Inheritance diagram for EvolutionaryAlgorithm< INDIVIDUAL >:

Public Member Functions
	EvolutionaryAlgorithm (const UINT populationSize=0, const UINT geneSize=0)

virtual	~EvolutionaryAlgorithm ()

INDIVIDUAL &	operator[] (const UINT &index)

virtual bool	initPopulation (const UINT populationSize, const UINT geneSize)

virtual bool	estimatePopulationFitness (const MatrixFloat &trainingData, Float &bestFitness, UINT &bestIndex)

virtual bool	evolvePopulation ()

virtual Float	evaluateFitness (INDIVIDUAL &individual, const MatrixFloat &trainingData)

virtual bool	train (const MatrixFloat &trainingData)

UINT	getPopulationSize () const

bool	getInitialized () const

Vector< INDIVIDUAL >	getPopulation () const

bool	setPopulationSize (const UINT populationSize)

bool	setMinNumIterationsNoChange (const UINT minNumIterationsNoChange)

bool	setMaxIterations (const UINT maxIteration)

bool	setStoreRate (const UINT storeRate)

bool	setStoreHistory (const bool storeHistory)

bool	setBaiseWeights (const bool baiseWeights)

bool	setBaiseCoeff (const Float baiseCoeff)

bool	setMutationRate (const Float mutationRate)

bool	setMinChange (const Float minChange)

virtual bool	setPopulation (const Vector< INDIVIDUAL > &newPopulation)

virtual Float	generateRandomGeneValue ()

virtual bool	customConvergenceCheck ()

virtual bool	printBest () const

Public Member Functions inherited from GRTBase
	GRTBase (void)

virtual	~GRTBase (void)

bool	copyGRTBaseVariables (const GRTBase *GRTBase)

std::string	getClassType () const

std::string	getLastWarningMessage () const

std::string	getLastErrorMessage () const

std::string	getLastInfoMessage () const

bool	setInfoLoggingEnabled (const bool loggingEnabled)

bool	setWarningLoggingEnabled (const bool loggingEnabled)

bool	setErrorLoggingEnabled (const bool loggingEnabled)

GRTBase *	getGRTBasePointer ()

const GRTBase *	getGRTBasePointer () const

Public Attributes
bool	initialized

bool	useElitism

bool	storeHistory

bool	baiseWeights

UINT	populationSize

UINT	geneSize

UINT	minNumIterationsNoChange

UINT	maxIteration

UINT	storeRate

UINT	bestIndividualIndex

Float	bestIndividualFitness

Float	mutationRate

Float	minChange

Float	baiseCoeff

Random	rand

Vector< INDIVIDUAL >	population

Vector< INDIVIDUAL >	parents

Vector< IndexedDouble >	populationWeights

Vector< Vector< INDIVIDUAL > >	populationHistory

Vector< IndexedDouble >	fitnessHistory

VectorFloat	accumSumLookup

Additional Inherited Members
Static Public Member Functions inherited from GRTBase
static std::string	getGRTVersion (bool returnRevision=true)

static std::string	getGRTRevison ()

Protected Member Functions inherited from GRTBase
Float	SQR (const Float &x) const

Protected Attributes inherited from GRTBase
std::string	classType

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

TrainingLog	trainingLog

TestingLog	testingLog

WarningLog	warningLog

Detailed Description

template<typename INDIVIDUAL>
class EvolutionaryAlgorithm< INDIVIDUAL >

Definition at line 41 of file EvolutionaryAlgorithm.h.

Constructor & Destructor Documentation

template<typename INDIVIDUAL >

EvolutionaryAlgorithm< INDIVIDUAL >::EvolutionaryAlgorithm	(	const UINT	populationSize = `0`,
		const UINT	geneSize = `0`
	)

inline

Default Constructor, if the populationSize and geneSize parameters are greater than zero then the algorithm will be initialized.

Parameters

populationSize	the number of individuals in the population. Default value = 0
geneSize	the number of elements in each individuals gene. Default value = 0

Definition at line 51 of file EvolutionaryAlgorithm.h.

template<typename INDIVIDUAL >

virtual EvolutionaryAlgorithm< INDIVIDUAL >::~EvolutionaryAlgorithm ( )

inlinevirtual

Default Destructor.

Definition at line 75 of file EvolutionaryAlgorithm.h.

Member Function Documentation

template<typename INDIVIDUAL >

virtual bool EvolutionaryAlgorithm< INDIVIDUAL >::estimatePopulationFitness	(	const MatrixFloat &	trainingData,
		Float &	bestFitness,
		UINT &	bestIndex
	)

inlinevirtual

This function estimates the populations fitness, based on the training data. It will return a reference to the bestFitness value and the index of the individual with the best fitness.

Parameters

trainingData	a reference to the trainingData that will be used to estimate the fitness
bestFitness	a reference that will return the best fitness value
bestIndex	a reference that will return the index of the individual with the best fitness

Returns: returns true if the population fitness was estimated, false otherwise

Definition at line 160 of file EvolutionaryAlgorithm.h.

template<typename INDIVIDUAL >

virtual Float EvolutionaryAlgorithm< INDIVIDUAL >::evaluateFitness	(	INDIVIDUAL &	individual,
		const MatrixFloat &	trainingData
	)

inlinevirtual

This function evaluates the fitness of an individual, using the training data. This function assumes that each row in the training data is an example, each column must therefore match the geneSize.

Parameters

individual	a reference to the individual you want to compute the fitness for
trainingData	a reference to the training data that will be used to compute the individual's fitness

Returns: returns the fitness of the individual

Definition at line 307 of file EvolutionaryAlgorithm.h.

template<typename INDIVIDUAL >

virtual bool EvolutionaryAlgorithm< INDIVIDUAL >::evolvePopulation ( )

inlinevirtual

This function evolves the current population, based on the fitness of each individual. You should compute the fitness of each individual first before using this function.

Returns: returns true if the population was evolved, false otherwise

Definition at line 196 of file EvolutionaryAlgorithm.h.

template<typename INDIVIDUAL >

virtual bool EvolutionaryAlgorithm< INDIVIDUAL >::initPopulation	(	const UINT	populationSize,
		const UINT	geneSize
	)

inlinevirtual

This function initalizes the population, setting each individual's genes to a random value in the range [0.0 1.0]. Both the populationSize and geneSize parameters must be greater than zero.

Parameters

populationSize	the number of individuals in the population
geneSize	the number of elements in each individuals gene

Returns: returns true if the population was initalized, false otherwise

Definition at line 98 of file EvolutionaryAlgorithm.h.

template<typename INDIVIDUAL >

INDIVIDUAL& EvolutionaryAlgorithm< INDIVIDUAL >::operator[] ( const UINT & index )

inline

This operator allows you to directly access each individual in the population. The user must ensure that index is a valid number in the range of [0 populationSize-1].

Parameters

index the index of the individual you want to access

Returns: returns a reference to the individual in the population at the specific index

Definition at line 86 of file EvolutionaryAlgorithm.h.

The documentation for this class was generated from the following file:

EvolutionaryAlgorithm.h

Public Member Functions

Public Attributes

Additional Inherited Members

Detailed Description

template<typename INDIVIDUAL> class EvolutionaryAlgorithm< INDIVIDUAL >

Constructor & Destructor Documentation

Member Function Documentation

template<typename INDIVIDUAL>
class EvolutionaryAlgorithm< INDIVIDUAL >