diff --git a/src/eva2/optimization/strategies/EvolutionStrategies.java b/src/eva2/optimization/strategies/EvolutionStrategies.java
index 30545332..1654d858 100644
--- a/src/eva2/optimization/strategies/EvolutionStrategies.java
+++ b/src/eva2/optimization/strategies/EvolutionStrategies.java
@@ -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"};
diff --git a/src/eva2/optimization/strategies/HillClimbing.java b/src/eva2/optimization/strategies/HillClimbing.java
index 6af9818f..c79950da 100644
--- a/src/eva2/optimization/strategies/HillClimbing.java
+++ b/src/eva2/optimization/strategies/HillClimbing.java
@@ -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
diff --git a/src/eva2/optimization/strategies/InterfaceOptimizer.java b/src/eva2/optimization/strategies/InterfaceOptimizer.java
index e4c5746b..4bd20685 100644
--- a/src/eva2/optimization/strategies/InterfaceOptimizer.java
+++ b/src/eva2/optimization/strategies/InterfaceOptimizer.java
@@ -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);
diff --git a/src/eva2/optimization/strategies/ScatterSearch.java b/src/eva2/optimization/strategies/ScatterSearch.java
index 7776215c..aa4c615e 100644
--- a/src/eva2/optimization/strategies/ScatterSearch.java
+++ b/src/eva2/optimization/strategies/ScatterSearch.java
@@ -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
*
* [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(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;
}
diff --git a/src/eva2/optimization/strategies/SqPSO.java b/src/eva2/optimization/strategies/SqPSO.java
index 418a6306..052d7675 100644
--- a/src/eva2/optimization/strategies/SqPSO.java
+++ b/src/eva2/optimization/strategies/SqPSO.java
@@ -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";
diff --git a/src/eva2/optimization/strategies/SteadyStateGA.java b/src/eva2/optimization/strategies/SteadyStateGA.java
index d74e762f..6d4aacd0 100644
--- a/src/eva2/optimization/strategies/SteadyStateGA.java
+++ b/src/eva2/optimization/strategies/SteadyStateGA.java
@@ -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() {