halfdan/eva2
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Lots of cleanup.

Browse Source
This commit is contained in:
Fabian Becker 2014-01-17 10:48:42 +01:00
parent 024279899c
commit f7b31611c2
6 changed files with 100 additions and 151 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
src/eva2/optimization/strategies/EvolutionStrategies.java
View File

@ -11,6 +11,7 @@ import eva2.optimization.population.Population;
import eva2.optimization.population.SolutionSet;
import eva2.optimization.problems.B1Problem;
import eva2.optimization.problems.InterfaceOptimizationProblem;
import eva2.util.annotation.Description;
/**
* Evolution strategies by Rechenberg and Schwefel, but please remember that
@ -20,13 +21,9 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
* 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 $
* Evolution Strategies.
*/
@Description(value = "This is an Evolution Strategy. Note that the population size depends on mu (number of parents) and lambda (number of offspring)")
public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializable {
protected int mu = 5;
@ -342,14 +339,6 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
/**
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is an Evolution Strategy. Note that the population size depends on mu (number of parents) and lambda (number of offspring).";
}
public String[] customPropertyOrder() {
return new String[]{"mu", "lambda"};

95
src/eva2/optimization/strategies/HillClimbing.java
View File

@ -8,39 +8,32 @@ import eva2.optimization.population.Population;
import eva2.optimization.population.SolutionSet;
import eva2.optimization.problems.B1Problem;
import eva2.optimization.problems.InterfaceOptimizationProblem;
import eva2.util.annotation.Description;
/**
* 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 $
* strategy sets the mutation rate temporarily to 1.0.
*/
@Description("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.")
public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private InterfaceOptimizationProblem optimizationProblem = new B1Problem();
private InterfaceMutation mutator = null;
// private int m_MultiRuns = 100;
// private int maxFitnessCalls = 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;
private Population m_Population;
transient private String identifier = "";
transient private InterfacePopulationChangedEventListener populationChangedEventListener;
private Population population;
public HillClimbing() {
this.m_Population = new Population();
this.m_Population.setTargetSize(10);
this.population = new Population();
this.population.setTargetSize(10);
}
public HillClimbing(HillClimbing a) {
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.population = (Population) a.population.clone();
this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone();
}
@Override
@ -49,21 +42,21 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
}
/**
* This method will init the HillClimber
* This method will initialize the HillClimber
*/
@Override
public void init() {
this.m_Problem.initializePopulation(this.m_Population);
this.m_Problem.evaluate(this.m_Population);
this.optimizationProblem.initializePopulation(this.population);
this.optimizationProblem.evaluate(this.population);
this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
}
@Override
public void initByPopulation(Population pop, boolean reset) {
this.m_Population = (Population) pop.clone();
this.population = (Population) pop.clone();
if (reset) {
this.m_Population.init();
this.m_Problem.evaluate(this.m_Population);
this.population.init();
this.optimizationProblem.evaluate(this.population);
this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
}
}
@ -74,12 +67,12 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
@Override
public void optimize() {
AbstractEAIndividual indy;
Population original = (Population) this.m_Population.clone();
Population original = (Population) this.population.clone();
double tmpD;
InterfaceMutation tmpMut;
for (int i = 0; i < this.m_Population.size(); i++) {
indy = ((AbstractEAIndividual) this.m_Population.get(i));
for (int i = 0; i < this.population.size(); i++) {
indy = ((AbstractEAIndividual) this.population.get(i));
tmpD = indy.getMutationProbability();
indy.setMutationProbability(1.0);
if (mutator == null) {
@ -89,17 +82,17 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
}
indy.setMutationProbability(tmpD);
}
this.m_Problem.evaluate(this.m_Population);
for (int i = 0; i < this.m_Population.size(); i++) {
if (((AbstractEAIndividual) original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual) this.m_Population.get(i)))) {
this.optimizationProblem.evaluate(this.population);
for (int i = 0; i < this.population.size(); i++) {
if (((AbstractEAIndividual) original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual) this.population.get(i)))) {
// this.population.remove(i);
// throw away mutated one and replace by old one
this.m_Population.set(i, original.get(i));
this.population.set(i, original.get(i));
} else {
// else: mutation improved the individual, so leave the new one
}
}
this.m_Population.incrGeneration();
this.population.incrGeneration();
// for (int i = 0; i < this.population.size(); i++) {
// indy1 = (AbstractEAIndividual) this.population.get(i);
// indy2 = (AbstractEAIndividual)(indy1).clone();
@ -139,12 +132,12 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.optimizationProblem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
return this.optimizationProblem;
}
// /** This method will init the HillClimber
@ -191,14 +184,14 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
*/
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
this.populationChangedEventListener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener == ea) {
m_Listener = null;
if (populationChangedEventListener == ea) {
populationChangedEventListener = null;
return true;
} else {
return false;
@ -209,8 +202,8 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
* Something has changed
*/
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
if (this.populationChangedEventListener != null) {
this.populationChangedEventListener.registerPopulationStateChanged(this, name);
}
}
@ -223,14 +216,14 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
@Override
public String getStringRepresentation() {
String result = "";
if (this.m_Population.size() > 1) {
result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
if (this.population.size() > 1) {
result += "Multi(" + this.population.size() + ")-Start Hill Climbing:\n";
} else {
result += "Hill Climbing:\n";
}
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
result += this.optimizationProblem.getStringRepresentationForProblem(this) + "\n";
result += this.population.getStringRepresentation();
return result;
}
@ -241,26 +234,18 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
this.identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
return this.identifier;
}
/**
* ********************************************************************************************************************
* These are for GUI
*/
/**
* 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
@ -274,12 +259,12 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
@Override
public Population getPopulation() {
return this.m_Population;
return this.population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
this.population = pop;
}
@Override

11
src/eva2/optimization/strategies/InterfaceOptimizer.java
View File

@ -9,16 +9,7 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
/**
* The general interface for optimizers giving the main methods necessary
* to perform a population based search.
* This is a simple implementation of Population Based Incremental Learning.
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
*
* @author Felix Streichert
* @version: $Revision: 306 $
* $Date: 2007-12-04 14:22:52 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
*/
public interface InterfaceOptimizer {
/**
@ -106,7 +97,7 @@ public interface InterfaceOptimizer {
* This method will set the problem that is to be optimized. The problem
* should be initialized when this method is called.
*
* @param problem
* @param problem The optimization problem.
*/
void setProblem(InterfaceOptimizationProblem problem);

17
src/eva2/optimization/strategies/ScatterSearch.java
View File

@ -22,6 +22,7 @@ import eva2.tools.Pair;
import eva2.tools.SelectedTag;
import eva2.tools.math.Mathematics;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.ArrayList;
@ -35,12 +36,12 @@ import java.util.ArrayList;
* values in the reference set or as an absolute value (in both cases only the
* first fitness criterion is regarded).
*
* @author mkron
* <p/>
* [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.
*/
@Description("A scatter search variant after Rodiguez-Fernandez, J.Egea and J.Banga: Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems, BMC Bioinf. 2006")
public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener {
transient private InterfacePopulationChangedEventListener m_Listener = null;
@ -64,11 +65,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
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;
// below this threshold a local search will be performed
// protected double fitThreshLocalSearch = 1000.;
protected boolean doLocalSearch = false;
private boolean relativeFitCriterion = false;
private double nelderMeadInitPerturbation = 0.01;
@ -430,9 +426,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
Population hcPop = new Population(1);
hcPop.add(cand);
int stepsDone = PostProcess.processWithHC(hcPop, problem, hcSteps);
// if (TRACE) {
// System.out.println("local search result: from " + BeanInspector.toString(fitBefore) + " to " + BeanInspector.toString(hcPop.getEAIndividual(0).getFitness()));
// }
return new Pair<AbstractEAIndividual, Integer>(hcPop.getEAIndividual(0), stepsDone);
}
@ -455,7 +448,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
*
* @param cand
* @param popCompGeno
* @param popCompPheno
* @param popComPheno
* @return
*/
private boolean diversityCriterionFulfilled(AbstractEAIndividual cand, Population popCompGeno, Population popComPheno) {
@ -782,10 +775,6 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
return "ScatterSearch";
}
public static String globalInfo() {
return "A scatter search variant after Rodiguez-Fernandez, J.Egea and J.Banga: Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems, BMC Bioinf. 2006";
}
private boolean useLSHC() {
return localSearchMethod.getSelectedTagID() == 0;
}

5
src/eva2/optimization/strategies/SqPSO.java
View File

@ -9,8 +9,6 @@ import eva2.optimization.operator.paramcontrol.ParamAdaption;
* A thunk class preconfiguring CBN-EA to function as a sequential niching method. This
* is to be comparable to parallel and semi-sequential niching (esp. using the same convergence
* criterion).
*
* @author mkron
*/
public class SqPSO extends ClusterBasedNichingEA {
public SqPSO() {
@ -32,12 +30,9 @@ public class SqPSO extends ClusterBasedNichingEA {
setOptimizer(new ParticleSwarmOptimization(popSize, 2.05, 2.05, PSOTopologyEnum.grid, 2));
ParamAdaption[] defAdpt = new ParamAdaption[]{};
setParameterControl(defAdpt);
// if (threshAdaption) addParameterControl(getDefaultThreshAdaption());
setPopulationSize(popSize);
}
// public void hideHideable()
@Override
public String getName() {
return "SqPSO";

106
src/eva2/optimization/strategies/SteadyStateGA.java
View File

@ -22,26 +22,26 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
*/
public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
private Population m_Population = new Population();
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private InterfaceSelection m_ParentSelection = new SelectTournament();
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;
private Population population = new Population();
private InterfaceOptimizationProblem optimizationProblem = new B1Problem();
private InterfaceSelection parentSelection = new SelectTournament();
private InterfaceSelection partnerSelection = new SelectTournament();
private InterfaceReplacement replacementSelection = new ReplaceWorst();
private int numberOfPartners = 1;
private String identifier = "";
transient private InterfacePopulationChangedEventListener populationChangedEventListener;
public SteadyStateGA() {
}
public SteadyStateGA(SteadyStateGA a) {
this.m_Population = (Population) a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
this.m_Identifier = a.m_Identifier;
this.m_NumberOfPartners = a.m_NumberOfPartners;
this.m_ParentSelection = (InterfaceSelection) a.m_ParentSelection.clone();
this.m_PartnerSelection = (InterfaceSelection) a.m_PartnerSelection.clone();
this.m_ReplacementSelection = (InterfaceReplacement) a.m_ReplacementSelection.clone();
this.population = (Population) a.population.clone();
this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone();
this.identifier = a.identifier;
this.numberOfPartners = a.numberOfPartners;
this.parentSelection = (InterfaceSelection) a.parentSelection.clone();
this.partnerSelection = (InterfaceSelection) a.partnerSelection.clone();
this.replacementSelection = (InterfaceReplacement) a.replacementSelection.clone();
}
@Override
@ -51,8 +51,8 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
@Override
public void init() {
this.m_Problem.initializePopulation(this.m_Population);
this.evaluatePopulation(this.m_Population);
this.optimizationProblem.initializePopulation(this.population);
this.evaluatePopulation(this.population);
this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
}
@ -63,10 +63,10 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
*/
@Override
public void initByPopulation(Population pop, boolean reset) {
this.m_Population = (Population) pop.clone();
this.population = (Population) pop.clone();
if (reset) {
this.m_Population.init();
this.evaluatePopulation(this.m_Population);
this.population.init();
this.evaluatePopulation(this.population);
this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
}
}
@ -77,7 +77,7 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
* @param population The population that is to be evaluated
*/
private void evaluatePopulation(Population population) {
this.m_Problem.evaluate(population);
this.optimizationProblem.evaluate(population);
population.incrGeneration();
}
@ -102,37 +102,37 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
* population of evaluated individuals.
*/
private void generateChildren() {
this.m_ParentSelection.prepareSelection(this.m_Population);
this.m_PartnerSelection.prepareSelection(this.m_Population);
Population parents = this.m_ParentSelection.selectFrom(this.m_Population, 1);
this.parentSelection.prepareSelection(this.population);
this.partnerSelection.prepareSelection(this.population);
Population parents = this.parentSelection.selectFrom(this.population, 1);
AbstractEAIndividual mother = (AbstractEAIndividual) parents.get(0);
parents = this.m_PartnerSelection.findPartnerFor(mother, this.m_Population, this.m_NumberOfPartners);
parents = this.partnerSelection.findPartnerFor(mother, this.population, this.numberOfPartners);
AbstractEAIndividual[] offSprings = mother.mateWith(parents);
offSprings[0].mutate();
this.m_Problem.evaluate(offSprings[0]);
this.m_ReplacementSelection.insertIndividual(offSprings[0], this.m_Population, parents);
this.optimizationProblem.evaluate(offSprings[0]);
this.replacementSelection.insertIndividual(offSprings[0], this.population, parents);
}
@Override
public void optimize() {
for (int i = 0; i < this.m_Population.size(); i++) {
for (int i = 0; i < this.population.size(); i++) {
this.generateChildren();
}
this.m_Population.incrFunctionCallsBy(this.m_Population.size());
this.m_Population.incrGeneration();
this.population.incrFunctionCallsBy(this.population.size());
this.population.incrGeneration();
this.firePropertyChangedEvent(Population.NEXT_GENERATION_PERFORMED);
}
@Override
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
this.populationChangedEventListener = ea;
}
@Override
public boolean removePopulationChangedEventListener(
InterfacePopulationChangedEventListener ea) {
if (m_Listener == ea) {
m_Listener = null;
if (populationChangedEventListener == ea) {
populationChangedEventListener = null;
return true;
} else {
return false;
@ -140,8 +140,8 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
}
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) {
this.m_Listener.registerPopulationStateChanged(this, name);
if (this.populationChangedEventListener != null) {
this.populationChangedEventListener.registerPopulationStateChanged(this, name);
}
}
@ -152,12 +152,12 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
*/
@Override
public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.optimizationProblem = problem;
}
@Override
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
return this.optimizationProblem;
}
/**
@ -171,8 +171,8 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
String result = "";
result += "Genetic Algorithm:\n";
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
result += this.m_Population.getStringRepresentation();
result += this.optimizationProblem.getStringRepresentationForProblem(this) + "\n";
result += this.population.getStringRepresentation();
return result;
}
@ -183,12 +183,12 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
*/
@Override
public void setIdentifier(String name) {
this.m_Identifier = name;
this.identifier = name;
}
@Override
public String getIdentifier() {
return this.m_Identifier;
return this.identifier;
}
/**
@ -215,20 +215,20 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
}
/**
* 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
* Assuming that all optimizers 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;
return this.population;
}
@Override
public void setPopulation(Population pop) {
this.m_Population = pop;
this.population = pop;
}
public String populationTipText() {
@ -246,11 +246,11 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
* @param selection
*/
public void setParentSelection(InterfaceSelection selection) {
this.m_ParentSelection = selection;
this.parentSelection = selection;
}
public InterfaceSelection getParentSelection() {
return this.m_ParentSelection;
return this.parentSelection;
}
public String parentSelectionTipText() {
@ -267,11 +267,11 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
if (partners < 0) {
partners = 0;
}
this.m_NumberOfPartners = partners;
this.numberOfPartners = partners;
}
public int getNumberOfPartners() {
return this.m_NumberOfPartners;
return this.numberOfPartners;
}
public String numberOfPartnersTipText() {
@ -285,11 +285,11 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
* @param selection
*/
public void setPartnerSelection(InterfaceSelection selection) {
this.m_PartnerSelection = selection;
this.partnerSelection = selection;
}
public InterfaceSelection getPartnerSelection() {
return this.m_PartnerSelection;
return this.partnerSelection;
}
public String partnerSelectionTipText() {
@ -302,11 +302,11 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
* @param selection
*/
public void setReplacementSelection(InterfaceReplacement selection) {
this.m_ReplacementSelection = selection;
this.replacementSelection = selection;
}
public InterfaceReplacement getReplacementSelection() {
return this.m_ReplacementSelection;
return this.replacementSelection;
}
public String replacementSelectionTipText() {