com.rapidminer.tools.math.optimization.ec.es
Class ESOptimization

java.lang.Object
  com.rapidminer.tools.math.optimization.ec.es.ESOptimization

All Implemented Interfaces:

Optimization

Direct Known Subclasses:

ClassificationEvoOptimization, ESParameterOptimization, KernelLogisticRegressionOptimization, LogisticRegressionOptimization, RegressionEvoOptimization

public abstract class ESOptimization
extends java.lang.Object
implements Optimization

Evolutionary Strategy approach for all real-valued optimization tasks.

Author:

Ingo Mierswa

Field Summary

static int	BOLTZMANN_SELECTION Indicates a Boltzmann selection scheme.
static int	CUT_SELECTION Indicates a cut selection scheme.
static int	GAUSSIAN_MUTATION Indicates a gaussian mutation.
static int	INIT_TYPE_MAX Indicates that the start population should be initialized with the maximum value.
static int	INIT_TYPE_MIN Indicates that the start population should be initialized with the minimum value.
static int	INIT_TYPE_ONE Indicates that the start population should be initialized with one.
static int	INIT_TYPE_RANDOM Indicates that the start population should be randomly initialized.
static int	INIT_TYPE_ZERO Indicates that the start population should be initialized with zero.
static java.lang.String[]	MUTATION_TYPES The names of the mutation types.
static int	NO_MUTATION Indicates no mutation.
static int	NON_DOMINATED_SORTING_SELECTION Indicates a multi-objective selection scheme (NSGA II).
static java.lang.String[]	POPULATION_INIT_TYPES The names of the initialization types.
static int	RANK_SELECTION Indicates a rank based selection scheme.
static int	ROULETTE_WHEEL Indicates a roulette wheel selection scheme.
static java.lang.String[]	SELECTION_TYPES The names of all available selection schemes.
static int	SPARSITY_MUTATION Indicates a hybrid between switching mutation and Gaussian mutation.
static int	STOCHASTIC_UNIVERSAL Indicates a stochastic universal sampling selection scheme.
static int	SWITCHING_MUTATION Indicates a switching mutation.
static int	TOURNAMENT_SELECTION Indicates a tournament selection scheme.
static int	UNIFORM_SELECTION Indicates a uniform sampling selection scheme.

Constructor Summary

ESOptimization(double[] minValues, double[] maxValues, int populationSize, int individualSize, int initType, int maxGenerations, int generationsWithoutImprovement, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double defaultSigma, double crossoverProb, boolean showConvergencePlot, boolean showPopulationPlot, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary SVM optimization.

ESOptimization(double minValue, double maxValue, int populationSize, int individualSize, int initType, int maxGenerations, int generationsWithoutImprovement, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double crossoverProb, boolean showConvergencePlot, boolean showPopulationPlot, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary SVM optimization.

Method Summary

protected void	evaluate(Population population) Evaluates the individuals of the given population.
abstract PerformanceVector	evaluateIndividual(Individual individual) Subclasses must implement this method to calculate the fitness of the given individual.
double	getBestFitnessEver() Returns the best fitness ever.
double	getBestFitnessInGeneration() Returns the best fitness in the current generation.
PerformanceVector	getBestPerformanceEver() Returns the best performance vector ever.
double[]	getBestValuesEver() Returns the best values ever.
int	getGeneration() Returns the current generation.
double	getMax(int index)
double	getMin(int index)
Population	getPopulation()
OptimizationValueType	getValueType(int index)
void	increaseCurrentEvaluationCounter() Getter and setter for subclasses
void	increaseTotalEvaluationCounter()
void	nextIteration() This method is invoked after each evaluation.
void	optimize() Starts the optimization.
void	setMax(int index, double v)
void	setMin(int index, double v)
void	setValueType(int index, OptimizationValueType type)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

SELECTION_TYPES

public static final java.lang.String[] SELECTION_TYPES

The names of all available selection schemes.

UNIFORM_SELECTION

public static final int UNIFORM_SELECTION

Indicates a uniform sampling selection scheme.

See Also:

Constant Field Values

CUT_SELECTION

public static final int CUT_SELECTION

Indicates a cut selection scheme.

See Also:

Constant Field Values

ROULETTE_WHEEL

public static final int ROULETTE_WHEEL

Indicates a roulette wheel selection scheme.

See Also:

Constant Field Values

STOCHASTIC_UNIVERSAL

public static final int STOCHASTIC_UNIVERSAL

Indicates a stochastic universal sampling selection scheme.

See Also:

Constant Field Values

BOLTZMANN_SELECTION

public static final int BOLTZMANN_SELECTION

Indicates a Boltzmann selection scheme.

See Also:

Constant Field Values

RANK_SELECTION

public static final int RANK_SELECTION

Indicates a rank based selection scheme.

See Also:

Constant Field Values

TOURNAMENT_SELECTION

public static final int TOURNAMENT_SELECTION

Indicates a tournament selection scheme.

See Also:

Constant Field Values

NON_DOMINATED_SORTING_SELECTION

public static final int NON_DOMINATED_SORTING_SELECTION

Indicates a multi-objective selection scheme (NSGA II).

See Also:

Constant Field Values

MUTATION_TYPES

public static final java.lang.String[] MUTATION_TYPES

The names of the mutation types.

NO_MUTATION

public static final int NO_MUTATION

Indicates no mutation.

See Also:

Constant Field Values

GAUSSIAN_MUTATION

public static final int GAUSSIAN_MUTATION

Indicates a gaussian mutation.

See Also:

Constant Field Values

SWITCHING_MUTATION

public static final int SWITCHING_MUTATION

Indicates a switching mutation.

See Also:

Constant Field Values

SPARSITY_MUTATION

public static final int SPARSITY_MUTATION

Indicates a hybrid between switching mutation and Gaussian mutation.

See Also:

Constant Field Values

POPULATION_INIT_TYPES

public static final java.lang.String[] POPULATION_INIT_TYPES

The names of the initialization types.

INIT_TYPE_RANDOM

public static final int INIT_TYPE_RANDOM

Indicates that the start population should be randomly initialized.

See Also:

Constant Field Values

INIT_TYPE_MIN

public static final int INIT_TYPE_MIN

Indicates that the start population should be initialized with the minimum value.

See Also:

Constant Field Values

INIT_TYPE_MAX

public static final int INIT_TYPE_MAX

Indicates that the start population should be initialized with the maximum value.

See Also:

Constant Field Values

INIT_TYPE_ONE

public static final int INIT_TYPE_ONE

Indicates that the start population should be initialized with one.

See Also:

Constant Field Values

INIT_TYPE_ZERO

public static final int INIT_TYPE_ZERO

Indicates that the start population should be initialized with zero.

See Also:

Constant Field Values

Constructor Detail

ESOptimization

public ESOptimization(double minValue,
                      double maxValue,
                      int populationSize,
                      int individualSize,
                      int initType,
                      int maxGenerations,
                      int generationsWithoutImprovement,
                      int selectionType,
                      double tournamentFraction,
                      boolean keepBest,
                      int mutationType,
                      double crossoverProb,
                      boolean showConvergencePlot,
                      boolean showPopulationPlot,
                      RandomGenerator random,
                      LoggingHandler logging)

Creates a new evolutionary SVM optimization.

ESOptimization

public ESOptimization(double[] minValues,
                      double[] maxValues,
                      int populationSize,
                      int individualSize,
                      int initType,
                      int maxGenerations,
                      int generationsWithoutImprovement,
                      int selectionType,
                      double tournamentFraction,
                      boolean keepBest,
                      int mutationType,
                      double defaultSigma,
                      double crossoverProb,
                      boolean showConvergencePlot,
                      boolean showPopulationPlot,
                      RandomGenerator random,
                      LoggingHandler logging)

Creates a new evolutionary SVM optimization.

Method Detail

evaluateIndividual

public abstract PerformanceVector evaluateIndividual(Individual individual)
                                              throws OperatorException

Subclasses must implement this method to calculate the fitness of the given individual. Please note that null might be returned for non-valid individuals. The fitness will be maximized.

Throws:

OperatorException

nextIteration

public void nextIteration()
                   throws OperatorException

This method is invoked after each evaluation. The default implementation does nothing but subclasses might implement this method to support online plotting or logging.

Throws:

OperatorException

getMin

public double getMin(int index)

getMax

public double getMax(int index)

setMin

public void setMin(int index,
                   double v)

setMax

public void setMax(int index,
                   double v)

getValueType

public OptimizationValueType getValueType(int index)

setValueType

public void setValueType(int index,
                         OptimizationValueType type)

optimize

public void optimize()
              throws OperatorException

Starts the optimization.

Specified by:

optimize in interface Optimization

Throws:

OperatorException

evaluate

protected void evaluate(Population population)
                 throws OperatorException

Evaluates the individuals of the given population.

Throws:

OperatorException

getGeneration

public int getGeneration()

Returns the current generation.

Specified by:

getGeneration in interface Optimization

getBestFitnessInGeneration

public double getBestFitnessInGeneration()

Returns the best fitness in the current generation.

Specified by:

getBestFitnessInGeneration in interface Optimization

getBestFitnessEver

public double getBestFitnessEver()

Returns the best fitness ever.

Specified by:

getBestFitnessEver in interface Optimization

getBestPerformanceEver

public PerformanceVector getBestPerformanceEver()

Returns the best performance vector ever.

Specified by:

getBestPerformanceEver in interface Optimization

getPopulation

public Population getPopulation()

getBestValuesEver

public double[] getBestValuesEver()

Returns the best values ever. Use this method after optimization to get the best result. Might returns null if the optimization did not work.

Specified by:

getBestValuesEver in interface Optimization

increaseCurrentEvaluationCounter

public void increaseCurrentEvaluationCounter()

Getter and setter for subclasses

increaseTotalEvaluationCounter

public void increaseTotalEvaluationCounter()