SingletonGenAlgAPI Class Reference

This is a facade for the gen. More...

#include <SingletonGenAlgAPI.h>

Collaboration diagram for SingletonGenAlgAPI:

[legend]List of all members.

Public Member Functions
void	select (bool createNextGeneration=true)
	starts the selection
void	crossover (RandGen *random)
	create the children from to fill up the next generation
void	update (double factor=1.5)
	update the internal statistical data
void	prepare (int startSize, int numChildren, RandGen *random, bool withUpdate=true)
	prepares the first generation and optional the enabled measure
void	prepare ()
	prepares the next generation and optional the enabled measure
void	measureStep (double time)
	makes a step in the measure
void	runGenAlg (int startSize, int numChildren, int numGeneration, RandGen *random)
	start the sequenz of select, crossover, update in a automatically loop
void	enableMeasure (std::list< PlotOption > &plotOptions)
	enables data measure with more than one plotOption.
void	enableMeasure (PlotOption &plotOption)
	enables da : public Storableta measure.
PlotOptionEngine *	getPlotOptionEngine (void) const
	returns the active plotOptionEngine for data measure.
void	enableGenContextMeasure (std::list< PlotOption > &plotOptions)
	enable data measure inside the GenContexts with more than one plotOption
void	enableGenContextMeasure (PlotOption &plotOption)
	enable data measure inside the GenContexts.
PlotOptionEngine *	getPlotOptionEngineForGenContext (void) const
	returns the active plotOptionEngine for data measure inside the GenContexts.
void	setGenerationSizeStrategy (IGenerationSizeStrategy *strategy)
	set the generation size strategy
void	setFitnessStrategy (IFitnessStrategy *strategy)
	set the fitness strategy
void	setSelectStrategy (ISelectStrategy *strategy)
	set the select strategy
SingletonGenEngine *	getEngine (void) const
	returns the GenAlgEngine
IFitnessStrategy *	createSumFitnessStrategy () const
	creates a SumFitnessStrategy.
IFitnessStrategy *	createEuclidicDistanceFitnessStrategy () const
	creates a EuclidicFitnessStrategy.
IFitnessStrategy *	createExtreamTestFitnessStrategy (IFitnessStrategy *fitness) const
	creates a TestFitnessStrategy which is the hardest test for a gen.
IFitnessStrategy *	createTestFitnessStrategy () const
	creates the test function fitness strategy from the papper tp this alg.
IFitnessStrategy *	createInvertedFitnessStrategy (IFitnessStrategy *strategy) const
	returns a fitness strategy which calculate the inverse value from a other strategy.
IRandomStrategy *	createDoubleRandomStrategy (RandGen *random, double base=0.0, double factor=1.0, double epsilon=0.0) const
	creates a random strategy for double values.
IMutationStrategy *	createValueMutationStrategy (IMutationFactorStrategy *strategy, int mutationProbability) const
	creates mutation strategy which change the old values by add a other value
IMutationFactorStrategy *	createFixMutationFactorStrategy (IValue *value) const
	creates a mutation factor strategy with a fix value
IMutationFactorStrategy *	createStandartMutationFactorStrategy (void) const
	creates a mutation factor strategy with a optimized value (varianz about all existing gens)
IGenerationSizeStrategy *	createFixGenerationSizeStrategy (int value) const
	creates a generation size strategy with a fix value
IGenerationSizeStrategy *	createStandartGenerationSizeStrategy (int startSize, int numGeneration) const
	creates a generation size strategy with a optimized size for the generation (changing by speed of development)
ISelectStrategy *	createEliteSelectStrategy (void) const
	creates a elite select strategy
ISelectStrategy *	createTournamentSelectStrategy (RandGen *random) const
	creates a tournament SelctStrategy.
ISelectStrategy *	createRandomSelectStrategy (RandGen *random) const
	creates a random select strategy.
IValue *	createDoubleValue (double value) const
	creates a IValue (TemplateValue<double) from type double.
bool	storeGA (FILE *f) const
	stores the object to the given file stream (binary).
bool	restoreGA (FILE *f) const
	loads the object from the given file stream (binary).
void	insertGenPrototype (GenPrototype *prototype)
	add a GenPrototype to the alg.
GenPrototype *	createPrototype (std::string name, IRandomStrategy *randomStrategy, IMutationStrategy *mutationStrategy) const
	create a prototype.
Individual *	getBestIndividual (void) const
	returns the best individual which the alg.
Static Public Member Functions
static SingletonGenAlgAPI *	getInstance (void)
	returns the one and only API to the alg.
static void	destroyAPI (bool cleanStrategies=false)
	destroy the api
Protected Attributes
PlotOptionEngine *	m_plotEngine
	plot option engine for data measure
PlotOptionEngine *	m_plotEngineGenContext
	plot option engine for gen contexts
Static Protected Attributes
static SingletonGenAlgAPI *	m_api = 0
	the api

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Detailed Description

This is a facade for the gen.

alg.

Over this is the class as singleton concepted. Only one API for a run.

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Member Function Documentation

IRandomStrategy * createDoubleRandomStrategy	(	RandGen *	random,
		double	base = 0.0,
		double	factor = 1.0,
		double	epsilon = 0.0
	)			const

creates a random strategy for double values.

The values will be generated in the intervals [base-epsilon:-epsilon] or [epsilon:factor+base+epsilon]

This means a random value in the interval [0:1] will be mul. with factor => [0:factor] After this it will be shifted by base => [base:factor+base] And than divided at the point zero and moved away by epsilon from the point zero => [base-epsilon:-epsilon], [epsilon:factor+base+epsilon]

For example: We want values in the intervals [-10:-5] and [5:10]. ==> epsilon=5 ==> [-5:5] ==> base=-5 ==> [0:10] ==> factor=10

Parameters:

	random	(RandGen*) random generator
	base	(double) normal=0.0 moves the ground interval
	factor	(double) normal=1.0 resize the interval
	epsilon	(double) normal=0.0 devided the interval at the point zero and move the values by epsilon away from point zero

Returns:

(IRandomStrategy*) the strategy

IValue * createDoubleValue

(

double

value

)

const

creates a IValue (TemplateValue<double) from type double.

Parameters:

value

(double) the value

Returns:

(IValue*) the new object

ISelectStrategy * createEliteSelectStrategy

(

void

)

const

creates a elite select strategy

Returns:

(ISelectStrategy*) the strategy

IFitnessStrategy * createEuclidicDistanceFitnessStrategy

(

)

const

creates a EuclidicFitnessStrategy.

This strategy calculate the euclidic distance of all gens with a double value.

Returns:

(IFitnessStrategy*) the strategy

IFitnessStrategy * createExtreamTestFitnessStrategy

(

IFitnessStrategy *

fitness

)

const

creates a TestFitnessStrategy which is the hardest test for a gen.

alg. Only a smale area has a fitness. All other gives no information!

Parameters:

fitness

(IFitnessStrategy*) a other fitness strat* Over this is the class as singleton concepted. Only one engine for a run.egy which define the smal area.

Returns:

(IFitnessStrategy*) the strategy

IGenerationSizeStrategy * createFixGenerationSizeStrategy

(

int

value

)

const

creates a generation size strategy with a fix value

Parameters:

value

(int) the size of the generation

Returns:

(IGenerationSizeStrategy*) the strategy

IMutationFactorStrategy * createFixMutationFactorStrategy

(

IValue *

value

)

const

creates a mutation factor strategy with a fix value

Parameters:

value

(IValue*) the fix value.

Returns:

(IMutationFactorStrategy*) the strategy

IFitnessStrategy * createInvertedFitnessStrategy

(

IFitnessStrategy *

strategy

)

const

returns a fitness strategy which calculate the inverse value from a other strategy.

Parameters:

strategy

(IFitnessStrategy*) the other strategy

Returns:

(IFitnessStrategy*) the resulting strategy

GenPrototype * createPrototype	(	std::string	name,
		IRandomStrategy *	randomStrategy,
		IMutationStrategy *	mutationStrategy
	)			const

create a prototype.

Parameters:

	name	(STRING) THE NAME OF THE PROTOTYPE
	randomStrategy	(IRandomStrategy*) the random strategy to generate values of the type which the prototype represent
	mutationStrategy	(IMutationStrategy*) the mutation strategy to change a value (Gen)

Returns:

(GenPrototype*) the prototype

ISelectStrategy * createRandomSelectStrategy

(

RandGen *

random

)

const

creates a random select strategy.

By this strategy will a random selected individual fight again a random number. If the random number higher than the fitness from the individual, than it will be die.

Parameters:

random

(RandGen*) ranom generator

Returns:

(ISelectStrategy*) the strategy

IGenerationSizeStrategy * createStandartGenerationSizeStrategy	(	int	startSize,
		int	numGeneration
	)			const

creates a generation size strategy with a optimized size for the generation (changing by speed of development)

Parameters:

	startSize	(int) the size at the start time
	numGeneration	(int) number of generation. (needed for the speed calculation

Returns:

(IGenerationSizeStrategy*) the strategy

IMutationFactorStrategy * createStandartMutationFactorStrategy

(

void

)

const

creates a mutation factor strategy with a optimized value (varianz about all existing gens)

Returns:

(IMutationFactorStrategy*) the strategy

IFitnessStrategy * createSumFitnessStrategy

(

)

const

creates a SumFitnessStrategy.

This strategy make the sum of all gens with a double value.

Returns:

(IFitnessStrategy*) the strategy

IFitnessStrategy * createTestFitnessStrategy

(

)

const

creates the test function fitness strategy from the papper tp this alg.

f(x,y) = 10.0 * (x² + 2.5y² - y) * exp(1 - (x² + y²)) + 2.4 + 0.1x² + 0.1y²

Returns:

(IFitnessStrategy*) the strategy

ISelectStrategy * createTournamentSelectStrategy

(

RandGen *

random

)

const

creates a tournament SelctStrategy.

By this strategy will two individual fight again (the individual with the higher fitness lives longer).

Parameters:

random

(RandGen*) random generator

Returns:

(ISelectStrategy*) the strategy

IMutationStrategy * createValueMutationStrategy	(	IMutationFactorStrategy *	strategy,
		int	mutationProbability
	)			const

creates mutation strategy which change the old values by add a other value

Parameters:

	strategy	(IMutationFactorStrategy*) this strategy define what the other value is.
	mutationProbability	(int) the probability of mutation in one per tausend (1/1000)

Returns:

(IMutationStrategy*) the strategy

void crossover

(

RandGen *

random

)

create the children from to fill up the next generation

Parameters:

random

(RandGen*) random generator

static void destroyAPI

(

bool

cleanStrategies = false

)

[inline, static]

destroy the api

Parameters:

cleanStrategies

(bool) default = false set a flag to clean the strategies, which are seted.

void enableGenContextMeasure

(

PlotOption &

plotOption

)

enable data measure inside the GenContexts.

Parameters:

plotOption

(PlotOption&) the plot option

void enableGenContextMeasure

(

std::list< PlotOption > &

plotOptions

)

enable data measure inside the GenContexts with more than one plotOption

Parameters:

plotOptions

(list<PlotOption>&) the list

void enableMeasure

(

PlotOption &

plotOption

)

enables da : public Storableta measure.

Parameters:

plotOption

(PlotOption&) the plot option

void enableMeasure

(

std::list< PlotOption > &

plotOptions

)

enables data measure with more than one plotOption.

Parameters:

plotOptions

(list<PöotOption>&) the list

Individual* getBestIndividual

(

void

)

const [inline]

returns the best individual which the alg.

have found

Returns:

(Individual*) the best

SingletonGenEngine * getEngine

(

void

)

const

returns the GenAlgEngine

Returns:

(SingletonGenEngine*) the engine

static SingletonGenAlgAPI* getInstance

(

void

)

[inline, static]

returns the one and only API to the alg.

Returns:

(SingletonGenAlgAPI*) the api

PlotOptionEngine* getPlotOptionEngine

(

void

)

const [inline]

returns the active plotOptionEngine for data measure.

Returns:

(PlotOptionEngine*) the plot option engine

PlotOptionEngine* getPlotOptionEngineForGenContext

(

void

)

const [inline]

returns the active plotOptionEngine for data measure inside the GenContexts.

Returns:

(PlotOptionEngine*) the plot option engine.

void insertGenPrototype

(

GenPrototype *

prototype

)

add a GenPrototype to the alg.

Parameters:

prototype

(GenPrototype*) the prototype

void measureStep

(

double

time

)

makes a step in the measure

Parameters:

time

(double) time stamp in the measure

void prepare

(

)

prepares the next generation and optional the enabled measure

void prepare	(	int	startSize,
		int	numChildren,
		RandGen *	random,
		bool	withUpdate = true
	)

prepares the first generation and optional the enabled measure

Parameters:

	startSize	(int) Number of individual at begin of the gen. alg.
	startChildren	(int) Number of individual which will be created by crossover
	random	(RandGen*) A random generator
	withUpdate	(bool) is needed for "generateFirstGeneration"

bool restoreGA

(

FILE *

f

)

const

loads the object from the given file stream (binary).

void runGenAlg	(	int	startSize,
		int	numChildren,
		int	numGeneration,
		RandGen *	random
	)

start the sequenz of select, crossover, update in a automatically loop

Parameters:

	startSize	(int) Number of individual at begin of the gen. alg.
	numChildren	(int) Number of individual which will be created by crossover
	numGeneration	(int) Number of generation which the alg. max. runs
	random	(RandGen*) random generator

void select

(

bool

createNextGeneration = true

)

starts the selection

Parameters:

createNextGeneration

(bool) normal=true should be the next generation be prepare?

void setFitnessStrategy

(

IFitnessStrategy *

strategy

)

[inline]

set the fitness strategy

Parameters:

strategy

(IFitnessStrategy*) the strategy

void setGenerationSizeStrategy

(

IGenerationSizeStrategy *

strategy

)

[inline]

set the generation size strategy

Parameters:

strategy

(IGenerationSizeStrategy*) the strategy

void setSelectStrategy

(

ISelectStrategy *

strategy

)

[inline]

set the select strategy

Parameters:

strategy

(ISelectStrategy*) the strategy

bool storeGA

(

FILE *

f

)

const

stores the object to the given file stream (binary).

void update

(

double

factor = 1.5

)

update the internal statistical data

Parameters:

factor

(double) normal=1.5 is needed for the whisker distance

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Member Data Documentation

SingletonGenAlgAPI * m_api = 0 [static, protected]

the api

PlotOptionEngine* m_plotEngine [protected]

plot option engine for data measure

PlotOptionEngine* m_plotEngineGenContext [protected]

plot option engine for gen contexts

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

• SingletonGenAlgAPI.h
• SingletonGenAlgAPI.cpp

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