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Check EvAInfo for change log.

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This commit is contained in:
Marcel Kronfeld 2008-06-24 14:39:56 +00:00
parent 6303437fe8
commit 1baa61018e
52 changed files with 757 additions and 317 deletions
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11
src/eva2/EvAInfo.java
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@ -3,19 +3,28 @@ package eva2;
/**
* Main product and version information strings.
*
* --- Changelog
*
* 2.026: Added DiversityTerminator and KnownOptimaTerminator, slightly changed InterfaceTerminator for these
* and InterfaceStatistics to provide termination message to text window.
* Killed redundant method getGenerations() in Population. Population.getAllSolutions now returns a
* SolutionSet combining last population with a set of possibly archived solutions.
* Post processing with HC may now use variable step size mutation.
* 2.025: FunctionArea may now plot circles easily. The FLensProblemViewer seems to be cured.
* 2.024: Cleaned up AbstractGOParams, deactivated parent logging (saving memory)
* 2.023: Cleaned up the PF strategy
* 2.022: Some changes to the SimpleProblemWrapper, not of great interest. However,
* simple problems may now access a plot quite easily.
*
* --- End Changelog
*
* @author mkron
*
*/
public class EvAInfo {
public static final String productName = "EvA 2";
public static final String productLongName = "Evolutionary Algorithms Workbench 2";
public static final String versionNum = new String ("2.025");
public static final String versionNum = new String ("2.026");
public static final String url = "http://www.ra.cs.uni-tuebingen.de/software/EvA2";
public static final String propertyFile = "resources/EvA2.props";

2
src/eva2/OptimizerRunnable.java
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@ -118,7 +118,7 @@ public class OptimizerRunnable implements Runnable {
return "Post processing finished";
} else {
InterfaceTerminator term = proc.getGOParams().getTerminator();
return term.terminatedBecause(proc.getGOParams().getOptimizer().getPopulation());
return term.lastTerminationMessage();
}
} else return "Not yet terminated";
}

39
src/eva2/server/go/InterfaceTerminator.java
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@ -1,26 +1,25 @@
package eva2.server.go;
/*
* Title: EvA2
* Description:
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
* @author Holger Ulmer, Felix Streichert, Hannes Planatscher
* @version: $Revision: 306 $
* $Date: 2007-12-04 14:22:52 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
*/
/*==========================================================================*
* IMPORTS
*==========================================================================*/
/*==========================================================================*
* INTERFACE DECLARATION
*==========================================================================*/
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/**
* Interface for a termination criterion.
*
* @author mkron, streiche
*
*/
public interface InterfaceTerminator {
public boolean isTerminated(PopulationInterface pop);
public String toString();
public String terminatedBecause(PopulationInterface pop);
public void init();
/**
* Test a given population for convergence with the criterion defined by the instance.
*
* @param pop the population to test
* @return true if the population fulfills the termination criterion, else false
*/
public boolean isTerminated(PopulationInterface pop);
public boolean isTerminated(InterfaceSolutionSet pop);
public String toString();
public String lastTerminationMessage();
public void init(InterfaceOptimizationProblem prob);
}

16
src/eva2/server/go/individuals/ESIndividualPermutationData.java
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@ -50,14 +50,14 @@ public class ESIndividualPermutationData extends AbstractEAIndividual implements
for (int i = 0; i < this.m_Genotype.length; i++) {
// if (individual.m_Phenotype != null) {
this.m_Genotype[i] = new double[individual.m_Genotype[i].length];
this.m_Range[i] = new double[individual.m_Genotype[i].length][2];
for (int j = 0; j < this.m_Genotype[i].length; j++) {
this.m_Genotype[i][j] = individual.m_Genotype[i][j];
this.m_Range[i][j][0] = individual.m_Range[i][j][0];
this.m_Range[i][j][1] = individual.m_Range[i][j][1];
// }
}
this.m_Genotype[i] = new double[individual.m_Genotype[i].length];
this.m_Range[i] = new double[individual.m_Genotype[i].length][2];
for (int j = 0; j < this.m_Genotype[i].length; j++) {
this.m_Genotype[i][j] = individual.m_Genotype[i][j];
this.m_Range[i][j][0] = individual.m_Range[i][j][0];
this.m_Range[i][j][1] = individual.m_Range[i][j][1];
// }
}
}
// cloning the members of AbstractEAIndividual

6
src/eva2/server/go/operators/distancemetric/GenotypeMetricBitSet.java
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@ -46,9 +46,9 @@ public class GenotypeMetricBitSet implements InterfaceDistanceMetric, java.io.Se
for (int i = 0; i < length; i++) {
if (dIndy1.get(i) == dIndy2.get(i)) result += 0;
else result += 1/(double)length;
else result += 1;
}
return result;
return result/(double)length;
}
/**********************************************************************************************************************
* These are for GUI
@ -57,7 +57,7 @@ public class GenotypeMetricBitSet implements InterfaceDistanceMetric, java.io.Se
* @return description
*/
public String globalInfo() {
return "This is a genotype based distance metric suited for binary data. Metric is computed on a normalized search space.";
return "This is a genotype based distance metric suited for binary data. The hamming distance is computed and normalized by chromosome length.";
}
/** This method will return a naming String
* @return The name of the algorithm

2
src/eva2/server/go/operators/distancemetric/ObjectiveSpaceMetric.java
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@ -48,7 +48,7 @@ public class ObjectiveSpaceMetric implements InterfaceDistanceMetric, java.io.Se
* @return description
*/
public String globalInfo() {
return "This is a objective space based metric.";
return "The objective space metric calculates euclidian distances on the fitness vectors.";
}
/** This method will return a naming String
* @return The name of the algorithm

20
src/eva2/server/go/operators/distancemetric/PhenotypeMetric.java
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@ -23,6 +23,7 @@ import eva2.server.go.individuals.codings.gp.InterfaceProgram;
*/
public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Serializable {
private static PhenotypeMetric pMetric = null;
private static GenotypeMetricBitSet bitMetric = null;
public PhenotypeMetric() {
}
@ -79,7 +80,8 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
return d[n][m];
}
/** This method allows you to compute the distance between two individuals.
/**
* This method allows you to compute the distance between two individuals.
* Depending on the metric this method may reject some types of individuals.
* The default return value would be 1.0.
* @param indy1 The first individual.
@ -88,18 +90,10 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
*/
public double distance(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
double result = 0;
// results are added up because individuals can implement several data types!
if ((indy1 instanceof InterfaceDataTypeBinary) && (indy2 instanceof InterfaceDataTypeBinary)) {
BitSet b1, b2;
int l1, l2;
double tmpResult = 0;
b1 = ((InterfaceDataTypeBinary) indy1).getBinaryData();
b2 = ((InterfaceDataTypeBinary) indy2).getBinaryData();
l1 = ((InterfaceDataTypeBinary) indy1).size();
l2 = ((InterfaceDataTypeBinary) indy2).size();
for (int i = 0; (i < l1) && (i < l2); i++) {
if (b1.get(i)==b2.get(i)) tmpResult += 1;
}
result += tmpResult/((double)Math.min(l1,l2));
if (bitMetric == null) bitMetric = new GenotypeMetricBitSet();
result += bitMetric.distance(indy1, indy2);
}
if ((indy1 instanceof InterfaceDataTypeInteger) && (indy2 instanceof InterfaceDataTypeInteger)) {
int[] d1,d2;
@ -222,7 +216,7 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
* @return description
*/
public String globalInfo() {
return "This is a phenotype based method suited for double data. Metric is computed on a normalized search space.";
return "This is a phenotype based metric which can be applied to binary, integer, double, permutation, and program data types. For the latter two, the Levenshtein distance is computed. All distance values are normed.";
}
/** This method will return a naming String
* @return The name of the algorithm

2
src/eva2/server/go/operators/distancemetric/SigmaSingleMetricGlobalMutation.java
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@ -64,7 +64,7 @@ public class SigmaSingleMetricGlobalMutation implements InterfaceDistanceMetric,
* @return description
*/
public String globalInfo() {
return "This is a phenotype based method suited for double data. Metric is computed on a normalized search space.";
return "This is an experimental method for individuals using global ES mutation.";
}
/** This method will return a naming String
* @return The name of the algorithm

38
src/eva2/server/go/operators/mutation/MutateESMutativeStepSizeControl.java
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@ -17,14 +17,32 @@ public class MutateESMutativeStepSizeControl implements InterfaceMutation, java.
protected double m_MutationStepSize = 0.2;
protected double m_Alpha = 1.2;
protected double m_LowerLimitStepSize = 0.0000005;
protected double m_UpperLimitStepSize = 0.4;
public MutateESMutativeStepSizeControl() {
}
public MutateESMutativeStepSizeControl(double initialStepSize, double lowerLimit, double upperLimit) {
m_MutationStepSize = initialStepSize;
if (m_LowerLimitStepSize > m_UpperLimitStepSize) {
System.err.println("Invalid step size bounds, switching upper and lower...");
double tmp = upperLimit;
upperLimit = lowerLimit;
lowerLimit = tmp;
}
m_LowerLimitStepSize = lowerLimit;
m_UpperLimitStepSize = upperLimit;
if (initialStepSize < lowerLimit || initialStepSize > upperLimit) {
m_MutationStepSize = (upperLimit + lowerLimit) /2.;
System.err.println("Invalid initial stepsize, setting it to " + m_MutationStepSize);
}
}
public MutateESMutativeStepSizeControl(MutateESMutativeStepSizeControl mutator) {
this.m_MutationStepSize = mutator.m_MutationStepSize;
this.m_Alpha = mutator.m_Alpha;
this.m_LowerLimitStepSize = mutator.m_LowerLimitStepSize;
this.m_UpperLimitStepSize = mutator.m_UpperLimitStepSize;
}
/** This method will enable you to clone a given mutation operator
@ -44,6 +62,7 @@ public class MutateESMutativeStepSizeControl implements InterfaceMutation, java.
if (this.m_MutationStepSize != mut.m_MutationStepSize) return false;
if (this.m_Alpha != mut.m_Alpha) return false;
if (this.m_LowerLimitStepSize != mut.m_LowerLimitStepSize) return false;
if (this.m_UpperLimitStepSize != mut.m_UpperLimitStepSize) return false;
return true;
} else return false;
}
@ -70,13 +89,14 @@ public class MutateESMutativeStepSizeControl implements InterfaceMutation, java.
else
this.m_MutationStepSize = this.m_MutationStepSize / this.m_Alpha;
if (this.m_MutationStepSize < this.m_LowerLimitStepSize) this.m_MutationStepSize = this.m_LowerLimitStepSize;
if (this.m_MutationStepSize > this.m_UpperLimitStepSize) this.m_MutationStepSize = this.m_UpperLimitStepSize;
for (int i = 0; i < x.length; i++) {
x[i] += ((range[i][1] -range[i][0])/2)*RNG.gaussianDouble(this.m_MutationStepSize);
if (range[i][0] > x[i]) x[i] = range[i][0];
if (range[i][1] < x[i]) x[i] = range[i][1];
}
((InterfaceESIndividual)individual).SetDGenotype(x);
System.out.println("new step size: " + m_MutationStepSize);
}
//System.out.println("After Mutate: " +((GAIndividual)individual).getSolutionRepresentationFor());
}
@ -133,7 +153,6 @@ public class MutateESMutativeStepSizeControl implements InterfaceMutation, java.
* @param d The mutation operator.
*/
public void setLowerLimitStepSize(double d) {
if (d < 1) d = 1;
this.m_LowerLimitStepSize = d;
}
public double getLowerLimitStepSize() {
@ -142,6 +161,19 @@ public class MutateESMutativeStepSizeControl implements InterfaceMutation, java.
public String lowerLimitStepSizeTipText() {
return "Set the lower limit for the mutation step size.";
}
/** Set the upper limit for the mutation step size with this method.
* @param d The mutation operator.
*/
public void setUpperLimitStepSize(double d) {
this.m_UpperLimitStepSize = d;
}
public double getUpperLimitStepSize() {
return this.m_UpperLimitStepSize;
}
public String upperLimitStepSizeTipText() {
return "Set the upper limit for the mutation step size.";
}
/** Set the value for Alpha with this method.
* @param d The mutation operator.

44
src/eva2/server/go/operators/postprocess/PostProcess.java
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@ -12,7 +12,9 @@ import eva2.server.go.operators.cluster.InterfaceClustering;
import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
import eva2.server.go.operators.distancemetric.PhenotypeMetric;
import eva2.server.go.operators.mutation.InterfaceMutation;
import eva2.server.go.operators.mutation.MutateESCovarianceMartixAdaption;
import eva2.server.go.operators.mutation.MutateESFixedStepSize;
import eva2.server.go.operators.mutation.MutateESMutativeStepSizeControl;
import eva2.server.go.operators.terminators.EvaluationTerminator;
import eva2.server.go.populations.Population;
import eva2.server.go.problems.AbstractMultiModalProblemKnown;
@ -35,6 +37,8 @@ public class PostProcess {
private static final boolean TRACE = false;
// the default mutation step size for HC post processing
private static double defaultMutationStepSize = 0.01;
// lower limit mutation step size for HC post processing
private static double minMutationStepSize = 0.0000000000000001;
// used for hill climbing post processing and only alive during that period
private static OptimizerRunnable hcRunnable = null;
@ -316,24 +320,38 @@ public class PostProcess {
/**
* Optimize a population with a default hill-climbing heuristic for a number of fitness evaluations.
* As mutation operator, a fixed step size ES mutation is used.
* As mutation operator, a mutative step size ES mutation is used, the step size of which is not allowed
* to increase above the initial stepSize. Returns the number of evaluations actually performed, which
* may be slightly above the maxSteps given.
*
* @param pop
* @param problem
* @param maxSteps
* @param fixedStepSize
* @param pop the set of individuals to work on
* @param problem the optimization problem
* @param maxSteps the number of evaluations to perform during HC
* @param stepSize the initial mutation step size
* @param minStepSize the minimal step size allowed for a mutation
* @return the number of evaluations actually performed
*/
public static int processWithHC(Population pop, AbstractOptimizationProblem problem, int maxSteps, double fixedStepSize) {
public static int processWithHC(Population pop, AbstractOptimizationProblem problem, int maxSteps, double stepSize, double minStepSize) {
// pop.SetFunctionCalls(0); // or else optimization wont restart on an "old" population
// pop.setGenerationTo(0);
int stepsBef = pop.getFunctionCalls();
processWithHC(pop, problem, new EvaluationTerminator(pop.getFunctionCalls()+maxSteps), new MutateESFixedStepSize(fixedStepSize));
processWithHC(pop, problem, new EvaluationTerminator(pop.getFunctionCalls()+maxSteps), new MutateESMutativeStepSizeControl(stepSize, minStepSize, stepSize));
return pop.getFunctionCalls()-stepsBef;
}
/**
* Perform hill climbing with default mutation parameters.
*
* @see processWithHC(Population pop, AbstractOptimizationProblem problem, int maxSteps, double stepSize, double minStepSize)
* @param pop
* @param problem
* @param maxSteps
* @return the number of evaluations actually performed
*/
public static int processWithHC(Population pop, AbstractOptimizationProblem problem, int maxSteps) {
return processWithHC(pop, problem, maxSteps, defaultMutationStepSize);
return processWithHC(pop, problem, maxSteps, defaultMutationStepSize, minMutationStepSize);
}
/**
* Optimize a population with a default hill-climbing heuristic with a given termination criterion and mutation operator.
*
@ -346,6 +364,7 @@ public class PostProcess {
HillClimbing hc = new HillClimbing();
// HC depends heavily on the selected mutation operator!
hc.SetProblem(problem);
mute.init(problem.getIndividualTemplate(), problem);
hc.SetMutationOperator(mute);
if (pop.size() != pop.getPopulationSize()) {
System.err.println(pop.size() + " vs. "+ pop.getPopulationSize());
@ -480,11 +499,12 @@ public class PostProcess {
if (listener != null) {
listener.println("number of known optima is " + mmkProb.getRealOptima().size());
listener.println("default epsilon is " + mmkProb.getEpsilon());
listener.println("optima found with default epsilon: " + getFoundOptima(solutions, mmkProb.getRealOptima(), mmkProb.getEpsilon(), true).size());
listener.println("max peak ratio is " + mmkProb.getMaximumPeakRatio(getFoundOptima(solutions, mmkProb.getRealOptima(), mmkProb.getEpsilon(), true)));
}
for (double epsilon=0.1; epsilon > 0.00000001; epsilon/=10.) {
// out.println("no optima found: " + ((InterfaceMultimodalProblemKnown)mmProb).getNumberOfFoundOptima(pop));
if (listener != null) listener.println("found " + getFoundOptima(solutions, mmkProb.getRealOptima(), epsilon, true).size() + " for epsilon = " + epsilon + ", maxPeakRatio: " + ((AbstractMultiModalProblemKnown)mmkProb).getMaximumPeakRatio(solutions, epsilon));
for (double epsilon=0.1; epsilon > 0.00000001; epsilon/=10.) {
// out.println("no optima found: " + ((InterfaceMultimodalProblemKnown)mmProb).getNumberOfFoundOptima(pop));
listener.println("found " + getFoundOptima(solutions, mmkProb.getRealOptima(), epsilon, true).size() + " for epsilon = " + epsilon + ", maxPeakRatio: " + ((AbstractMultiModalProblemKnown)mmkProb).getMaximumPeakRatio(solutions, epsilon));
}
}
}

43
src/eva2/server/go/operators/terminators/CombinedTerminator.java
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@ -4,6 +4,8 @@ import java.io.Serializable;
import eva2.server.go.InterfaceTerminator;
import eva2.server.go.PopulationInterface;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.SelectedTag;
@ -15,6 +17,7 @@ public class CombinedTerminator implements InterfaceTerminator, Serializable {
private InterfaceTerminator t1 = new FitnessConvergenceTerminator();
private InterfaceTerminator t2 = new EvaluationTerminator();
private SelectedTag andOrTag = new SelectedTag("OR", "AND");
private String msg = null;
public static final boolean AND = true;
public static final boolean OR = false;
@ -38,46 +41,52 @@ public class CombinedTerminator implements InterfaceTerminator, Serializable {
return "Boolean combination of two terminators.";
}
public void init() {
if (t1 != null) t1.init();
if (t2 != null) t2.init();
public void init(InterfaceOptimizationProblem prob) {
if (t1 != null) t1.init(prob);
if (t2 != null) t2.init(prob);
msg = "Not terminated.";
}
public boolean isTerminated(InterfaceSolutionSet solSet) {
return isTerminated(solSet.getCurrentPopulation());
}
public boolean isTerminated(PopulationInterface pop) {
boolean ret;
if ((t1 == null) && (t2 == null)) {
System.err.println("Error: No terminator set in CombinedTerminator");
return true;
}
if (t1 == null) return t2.isTerminated(pop);
if (t2 == null) return t1.isTerminated(pop);
if (t1 == null) {
ret = t2.isTerminated(pop);
msg = t2.lastTerminationMessage();
return ret;
}
if (t2 == null) {
ret = t1.isTerminated(pop);
msg = t1.lastTerminationMessage();
return ret;
}
if (andOrTag.isSelectedString("AND")) {
// make sure that both terminators are triggered by every call, because some judge
// time-dependently and store information on the population.
ret = t1.isTerminated(pop);
ret = ret && t2.isTerminated(pop);
if (ret) msg = "Terminated because both: " + t1.lastTerminationMessage() + " And " + t2.lastTerminationMessage();
} else { // OR
// make sure that both terminators are triggered by every call, because some judge
// time-dependently and store information on the population.
ret = t1.isTerminated(pop);
if (ret) msg = t1.lastTerminationMessage();
ret = ret || t2.isTerminated(pop);
if (ret) msg = t2.lastTerminationMessage();
}
return ret;
}
public String terminatedBecause(PopulationInterface pop) {
if (isTerminated(pop)) {
if (andOrTag.isSelectedString("AND")) {
return "Terminated because both: " + t1.terminatedBecause(pop) + " And " + t2.terminatedBecause(pop);
} else {
if ((t1 != null) && (t1.isTerminated(pop))) {
return t1.terminatedBecause(pop);
} else {
return t2.terminatedBecause(pop);
}
}
} else return "not terminated";
public String lastTerminationMessage() {
return msg;
}
/**

143
src/eva2/server/go/operators/terminators/DiversityTerminator.java Normal file
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@ -0,0 +1,143 @@
package eva2.server.go.operators.terminators;
import java.io.Serializable;
import eva2.server.go.InterfaceTerminator;
import eva2.server.go.PopulationInterface;
import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
import eva2.server.go.operators.distancemetric.PhenotypeMetric;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.SelectedTag;
/**
* The diversity terminator uses the distance of individuals
* in the population as termination criterion. If the average distance
* sinks below the given limit, the optimization stops.
* May be computationally expensive.
*
* @see Population.getPopulationMeasures()
* @see PhenotypeMetric
* @author mkron
*
*/
public class DiversityTerminator implements InterfaceTerminator, Serializable {
private double distanceLimit = 0.001;
private InterfaceDistanceMetric metric = new PhenotypeMetric();
private String msg = "";
// leave the string order for this SelectedTag! (analogy to Population.getPopulationMeasures)
private SelectedTag criterion = new SelectedTag("Average distance", "Minimum distance", "Maximum distance");
public DiversityTerminator() {}
/**
* The default uses Phenotype metric and average distance as criterion.
*
* @param limit
*/
public DiversityTerminator(double limit) {
distanceLimit = limit;
metric = new PhenotypeMetric();
criterion.setSelectedTag(0);
}
/**
* Create a special DiversityTerminator defining distance limit, metric and distance
* criterion to use (average, minimum or maximum distance).
*
* @param limit the distance limit of individuals below which termination is triggered
* @param metric the metric to be used to calculate individual distances
* @param criterionID 0 for average distance, 1 for minimum distance and 2 for maximum distance
*/
public DiversityTerminator(double limit, InterfaceDistanceMetric metric, int criterionID) {
distanceLimit = limit;
this.metric = metric;
criterion.setSelectedTag(criterionID);
}
public void init(InterfaceOptimizationProblem prob) {
msg = "Not terminated.";
}
public String globalInfo() {
return "The diversity terminator uses the distance of individuals in the population as a termination criterion.";
}
/**
* Returns true if the average phenotypic distance within the given
* population is below the limit set in the terminator instance.
*
* @return true if the population is seen as converged due to low average phenotypic distance, else false
*/
public boolean isTerminated(PopulationInterface pop) {
double[] measures = ((Population)pop).getPopulationMeasures(metric);
int measureIndex = criterion.getSelectedTagID();
if (measures[measureIndex] < distanceLimit) {
msg = "Average individual distance below " + distanceLimit;
return true;
} else return false;
}
public boolean isTerminated(InterfaceSolutionSet sols) {
return isTerminated(sols.getCurrentPopulation());
}
public String lastTerminationMessage() {
return msg;
}
/**
* @return the avgDistanceLimit
*/
public double getDistanceLimit() {
return distanceLimit;
}
/**
* @param avgDistanceLimit the avgDistanceLimit to set
*/
public void setDistanceLimit(double avgDistanceLimit) {
this.distanceLimit = avgDistanceLimit;
}
public String distanceLimitTipText() {
return "Set the distance limit of individuals below which termination is triggered.";
}
/**
* @return the metric
*/
public InterfaceDistanceMetric getMetric() {
return metric;
}
/**
* @param metric the metric to set
*/
public void setMetric(InterfaceDistanceMetric metric) {
this.metric = metric;
}
public String metricTipText() {
return "Set the metric to be used to calculate individual distances.";
}
/**
* @return the criterion
*/
public SelectedTag getCriterion() {
return criterion;
}
/**
* @param criterion the criterion to set
*/
public void setCriterion(SelectedTag criterion) {
this.criterion = criterion;
}
public String criterionTipText() {
return "Define the distance criterion to check for in a population.";
}
}

138
src/eva2/server/go/operators/terminators/EvaluationTerminator.java
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@ -16,80 +16,80 @@ import java.io.Serializable;
import eva2.server.go.InterfaceTerminator;
import eva2.server.go.PopulationInterface;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/*==========================================================================*
* CLASS DECLARATION
*==========================================================================*/
* CLASS DECLARATION
*==========================================================================*/
/**
*
*/
public class EvaluationTerminator implements InterfaceTerminator,
Serializable {
/**
* Number of fitness calls on the problem which is optimized.
*/
protected int m_FitnessCalls = 1000;
/**
*
*/
public EvaluationTerminator() {
}
public void init(){}
/**
*
*/
public String globalInfo() {
return "Terminates after the given number of fitness calls.";
}
/**
* Construct Terminator with a maximum number of fitness calls.
* @param maximum number of fitness calls
*/
public EvaluationTerminator(int x) {
m_FitnessCalls = x;
}
/**
*
*/
public boolean isTerminated(PopulationInterface pop) {
//System.out.println("m_FitnessCalls="+m_FitnessCalls);
if (m_FitnessCalls>pop.getFunctionCalls())
return false;
return true;
}
public String terminatedBecause(PopulationInterface pop) {
if (isTerminated(pop)) return m_FitnessCalls + " fitness calls were reached.";
else return "Not yet terminated.";
}
/**
*
*/
public String toString() {
String ret = "EvaluationTerminator,calls="+m_FitnessCalls;
return ret;
}
/**
*
*/
public void setFitnessCalls(int x) {
//System.out.println("setFitnessCalls"+x);
m_FitnessCalls = x;
}
/**
*
*/
public int getFitnessCalls() {
//System.out.println("getFitnessCalls"+m_FitnessCalls);
return m_FitnessCalls;
}
/**
* Returns the tip text for this property
* @return tip text for this property
*/
public String fitnessCallsTipText() {
return "number of calls to fitness function.";
}
Serializable {
private String msg = "Not terminated.";
/**
* Number of fitness calls on the problem which is optimized.
*/
protected int m_FitnessCalls = 1000;
public EvaluationTerminator() {
}
public void init(InterfaceOptimizationProblem prob){
msg = "Not terminated.";
}
public String globalInfo() {
return "Terminates after the given number of fitness calls.";
}
/**
* Construct Terminator with a maximum number of fitness calls.
* @param maximum number of fitness calls
*/
public EvaluationTerminator(int x) {
m_FitnessCalls = x;
}
public boolean isTerminated(InterfaceSolutionSet solSet) {
return isTerminated(solSet.getCurrentPopulation());
}
public boolean isTerminated(PopulationInterface pop) {
//System.out.println("m_FitnessCalls="+m_FitnessCalls);
if (m_FitnessCalls>pop.getFunctionCalls()) return false;
else {
msg = m_FitnessCalls + " fitness calls were reached.";
return true;
}
}
public String lastTerminationMessage() {
return msg;
}
public String toString() {
String ret = "EvaluationTerminator,calls="+m_FitnessCalls;
return ret;
}
public void setFitnessCalls(int x) {
//System.out.println("setFitnessCalls"+x);
m_FitnessCalls = x;
}
public int getFitnessCalls() {
//System.out.println("getFitnessCalls"+m_FitnessCalls);
return m_FitnessCalls;
}
/**
* Returns the tip text for this property
* @return tip text for this property
*/
public String fitnessCallsTipText() {
return "number of calls to fitness function.";
}
}

19
src/eva2/server/go/operators/terminators/FitnessConvergenceTerminator.java
View File

@ -21,6 +21,8 @@ import eva2.server.go.InterfaceTerminator;
import eva2.server.go.PopulationInterface;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.operators.distancemetric.PhenotypeMetric;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.SelectedTag;
@ -43,6 +45,8 @@ Serializable {
protected double oldNorm;
private SelectedTag stagnationMeasure = new SelectedTag("Fitness calls", "Generations");
private SelectedTag convergenceCondition = new SelectedTag("Relative", "Absolute");
private String msg="";
protected String tagString = "Fitness converged";
PhenotypeMetric pMetric = null;
public FitnessConvergenceTerminator() {
@ -66,15 +70,22 @@ Serializable {
return "Stop if a fitness convergence criterion has been met.";
}
public void init() {
public void init(InterfaceOptimizationProblem prob) {
if (pMetric == null) pMetric = new PhenotypeMetric();
firstTime = true;
msg = "Not terminated.";
tagString = "Fitness converged";
}
public boolean isTerminated(InterfaceSolutionSet solSet) {
return isTerminated(solSet.getCurrentPopulation());
}
public boolean isTerminated(PopulationInterface Pop) {
if (!firstTime && isStillConverged(Pop.getBestIndividual())) {
if (stagnationTimeHasPassed(Pop)) {
// population hasnt improved much for max time, criterion is met
msg = getTerminationMessage(tagString);
return true;
} else {
// population hasnt improved much for i<max time, keep running
@ -88,10 +99,8 @@ Serializable {
}
}
public String terminatedBecause(PopulationInterface pop) {
if (isTerminated(pop)) {
return getTerminationMessage("Fitness converged");
} else return "Not yet terminated.";
public String lastTerminationMessage() {
return msg;
}
protected String getTerminationMessage(String prefix) {

22
src/eva2/server/go/operators/terminators/FitnessValueTerminator.java
View File

@ -17,6 +17,8 @@ import java.io.Serializable;
import eva2.gui.BeanInspector;
import eva2.server.go.InterfaceTerminator;
import eva2.server.go.PopulationInterface;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/*==========================================================================*
* CLASS DECLARATION
@ -27,6 +29,7 @@ import eva2.server.go.PopulationInterface;
public class FitnessValueTerminator implements InterfaceTerminator,
Serializable {
protected double[] m_FitnessValue;
private String msg = "";
/**
*
*/
@ -34,7 +37,9 @@ Serializable {
m_FitnessValue = new double []{0.1};
}
public void init(){}
public void init(InterfaceOptimizationProblem prob){
msg = "Not terminated.";
}
/**
*
*/
@ -47,21 +52,22 @@ Serializable {
public FitnessValueTerminator( double[] v) {
m_FitnessValue = (double[])v.clone();
}
/**
*
*/
public boolean isTerminated(InterfaceSolutionSet solSet) {
return isTerminated(solSet.getCurrentPopulation());
}
public boolean isTerminated(PopulationInterface Pop) {
double[] fit = Pop.getBestFitness();
for (int i = 0; i < fit.length; i++) {
if (m_FitnessValue[i]>fit[i]) return false;
}
msg = "Fitness value below " + BeanInspector.toString(m_FitnessValue);
return true;
}
public String terminatedBecause(PopulationInterface pop) {
if (isTerminated(pop)) {
return "Fitness value below " + BeanInspector.toString(m_FitnessValue);
} else return "Not yet terminated.";
public String lastTerminationMessage() {
return msg;
}
/**

125
src/eva2/server/go/operators/terminators/GenerationTerminator.java
View File

@ -17,75 +17,72 @@ import java.io.Serializable;
import eva2.gui.BeanInspector;
import eva2.server.go.InterfaceTerminator;
import eva2.server.go.PopulationInterface;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/*==========================================================================*
* CLASS DECLARATION
*==========================================================================*/
* CLASS DECLARATION
*==========================================================================*/
/**
*
*/
public class GenerationTerminator implements InterfaceTerminator,
Serializable {
/**
* Number of fitness calls on the problem which is optimized
*/
protected int m_Generations = 100;
public void init(){}
/**
*
*/
public String globalInfo() {
return "Terminate after the given number of generations";
}
/**
*
*/
public GenerationTerminator() {
}
/**
*
*/
public GenerationTerminator(int gens) {
m_Generations = gens;
}
/**
*
*/
public boolean isTerminated(PopulationInterface Pop) {
if (m_Generations<Pop.getGeneration())
return true;
return false;
}
public String terminatedBecause(PopulationInterface pop) {
if (isTerminated(pop)) {
return m_Generations + " generations reached.";
} else return "Not yet terminated.";
Serializable {
/**
* Number of fitness calls on the problem which is optimized
*/
protected int m_Generations = 100;
private String msg = "";
public void init(InterfaceOptimizationProblem prob){
msg = "Not terminated.";
}
public String globalInfo() {
return "Terminate after the given number of generations";
}
public GenerationTerminator() {
}
public GenerationTerminator(int gens) {
m_Generations = gens;
}
public boolean isTerminated(InterfaceSolutionSet solSet) {
return isTerminated(solSet.getCurrentPopulation());
}
public boolean isTerminated(PopulationInterface Pop) {
if (m_Generations<Pop.getGeneration()) {
msg = m_Generations + " generations reached.";
return true;
}
return false;
}
public String lastTerminationMessage() {
return msg;
}
public String toString() {
String ret = "Generations calls="+m_Generations;
return ret;
}
public void setGenerations(int x) {
m_Generations = x;
}
public int getGenerations() {
return m_Generations;
}
/**
* Returns the tip text for this property
* @return tip text for this property
*/
public String generationsTipText() {
return "number of generations to evaluate.";
}
/**
*
*/
public String toString() {
String ret = "Generations calls="+m_Generations;
return ret;
}
/**
*
*/
public void setGenerations(int x) {
m_Generations = x;
}
/**
*
*/
public int getGenerations() {
return m_Generations;
}
/**
* Returns the tip text for this property
* @return tip text for this property
*/
public String generationsTipText() {
return "number of generations to evaluate.";
}
}

85
src/eva2/server/go/operators/terminators/KnownOptimaFoundTerminator.java Normal file
View File

@ -0,0 +1,85 @@
package eva2.server.go.operators.terminators;
import java.io.Serializable;
import eva2.server.go.InterfaceTerminator;
import eva2.server.go.PopulationInterface;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.problems.InterfaceMultimodalProblemKnown;
import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.EVAERROR;
/**
* This terminator counts the number of found optima for a problem that
* implements InterfaceMultimodalProblemKnown. A population is regarded as terminated
* if the preset number of optima is identified.
* For any other problem types, this terminator will not make sense, so take care.
*
* @author mkron
*
*/
public class KnownOptimaFoundTerminator implements InterfaceTerminator, Serializable {
InterfaceMultimodalProblemKnown mProblem = null;
int reqOptima = 1;
private String msg = "";
public KnownOptimaFoundTerminator() {
}
public void init(InterfaceOptimizationProblem prob) {
if (prob != null) {
if (prob instanceof InterfaceMultimodalProblemKnown) {
mProblem = (InterfaceMultimodalProblemKnown)prob;
} else System.err.println("KnownOptimaFoundTerminator only works with InterfaceMultimodalProblemKnown instances!");
} else System.err.println("KnownOptimaFoundTerminator wont work with null problem!");
msg = "Not terminated.";
}
public boolean isTerminated(InterfaceSolutionSet solSet) {
return isTerm(solSet.getCurrentPopulation());
}
public boolean isTerminated(PopulationInterface pop) {
EVAERROR.errorMsgOnce("Warning, the KnownOptimaFoundTerminator is supposed to work on a final population.");
return isTerm((Population)pop);
}
private boolean isTerm(Population pop) {
int found = mProblem.getNumberOfFoundOptima(pop);
if (found >= reqOptima) {
msg = "There were " + reqOptima + " optima found.";
return true;
} else return false;
}
public String lastTerminationMessage() {
return msg;
}
/**
* @return the reqOptima
*/
public int getReqOptima() {
return reqOptima;
}
/**
* @param reqOptima the reqOptima to set
*/
public void setReqOptima(int reqOptima) {
this.reqOptima = reqOptima;
}
public String reqOptimaTipText() {
return "The number of optima that need to be found to terminate the optimization.";
}
public String toString() {
return "KnownOptimaFoundTerminator requiring " + reqOptima + " optima.";
}
public String globalInfo() {
return "Terminate if a given number of optima has been found. Works for problems implementing InterfaceMultimodalProblemKnown, e.g. FM0.";
}
}

15
src/eva2/server/go/operators/terminators/PhenotypeConvergenceTerminator.java
View File

@ -6,6 +6,7 @@ import eva2.server.go.InterfaceTerminator;
import eva2.server.go.PopulationInterface;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.operators.distancemetric.PhenotypeMetric;
import eva2.server.go.problems.InterfaceOptimizationProblem;
public class PhenotypeConvergenceTerminator extends FitnessConvergenceTerminator implements InterfaceTerminator {
AbstractEAIndividual oldIndy = null;
@ -13,11 +14,18 @@ public class PhenotypeConvergenceTerminator extends FitnessConvergenceTerminator
public PhenotypeConvergenceTerminator() {
super();
tagString = "Phenotype converged";
}
public PhenotypeConvergenceTerminator(double thresh, int stagnTime, boolean bFitCallBased, boolean bAbsolute) {
super(thresh, stagnTime, bFitCallBased, bAbsolute);
}
public void init(InterfaceOptimizationProblem prob) {
super.init(prob);
tagString = "Phenotype converged";
}
/**
* Return true if |oldPhen - curPhen| < |oldPhen| * thresh (relative case)
* and if |oldFit - curFit| < thresh (absolute case).
@ -36,14 +44,7 @@ public class PhenotypeConvergenceTerminator extends FitnessConvergenceTerminator
if (TRACE) System.out.println("isStillConverged returns " + ret + ", dist " + dist + ", old indy " + BeanInspector.toString(oldIndy) + ", cur indy" + BeanInspector.toString(indy));
return ret;
}
public String terminatedBecause(PopulationInterface pop) {
if (isTerminated(pop)) {
return getTerminationMessage("Phenotype converged");
} else return "Not yet terminated.";
}
protected void saveState(PopulationInterface Pop) {
super.saveState(Pop);
oldIndy = (AbstractEAIndividual)((AbstractEAIndividual)Pop.getBestIndividual()).clone();

16
src/eva2/server/go/populations/InterfaceSolutionSet.java Normal file
View File

@ -0,0 +1,16 @@
package eva2.server.go.populations;
/**
* This is just a wrapper type to distinct between a Population (current solution set
* of an optimizer) and a final solution set, in which archived and deactivated
* individuals may be contained as well. Both may be equal if the optimizer doesnt
* make this distinction.
*
* @author mkron
*
*/
public interface InterfaceSolutionSet {
public Population getSolutions();
public Population getCurrentPopulation();
public SolutionSet clone();
}

23
src/eva2/server/go/populations/Population.java
View File

@ -13,6 +13,7 @@ import eva2.server.go.PopulationInterface;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.AbstractEAIndividualComparator;
import eva2.server.go.individuals.GAIndividualBinaryData;
import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
import eva2.server.go.operators.distancemetric.PhenotypeMetric;
import eva2.tools.EVAERROR;
import eva2.tools.Mathematics;
@ -90,6 +91,16 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
}
}
public void addData(String key, Object value) {
if (additionalPopData == null) additionalPopData = new HashMap<String, Object>();
additionalPopData.put(key, value);
}
public Object getData(String key) {
if (additionalPopData == null) return null;
else return additionalPopData.get(key);
}
public Object clone() {
return (Object) new Population(this);
}
@ -724,8 +735,16 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
* @return the average, minimal and maximal mean distance of individuals in an array of three
*/
public double[] getPopulationMeasures() {
PhenotypeMetric metric = new PhenotypeMetric();
return getPopulationMeasures(new PhenotypeMetric());
}
/**
* Returns the average, minimal and maximal individual distance as diversity measure for the population.
* This is of course rather expensive computationally.
*
* @return the average, minimal and maximal mean distance of individuals in an array of three
*/
public double[] getPopulationMeasures(InterfaceDistanceMetric metric) {
double d;
double[] res = new double[3];

34
src/eva2/server/go/populations/SolutionSet.java Normal file
View File

@ -0,0 +1,34 @@
package eva2.server.go.populations;
public class SolutionSet implements InterfaceSolutionSet {
Population pop = null;
Population sols = null;
public SolutionSet(Population currentPop, Population allSols) {
pop = currentPop;
sols = allSols;
}
/**
* Create a solution set from a single population. This can be used by optimizers which
* make no distinction between current solution set and archived solution set.
*
* @param p
*/
public SolutionSet(Population p) {
pop = p;
sols = p;
}
public SolutionSet clone() {
return new SolutionSet((Population)pop.clone(), (Population)sols.clone());
}
public Population getSolutions() {
return sols;
}
public Population getCurrentPopulation() {
return pop;
}
}

2
src/eva2/server/go/problems/MatlabProblem.java
View File

@ -444,7 +444,7 @@ class WaitForEvARunnable implements Runnable {
}
try {
mp.log("runnable.getSolution: " + BeanInspector.toString(runnable.getDoubleSolution()));
mp.log("\ngetAllSols best: " + AbstractEAIndividual.getDefaultDataString(runnable.getGOParams().getOptimizer().getAllSolutions().getBestEAIndividual()));
mp.log("\ngetAllSols best: " + AbstractEAIndividual.getDefaultDataString(runnable.getGOParams().getOptimizer().getAllSolutions().getSolutions().getBestEAIndividual()));
mp.log("\n");
// write results back to matlab
mp.exportResultToMatlab(runnable.getDoubleSolution());

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

@ -3,17 +3,18 @@ package eva2.server.go.strategies;
import java.util.BitSet;
import wsi.ra.math.RNG;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.InterfaceGAIndividual;
import eva2.server.go.operators.selection.InterfaceSelection;
import eva2.server.go.operators.selection.SelectBestSingle;
import eva2.server.go.operators.selection.SelectRandom;
import eva2.server.go.populations.PBILPopulation;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
import wsi.ra.math.RNG;
/** This is an implementation of the CHC Adaptive Search Algorithm by Eselman. It is
* limited to binary data and is based on massively distruptive crossover. I'm not
@ -295,8 +296,8 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
// /** This method will set the normation method that is to be used.

13
src/eva2/server/go/strategies/ClusterBasedNichingEA.java
View File

@ -19,8 +19,9 @@ import eva2.server.go.operators.cluster.ClusteringDensityBased;
import eva2.server.go.operators.cluster.InterfaceClustering;
import eva2.server.go.operators.mutation.InterfaceMutation;
import eva2.server.go.operators.mutation.MutateESGlobal;
//import eva2.server.go.populations.Distraction;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.Interface2DBorderProblem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -730,12 +731,12 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
public InterfaceSolutionSet getAllSolutions() {
// return inactive species
Population pop = (Population)m_Archive.clone();
pop.addPopulation(getPopulation());
pop.setPopulationSize(pop.size());
return pop;
Population sols = (Population)m_Archive.clone();
sols.addPopulation(getPopulation());
sols.setPopulationSize(sols.size());
return new SolutionSet(getPopulation(), sols);
}
/** This method allows you to set/get the switch that toggles the use

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

@ -6,7 +6,9 @@ import eva2.gui.GenericObjectEditor;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.operators.mutation.MutateESFixedStepSize;
import eva2.server.go.operators.postprocess.PostProcess;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.AbstractOptimizationProblem;
import eva2.server.go.problems.F1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -207,13 +209,16 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
return "Change the number of starting individuals stored (Cluster-HC).";
}
public Population getAllSolutions() {
public InterfaceSolutionSet getAllSolutions() {
Population tmp = new Population();
tmp.addPopulation(archive);
tmp.addPopulation(m_Population);
tmp.SetFunctionCalls(m_Population.getFunctionCalls());
tmp.setGenerationTo(m_Population.getGeneration());
// tmp = PostProcessInterim.clusterBest(tmp, sigma, 0, PostProcessInterim.KEEP_LONERS, PostProcessInterim.BEST_ONLY);
return tmp;
return new SolutionSet(m_Population, tmp);
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
* @return A descriptive string

7
src/eva2/server/go/strategies/DifferentialEvolution.java
View File

@ -8,7 +8,9 @@ import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.InterfaceESIndividual;
import eva2.server.go.operators.selection.replacement.ReplacementCrowding;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.AbstractMultiObjectiveOptimizationProblem;
import eva2.server.go.problems.AbstractOptimizationProblem;
import eva2.server.go.problems.F1Problem;
@ -516,8 +518,9 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
Population pop = getPopulation();
return new SolutionSet(pop, pop);
}
/** F is a real and constant factor which controls the amplification of the differential variation

6
src/eva2/server/go/strategies/EvolutionStrategies.java
View File

@ -6,7 +6,9 @@ import eva2.server.go.operators.mutation.MutateESSuccessRule;
import eva2.server.go.operators.selection.InterfaceSelection;
import eva2.server.go.operators.selection.SelectBestIndividuals;
import eva2.server.go.operators.selection.SelectRandom;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -373,8 +375,8 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
// /** This method will set the normation method that is to be used.
// * @param normation

6
src/eva2/server/go/strategies/EvolutionaryProgramming.java
View File

@ -5,7 +5,9 @@ import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.GAIndividualBinaryData;
import eva2.server.go.operators.selection.InterfaceSelection;
import eva2.server.go.operators.selection.SelectEPTournaments;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.F1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -199,8 +201,8 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** Choose a method for selecting the reduced population.
* @param selection

6
src/eva2/server/go/strategies/FloodAlgorithm.java
View File

@ -3,7 +3,9 @@ package eva2.server.go.strategies;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.GAIndividualBinaryData;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -234,8 +236,8 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This methods allow you to set/get the temperatur of the flood
* algorithm procedure

6
src/eva2/server/go/strategies/GeneticAlgorithm.java
View File

@ -5,7 +5,9 @@ import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.GAIndividualBinaryData;
import eva2.server.go.operators.selection.InterfaceSelection;
import eva2.server.go.operators.selection.SelectTournament;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -238,8 +240,8 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
// /** This method will set the normation method that is to be used.
// * @param normation

6
src/eva2/server/go/strategies/GradientDescentAlgorithm.java
View File

@ -5,7 +5,9 @@ import java.util.*;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.InterfaceDataTypeDouble;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.F1Problem;
import eva2.server.go.problems.InterfaceFirstOrderDerivableProblem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -256,8 +258,8 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
return this.m_Population;
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
public void setPopulation(Population pop) {

6
src/eva2/server/go/strategies/HillClimbing.java
View File

@ -4,7 +4,9 @@ import eva2.gui.BeanInspector;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.operators.mutation.InterfaceMutation;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -235,8 +237,8 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
this.m_Population = pop;
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
public String populationTipText() {
return "Change the number of best individuals stored (MS-HC).";

14
src/eva2/server/go/strategies/InterfaceOptimizer.java
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@ -1,9 +1,7 @@
package eva2.server.go.strategies;
import java.util.List;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -62,11 +60,15 @@ public interface InterfaceOptimizer {
public void setPopulation(Population pop);
/**
* Return all found solutions (local optima) if they are not contained in the last population.
* Return all found solutions (local optima) if they are not contained in the current population. Be
* sure to set the Population properties, especially function calls and generation, with respect
* to the ongoing optimization.
* May return the same as getPopulation if the optimizer makes no distinction, i.e. does not collect
* solutions outside the current population.
*
* @return A population of found solutions or null if they are contained in the population.
* @return A population of found solutions.
*/
public Population getAllSolutions();
public InterfaceSolutionSet getAllSolutions();
/**
* This method allows you to set an identifier for the algorithm

7
src/eva2/server/go/strategies/IslandModelEA.java
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@ -9,7 +9,9 @@ import eva2.server.go.operators.migration.MOBestMigration;
import eva2.server.go.operators.migration.MOClusteringSeparation;
import eva2.server.go.operators.migration.MOConeSeparation;
import eva2.server.go.operators.migration.SOBestMigration;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.F1Problem;
import eva2.server.go.problems.F8Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -524,10 +526,9 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
return "(Defunct)";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method allows you to set the number of processors in local mode
* @param n Number of processors.
*/

6
src/eva2/server/go/strategies/MemeticAlgorithm.java
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@ -6,7 +6,9 @@ import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.operators.selection.InterfaceSelection;
import eva2.server.go.operators.selection.SelectBestIndividuals;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.F1Problem;
import eva2.server.go.problems.InterfaceLocalSearchable;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -364,8 +366,8 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
this.subsetsize = subsetsize;
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
public int getSubsetsize() {
return subsetsize;

6
src/eva2/server/go/strategies/MonteCarloSearch.java
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@ -3,7 +3,9 @@ package eva2.server.go.strategies;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.GAIndividualBinaryData;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -200,7 +202,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
return "Change the number of best individuals stored.";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
}

6
src/eva2/server/go/strategies/MultiObjectiveEA.java
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@ -7,7 +7,9 @@ import eva2.server.go.operators.archiving.InformationRetrievalInserting;
import eva2.server.go.operators.archiving.InterfaceArchiving;
import eva2.server.go.operators.archiving.InterfaceInformationRetrieval;
import eva2.server.go.operators.selection.SelectMONonDominated;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.FM0Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -227,8 +229,8 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
return "Edit the properties of the Population used.";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method allows you to set/get the optimizing technique to use.

6
src/eva2/server/go/strategies/MultiObjectiveMemeticAlgorithmII.java
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@ -8,7 +8,9 @@ import eva2.server.go.operators.archiving.InterfaceArchiving;
import eva2.server.go.operators.archiving.InterfaceInformationRetrieval;
import eva2.server.go.operators.selection.SelectMOMAIIDominanceCounter;
import eva2.server.go.operators.selection.SelectMONonDominated;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.FM0Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -163,8 +165,8 @@ public class MultiObjectiveMemeticAlgorithmII implements InterfaceOptimizer, jav
return "Edit the properties of the Population used.";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method allows you to set/get the optimizing technique to use.
* @return The current optimizing method

6
src/eva2/server/go/strategies/ParticleFilterOptimization.java
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@ -9,7 +9,9 @@ import eva2.server.go.individuals.InterfaceESIndividual;
import eva2.server.go.operators.mutation.MutateESFixedStepSize;
import eva2.server.go.operators.selection.InterfaceSelection;
import eva2.server.go.operators.selection.SelectParticleWheel;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.F1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -254,8 +256,8 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method will set the selection method that is to be used
* @param selection

6
src/eva2/server/go/strategies/ParticleSwarmOptimization.java
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@ -12,7 +12,9 @@ import eva2.server.go.individuals.AbstractEAIndividualComparator;
import eva2.server.go.individuals.InterfaceDataTypeDouble;
import eva2.server.go.individuals.InterfaceESIndividual;
import eva2.server.go.operators.distancemetric.PhenotypeMetric;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.F1Problem;
import eva2.server.go.problems.Interface2DBorderProblem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -1538,8 +1540,8 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method will set the initial velocity
* @param f

6
src/eva2/server/go/strategies/PopulationBasedIncrementalLearning.java
View File

@ -5,8 +5,10 @@ import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.InterfaceGAIndividual;
import eva2.server.go.operators.selection.InterfaceSelection;
import eva2.server.go.operators.selection.SelectBestIndividuals;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.PBILPopulation;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.AbstractOptimizationProblem;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -206,8 +208,8 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
return "Edit the properties of the PBIL population used.";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
// /** This method will set the normation method that is to be used.
// * @param normation

6
src/eva2/server/go/strategies/SimulatedAnnealing.java
View File

@ -3,7 +3,9 @@ package eva2.server.go.strategies;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.GAIndividualBinaryData;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
import wsi.ra.math.RNG;
@ -238,8 +240,8 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
return "Change the number of best individuals stored (MS-SA)).";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** Set the initial temperature
* @return The initial temperature.

6
src/eva2/server/go/strategies/SteadyStateGA.java
View File

@ -7,7 +7,9 @@ import eva2.server.go.operators.selection.InterfaceSelection;
import eva2.server.go.operators.selection.SelectTournament;
import eva2.server.go.operators.selection.replacement.InterfaceReplacement;
import eva2.server.go.operators.selection.replacement.ReplaceWorst;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -191,8 +193,8 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method will set the parent selection method that is to be used
* @param selection

6
src/eva2/server/go/strategies/ThresholdAlgorithm.java
View File

@ -3,7 +3,9 @@ package eva2.server.go.strategies;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.GAIndividualBinaryData;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
import wsi.ra.math.RNG;
@ -230,8 +232,8 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
return "Change the number of best individuals stored (MS-TA).";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** Set the initial threshold
* @return The initial temperature.

10
src/eva2/server/go/strategies/Tribes.java
View File

@ -8,7 +8,9 @@ import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.InterfaceDataTypeDouble;
import eva2.server.go.individuals.InterfaceESIndividual;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.AbstractOptimizationProblem;
import eva2.server.go.problems.InterfaceHasInitRange;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -658,12 +660,12 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
/**
* Return a Population of TribesExplorers (AbstractEAIndividuals) of which some where
* 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.
*/
public Population getAllSolutions() {
public InterfaceSolutionSet getAllSolutions() {
// return population and memories?
Population all = (Population)population.clone();
List<TribesPosition> mems = swarm.collectMem();
@ -671,8 +673,10 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
TribesPosition tp = iterator.next();
all.add(positionToExplorer(tp));
}
all.SetFunctionCalls(population.getFunctionCalls());
all.setGenerationTo(population.getGeneration());
//all.addPopulation(pop);
return all;
return new SolutionSet(population, all);
}
protected TribesExplorer positionToExplorer(TribesPosition pos) {

6
src/eva2/server/go/strategies/WingedMultiObjectiveEA.java
View File

@ -9,7 +9,9 @@ import eva2.server.go.operators.archiving.InterfaceArchiving;
import eva2.server.go.operators.archiving.InterfaceInformationRetrieval;
import eva2.server.go.operators.moso.MOSOWeightedFitness;
import eva2.server.go.operators.selection.SelectMONonDominated;
import eva2.server.go.populations.InterfaceSolutionSet;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.AbstractMultiObjectiveOptimizationProblem;
import eva2.server.go.problems.FM0Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
@ -286,8 +288,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
return "(Defunct)";
}
public Population getAllSolutions() {
return getPopulation();
public InterfaceSolutionSet getAllSolutions() {
return new SolutionSet(getPopulation());
}
/** This method allows you to set/get the optimizing technique to use.
* @return The current optimizing method

21
src/eva2/server/modules/Processor.java
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@ -194,7 +194,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
this.goParams.getProblem().initProblem();
this.goParams.getOptimizer().SetProblem(this.goParams.getProblem());
if (this.m_createInitialPopulations) this.goParams.getOptimizer().init();
this.goParams.getTerminator().init();
this.goParams.getTerminator().init(this.goParams.getProblem());
//m_Statistics.createNextGenerationPerformed((PopulationInterface)this.m_ModulParameter.getOptimizer().getPopulation());
if (m_ListenerModule!=null) m_ListenerModule.updateProgress(getStatusPercent(goParams.getOptimizer().getPopulation(), runCounter, m_Statistics.getStatisticsParameter().getMultiRuns()), null);
@ -204,26 +204,17 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
// registerPopulationStateChanged *SHOULD* be fired by the optimizer or resp. the population
// as we are event listener
if (popLog != null) EVAHELP.logString(this.goParams.getOptimizer().getPopulation().getIndyList(), popLog);
} while (isOptRunning() && !this.goParams.getTerminator().isTerminated(this.goParams.getOptimizer().getPopulation()));
} while (isOptRunning() && !this.goParams.getTerminator().isTerminated(this.goParams.getOptimizer().getAllSolutions()));
runCounter++;
//////////////// Default stats
m_Statistics.stopOptPerformed(isOptRunning()); // stop is "normal" if opt wasnt set false by the user (and thus still true)
m_Statistics.stopOptPerformed(isOptRunning(), goParams.getTerminator().lastTerminationMessage()); // stop is "normal" if opt wasnt set false by the user (and thus still true)
//////////////// PP or set results without further PP
if (isOptRunning()) {
resultPop = performPostProcessing();
} else resultPop = goParams.getOptimizer().getAllSolutions();
/**
if (isOptRunning()) {
resultPop = performPostProcessing();
//System.out.println("calling getall after PP returned " + (resultPop == null ? "null" : resultPop.size()));
}
if (resultPop == null) {
resultPop = goParams.getOptimizer().getAllSolutions();
//System.out.println("calling getall returned " + resultPop.size());
}
**/
} else resultPop = goParams.getOptimizer().getAllSolutions().getSolutions();
}
setOptRunning(false); // normal finish
if (m_ListenerModule!=null) m_ListenerModule.performedStop(); // is only needed in client server mode
@ -345,7 +336,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
// if textwindow was closed, check if it should be reopened for pp
if (m_Statistics instanceof StatisticsWithGUI) ((StatisticsWithGUI)m_Statistics).maybeShowProxyPrinter();
}
Population resultPop = (Population)(goParams.getOptimizer().getAllSolutions()).clone();
Population resultPop = (Population)(goParams.getOptimizer().getAllSolutions().getSolutions().clone());
if (resultPop.getFunctionCalls() != goParams.getOptimizer().getPopulation().getFunctionCalls()) {
// System.err.println("bad case in Processor::performNewPostProcessing ");
resultPop.SetFunctionCalls(goParams.getOptimizer().getPopulation().getFunctionCalls());

4
src/eva2/server/stat/AbstractStatistics.java
View File

@ -134,7 +134,7 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
functionCalls = 0;
}
public void stopOptPerformed(boolean normal) {
public void stopOptPerformed(boolean normal, String stopMessage) {
if (TRACE) System.out.println("AbstractStatistics.stopOptPerformed");
if (runIterCnt < meanCollection.size()) {
// no good: later run was shorter than the first one. What to do? Discard the longer one:
@ -143,6 +143,8 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
}
optRunsPerformed++;
functionCallSum += functionCalls;
if (printRunStoppedVerbosity() && (stopMessage != null)) printToTextListener(" Termination message: " + stopMessage + "\n");
if (printRunStoppedVerbosity()) printToTextListener(" Function calls run: " + functionCalls + ", sum: " + functionCallSum + "\n");
// check for convergence
if (bestCurrentIndividual != null) {
if (Mathematics.norm(bestCurrentIndividual.getFitness()) < this.m_StatsParams.getConvergenceRateThreshold()) {

2
src/eva2/server/stat/InterfaceStatistics.java
View File

@ -30,7 +30,7 @@ public interface InterfaceStatistics {
/**
* Finalize statistics computations.
*/
public void stopOptPerformed(boolean normal); // called from processor
public void stopOptPerformed(boolean normal, String stopMessage); // called from processor
public void addTextListener(InterfaceTextListener listener);
public boolean removeTextListener(InterfaceTextListener listener);
public void printToTextListener(String s);

4
src/eva2/server/stat/StatisticsStandalone.java
View File

@ -125,8 +125,8 @@ public class StatisticsStandalone extends AbstractStatistics implements Interfac
m_InfoString = infoString;
}
public void stopOptPerformed(boolean normal) {
super.stopOptPerformed(normal);
public void stopOptPerformed(boolean normal, String stopMessage) {
super.stopOptPerformed(normal, stopMessage);
if (bestCurrentIndividual != null) {
m_SumOfBestFit = m_SumOfBestFit + bestCurrentIndividual.getFitness()[0];

4
src/eva2/server/stat/StatisticsWithGUI.java
View File

@ -107,8 +107,8 @@ public class StatisticsWithGUI extends AbstractStatistics implements Serializabl
m_PlotCounter = m_StatsParams.GetPlotoutput();
}
public void stopOptPerformed(boolean normal) {
super.stopOptPerformed(normal);
public void stopOptPerformed(boolean normal, String stopMessage) {
super.stopOptPerformed(normal, stopMessage);
if (optRunsPerformed > m_StatsParams.getMultiRuns()) System.err.println("error: this shouldnt happen (StatisticsWithGUI::stopOptPerformed)");
// unite the graphs only if the break was "normal"

23
src/wsi/ra/math/RNG.java
View File

@ -1,5 +1,6 @@
package wsi.ra.math;
import java.util.ArrayList;
import java.util.Random;
import eva2.tools.Mathematics;
@ -98,6 +99,28 @@ public class RNG extends Random {
}
return result;
}
/** This method returns a random permutation of n int values
* @param length The number of int values
* @return The permutation [0-length-1]
*/
public static int[] randomPerm(int length) {
ArrayList<Integer> intList = new ArrayList<Integer>(length);
int[] result = new int[length];
for (int i = 0; i < length; i++) {
intList.add(new Integer(i));
}
for (int i = 0; i < length-1; i++) {
int index = randomInt(intList.size());
result[i] = intList.get(index);
intList.remove(index);
}
if (intList.size()>1) System.err.println("Error in randomPerm!");
result[length-1] = intList.get(0);
return result;
}
/**
*
*/