
@@ -383,253 +376,161 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
/**
public synchronized void search(param pb, PrintStream runSave, PrintStream synthSave) {
double epsMean, epsMin, epsMax;
double evalMean;
int n;
int run, run1;
int successNb;
// Exploratrices générées
explorer explorer[] = new explorer[
Tribes.maxExplorerNb];
double[] eps = new double[pb.maxRun];
double[] evalNb = new double[pb.maxRun];
double[] temp = new double[3];
successNb = 0;
for (n = 0; n < 9; n++) { // For information
Tribes.strategies[n] = 0;
Tribes.status[n] = 0;
}
;
epsMin = Tribes.infinity;
epsMax = 0;
// Titles
print("\nIter. Eval. Best_fitness", displayPb);
save("\n\n PROBLEM "+pb.function[0],synthSave);
save("\nRun Iter. Eval. Best_fitness Position", synthSave);
save("\n\n PROBLEM "+pb.function[0],runSave);
// **
// * Loop on runs
// *
for (run = 0; run < pb.maxRun; run++) {
run1 = run + 1;
save("\n" + run1 + " ", synthSave);
temp = solve(pb, Tribes.initExplorerNb,runSave,synthSave);
eps[run] = temp[0];
evalNb[run] = temp[1];
successNb = successNb + (int) temp[2];
if (eps[run] < epsMin) {
epsMin = eps[run];
}
if (eps[run] > epsMax) {
epsMax = eps[run];
}
}
// Mean values
epsMean = 0;
evalMean = 0;
for (run = 0; run < pb.maxRun; run++) {
epsMean = epsMean + eps[run];
evalMean = evalMean + evalNb[run];
}
epsMean = epsMean / pb.maxRun;
evalMean = evalMean / pb.maxRun;
print("\nStatuses ", displayPb);
for (n = 1; n < 10; n++) {
print("\n" + n + " " + Tribes.status[n - 1] + " times",
displayPb);
}
print("\nStrategies ", displayPb);
for (n = 1; n < 10; n++) {
print("\n" + n + " " + Tribes.strategies[n - 1] + " times",
displayPb);
}
print("\nMIN BEST TOTAL_ERROR " + epsMin, displayPb);
print("\nMEAN BEST TOTAL_ERROR " + epsMean, displayPb);
print("\nMEAN EVAL. NUMBER " + evalMean, displayPb);
print("\n SUCCESS RATE " + (double) successNb / pb.maxRun,
displayPb);
save("\nMIN BEST TOTAL_ERROR " + epsMin, synthSave);
save("\nMEAN BEST TOTAL_ERROR " + epsMean, synthSave);
save("\nMEAN EVAL. NUMBER " + evalMean, synthSave);
save("\n SUCCESS RATE " + (double) successNb / pb.maxRun, synthSave);
save("\n-1", runSave); // Special value for the end of the file. Used for graphics
} // End of search()
public double[] solve(param pb, int initExplorerNb, PrintStream runSave, PrintStream synthSave) {
int adapt, adaptMax;
int adaptThreshold;
int d, D = pb.H.Dimension;
int evalF;
int iter;
int n;
boolean stop;
double[] temp = new double[3];
int informOption;
// For the best informant.
// -1 => really the best
// 1 => the best according to a pseudo-gradient method
// -----------INIT START
// Generate a swarm
evalF=0;
swarm swarm = new swarm();
evalF=swarm.generateSwarm(pb, initExplorerNb, pb.initType, displayPb,evalF);
// swarm.displaySwarm(swarm,out);
// print("\n Best after init: "+swarm.Best.position.fitness,out);
// Move the swarm as long as the stop criterion is false
iter = 0;
adapt = 0;
stop = false;
informOption = -1;
// Hard coded option
// -1 = absolute best informant
// 1 = relative (pseudo-gradient) best informant. For "niching"
// See also moveExplorer, which can be modified in order to avoid this parameter
//
// -----------INIT END
// -----------OPTIMIZE START
iterations:while (!stop) {
swarm.size = swarm.swarmSize(swarm);
iter++;
// swarm.Best.positionPrev = swarm.Best.position;
evalF=swarm.moveSwarm(pb, swarm, informOption, displayPb, evalF); //*** HERE IT MOVES
// Some display each time there is "enough" improvement
// fduring the process
double enough = 0.005;
if ((1 - enough) * swarm.Best.positionPrev.totalError >
swarm.Best.position.totalError) {
print("\nIter. " + iter, displayPb);
print(" Eval. " + evalF, displayPb);
print(" totalError " + swarm.Best.position.totalError,
displayPb);
print(" " + swarm.size + " particles", displayPb);
}
// Save run info
save("\n" + iter + " " + evalF + " " +
swarm.Best.position.totalError + " " + swarm.size, runSave);
// Evaluate the stop criterion
stop = evalF >= pb.maxEval ||
pb.accuracy > swarm.Best.position.totalError;
if (Tribes.adaptOption == 0) {
continue iterations;
}
if (Tribes.adaptOption == 1) { // Just reinitialize the swarm
adaptThreshold = iter - adapt;
// adaptMax=swarmSize;
adaptMax = swarm.linkNb(swarm);
if (adaptThreshold >= adaptMax) {
if (swarm.Best.positionPrev.totalError <=
swarm.Best.position.totalError) {
adapt = iter; // Memorize at which iteration adaptation occurs
for (n = 0; n < swarm.tribeNb; n++) {
evalF=swarm.tribes[n].reinitTribe(pb,evalF);
}
}
}
continue iterations;
}
// if(swarm.Best.positionPrev.totalError<=swarm.Best.position.totalError)
{
// Structural adaptations
//swarmSize = swarm.swarmSize(swarm);
// print("\nSwarm size (explorers): " + swarmSize,out);
// print(" Tribes: (explorers/memories) ",out);
// for (n = 0; n < swarm.tribeNumber; n++) {
// print(swarm.tribes[n].explorerNb + "/" +
// swarm.tribes[n].memoryNb + " ",out);
// }
//
// On "laisse le temps" à chaque tribu de bouger avant éventuelle adaptation
// La règle est empirique et peut être modifiée
//
adaptThreshold = iter - adapt;
// adaptMax=swarmSize;
adaptMax = swarm.linkNb(swarm);
if (adaptThreshold >= adaptMax) {
adapt = iter; // Memorize at which iteration adaptation occurs
evalF=swarm.adaptSwarm(pb, Tribes.adaptOption, swarm,
displayPb,evalF); // Réalise l'adaptation
// Modifie la recherche de la meilleure informatrice
// normale (la "vraie" meilleure) ou dépendant d'un pseudo-gradient
// (cf. informExplorer)
//
// informOption=-informOption;
}
// print("\n Nb of tribes: " + swarm.tribeNumber +
// "\n Particles/tribe:",out);
// for (n = 0; n < swarm.tribeNumber; n++) {
// print(swarm.tribes[n].explorerNb + " ",out);
// }
//
// print("\n Statuses :");
// for (n = 0; n < swarm.tribeNumber; n++) {
// print(swarm.tribes[n].status + " ",out);
// }
}
// -----------OPTIMIZE END
}
// Result of the run
print("\nBest: eval.= " + evalF + "\n", displayPb);
swarm.Best.displayMemory(displayPb);
save(" " + iter + " " + evalF+ " ", synthSave);
for (d = 0; d < pb.fitnessSize; d++) {
save(swarm.Best.position.fitness[d] + " ", synthSave);
}
for (d = 0; d < D; d++) {
save(" " + swarm.Best.position.x[d], synthSave);
}
// Prepare return
temp[0] = swarm.Best.position.totalError;
temp[1] = evalF;
if (evalF < pb.maxEval) {
temp[2] = 1;
} else {
temp[2] = 0;
}
return temp;
}
**/
*
* public synchronized void search(param pb, PrintStream runSave,
* PrintStream synthSave) {
*
* double epsMean, epsMin, epsMax; double evalMean; int n; int run, run1;
* int successNb;
*
* // Exploratrices générées explorer explorer[] = new explorer[
* Tribes.maxExplorerNb];
*
* double[] eps = new double[pb.maxRun]; double[] evalNb = new
* double[pb.maxRun]; double[] temp = new double[3]; successNb = 0; for (n =
* 0; n < 9; n++) { // For information Tribes.strategies[n] = 0;
* Tribes.status[n] = 0; } ; epsMin = Tribes.infinity; epsMax = 0;
*
* // Titles
*
* print("\nIter. Eval. Best_fitness", displayPb); save("\n\n PROBLEM
* "+pb.function[0],synthSave); save("\nRun Iter. Eval. Best_fitness
* Position", synthSave); save("\n\n PROBLEM "+pb.function[0],runSave);
*
* // ** // * Loop on runs // * for (run = 0; run < pb.maxRun; run++) { run1
* = run + 1; save("\n" + run1 + " ", synthSave); temp = solve(pb,
* Tribes.initExplorerNb,runSave,synthSave);
*
* eps[run] = temp[0]; evalNb[run] = temp[1]; successNb = successNb + (int)
* temp[2]; if (eps[run] < epsMin) { epsMin = eps[run]; } if (eps[run] >
* epsMax) { epsMax = eps[run]; } }
*
* // Mean values epsMean = 0; evalMean = 0; for (run = 0; run < pb.maxRun;
* run++) { epsMean = epsMean + eps[run]; evalMean = evalMean + evalNb[run];
* }
*
* epsMean = epsMean / pb.maxRun; evalMean = evalMean / pb.maxRun;
* print("\nStatuses ", displayPb); for (n = 1; n < 10; n++) { print("\n" +
* n + " " + Tribes.status[n - 1] + " times", displayPb); }
* print("\nStrategies ", displayPb); for (n = 1; n < 10; n++) { print("\n"
* + n + " " + Tribes.strategies[n - 1] + " times", displayPb); }
* print("\nMIN BEST TOTAL_ERROR " + epsMin, displayPb); print("\nMEAN BEST
* TOTAL_ERROR " + epsMean, displayPb); print("\nMEAN EVAL. NUMBER " +
* evalMean, displayPb); print("\n SUCCESS RATE " + (double) successNb /
* pb.maxRun, displayPb);
*
* save("\nMIN BEST TOTAL_ERROR " + epsMin, synthSave); save("\nMEAN BEST
* TOTAL_ERROR " + epsMean, synthSave); save("\nMEAN EVAL. NUMBER " +
* evalMean, synthSave); save("\n SUCCESS RATE " + (double) successNb /
* pb.maxRun, synthSave);
*
* save("\n-1", runSave); // Special value for the end of the file. Used for
* graphics
*
* } // End of search()
*
* public double[] solve(param pb, int initExplorerNb, PrintStream runSave,
* PrintStream synthSave) { int adapt, adaptMax; int adaptThreshold; int d,
* D = pb.H.Dimension; int evalF; int iter; int n; boolean stop; double[]
* temp = new double[3]; int informOption; // For the best informant. // -1
* => really the best // 1 => the best according to a pseudo-gradient method
*
* // -----------INIT START // Generate a swarm evalF=0; swarm swarm = new
* swarm(); evalF=swarm.generateSwarm(pb, initExplorerNb, pb.initType,
* displayPb,evalF);
*
* // swarm.displaySwarm(swarm,out); // print("\n Best after init:
* "+swarm.Best.position.fitness,out);
*
* // Move the swarm as long as the stop criterion is false iter = 0; adapt
* = 0; stop = false; informOption = -1; // Hard coded option // -1 =
* absolute best informant // 1 = relative (pseudo-gradient) best informant.
* For "niching" // See also moveExplorer, which can be modified in order to
* avoid this parameter // // -----------INIT END
*
*
* // -----------OPTIMIZE START iterations:while (!stop) {
*
* swarm.size = swarm.swarmSize(swarm); iter++; // swarm.Best.positionPrev =
* swarm.Best.position;
*
* evalF=swarm.moveSwarm(pb, swarm, informOption, displayPb, evalF); //***
* HERE IT MOVES
*
* // Some display each time there is "enough" improvement // fduring the
* process
*
* double enough = 0.005; if ((1 - enough) *
* swarm.Best.positionPrev.totalError > swarm.Best.position.totalError) {
* print("\nIter. " + iter, displayPb); print(" Eval. " + evalF, displayPb);
* print(" totalError " + swarm.Best.position.totalError, displayPb);
* print(" " + swarm.size + " particles", displayPb); } // Save run info
* save("\n" + iter + " " + evalF + " " + swarm.Best.position.totalError + "
* " + swarm.size, runSave);
*
* // Evaluate the stop criterion stop = evalF >= pb.maxEval || pb.accuracy
* > swarm.Best.position.totalError;
*
* if (Tribes.adaptOption == 0) { continue iterations; }
*
* if (Tribes.adaptOption == 1) { // Just reinitialize the swarm
* adaptThreshold = iter - adapt; // adaptMax=swarmSize; adaptMax =
* swarm.linkNb(swarm); if (adaptThreshold >= adaptMax) { if
* (swarm.Best.positionPrev.totalError <= swarm.Best.position.totalError) {
* adapt = iter; // Memorize at which iteration adaptation occurs
*
* for (n = 0; n < swarm.tribeNb; n++) {
* evalF=swarm.tribes[n].reinitTribe(pb,evalF); } } } continue iterations; }
*
* // if(swarm.Best.positionPrev.totalError<=swarm.Best.position.totalError)
* { // Structural adaptations
*
* //swarmSize = swarm.swarmSize(swarm);
*
* // print("\nSwarm size (explorers): " + swarmSize,out); // print("
* Tribes: (explorers/memories) ",out); // for (n = 0; n <
* swarm.tribeNumber; n++) { // print(swarm.tribes[n].explorerNb + "/" + //
* swarm.tribes[n].memoryNb + " ",out); // } // // On "laisse le temps" à
* chaque tribu de bouger avant éventuelle adaptation // La règle est
* empirique et peut être modifiée //
*
* adaptThreshold = iter - adapt; // adaptMax=swarmSize; adaptMax =
* swarm.linkNb(swarm);
*
* if (adaptThreshold >= adaptMax) { adapt = iter; // Memorize at which
* iteration adaptation occurs evalF=swarm.adaptSwarm(pb,
* Tribes.adaptOption, swarm, displayPb,evalF); // Réalise l'adaptation
*
* // Modifie la recherche de la meilleure informatrice // normale (la
* "vraie" meilleure) ou dépendant d'un pseudo-gradient // (cf.
* informExplorer) // // informOption=-informOption; } // print("\n Nb of
* tribes: " + swarm.tribeNumber + // "\n Particles/tribe:",out); // for (n
* = 0; n < swarm.tribeNumber; n++) { // print(swarm.tribes[n].explorerNb +
* " ",out); // } // // print("\n Statuses :"); // for (n = 0; n <
* swarm.tribeNumber; n++) { // print(swarm.tribes[n].status + " ",out); //
* }
*
*
* }
* // -----------OPTIMIZE END }
*
* // Result of the run print("\nBest: eval.= " + evalF + "\n", displayPb);
* swarm.Best.displayMemory(displayPb);
*
* save(" " + iter + " " + evalF+ " ", synthSave);
*
* for (d = 0; d < pb.fitnessSize; d++) {
* save(swarm.Best.position.fitness[d] + " ", synthSave); }
*
* for (d = 0; d < D; d++) { save(" " + swarm.Best.position.x[d],
* synthSave); }
*
* // Prepare return temp[0] = swarm.Best.position.totalError; temp[1] =
* evalF; if (evalF < pb.maxEval) { temp[2] = 1; } else { temp[2] = 0; }
*
* return temp; }
*
*/
/**
* Population will be hidden.
*/
@@ -638,9 +539,10 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
/**
* As TRIBES manages an own structured set of particles (the list of Tribes containing explorers
* and memories), the setPopulation method is only telling Tribes the range
* of the indiviuals in the beginning of the run, the individuals will be discarded.
* As TRIBES manages an own structured set of particles (the list of Tribes
* containing explorers and memories), the setPopulation method is only
* telling Tribes the range of the indiviuals in the beginning of the run,
* the individuals will be discarded.
*/
@Override
public void setPopulation(Population pop) {
@@ -666,10 +568,11 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
/**
* Be aware that TRIBES uses two kinds of particles: explorers and memories. As memories
* are inactive in that they dont search the problem space directly, they are not included
* in the returned population. This, however, means that the best found solution might not
* be inluded as well at several if not most stages of the search.
* Be aware that TRIBES uses two kinds of particles: explorers and memories.
* As memories are inactive in that they dont search the problem space
* directly, they are not included in the returned population. This,
* however, means that the best found solution might not be inluded as well
* at several if not most stages of the search.
*/
@Override
public Population getPopulation() {
@@ -677,8 +580,9 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
/**
* Return a SolutionSet of TribesExplorers (AbstractEAIndividuals) of which some where
* memory particles, thus the returned population is larger than the current population.
* Return a SolutionSet of TribesExplorers (AbstractEAIndividuals) of which
* some where memory particles, thus the returned population is larger than
* the current population.
*
* @return a population of possible solutions.
*/
@@ -709,13 +613,16 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
return indy;
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* This method allows you to add the LectureGUI as listener to the Optimizer
*
* @param ea
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -726,6 +633,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
return false;
}
}
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
@@ -736,13 +644,11 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
return (evals % notifyGenChangedEvery) == 0;
}
@Override
public void freeWilly() {}
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return m_Identifier;
@@ -821,7 +727,6 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
// public void setUseAnchor(boolean useAnchor) {
// this.useAnchor = useAnchor;
// }
/**
* @return the m_Show
*/

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.InterfaceOptimizationProblem;
/** The winged MOEA was a nice idea, which didn't really work out.
* Here a standard MOEA is assisted by n additional local searchers, each
* optimizing just one objective. The idea was that these local optimizers
* would span the search space and would allow the MOEA to converge faster.
* But in the end the performance of this algorithm strongly depends on the
* optimization problem.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 16.02.2005
* Time: 16:34:22
* To change this template use File | Settings | File Templates.
/**
* The winged MOEA was a nice idea, which didn't really work out. Here a
* standard MOEA is assisted by n additional local searchers, each optimizing
* just one objective. The idea was that these local optimizers would span the
* search space and would allow the MOEA to converge faster. But in the end the
* performance of this algorithm strongly depends on the optimization problem.
* Created by IntelliJ IDEA. User: streiche Date: 16.02.2005 Time: 16:34:22 To
* change this template use File | Settings | File Templates.
*/
public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable {
@@ -94,8 +91,9 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will init the optimizer with a given population
/**
* This method will init the optimizer with a given population
*
* @param pop The initial population
* @param reset If true the population is reset.
*/
@@ -135,7 +133,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** The optimize method will compute a 'improved' and evaluated population
/**
* The optimize method will compute a 'improved' and evaluated population
*/
@Override
public void optimize() {
@@ -154,8 +153,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/** This method will manage comunication between the
* islands
/**
* This method will manage comunication between the islands
*/
private void communicate() {
int oldFunctionCalls;
@@ -180,7 +179,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
this.migrate();
}
/** This method implements the migration between the optimzers
/**
* This method implements the migration between the optimzers
*
*/
private void migrate() {
@@ -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
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
@@ -227,7 +230,9 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
return false;
}
}
/** Something has changed
/**
* Something has changed
*/
protected void firePropertyChangedEvent(String name) {
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
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
/**
* This method will return a string describing all properties of the
* optimizer and the applied methods.
*
* @return A descriptive string
*/
@Override
@@ -260,35 +270,38 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
result += this.m_Population.getStringRepresentation();
return result;
}
/** This method allows you to set an identifier for the algorithm
/**
* This method allows you to set an identifier for the algorithm
*
* @param name The indenifier
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
}
/** This method is required to free the memory on a RMIServer,
* but there is nothing to implement.
*/
@Override
public void freeWilly() {
}
/**********************************************************************************************************************
/**
* ********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is Evolutionary Multi-Criteria Optimization Algorithm hybridized with Local Searchers to span the Pareto-Front.";
}
/** This method will return a naming String
/**
* This method will return a naming String
*
* @return The name of the algorithm
*/
@Override
@@ -296,19 +309,23 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
return "EMO-LS";
}
/** Assuming that all optimizer will store their data in a population
* we will allow access to this population to query to current state
* of the optimizer.
/**
* Assuming that all optimizer will store their data in a population we will
* allow access to this population to query to current state of the
* optimizer.
*
* @return The population of current solutions to a given problem.
*/
@Override
public Population getPopulation() {
return this.m_Population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
}
public String populationTipText() {
return "(Defunct)";
}
@@ -317,41 +334,54 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method allows you to set/get the optimizing technique to use.
/**
* This method allows you to set/get the optimizing technique to use.
*
* @return The current optimizing method
*/
public InterfaceOptimizer getMOOptimizer() {
return this.m_MOOptimizer;
}
public void setMOOptimizer(InterfaceOptimizer b) {
this.m_MOOptimizer = b;
}
public String mOOptimizerTipText() {
return "Choose a population based optimizing technique to use.";
}
/** This method allows you to set/get the optimizing technique to use.
/**
* This method allows you to set/get the optimizing technique to use.
*
* @return The current optimizing method
*/
public InterfaceOptimizer getSOOptimizer() {
return this.m_SOOptimizer;
}
public void setSOOptimizer(InterfaceOptimizer b) {
this.m_SOOptimizer = b;
}
public String sOOptimizerTipText() {
return "Choose a population based optimizing technique to use.";
}
/** This method allows you to set/get the archiving strategy to use.
/**
* This method allows you to set/get the archiving strategy to use.
*
* @return The current optimizing method
*/
public int getMigrationRate() {
return this.m_MigrationRate;
}
public void setMigrationRate(int b) {
this.m_MigrationRate = b;
}
public String migrationRateTipText() {
return "Choose a proper migration rate.";
}