java.lang.Object
- adams.core.logging.LoggingObject
- - adams.core.logging.CustomLoggingLevelObject
  - - adams.core.option.AbstractOptionHandler
    - - adams.opt.optimise.AbstractOptimiser
      - adams.opt.optimise.genetic.AbstractGeneticAlgorithm

All Implemented Interfaces:

CleanUpHandler, Destroyable, GlobalInfoSupporter, LoggingLevelHandler, LoggingSupporter, OptionHandler, SizeOfHandler, Serializable, Comparable

Direct Known Subclasses:

PackDataGeneticAlgorithm
```
public abstract class AbstractGeneticAlgorithm
extends AbstractOptimiser
```
Base class for genetic algorithms.

Version:

$Revision$

Author:

Dale (dale at cs dot waikato dot ac dot nz)

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field	Description
`protected Range`	`m_BestRange`	stores the best range of attribtues.
`protected boolean`	`m_FavorZeroes`	whether to favor 0s instead of 1s.
`protected double[]`	`m_Fitness`	the fitness of the genes.
`protected BitSet[]`	`m_Genes`	the genes.
`protected int`	`m_Iterations`	number of iterations.
`protected int`	`m_MaxTrainTime`	the maximum number of seconds to train.
`protected int`	`m_NumChrom`	number of chromosomes.
`protected int`	`m_NumGenes`	number of genes per chromosome.
`protected Random`	`m_Random`	the random number generator.
`protected boolean`	`m_Running`	whether the algorithm is still running.
`protected int`	`m_Seed`	the seed value.
`protected long`	`m_TrainStart`	the time when training commenced.
`static String`	`PROPS_FILTER`	the key for a filter setup in the setup properties.
`static String`	`PROPS_MASK`	the key for the mask in the setup properties.
`static String`	`PROPS_RELATION`	the key for the relation name in the generated properties file.

Fields inherited from class adams.opt.optimise.AbstractOptimiser
m_bestf, m_bestv

Fields inherited from class adams.core.option.AbstractOptionHandler
m_OptionManager

Fields inherited from class adams.core.logging.LoggingObject
m_Logger, m_LoggingIsEnabled, m_LoggingLevel

Constructor Summary

Constructors
Constructor Description

AbstractGeneticAlgorithm()

Method Summary

All Methods Static Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method	Description
`String`	`bestRangeTipText()`	Returns the tip text for this property.
`abstract void`	`calcFitness()`	Override the following function in sub-classes.
`protected void`	`copyGene(int to, int from)`	Copies the values of one gene to another.
`void`	`defineOptions()`	Adds options to the internal list of options.
`void`	`doCrossovers()`	Performs cross-over.
`void`	`doMutations()`	Performs mutations.
`void`	`doMutations2()`	Performs mutations.
`String`	`favorZeroesTipText()`	Returns the tip text for this property.
`static AbstractGeneticAlgorithm`	`forCommandLine(String cmdline)`	Instantiates the genetic algorithm from the given commandline (i.e., classname and optional options).
`static AbstractGeneticAlgorithm`	`forName(String classname, String[] options)`	Instantiates the genetic algorithm with the given options.
`String`	`getBestRange()`	Returns the best range of attributes.
`boolean`	`getFavorZeroes()`	Returns whether 0s are favored over 1s.
`double[]`	`getFitness()`	Returns the current fitness values.
`boolean`	`getGene(int chromosome, int gene)`	Returns the value of the specified gene.
`static String[]`	`getGeneticAlgorithms()`	Returns a list with classnames of genetic algorithms.
`abstract Vector<int[]>`	`getInitialSetups()`
`int`	`getIterations()`	Gets the iterations use.
`int`	`getMaxTrainTime()`	Returns the maximum number of seconds to perform training.
`int`	`getNumChrom()`	Returns the number of chromosomes to use.
`int`	`getNumGenes()`	Returns the number of genes to use.
`int`	`getSeed()`	Returns the current seed value.
`boolean`	`hasMoreZeroes(BitSet a, BitSet b)`	return if a has more zeroes than b.
`protected void`	`init(int ch, int genes)`	Initializes the algorithm.
`protected void`	`initialize()`	Initializes the members.
`boolean`	`isRunning()`	Returns whether the algorithm is still running.
`String`	`iterationsTipText()`	Returns the tip text for this property.
`String`	`maxTrainTimeTipText()`	Returns the tip text for this property.
`String`	`numChromTipText()`	Returns the tip text for this property.
`protected void`	`postRun()`	Further clean-ups in derived classes.
`protected void`	`preRun()`	Further initializations in derived classes.
`void`	`reset()`	Resets the genetic algorihtm.
`boolean`	`run()`	Runs the genetic algorithm.
`static void`	`runGeneticAlgorithm(Class env, Class genetic, String[] options)`	Runs the genetic algorithm with the given options.
`String`	`seedTipText()`	Returns the tip text for this property.
`protected void`	`setBestRange(Range value)`	Sets the best range of attributes.
`void`	`setBestRange(String value)`	Sets the best range of attributes.
`void`	`setFavorZeroes(boolean value)`	Sets whether 0s are favored over 1s.
`void`	`setGene(int chromosome, int gene, boolean value)`	Sets the value of the specified gene.
`void`	`setGene(int chromosome, int gene, int value)`	Sets the value of the specified gene.
`void`	`setIterations(int value)`	Sets the iterations to use.
`void`	`setMaxTrainTime(int value)`	Sets the maximum number of seconds to perform training.
`void`	`setNumChrom(int value)`	Sets the number of chromosomes to use.
`void`	`setSeed(int value)`	Sets the seed value to use, resets the random number generator.
`void`	`sort()`	Sorts genes and fitness arrays according to fitness.
`void`	`stop()`	Stops the execution of the algorithm.
`protected Properties`	`storeSetup(weka.core.Instances data)`	Generates a Properties file that stores information on the setup of the genetic algorithm.
`protected weka.core.Instances`	`updateHeader(weka.core.Instances data)`	Creates a new dataset, with the setup as the new relation name.

Methods inherited from class adams.opt.optimise.AbstractOptimiser
checkBest, cleanUp, compareTo, destroy, equals, getOptimisers, optimise, shallowCopy, shallowCopy

Methods inherited from class adams.core.option.AbstractOptionHandler
cleanUpOptions, finishInit, getDefaultLoggingLevel, getOptionManager, globalInfo, loggingLevelTipText, newOptionManager, setLoggingLevel, toCommandLine, toString

Methods inherited from class adams.core.logging.LoggingObject
configureLogger, getLogger, getLoggingLevel, initializeLogging, isLoggingEnabled, sizeOf

Methods inherited from class java.lang.Object
clone, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface adams.core.logging.LoggingLevelHandler
getLoggingLevel

- Field Detail
  - PROPS_RELATION
```
public static final String PROPS_RELATION
```
    the key for the relation name in the generated properties file.
    
    See Also:
    
    Constant Field Values
  - PROPS_FILTER
```
public static final String PROPS_FILTER
```
    the key for a filter setup in the setup properties.
    
    See Also:
    
    Constant Field Values
  - PROPS_MASK
```
public static final String PROPS_MASK
```
    the key for the mask in the setup properties.
    
    See Also:
    
    Constant Field Values
  - m_NumGenes
```
protected int m_NumGenes
```
    number of genes per chromosome. NB: must be initialized by the algorithm!
  - m_NumChrom
```
protected int m_NumChrom
```
    number of chromosomes.
  - m_Genes
```
protected BitSet[] m_Genes
```
    the genes.
  - m_Fitness
```
protected double[] m_Fitness
```
    the fitness of the genes.
  - m_BestRange
```
protected Range m_BestRange
```
    stores the best range of attribtues.
  - m_FavorZeroes
```
protected boolean m_FavorZeroes
```
    whether to favor 0s instead of 1s.
  - m_Seed
```
protected int m_Seed
```
    the seed value.
  - m_Random
```
protected Random m_Random
```
    the random number generator.
  - m_Running
```
protected boolean m_Running
```
    whether the algorithm is still running.
  - m_MaxTrainTime
```
protected int m_MaxTrainTime
```
    the maximum number of seconds to train.
  - m_TrainStart
```
protected long m_TrainStart
```
    the time when training commenced.
  - m_Iterations
```
protected int m_Iterations
```
    number of iterations.
- Constructor Detail
  - AbstractGeneticAlgorithm
```
public AbstractGeneticAlgorithm()
```
- Method Detail
  - getInitialSetups
```
public abstract Vector<int[]> getInitialSetups()
```
  - initialize
```
protected void initialize()
```
    Initializes the members.
    
    Overrides:
    
    initialize in class AbstractOptimiser
  - reset
```
public void reset()
```
    Resets the genetic algorihtm. Derived classes must call this method in set-methods of parameters to assure the invalidation of previously generated data.
    
    Overrides:
    
    reset in class AbstractOptimiser
  - defineOptions
```
public void defineOptions()
```
    Adds options to the internal list of options.
    
    Specified by:
    
    defineOptions in interface OptionHandler
    
    Overrides:
    
    defineOptions in class AbstractOptionHandler
  - setBestRange
```
protected void setBestRange(Range value)
```
    Sets the best range of attributes.
    
    Parameters:
    
    value - the range
  - setBestRange
```
public void setBestRange(String value)
```
    Sets the best range of attributes.
    
    Parameters:
    
    value - the range
  - getBestRange
```
public String getBestRange()
```
    Returns the best range of attributes.
    
    Returns:
    
    the range
  - bestRangeTipText
```
public String bestRangeTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setSeed
```
public void setSeed(int value)
```
    Sets the seed value to use, resets the random number generator.
    
    Parameters:
    
    value - the seed to use
  - getSeed
```
public int getSeed()
```
    Returns the current seed value.
    
    Returns:
    
    the seed value
  - seedTipText
```
public String seedTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setFavorZeroes
```
public void setFavorZeroes(boolean value)
```
    Sets whether 0s are favored over 1s.
    
    Parameters:
    
    value - if true then 0s are favored over 1s
  - getFavorZeroes
```
public boolean getFavorZeroes()
```
    Returns whether 0s are favored over 1s.
    
    Returns:
    
    true if 0s are favored over 1s
  - favorZeroesTipText
```
public String favorZeroesTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - getNumGenes
```
public int getNumGenes()
```
    Returns the number of genes to use.
    
    Returns:
    
    the number
  - setNumChrom
```
public void setNumChrom(int value)
```
    Sets the number of chromosomes to use.
    
    Parameters:
    
    value - the number
  - getNumChrom
```
public int getNumChrom()
```
    Returns the number of chromosomes to use.
    
    Returns:
    
    the number
  - numChromTipText
```
public String numChromTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - setMaxTrainTime
```
public void setMaxTrainTime(int value)
```
    Sets the maximum number of seconds to perform training.
    
    Parameters:
    
    value - the number of seconds
  - getMaxTrainTime
```
public int getMaxTrainTime()
```
    Returns the maximum number of seconds to perform training.
    
    Returns:
    
    the seed value
  - maxTrainTimeTipText
```
public String maxTrainTimeTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - stop
```
public void stop()
```
    Stops the execution of the algorithm.
  - isRunning
```
public boolean isRunning()
```
    Returns whether the algorithm is still running.
    
    Returns:
    
    true if the algorithm is still running
  - getFitness
```
public double[] getFitness()
```
    Returns the current fitness values.
    
    Returns:
    
    the fitness values
  - setIterations
```
public void setIterations(int value)
```
    Sets the iterations to use.
    
    Parameters:
    
    value - the iterations.
  - getIterations
```
public int getIterations()
```
    Gets the iterations use.
    
    Returns:
    
    the iterations
  - iterationsTipText
```
public String iterationsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the GUI or for listing the options.
  - init
```
protected void init(int ch,
                    int genes)
```
    Initializes the algorithm.
    
    Parameters:
    
    ch - the chromosome
    
    genes - the number of genes
  - getGene
```
public boolean getGene(int chromosome,
                       int gene)
```
    Returns the value of the specified gene.
    
    Parameters:
    
    chromosome - the chromosome index
    
    gene - the gene index
    
    Returns:
    
    true if gene is active
  - setGene
```
public void setGene(int chromosome,
                    int gene,
                    int value)
```
    Sets the value of the specified gene.
    
    Parameters:
    
    chromosome - the chromosome index
    
    gene - the gene index
    
    value - the value to set, 0=clear, otherwise activate it
  - setGene
```
public void setGene(int chromosome,
                    int gene,
                    boolean value)
```
    Sets the value of the specified gene.
    
    Parameters:
    
    chromosome - the chromosome index
    
    gene - the gene index
    
    value - false=clear, true=activate it
  - hasMoreZeroes
```
public boolean hasMoreZeroes(BitSet a,
                             BitSet b)
```
    return if a has more zeroes than b.
    
    Parameters:
    
    a - the first gene
    
    b - the second gene
    
    Returns:
    
    true if a has more zeroes than b
  - sort
```
public void sort()
```
    Sorts genes and fitness arrays according to fitness.
  - doCrossovers
```
public void doCrossovers()
```
    Performs cross-over.
  - copyGene
```
protected void copyGene(int to,
                        int from)
```
    Copies the values of one gene to another.
    
    Parameters:
    
    to - the gene to copy to
    
    from - the gene to copy from
  - doMutations
```
public void doMutations()
```
    Performs mutations.
  - doMutations2
```
public void doMutations2()
```
    Performs mutations.
  - calcFitness
```
public abstract void calcFitness()
```
    Override the following function in sub-classes.
  - storeSetup
```
protected Properties storeSetup(weka.core.Instances data)
```
    Generates a Properties file that stores information on the setup of the genetic algorithm. E.g., it backs up the original relation name. The generated properties file will be used as new relation name for the data. Derived classes can add additional parameters to this properties file.
    
    Parameters:
    
    data - the data to create the setup for
    
    job - the associated job
    
    Returns:
    
    the generated setup
    
    See Also:
    
    PROPS_RELATION
  - updateHeader
```
protected weka.core.Instances updateHeader(weka.core.Instances data)
```
    Creates a new dataset, with the setup as the new relation name.
    
    Parameters:
    
    data - the data to replace the relation name with the setup
    
    job - the associated job
    
    Returns:
    
    the updated dataset
  - preRun
```
protected void preRun()
```
    Further initializations in derived classes.
  - run
```
public boolean run()
```
    Runs the genetic algorithm.
    
    Returns:
    
    true if successfully finished, false when interrupted
  - postRun
```
protected void postRun()
                throws Exception
```
    Further clean-ups in derived classes.
    
    Throws:
    
    Exception - if something goes wrong
  - runGeneticAlgorithm
```
public static void runGeneticAlgorithm(Class env,
                                       Class genetic,
                                       String[] options)
```
    Runs the genetic algorithm with the given options.
    
    Parameters:
    
    env - the environment class to use
    
    genetic - the genetic algorithm to run
    
    options - the options to set
  - getGeneticAlgorithms
```
public static String[] getGeneticAlgorithms()
```
    Returns a list with classnames of genetic algorithms.
    
    Returns:
    
    the genetic algorithm classnames
  - forName
```
public static AbstractGeneticAlgorithm forName(String classname,
                                               String[] options)
```
    Instantiates the genetic algorithm with the given options.
    
    Parameters:
    
    classname - the classname of the genetic algorithm to instantiate
    
    options - the options for the genetic algorithm
    
    Returns:
    
    the instantiated genetic algorithm or null if an error occurred
  - forCommandLine
```
public static AbstractGeneticAlgorithm forCommandLine(String cmdline)
```
    Instantiates the genetic algorithm from the given commandline (i.e., classname and optional options).
    
    Parameters:
    
    cmdline - the classname (and optional options) of the genetic algorithm to instantiate
    
    Returns:
    
    the instantiated genetic algorithm or null if an error occurred

Class AbstractGeneticAlgorithm

Field Summary

Fields inherited from class adams.opt.optimise.AbstractOptimiser

Fields inherited from class adams.core.option.AbstractOptionHandler

Fields inherited from class adams.core.logging.LoggingObject

Constructor Summary

Method Summary

Methods inherited from class adams.opt.optimise.AbstractOptimiser

Methods inherited from class adams.core.option.AbstractOptionHandler

Methods inherited from class adams.core.logging.LoggingObject

Methods inherited from class java.lang.Object

Methods inherited from interface adams.core.logging.LoggingLevelHandler

Field Detail

PROPS_RELATION

PROPS_FILTER

PROPS_MASK

m_NumGenes

m_NumChrom

m_Genes

m_Fitness

m_BestRange

m_FavorZeroes

m_Seed

m_Random

m_Running

m_MaxTrainTime

m_TrainStart

m_Iterations

Constructor Detail

AbstractGeneticAlgorithm

Method Detail

getInitialSetups

initialize

reset

defineOptions

setBestRange

setBestRange

getBestRange

bestRangeTipText

setSeed

getSeed

seedTipText

setFavorZeroes

getFavorZeroes

favorZeroesTipText

getNumGenes

setNumChrom

getNumChrom

numChromTipText

setMaxTrainTime

getMaxTrainTime

maxTrainTimeTipText

stop

isRunning

getFitness

setIterations

getIterations

iterationsTipText

init

getGene

setGene

setGene

hasMoreZeroes

sort

doCrossovers

copyGene

doMutations

doMutations2

calcFitness

storeSetup

updateHeader

preRun

run

postRun

runGeneticAlgorithm

getGeneticAlgorithms

forName

forCommandLine