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Fix performance issue with CLI execution (SnakeYAML doesn't perform well

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when serializing large objects)
Implement easier access for terminators.
This commit is contained in:
Fabian Becker 2014-11-14 17:55:23 +01:00
parent 5a80cb781e
commit ca108a5d97
5 changed files with 30 additions and 58 deletions
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7
src/eva2/cli/Main.java
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@ -96,7 +96,12 @@ final class YamlStatistics implements InterfaceStatistics {
public void stopOptimizationPerformed(boolean normal, String stopMessage) {
this.currentRun.put("stopMessage", stopMessage);
this.currentRun.put("totalFunctionCalls", this.currentParameters.getOptimizer().getPopulation().getFunctionCalls());
this.currentRun.put("generations", currentGenerations);
// ToDo: Figure out a sane way to do this. Having multirun > 1 increases SnakeYAML memory consumption to beyond infinity
//this.currentRun.put("generations", currentGenerations);
Population pop = this.currentParameters.getOptimizer().getAllSolutions().getSolutions();
this.currentRun.put("solution", pop.getBestEAIndividual().getDoublePosition().clone());
this.currentRun.put("bestFitness", pop.getBestFitness().clone());
this.currentRun.put("meanFitness", pop.getMeanFitness().clone());
this.runs.add(currentRun);
}

13
src/eva2/cli/OptimizationBuilder.java
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@ -168,9 +168,10 @@ public final class OptimizationBuilder {
if (type.isPrimitive() && ((ArgumentTree)tree.get(name)).getValue() != null) {
obj = BeanInspector.stringToPrimitive((String) ((ArgumentTree) tree.get(name)).getValue(), type);
} else if (type.isArray() && ((ArgumentTree)tree.get(name)).getValue() != null) {
// ToDo: Implement array parsing
} else if (type.isEnum() && ((ArgumentTree)tree.get(name)).getValue() != null) {
int enumIndex = Integer.parseInt((String)((ArgumentTree)tree.get(name)).getValue());
// ToDo: Properly check
obj = type.getEnumConstants()[enumIndex];
} else {
@ -180,7 +181,11 @@ public final class OptimizationBuilder {
Class subType;
if (className != null) {
// Try to get the actual class from its name
subType = getClassFromName(className, type);
if (className.endsWith("Problem")) {
subType = getClassFromName("eva2.problems", className, type);
} else {
subType = getClassFromName("eva2.optimization", className, type);
}
} else {
subType = type;
}
@ -207,8 +212,8 @@ public final class OptimizationBuilder {
return instance;
}
private static Class<?> getClassFromName(String name, Class type) {
Class<?>[] classes = ReflectPackage.getAssignableClassesInPackage("eva2", type, true, true);
private static Class<?> getClassFromName(String packageName, String name, Class type) {
Class<?>[] classes = ReflectPackage.getAssignableClassesInPackage(packageName, type, true, true);
for (Class clazz : classes) {
// We allow both the fully qualified name (eva2.optimization.strategies.GeneticAlgorithm
// and the simple name (GeneticAlgorithm)

2
src/eva2/optimization/operator/terminators/CombinedTerminator.java
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@ -129,7 +129,7 @@ public class CombinedTerminator implements InterfaceTerminator, Serializable {
/**
* @param logicalOperator the logicalOperator to set
*/
@Parameter(description = "Set the boolean operator to be used to combine the two terminators.")
@Parameter(name = "operator", description = "Set the boolean operator to be used to combine the two terminators.")
public void setLogicalOperator(LogicalOperator logicalOperator) {
this.logicalOperator = logicalOperator;
}

8
src/eva2/optimization/operator/terminators/FitnessValueTerminator.java
View File

@ -5,6 +5,7 @@ import eva2.optimization.population.InterfaceSolutionSet;
import eva2.optimization.population.PopulationInterface;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.util.annotation.Description;
import eva2.util.annotation.Parameter;
import java.io.Serializable;
import java.util.Arrays;
@ -22,7 +23,7 @@ public class FitnessValueTerminator implements InterfaceTerminator,
*
*/
public FitnessValueTerminator() {
fitnessValue = new double[]{0.1};
fitnessValue = new double[]{10E-4};
}
@Override
@ -70,6 +71,7 @@ public class FitnessValueTerminator implements InterfaceTerminator,
/**
*
*/
@Parameter(name = "fitness", description = "Set the fitness objective value.")
public void setFitnessValue(double[] x) {
fitnessValue = x;
}
@ -80,8 +82,4 @@ public class FitnessValueTerminator implements InterfaceTerminator,
public double[] getFitnessValue() {
return fitnessValue;
}
public String fitnessValueTipText() {
return "Set the fitness objective value.";
}
}

58
src/eva2/optimization/strategies/DifferentialEvolution.java
View File

@ -693,7 +693,7 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
*
* @param f
*/
@Parameter(name = "F", description = "Differential Weight")
@Parameter(name = "F", description = "F is a real and constant factor which controls the amplification of the differential variation.")
public void setDifferentialWeight(double f) {
this.differentialWeight = f;
}
@ -702,17 +702,13 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
return this.differentialWeight;
}
public String differentialWeightTipText() {
return "F is a real and constant factor which controls the amplification of the differential variation.";
}
/**
* Probability of alteration through DE (something like a discrete uniform
* crossover is performed here)
*
* @param k
*/
@Parameter(name = "CR", description = "Crossover Rate")
@Parameter(name = "CR", description = "Probability of alteration through DE (a.k.a. CR, similar to discrete uniform crossover).")
public void setCrossoverRate(double k) {
if (k < 0) {
k = 0;
@ -727,10 +723,6 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
return this.crossoverRate;
}
public String crossoverrateTipText() {
return "Probability of alteration through DE (a.k.a. CR, similar to discrete uniform crossover).";
}
/**
* Enhance greediness through amplification of the differential vector to
* the best individual for DE2
@ -751,6 +743,7 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
*
* @param l
*/
@Parameter(description = "In case of trigonometric mutation DE, the TMO is applied with probability Mt.")
public void setMt(double l) {
this.mt = l;
if (this.mt < 0) {
@ -765,16 +758,12 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
return this.mt;
}
public String mtTipText() {
return "In case of trigonometric mutation DE, the TMO is applied with probability Mt.";
}
/**
* This method allows you to choose the type of Differential Evolution.
*
* @param s The type.
*/
@Parameter(name = "type", description = "Mutation type for DE")
@Parameter(name = "type", description = "Choose the type of Differential Evolution.")
public void setDEType(eva2.optimization.enums.DEType s) {
this.DEType = s;
// show mt for trig. DE only
@ -786,10 +775,6 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
return this.DEType;
}
public String dETypeTipText() {
return "Choose the type of Differential Evolution.";
}
/**
* @return the maximumAge
*/
@ -800,14 +785,11 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
/**
* @param maximumAge the maximumAge to set
*/
@Parameter(description = "The maximum age of individuals, older ones are discarded. Set to -1 (or 0) to deactivate")
public void setMaximumAge(int maximumAge) {
this.maximumAge = maximumAge;
}
public String maximumAgeTipText() {
return "The maximum age of individuals, older ones are discarded. Set to -1 (or 0) to deactivate";
}
/**
* Check whether the problem range will be enforced.
*
@ -820,62 +802,47 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
/**
* @param forceRange the forceRange to set
*/
@Parameter(description = "Set whether to enforce the problem range.")
public void setCheckRange(boolean forceRange) {
this.forceRange = forceRange;
}
public String checkRangeTipText() {
return "Set whether to enforce the problem range.";
}
public boolean isRandomizeFKLambda() {
return randomizeFKLambda;
}
@Parameter(description = "If true, values for k, f, lambda are randomly sampled around +/- 20% of the given values.")
public void setRandomizeFKLambda(boolean randomizeFK) {
this.randomizeFKLambda = randomizeFK;
}
public String randomizeFKLambdaTipText() {
return "If true, values for k, f, lambda are randomly sampled around +/- 20% of the given values.";
}
public boolean isCompareToParent() {
return compareToParent;
}
@Parameter(description = "Compare a challenge individual to its original parent instead of a random one.")
public void setCompareToParent(boolean compareToParent) {
this.compareToParent = compareToParent;
}
public String compareToParentTipText() {
return "Compare a challenge individual to its original parent instead of a random one.";
}
public boolean isGenerational() {
return generational;
}
@Parameter(description = "Switch to generational DE as opposed to standard steady-state DE")
public void setGenerational(boolean generational) {
this.generational = generational;
}
public String generationalTipText() {
return "Switch to generational DE as opposed to standard steady-state DE";
}
public boolean isCyclePop() {
return cyclePop;
}
@Parameter(description = "if true, individuals are used as parents in a cyclic sequence - otherwise randomly ")
public void setCyclePop(boolean cycle) {
this.cyclePop = cycle;
}
public String cyclePopTipText() {
return "if true, individuals are used as parents in a cyclic sequence - otherwise randomly ";
}
/**
* @return the maximumAge
*/
@ -885,11 +852,8 @@ public class DifferentialEvolution extends AbstractOptimizer implements java.io.
/**
*/
@Parameter(description = "Re-evaluates individuals which are older than maximum age instead of discarding them")
public void setReEvaluate(boolean reEvaluate) {
this.reEvaluate = reEvaluate;
}
public String reEvaluateTipText() {
return "Re-evaluates individuals which are older than maximum age instead of discarding them";
}
}