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More refactoring.

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This commit is contained in:
Fabian Becker 2013-09-11 16:40:56 +02:00
parent 2f99e23676
commit c3dce7ac1b
61 changed files with 130 additions and 139 deletions
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4
src/eva2/optimization/individuals/AbstractEAIndividual.java
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@ -1262,10 +1262,10 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
*/
public static boolean setDoublePosition(AbstractEAIndividual indy, double[] pos) {
if (indy instanceof InterfaceESIndividual) {
((InterfaceESIndividual) indy).SetDGenotype(pos);
((InterfaceESIndividual) indy).setDGenotype(pos);
return true;
} else if (indy instanceof InterfaceDataTypeDouble) {
((InterfaceDataTypeDouble) indy).SetDoubleGenotype(pos);
((InterfaceDataTypeDouble) indy).setDoubleGenotype(pos);
return true;
} else if (indy instanceof InterfaceDataTypeInteger) {
EVAERROR.errorMsgOnce("Warning, double position truncated to integer! (AbstractEAIndividual.setDoublePosition)");

4
src/eva2/optimization/individuals/ESIndividualBinaryData.java
View File

@ -279,7 +279,7 @@ public class ESIndividualBinaryData extends AbstractEAIndividual implements Inte
* @param b The new genotype of the Individual
*/
@Override
public void SetDGenotype(double[] b) {
public void setDGenotype(double[] b) {
this.m_Genotype = b;
for (int i = 0; i < this.m_Genotype.length; i++) {
if (this.m_Genotype[i] < this.m_Range[i][0]) {
@ -296,7 +296,7 @@ public class ESIndividualBinaryData extends AbstractEAIndividual implements Inte
// * ranges.
// * @param range The new range for the double data.
// */
// public void SetDoubleRange(double[][] range) {
// public void setDoubleRange(double[][] range) {
// this.m_Range = range;
// }

26
src/eva2/optimization/individuals/ESIndividualDoubleData.java
View File

@ -168,10 +168,10 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
* @param range The new range for the double data.
*/
@Override
public void SetDoubleRange(double[][] range) {
public void setDoubleRange(double[][] range) {
if (range.length != this.m_Range.length) {
System.out.println("Warning: Trying to set a range of length " + range.length + " to a vector of length "
+ this.m_Range.length + "!\n Use method setDoubleDataLength first! (ESIndividualDoubleData:SetDoubleRange)");
+ this.m_Range.length + "!\n Use method setDoubleDataLength first! (ESIndividualDoubleData:setDoubleRange)");
}
for (int i = 0; ((i < this.m_Range.length) && (i < range.length)); i++) {
this.m_Range[i][0] = range[i][0];
@ -230,7 +230,7 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
* @param doubleData The new double data.
*/
@Override
public void SetDoublePhenotype(double[] doubleData) {
public void setDoublePhenotype(double[] doubleData) {
this.m_Phenotype = doubleData;
}
@ -241,9 +241,9 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
* @param doubleData The new double data.
*/
@Override
public void SetDoubleGenotype(double[] doubleData) {
// this.SetDoublePhenotype(doubleData);
this.SetDoublePhenotype(null); // tag it as invalid
public void setDoubleGenotype(double[] doubleData) {
// this.setDoublePhenotype(doubleData);
this.setDoublePhenotype(null); // tag it as invalid
this.m_Genotype = new double[doubleData.length];
System.arraycopy(doubleData, 0, this.m_Genotype, 0, doubleData.length);
}
@ -279,7 +279,7 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
if (bs.length != this.m_Genotype.length) {
System.out.println("Init value and requested length doesn't match!");
}
this.SetDoubleGenotype(bs);
this.setDoubleGenotype(bs);
} else {
this.defaultInit(opt);
System.out.println("Initial value for ESIndividualDoubleData is not double[]!");
@ -336,7 +336,7 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
* @param b The new genotype of the Individual
*/
@Override
public void SetDGenotype(double[] b) {
public void setDGenotype(double[] b) {
this.m_Genotype = b;
this.m_Phenotype = null; // mark it as invalid
for (int i = 0; i < this.m_Genotype.length; i++) {
@ -354,7 +354,7 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
*
* @param b The new genotype of the Individual
*/
public void SetDGenotypeNocheck(double[] b) {
public void setDGenotypeNocheck(double[] b) {
this.m_Phenotype = null; // mark it as invalid
this.m_Genotype = b;
}
@ -432,12 +432,4 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
public static String globalInfo() {
return "This is an ES individual suited to optimize double values.";
}
// public String toString() {
// String str = "Ind " + m_Genotype[0];
// for (int i=1; i<this.m_Genotype.length; i++) str += "/" + m_Genotype[i];
// str += "~" + fitness[0];
// for (int i=1; i<this.fitness.length; i++) str += "/" + fitness[i];
// return str;
// }
}

2
src/eva2/optimization/individuals/ESIndividualIntegerData.java
View File

@ -297,7 +297,7 @@ public class ESIndividualIntegerData extends AbstractEAIndividual implements Int
* @param b The new genotype of the Individual
*/
@Override
public void SetDGenotype(double[] b) {
public void setDGenotype(double[] b) {
this.m_Genotype = b;
for (int i = 0; i < this.m_Genotype.length; i++) {
if (this.m_Genotype[i] < this.m_Range[i][0]) {

2
src/eva2/optimization/individuals/ESIndividualPermutationData.java
View File

@ -348,7 +348,7 @@ public class ESIndividualPermutationData extends AbstractEAIndividual implements
* @param b The new genotype of the Individual
*/
@Override
public void SetDGenotype(double[] b) {
public void setDGenotype(double[] b) {
this.m_Genotype = mapVectorToMatrix(b, this.sizePermutation());
for (int i = 0; i < this.m_Genotype.length; i++) {
for (int j = 0; j < this.m_Genotype[i].length; j++) {

12
src/eva2/optimization/individuals/GAESIndividualBinaryDoubleData.java
View File

@ -246,8 +246,8 @@ public class GAESIndividualBinaryDoubleData extends AbstractEAIndividual impleme
* @param range The new range for the double data.
*/
@Override
public void SetDoubleRange(double[][] range) {
this.m_Numbers.SetDoubleRange(range);
public void setDoubleRange(double[][] range) {
this.m_Numbers.setDoubleRange(range);
}
/**
@ -288,8 +288,8 @@ public class GAESIndividualBinaryDoubleData extends AbstractEAIndividual impleme
* @see InterfaceDataTypeDouble.SetDoubleData()
*/
@Override
public void SetDoublePhenotype(double[] doubleData) {
this.m_Numbers.SetDoublePhenotype(doubleData);
public void setDoublePhenotype(double[] doubleData) {
this.m_Numbers.setDoublePhenotype(doubleData);
}
/**
@ -300,8 +300,8 @@ public class GAESIndividualBinaryDoubleData extends AbstractEAIndividual impleme
* @see InterfaceDataTypeDouble.SetDoubleDataLamarckian()
*/
@Override
public void SetDoubleGenotype(double[] doubleData) {
this.m_Numbers.SetDoubleGenotype(doubleData);
public void setDoubleGenotype(double[] doubleData) {
this.m_Numbers.setDoubleGenotype(doubleData);
}
/**

10
src/eva2/optimization/individuals/GAIndividualDoubleData.java
View File

@ -167,7 +167,7 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
* @param range The new range for the double data.
*/
@Override
public void SetDoubleRange(double[][] range) {
public void setDoubleRange(double[][] range) {
if (range.length != this.m_Range.length) {
System.out.println("Warning: Trying to set a range of length " + range.length + " to a vector of length "
+ this.m_Range.length + "!\n Use method setDoubleDataLength first!");
@ -223,7 +223,7 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
* @param doubleData The new double data.
*/
@Override
public void SetDoublePhenotype(double[] doubleData) {
public void setDoublePhenotype(double[] doubleData) {
this.m_Phenotype = doubleData;
}
@ -234,8 +234,8 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
* @param doubleData The new double data.
*/
@Override
public void SetDoubleGenotype(double[] doubleData) {
this.SetDoublePhenotype(doubleData);
public void setDoubleGenotype(double[] doubleData) {
this.setDoublePhenotype(doubleData);
int[] locus = new int[2];
for (int i = 0; i < doubleData.length; i++) {
locus[0] = i * this.m_Precision;
@ -262,7 +262,7 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
if (bs.length != this.m_Range.length) {
System.out.println("Init value and requested length doesn't match!");
}
this.SetDoubleGenotype(bs);
this.setDoubleGenotype(bs);
} else {
this.defaultInit(opt);
System.out.println("Initial value for GAIndividualDoubleData is not double[]!");

12
src/eva2/optimization/individuals/GAPIndividualProgramData.java
View File

@ -229,8 +229,8 @@ public class GAPIndividualProgramData extends AbstractEAIndividual implements In
* @param range The new range for the double data.
*/
@Override
public void SetDoubleRange(double[][] range) {
this.m_Numbers.SetDoubleRange(range);
public void setDoubleRange(double[][] range) {
this.m_Numbers.setDoubleRange(range);
}
/**
@ -271,8 +271,8 @@ public class GAPIndividualProgramData extends AbstractEAIndividual implements In
* @param doubleData The new double data.
*/
@Override
public void SetDoublePhenotype(double[] doubleData) {
this.m_Numbers.SetDoublePhenotype(doubleData);
public void setDoublePhenotype(double[] doubleData) {
this.m_Numbers.setDoublePhenotype(doubleData);
}
/**
@ -282,8 +282,8 @@ public class GAPIndividualProgramData extends AbstractEAIndividual implements In
* @param doubleData The new double data.
*/
@Override
public void SetDoubleGenotype(double[] doubleData) {
this.m_Numbers.SetDoubleGenotype(doubleData);
public void setDoubleGenotype(double[] doubleData) {
this.m_Numbers.setDoubleGenotype(doubleData);
}
/************************************************************************************

6
src/eva2/optimization/individuals/InterfaceDataTypeDouble.java
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@ -33,7 +33,7 @@ public interface InterfaceDataTypeDouble {
*
* @param range The new range for the double data.
*/
public void SetDoubleRange(double[][] range);
public void setDoubleRange(double[][] range);
/**
* This method will return the range for all double attributes.
@ -63,7 +63,7 @@ public interface InterfaceDataTypeDouble {
*
* @param doubleData The new double data.
*/
public void SetDoublePhenotype(double[] doubleData);
public void setDoublePhenotype(double[] doubleData);
/**
* This method allows you to set the double data, this can be used for
@ -71,5 +71,5 @@ public interface InterfaceDataTypeDouble {
*
* @param doubleData The new double data.
*/
public void SetDoubleGenotype(double[] doubleData);
public void setDoubleGenotype(double[] doubleData);
}

4
src/eva2/optimization/individuals/InterfaceESIndividual.java
View File

@ -24,14 +24,14 @@ public interface InterfaceESIndividual {
*
* @param b The new genotype of the Individual
*/
public void SetDGenotype(double[] b);
public void setDGenotype(double[] b);
//
// /** This method will set the range of the double attributes.
// * Note: range[d][0] gives the lower bound and range[d] gives the upper bound
// * for dimension d.
// * @param range The new range for the double data.
// */
// public void SetDoubleRange(double[][] range);
// public void setDoubleRange(double[][] range);
/**
* This method will return the range for all double attributes.

2
src/eva2/optimization/operator/cluster/ClusteringKMeans.java
View File

@ -316,7 +316,7 @@ public class ClusteringKMeans implements InterfaceClustering, java.io.Serializab
*/
private double distance(AbstractEAIndividual indy, double[] p) {
if (m_UseSearchSpace) {
((InterfaceDataTypeDouble) tmpIndy).SetDoubleGenotype(p);
((InterfaceDataTypeDouble) tmpIndy).setDoubleGenotype(p);
} else {
tmpIndy.setFitness(p);
}

2
src/eva2/optimization/operator/cluster/ClusteringXMeans.java
View File

@ -356,7 +356,7 @@ public class ClusteringXMeans implements InterfaceClustering, java.io.Serializab
x[0] = 10;
x[1] = 10;
}
((InterfaceDataTypeDouble) pop.get(i)).SetDoubleGenotype(x);
((InterfaceDataTypeDouble) pop.get(i)).setDoubleGenotype(x);
}
} else {
f1.initializePopulation(pop);

3
src/eva2/optimization/operator/constraint/InterfaceDoubleConstraint.java
View File

@ -15,7 +15,7 @@ public interface InterfaceDoubleConstraint {
/**
* Returns the boolean information whether the constraint is satisfied.
*
* @param indy
* @param indyX
* @return
*/
public boolean isSatisfied(double[] indyX);
@ -24,7 +24,6 @@ public interface InterfaceDoubleConstraint {
* Return the absolute (positive) degree of violation or zero if the constraint
* is fulfilled.
*
* @param indy The individual to check.
* @param indyX possibly the decoded individual position
* @return true if valid false else.
*/

2
src/eva2/optimization/operator/crossover/CrossoverESArithmetical.java
View File

@ -89,7 +89,7 @@ public class CrossoverESArithmetical implements InterfaceCrossover, java.io.Seri
}
// write the result back
for (int i = 0; i < result.length; i++) {
((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
}
}
//in case the crossover was successful lets give the mutation operators a chance to mate the strategy parameters

2
src/eva2/optimization/operator/crossover/CrossoverESBLXAlpha.java
View File

@ -84,7 +84,7 @@ public class CrossoverESBLXAlpha implements InterfaceCrossover, java.io.Serializ
}
// write the result back
for (int i = 0; i < result.length; i++) {
((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
}
}
//in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters

2
src/eva2/optimization/operator/crossover/CrossoverESDefault.java
View File

@ -84,7 +84,7 @@ public class CrossoverESDefault implements InterfaceCrossover, java.io.Serializa
}
// write the result back
for (int i = 0; i < result.length; i++) {
((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
}
}
//in case the crossover was successful lets give the mutation operators a chance to mate the strategy parameters

2
src/eva2/optimization/operator/crossover/CrossoverESFlat.java
View File

@ -88,7 +88,7 @@ public class CrossoverESFlat implements InterfaceCrossover, java.io.Serializable
}
// write the result back
for (int i = 0; i < result.length; i++) {
((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
}
}
//in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters

2
src/eva2/optimization/operator/crossover/CrossoverESIntermediate.java
View File

@ -75,7 +75,7 @@ public class CrossoverESIntermediate implements InterfaceCrossover, java.io.Seri
}
// write the result back
for (int i = 0; i < result.length; i++) {
((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
}
}
//in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters

2
src/eva2/optimization/operator/crossover/CrossoverESNPointDiscrete.java
View File

@ -84,7 +84,7 @@ public class CrossoverESNPointDiscrete implements InterfaceCrossover, java.io.Se
// write the result back
for (int i = 0; i < result.length; i++) {
((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
}
}
//in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters

2
src/eva2/optimization/operator/crossover/CrossoverESNPointDiscreteDislocation.java
View File

@ -99,7 +99,7 @@ public class CrossoverESNPointDiscreteDislocation implements InterfaceCrossover,
// write the result back
for (int i = 0; i < result.length; i++) {
((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
}
}
//in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters

4
src/eva2/optimization/operator/crossover/CrossoverESPCX.java
View File

@ -118,7 +118,7 @@ public class CrossoverESPCX implements InterfaceCrossover, java.io.Serializable
// write the result back
for (int i = 0; i < result.length; i++) {
((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
}
}
//in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters
@ -238,7 +238,7 @@ public class CrossoverESPCX implements InterfaceCrossover, java.io.Serializable
range[i][1] = 2;
}
indy1.setDoubleDataLength(n);
indy1.SetDoubleRange(range);
indy1.setDoubleRange(range);
// init values
indy2 = (ESIndividualDoubleData) indy1.clone();
indy3 = (ESIndividualDoubleData) indy1.clone();

4
src/eva2/optimization/operator/crossover/CrossoverESSBX.java
View File

@ -86,7 +86,7 @@ public class CrossoverESSBX implements InterfaceCrossover, java.io.Serializable
}
// write the result back
for (int i = 0; i < result.length; i++) {
((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
}
}
//in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters
@ -144,7 +144,7 @@ public class CrossoverESSBX implements InterfaceCrossover, java.io.Serializable
range[1][0] = -2.0;
range[1][1] = 2.0;
indy1.setDoubleDataLength(2);
indy1.SetDoubleRange(range);
indy1.setDoubleRange(range);
// init values
indy2 = (ESIndividualDoubleData) indy1.clone();
indy3 = (ESIndividualDoubleData) indy1.clone();

4
src/eva2/optimization/operator/crossover/CrossoverESSPX.java
View File

@ -97,7 +97,7 @@ public class CrossoverESSPX implements InterfaceCrossover, java.io.Serializable
// write the result back
for (int i = 0; i < result.length; i++) {
((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
}
}
//in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters
@ -156,7 +156,7 @@ public class CrossoverESSPX implements InterfaceCrossover, java.io.Serializable
range[i][1] = 2.0;
}
indy1.setDoubleDataLength(n);
indy1.SetDoubleRange(range);
indy1.setDoubleRange(range);
// init values
indy2 = (ESIndividualDoubleData) indy1.clone();
indy3 = (ESIndividualDoubleData) indy1.clone();

4
src/eva2/optimization/operator/crossover/CrossoverESUNDX.java
View File

@ -122,7 +122,7 @@ public class CrossoverESUNDX implements InterfaceCrossover, java.io.Serializable
// write the result back
for (int i = 0; i < result.length; i++) {
((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
}
}
//in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters
@ -240,7 +240,7 @@ public class CrossoverESUNDX implements InterfaceCrossover, java.io.Serializable
range[i][1] = 2.0;
}
indy1.setDoubleDataLength(n);
indy1.SetDoubleRange(range);
indy1.setDoubleRange(range);
// init values
indy2 = (ESIndividualDoubleData) indy1.clone();
indy3 = (ESIndividualDoubleData) indy1.clone();

2
src/eva2/optimization/operator/crossover/CrossoverESUniformDiscrete.java
View File

@ -76,7 +76,7 @@ public class CrossoverESUniformDiscrete implements InterfaceCrossover, java.io.S
// write the result back
for (int i = 0; i < result.length; i++) {
((InterfaceESIndividual) result[i]).SetDGenotype(children[i]);
((InterfaceESIndividual) result[i]).setDGenotype(children[i]);
}
}
//in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters

6
src/eva2/optimization/operator/crossover/TestESCrossover.java
View File

@ -107,7 +107,7 @@ public class TestESCrossover implements java.io.Serializable {
newRange[i][1] = 2;
}
tmpIndyD.setDoubleDataLength(m_Dimension);
tmpIndyD.SetDoubleRange(newRange);
tmpIndyD.setDoubleRange(newRange);
for (int i = 0; i < m_Partners.getTargetSize(); i++) {
tmpIndyEA = (AbstractEAIndividual) ((AbstractEAIndividual) tmpIndyD).clone();
tmpIndyEA.init(m_Problem);
@ -146,7 +146,7 @@ public class TestESCrossover implements java.io.Serializable {
newRange[i][1] = 2;
}
tmpIndyD.setDoubleDataLength(m_Dimension);
tmpIndyD.SetDoubleRange(newRange);
tmpIndyD.setDoubleRange(newRange);
double[] tmpD = new double[2];
tmpD[0] = 1;
@ -190,7 +190,7 @@ public class TestESCrossover implements java.io.Serializable {
newRange[i][1] = 2;
}
tmpIndyD.setDoubleDataLength(m_Dimension);
tmpIndyD.SetDoubleRange(newRange);
tmpIndyD.setDoubleRange(newRange);
double[] tmpD = new double[2];
tmpD[0] = 0.5;

2
src/eva2/optimization/operator/mutation/MutateESCorrVector.java
View File

@ -178,7 +178,7 @@ public class MutateESCorrVector implements InterfaceMutation, java.io.Serializab
}
// write genotype back
((InterfaceESIndividual) individual).SetDGenotype(genes);
((InterfaceESIndividual) individual).setDGenotype(genes);
}
// if (TRACE) System.out.println("After Mutate: " + AbstractEAIndividual.getDefaultDataString(individual));

4
src/eva2/optimization/operator/mutation/MutateESCorrolated.java
View File

@ -181,7 +181,7 @@ public class MutateESCorrolated implements InterfaceMutation, java.io.Serializab
// if (range[i][1] < x[i]) x[i] = range[i][1];
// }
//
// ((InterfaceESIndividual)individual).SetDGenotype(x);
// ((InterfaceESIndividual)individual).setDGenotype(x);
//
// //turn mutationvector with alphas
//// for (int i = 0; i < x.length-1; i++) {
@ -250,7 +250,7 @@ public class MutateESCorrolated implements InterfaceMutation, java.io.Serializab
}
}
((InterfaceESIndividual) individual).SetDGenotype(x);
((InterfaceESIndividual) individual).setDGenotype(x);
}
}

2
src/eva2/optimization/operator/mutation/MutateESCovarianceMatrixAdaption.java
View File

@ -202,7 +202,7 @@ public class MutateESCovarianceMatrixAdaption implements InterfaceMutation, java
x[i] = ranges[i][1];
}
}
((InterfaceESIndividual) individual).SetDGenotype(x);
((InterfaceESIndividual) individual).setDGenotype(x);
}
//System.out.println("After Mutate: " +((GAIndividual)individual).getSolutionRepresentationFor());
}

2
src/eva2/optimization/operator/mutation/MutateESFixedStepSize.java
View File

@ -88,7 +88,7 @@ public class MutateESFixedStepSize implements InterfaceMutation, java.io.Seriali
x[i] = range[i][1];
}
}
((InterfaceESIndividual) individual).SetDGenotype(x);
((InterfaceESIndividual) individual).setDGenotype(x);
}
//System.out.println("After Mutate: " +((GAIndividual)individual).getSolutionRepresentationFor());

2
src/eva2/optimization/operator/mutation/MutateESGlobal.java
View File

@ -125,7 +125,7 @@ public class MutateESGlobal implements InterfaceMutation, java.io.Serializable,
x[i] = range[i][1];
}
}
((InterfaceESIndividual) individual).SetDGenotype(x);
((InterfaceESIndividual) individual).setDGenotype(x);
}
//System.out.println("After Mutate: " +((GAIndividual)individual).getSolutionRepresentationFor());

2
src/eva2/optimization/operator/mutation/MutateESLocal.java
View File

@ -144,7 +144,7 @@ public class MutateESLocal implements InterfaceMutation, InterfaceAdditionalPopu
x[i] = range[i][1];
}
}
((InterfaceESIndividual) individual).SetDGenotype(x);
((InterfaceESIndividual) individual).setDGenotype(x);
}
//System.out.println("After Mutate: " +((GAIndividual)individual).getSolutionRepresentationFor());
}

2
src/eva2/optimization/operator/mutation/MutateESMainVectorAdaption.java
View File

@ -157,7 +157,7 @@ public class MutateESMainVectorAdaption implements InterfaceMutation, java.io.Se
Z1 = RNG.gaussianDouble(1.0);
evaluateNewObjectX(x, ranges);
((InterfaceESIndividual) individual).SetDGenotype(x);
((InterfaceESIndividual) individual).setDGenotype(x);
}
//System.out.println("After Mutate: " +((GAIndividual)individual).getSolutionRepresentationFor());
}

2
src/eva2/optimization/operator/mutation/MutateESMutativeStepSizeControl.java
View File

@ -126,7 +126,7 @@ public class MutateESMutativeStepSizeControl implements InterfaceMutation, java.
x[i] = range[i][1];
}
}
((InterfaceESIndividual) individual).SetDGenotype(x);
((InterfaceESIndividual) individual).setDGenotype(x);
// System.out.println("new step size: " + mutationStepSize);
}
//System.out.println("After Mutate: " +((GAIndividual)individual).getSolutionRepresentationFor());

2
src/eva2/optimization/operator/mutation/MutateESPathLengthAdaption.java
View File

@ -150,7 +150,7 @@ public class MutateESPathLengthAdaption implements InterfaceMutation, java.io.Se
this.mutateX(x, ranges, true); // this performs new mutation
((InterfaceESIndividual) individual).SetDGenotype(x);
((InterfaceESIndividual) individual).setDGenotype(x);
}
//System.out.println("After Mutate: " +((GAIndividual)individual).getSolutionRepresentationFor());
}

2
src/eva2/optimization/operator/mutation/MutateESPolynomial.java
View File

@ -97,7 +97,7 @@ public class MutateESPolynomial implements InterfaceMutation, java.io.Serializab
x[i] = range[i][1];
}
}
((InterfaceESIndividual) individual).SetDGenotype(x);
((InterfaceESIndividual) individual).setDGenotype(x);
}
//System.out.println("After Mutate: " +((GAIndividual)individual).getSolutionRepresentationFor());

2
src/eva2/optimization/operator/mutation/MutateESRankMuCMA.java
View File

@ -612,7 +612,7 @@ public class MutateESRankMuCMA implements InterfaceAdaptOperatorGenerational, In
// this is a critical point: where do the CMA parameters for this individual's mutation come from?
// for GA and ES we can expect that selection occured directly before the mutation cycle,
// so we take the parameter set from the last adpation step.
((InterfaceDataTypeDouble) individual).SetDoubleGenotype(mutate(lastParams, x, range, 0));
((InterfaceDataTypeDouble) individual).setDoubleGenotype(mutate(lastParams, x, range, 0));
// if (TRACE) System.out.println("WCMA mutate, aft: " + BeanInspector.toString(x));
} else {

2
src/eva2/optimization/operator/mutation/MutateESStandard.java
View File

@ -66,7 +66,7 @@ package eva2.optimization.operator.mutation;
// 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);
// ((InterfaceESIndividual)individual).setDGenotype(x);
//
// }
// //System.out.println("After Mutate: " +((GAIndividual)individual).getSolutionRepresentationFor());

4
src/eva2/optimization/operator/postprocess/PostProcess.java
View File

@ -797,9 +797,9 @@ public class PostProcess {
public static void setDoubleData(AbstractEAIndividual indy, double[] data) {
if (indy instanceof InterfaceDataTypeDouble || (indy instanceof InterfaceESIndividual)) {
if (indy instanceof InterfaceESIndividual) {
((InterfaceESIndividual) indy).SetDGenotype(data);
((InterfaceESIndividual) indy).setDGenotype(data);
} else {
((InterfaceDataTypeDouble) indy).SetDoubleGenotype(data);
((InterfaceDataTypeDouble) indy).setDoubleGenotype(data);
}
} else {
throw new RuntimeException("Error, unable to set double data to individual instance " + indy.getClass() + " in PostProcess.setDoubleData");

6
src/eva2/optimization/population/Population.java
View File

@ -515,7 +515,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
Matrix rlhM = StatisticUtils.rlh(pop.size(), range, true);
for (int i = 0; i < pop.size(); i++) {
AbstractEAIndividual tmpIndy = pop.getEAIndividual(i);
((InterfaceDataTypeDouble) tmpIndy).SetDoubleGenotype(rlhM.getRowShallow(i));
((InterfaceDataTypeDouble) tmpIndy).setDoubleGenotype(rlhM.getRowShallow(i));
}
} else {
System.err.println("Error: data type double required for Population.createUniformSampling");
@ -2383,11 +2383,11 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
*/
public void updateRange(double[][] range, boolean forceRange) {
for (int i = 0; i < size(); i++) {
((InterfaceDataTypeDouble) getEAIndividual(i)).SetDoubleRange(range);
((InterfaceDataTypeDouble) getEAIndividual(i)).setDoubleRange(range);
double[] pos = ((InterfaceDataTypeDouble) getEAIndividual(i)).getDoubleData();
if (!Mathematics.isInRange(pos, range)) {
Mathematics.projectToRange(pos, range);
((InterfaceDataTypeDouble) getEAIndividual(i)).SetDoubleGenotype(pos);
((InterfaceDataTypeDouble) getEAIndividual(i)).setDoubleGenotype(pos);
}
}
}

2
src/eva2/optimization/problems/AbstractDynTransProblem.java
View File

@ -103,7 +103,7 @@ public abstract class AbstractDynTransProblem extends AbstractSynchronousOptimiz
/* individuum moves towords untranslated problem */
indyData[i] -= getTranslation(i, time);
}
((InterfaceDataTypeDouble) individual).SetDoubleGenotype(indyData);
((InterfaceDataTypeDouble) individual).setDoubleGenotype(indyData);
}
/*

6
src/eva2/optimization/problems/AbstractMultiModalProblemKnown.java
View File

@ -55,7 +55,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
population.clear();
((InterfaceDataTypeDouble) this.template).setDoubleDataLength(this.problemDimension);
((InterfaceDataTypeDouble) this.template).SetDoubleRange(makeRange());
((InterfaceDataTypeDouble) this.template).setDoubleRange(makeRange());
for (int i = 0; i < population.getTargetSize(); i++) {
tmpIndy = (AbstractEAIndividual) ((AbstractEAIndividual) this.template).clone();
tmpIndy.init(this);
@ -148,7 +148,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
protected void addOptimum(double[] point) {
InterfaceDataTypeDouble tmpIndy;
tmpIndy = (InterfaceDataTypeDouble) ((AbstractEAIndividual) this.template).clone();
tmpIndy.SetDoubleGenotype(point);
tmpIndy.setDoubleGenotype(point);
((AbstractEAIndividual) tmpIndy).setFitness(evalUnnormalized(point));
if (((AbstractEAIndividual) tmpIndy).getFitness(0) >= globalOptimum) {
globalOptimum = ((AbstractEAIndividual) tmpIndy).getFitness(0);
@ -166,7 +166,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
}
if (isDoRotation()) {
point = inverseRotateMaybe(point); // theres an inverse rotation required
tmpIndy.SetDoubleGenotype(point);
tmpIndy.setDoubleGenotype(point);
}
this.listOfOptima.add(tmpIndy);
}

10
src/eva2/optimization/problems/AbstractProblemDouble.java
View File

@ -80,7 +80,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
((InterfaceDataTypeDouble) this.template)
.setDoubleDataLength(getProblemDimension());
((InterfaceDataTypeDouble) this.template)
.SetDoubleRange(makeRange());
.setDoubleRange(makeRange());
}
}
@ -136,7 +136,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
double[] fitness;
x = getEvalArray(individual);
((InterfaceDataTypeDouble) individual).SetDoublePhenotype(x);
((InterfaceDataTypeDouble) individual).setDoublePhenotype(x);
// evaluate the vector
fitness = this.eval(x);
// if indicated, add Gaussian noise
@ -427,11 +427,11 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
InterfaceDataTypeDouble tmpIndy;
tmpIndy = (InterfaceDataTypeDouble) prob.getIndividualTemplate()
.clone();
tmpIndy.SetDoubleGenotype(pos);
tmpIndy.setDoubleGenotype(pos);
if (prob.isDoRotation()) {
pos = prob.inverseRotateMaybe(pos); // theres an inverse rotation
// required
tmpIndy.SetDoubleGenotype(pos);
tmpIndy.setDoubleGenotype(pos);
}
((AbstractEAIndividual) tmpIndy).setFitness(prob.eval(pos));
if (!Mathematics.isInRange(pos, prob.makeRange())) {
@ -453,7 +453,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
InterfaceDataTypeDouble tmpIndy;
tmpIndy = (InterfaceDataTypeDouble) prob.getIndividualTemplate()
.clone();
tmpIndy.SetDoubleGenotype(pos);
tmpIndy.setDoubleGenotype(pos);
((AbstractEAIndividual) tmpIndy).setFitness(prob.eval(pos));
pop.add(tmpIndy);
FitnessConvergenceTerminator convTerm = new FitnessConvergenceTerminator(

4
src/eva2/optimization/problems/ExternalRuntimeProblem.java
View File

@ -77,7 +77,7 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem
public ExternalRuntimeProblem() {
this.template = new ESIndividualDoubleData();
((ESIndividualDoubleData) this.template).setDoubleDataLength(m_ProblemDimension);
((ESIndividualDoubleData) this.template).SetDoubleRange(makeRange());
((ESIndividualDoubleData) this.template).setDoubleRange(makeRange());
}
public ExternalRuntimeProblem(ExternalRuntimeProblem b) {
@ -153,7 +153,7 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem
this.m_OverallBest = null;
((InterfaceDataTypeDouble) this.template).setDoubleDataLength(this.m_ProblemDimension);
((InterfaceDataTypeDouble) this.template).SetDoubleRange(makeRange());
((InterfaceDataTypeDouble) this.template).setDoubleRange(makeRange());
AbstractOptimizationProblem.defaultInitPopulation(population, template, this);
}

2
src/eva2/optimization/problems/F8Problem.java
View File

@ -315,7 +315,7 @@ public class F8Problem extends AbstractProblemDoubleOffset
// Population population = new Population();
// InterfaceDataTypeDouble tmpIndy;
// tmpIndy = (InterfaceDataTypeDouble)((AbstractEAIndividual)this.template).clone();
// tmpIndy.SetDoubleGenotype(pos);
// tmpIndy.setDoubleGenotype(pos);
// ((AbstractEAIndividual)tmpIndy).SetFitness(eval(pos));
// population.add(tmpIndy);
// FitnessConvergenceTerminator convTerm = new FitnessConvergenceTerminator(1e-15, 10, false, true);

2
src/eva2/optimization/problems/FLensProblem.java
View File

@ -329,7 +329,7 @@ public class FLensProblem extends AbstractOptimizationProblem
range[i][0] = 0.1;
range[i][1] = 5.0;
}
((InterfaceDataTypeDouble) this.template).SetDoubleRange(range);
((InterfaceDataTypeDouble) this.template).setDoubleRange(range);
AbstractOptimizationProblem.defaultInitPopulation(population, template, this);
if (this.m_Show) {

10
src/eva2/optimization/problems/FM0Problem.java
View File

@ -17,11 +17,11 @@ public class FM0Problem extends AbstractMultiModalProblemKnown implements Interf
this.problemDimension = 2;
this.template = new ESIndividualDoubleData();
// this.m_Extrema = new double[2];
// this.m_Range = new double [this.problemDimension][2];
// this.m_Range[0][0] = -2.0;
// this.m_Range[0][1] = 2.0;
// this.m_Range[1][0] = -2.8;
// this.m_Range[1][1] = 2.8;
// this.range = new double [this.problemDimension][2];
// this.range[0][0] = -2.0;
// this.range[0][1] = 2.0;
// this.range[1][0] = -2.8;
// this.range[1][1] = 2.8;
// this.m_Extrema[0] = -2;
// this.m_Extrema[1] = 6;
}

4
src/eva2/optimization/problems/MatlabProblem.java
View File

@ -142,7 +142,7 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
}
if (getProblemDimension() > 0) { // avoid evil case setting dim to 0 during object init
((InterfaceDataTypeDouble) this.template).setDoubleDataLength(getProblemDimension());
((InterfaceDataTypeDouble) this.template).SetDoubleRange(range);
((InterfaceDataTypeDouble) this.template).setDoubleRange(range);
}
break;
case typeBinary:
@ -330,7 +330,7 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
private void setIndyGenotype(AbstractEAIndividual indy, double[] ds) {
switch (dataType) {
case typeDouble:
((InterfaceDataTypeDouble) indy).SetDoubleGenotype(ds);
((InterfaceDataTypeDouble) indy).setDoubleGenotype(ds);
break;
case typeBinary:
((InterfaceDataTypeBinary) indy).SetBinaryGenotype(toBinary(ds));

2
src/eva2/optimization/problems/SimpleProblemWrapper.java
View File

@ -129,7 +129,7 @@ public class SimpleProblemWrapper extends AbstractOptimizationProblem {
}
if (template instanceof InterfaceDataTypeDouble) {
((InterfaceDataTypeDouble) this.template).setDoubleDataLength(simProb.getProblemDimension());
((InterfaceDataTypeDouble) this.template).SetDoubleRange(makeRange());
((InterfaceDataTypeDouble) this.template).setDoubleRange(makeRange());
} else if (template instanceof InterfaceDataTypeBinary) {
((InterfaceDataTypeBinary) this.template).setBinaryDataLength(simProb.getProblemDimension());
} else {

2
src/eva2/optimization/problems/TF1Problem.java
View File

@ -99,7 +99,7 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem
double[][] newRange = makeRange();
((InterfaceDataTypeDouble) this.template).setDoubleDataLength(this.m_ProblemDimension);
((InterfaceDataTypeDouble) this.template).SetDoubleRange(newRange);
((InterfaceDataTypeDouble) this.template).setDoubleRange(newRange);
AbstractOptimizationProblem.defaultInitPopulation(population, template, this);
}

2
src/eva2/optimization/strategies/DifferentialEvolution.java
View File

@ -448,7 +448,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
if (forceRange) {
Mathematics.projectToRange(nX, esIndy.getDoubleRange());
} // why did this never happen before?
esIndy.SetDoubleGenotype(nX);
esIndy.setDoubleGenotype(nX);
indy.SetAge(0);
indy.resetConstraintViolation();
double[] fit = new double[1];

4
src/eva2/optimization/strategies/DynamicParticleSwarmOptimization.java
View File

@ -158,9 +158,9 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
}
if (indy instanceof InterfaceDataTypeDouble) {
((InterfaceDataTypeDouble) indy).SetDoubleGenotype(newPos);
((InterfaceDataTypeDouble) indy).setDoubleGenotype(newPos);
} else {
endy.SetDoubleGenotype(newPos);
endy.setDoubleGenotype(newPos);
}
resetFitness(indy);

4
src/eva2/optimization/strategies/GradientDescentAlgorithm.java
View File

@ -229,7 +229,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
} // end loop iterations
((InterfaceDataTypeDouble) indy).SetDoubleGenotype(params);
((InterfaceDataTypeDouble) indy).setDoubleGenotype(params);
} // end if ((this.problem instanceof InterfaceFirstOrderDerivableProblem) && (indy instanceof InterfaceDataTypeDouble)) {
else {
@ -256,7 +256,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
if (TRACE) {
System.out.println("Gradient Descent: Fitness critical:" + indy.getFitness()[0]);
}
((InterfaceDataTypeDouble) indy).SetDoublePhenotype((double[]) indy.getData(oldParamsKey));
((InterfaceDataTypeDouble) indy).setDoublePhenotype((double[]) indy.getData(oldParamsKey));
double[] changes = (double[]) indy.getData(changesKey);
int[] lock = (int[]) indy.getData(lockKey);

12
src/eva2/optimization/strategies/NelderMeadSimplex.java
View File

@ -163,7 +163,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
}
}
// AbstractEAIndividual reflectedInd = (AbstractEAIndividual)((AbstractEAIndividual)bestpop.getIndividual(1)).clone();
// ((InterfaceDataTypeDouble)reflectedInd).SetDoubleGenotype(r);
// ((InterfaceDataTypeDouble)reflectedInd).setDoubleGenotype(r);
//
// problem.evaluate(reflectedInd);
AbstractEAIndividual reflectedInd = createEvalIndy(bestpop, r);
@ -199,7 +199,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
}
// AbstractEAIndividual c_ind = (AbstractEAIndividual)((AbstractEAIndividual)bestpop.getIndividual(1)).clone();
// ((InterfaceDataTypeDouble)c_ind).SetDoubleGenotype(c);
// ((InterfaceDataTypeDouble)c_ind).setDoubleGenotype(c);
// problem.evaluate(c_ind);
AbstractEAIndividual c_ind = createEvalIndy(bestpop, c);
this.m_Population.incrFunctionCalls();
@ -212,7 +212,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
private AbstractEAIndividual createEvalIndy(Population pop, double[] newGenotype) {
AbstractEAIndividual e_ind = (AbstractEAIndividual) ((AbstractEAIndividual) pop.getIndividual(1)).clone();
((InterfaceDataTypeDouble) e_ind).SetDoubleGenotype(newGenotype);
((InterfaceDataTypeDouble) e_ind).setDoubleGenotype(newGenotype);
e_ind.resetConstraintViolation();
m_Problem.evaluate(e_ind);
if (e_ind.getFitness(0) < 6000) {
@ -294,7 +294,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
if (!Mathematics.isInRange(x, range)) {
System.err.println("WARNING: nelder mead step produced indy out of range!");
// Mathematics.projectToRange(x, range);
// ((InterfaceDataTypeDouble)ind).SetDoubleGenotype(x);
// ((InterfaceDataTypeDouble)ind).setDoubleGenotype(x);
// problem.evaluate(ind);
// this.m_Population.incrFunctionCalls();
}
@ -308,7 +308,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
for (int i = 0; i < c.length; i++) {
c[i] = 0.5 * c[i] + 0.5 * u_1[i];
}
((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).SetDoubleGenotype(c);
((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).setDoubleGenotype(c);
// m_Population.getEAIndividual(j).resetConstraintViolation(); // not a good idea because during evaluation, a stats update may be performed which mustnt see indies which are evaluated, but possible constraints have been reset.
}
m_Problem.evaluate(m_Population);
@ -470,7 +470,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
} else {
dat[i] = Math.min(dat[i] + curPerturb, range[i][1]);
}
((InterfaceDataTypeDouble) indy).SetDoubleGenotype(dat);
((InterfaceDataTypeDouble) indy).setDoubleGenotype(dat);
indy.resetConstraintViolation();
initialPop.add((AbstractEAIndividual) indy.clone());
}

2
src/eva2/optimization/strategies/PDDifferentialEvolution.java
View File

@ -414,7 +414,7 @@ public class PDDifferentialEvolution implements InterfaceOptimizer, java.io.Seri
if (forceRange) {
Mathematics.projectToRange(nX, esIndy.getDoubleRange());
} // why did this never happen before?
esIndy.SetDoubleGenotype(nX);
esIndy.setDoubleGenotype(nX);
indy.SetAge(0);
indy.resetConstraintViolation();
double[] fit = new double[1];

4
src/eva2/optimization/strategies/ParticleSubSwarmOptimization.java
View File

@ -373,8 +373,8 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
minValInDim[i] = range[i][0]; // get lower boarder for dimension i
maxValInDim[i] = range[i][1]; // get upper boarder for dimension i
}
// min.SetDoubleGenotype(minValInDim); // set all dimensions to min
// max.SetDoubleGenotype(maxValInDim); // set all dimensions to max
// min.setDoubleGenotype(minValInDim); // set all dimensions to min
// max.setDoubleGenotype(maxValInDim); // set all dimensions to max
this.maxPosDist = Mathematics.euclidianDist(minValInDim, maxValInDim);
}

8
src/eva2/optimization/strategies/ParticleSwarmOptimization.java
View File

@ -1253,16 +1253,16 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
// finally set the new position and the current velocity
if (indy instanceof InterfaceDataTypeDouble) {
((InterfaceDataTypeDouble) indy).SetDoubleGenotype(newPosition);
((InterfaceDataTypeDouble) indy).setDoubleGenotype(newPosition);
} else {
((InterfaceDataTypeDouble) indy).SetDoubleGenotype(newPosition); // WARNING, this does a checkBounds in any case!
((InterfaceDataTypeDouble) indy).setDoubleGenotype(newPosition); // WARNING, this does a checkBounds in any case!
if (!m_CheckRange) {
System.err.println("warning, checkbounds will be forced by InterfaceESIndividual!");
}
}
indy.putData(partVelKey, curVelocity);
// ((InterfaceESIndividual) indy).SetDGenotype(newPosition);
// ((InterfaceESIndividual) indy).setDGenotype(newPosition);
}
/**
@ -2228,7 +2228,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
System.err.println("partInfo: " + i + " - " + getParticleInfo(population.getEAIndividual(i)));
throw new RuntimeException("Mismatching best fitness!! " + personalBestfit[0] + " vs. " + ((InterfaceProblemDouble) m_Problem).eval(personalBestPos)[0]);
}
((InterfaceDataTypeDouble) indy).SetDoubleGenotype(personalBestPos);
((InterfaceDataTypeDouble) indy).setDoubleGenotype(personalBestPos);
indy.setFitness(personalBestfit);
bests.add((AbstractEAIndividual) indy.clone());
}

8
src/eva2/optimization/strategies/ScatterSearch.java
View File

@ -187,7 +187,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
// public double eval(double[] x) {
// AbstractEAIndividual indy = (AbstractEAIndividual)template.clone();
// ((InterfaceDataTypeDouble)indy).SetDoubleGenotype(x);
// ((InterfaceDataTypeDouble)indy).setDoubleGenotype(x);
// problem.evaluate(indy);
// return indy.getFitness(0);
// }
@ -554,7 +554,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
if (checkRange) {
Mathematics.projectToRange(combi, range);
}
((InterfaceDataTypeDouble) resIndy).SetDoubleGenotype(combi);
((InterfaceDataTypeDouble) resIndy).setDoubleGenotype(combi);
problem.evaluate(resIndy);
refSet.incrFunctionCalls();
return resIndy;
@ -673,7 +673,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
freq[i][interv]++;
}
dblIndy.SetDoubleGenotype(genes);
dblIndy.setDoubleGenotype(genes);
return indy;
}
@ -717,7 +717,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
for (int i = 0; i < probDim; i++) {
genes[i] = randInRangeInterval(i, interval);
}
dblIndy.SetDoubleGenotype(genes);
dblIndy.setDoubleGenotype(genes);
return indy;
}

4
src/eva2/optimization/strategies/Tribes.java
View File

@ -253,7 +253,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
if (bestMemPos.firstIsBetter(bestMemPos.getFitness(), bestExp.getFitness())) {
AbstractEAIndividual indy = (AbstractEAIndividual) bestExp.clone();
indy.setFitness(bestMemPos.getFitness());
((InterfaceDataTypeDouble) indy).SetDoubleGenotype(bestMemPos.getPos());
((InterfaceDataTypeDouble) indy).setDoubleGenotype(bestMemPos.getPos());
return indy;
} else {
return bestExp;
@ -609,7 +609,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
}
TribesExplorer indy = tmp.clone();
indy.clearPosVel();
indy.SetDoubleGenotype(pos.getPos());
indy.setDoubleGenotype(pos.getPos());
indy.setFitness(pos.getFitness());
return indy;
}

12
src/eva2/optimization/strategies/tribes/TribesExplorer.java
View File

@ -41,7 +41,7 @@ public class TribesExplorer extends AbstractEAIndividual implements InterfaceDat
public TribesExplorer(double[][] range, double objFirstDim) {
init(range.length, Tribes.maxTribeNb);
SetDoubleRange(range);
setDoubleRange(range);
objectiveValueFirstDim = objFirstDim;
}
@ -981,7 +981,7 @@ v[d] = cmin * v[d];
if (x.length != position.x.length) {
System.err.println("Init value and requested length doesn't match!");
}
this.SetDoubleGenotype(x);
this.setDoubleGenotype(x);
} else {
this.init(opt);
System.err.println("Initial value for ESIndividualDoubleData is not double[]!");
@ -1000,17 +1000,17 @@ v[d] = cmin * v[d];
}
@Override
public void SetDoublePhenotype(double[] doubleData) {
public void setDoublePhenotype(double[] doubleData) {
position.setDoubleArray(doubleData);
}
@Override
public void SetDoubleGenotype(double[] doubleData) {
public void setDoubleGenotype(double[] doubleData) {
position.setDoubleArray(doubleData);
}
@Override
public void SetDoubleRange(double[][] range) {
public void setDoubleRange(double[][] range) {
if (position.x.length != range.length) { // we will need to fully reinit the particle
initPositions(range.length);
}
@ -1048,7 +1048,7 @@ v[d] = cmin * v[d];
return position.x.length;
}
// public void SetDGenotype(double[] b) {
// public void setDGenotype(double[] b) {
// position.setDoubleArray(b);
// }
//

2
src/eva2/optimization/strategies/tribes/TribesSwarm.java
View File

@ -599,7 +599,7 @@ public class TribesSwarm implements java.io.Serializable {
*/
TribesExplorer expl = new TribesExplorer(range, masterTribe.getObjectiveFirstDim());
expl.SetDoubleRange(range);
expl.setDoubleRange(range);
// System.out.println("generating expl, option " + option + ", init " + initType + ", from tribe " + fromTribe);