Class to represent an Abstract Genetic Algorithm. Base skeleton is defined here, to extend in particular versions of GAs. This class extends AbstractEA. Use this class to create your own version of GAs. More...

#include <AbstractGA.h>

Inheritance diagram for AbstractGA< S >:

Public Member Functions
	AbstractGA ()
	Creates a new AbstractGA which initialises all parameter to default. The operators will be set to null. More...

virtual string	getName () const =0
	Returns the name of the algorithm, this is used in the to_json method. Must be implemented in the subclasses. More...

json	to_json () const override
	Creates and returns JSON object with the GA information. More...

virtual Front< S >	getResults () const
	Returns a front with the feasible non repeated individuals in the last population of the Genetic Algorithm. This is the final solution of the algorithm to the problem at hand. More...

void	run () override
	Runs the evolutionary process. This is the main EA method. This methods is in charge of: More...

void	setProblem (shared_ptr< Problem< S >> prob) override
	Uptades the problem to solve using a share_ptr. The problem must be single-objective. More...

void	setProblem (Problem< S > *prob) override
	Uptades the problem to solve using a raw pointer. The problem must be single-objective. More...

double	getMutationRate () const
	Gets the mutation rate. More...

void	setMutationRate (double mutationRate)
	Updates the mutation rate. More...

double	getCrossRate () const
	Gets the crossover rate. More...

void	setCrossRate (double crossRate)
	Updates the crossover rate. More...

const Mutation< S > *	getMutation () const
	Gets a pointer to the mutation operator. More...

void	setMutation (unique_ptr< Mutation< S >> mutation)
	Updates the mutation operator. Takes a unique_ptr pointing to the new Mutation operator and takes the ownership. More...

const Crossover< S > *	getCrossover () const
	Returns a raw pointer to the crossover operator. More...

void	setCrossover (unique_ptr< Crossover< S >> crossover)
	Updates the crossover operator. Takes a unique_ptr pointing to the new Crossover operator and takes the ownership. More...

const Selection< S > *	getSelection () const
	Returns a raw pointer of the selection operator. More...

void	setSelection (unique_ptr< Selection< S >> selectionOperator)
	Updates the selection operator. Takes a unique_ptr pointing to the new Selection operator and takes the ownership. More...

Public Member Functions inherited from AbstractEA< S >
	AbstractEA ()
	Construct a new Abstract EA object with default parameter values. More...

	AbstractEA (const int &maxEvals, const int &popsize)
	AbstractEA constructor which initialises the parameters maxEvalautions and populationSize. More...

	AbstractEA (unique_ptr< PopulationEvaluator< S >>, const int &maxEvals, const int &popsize)
	AbstractEA constructor which initialises the parameters maxEvalautions, the populationSize and the evaluator approach. More...

virtual string	getID () const =0
	Returns the identificator of the algorithm, this is used in the to_json method. Must be implemented in the subclasses. It should return the acronym of useful id for a particular configuration. More...

int	getPopulationSize () const
	Get the population size. More...

void	setPopulationSize (int pSize)
	Setter to update the population size. More...

double	getElapsedTime () const
	Get the elapsed time of the evolutionary process. More...

const vector< S > &	getPopulation () const
	Gets a reference of the population. More...

int	getMaxEvaluations () const
	Get the maximum number of evaluations to perform. More...

void	setMaxEvaluations (int maxEval)
	Set a new maximum number of evaluations to perform. More...

const Problem< S > *	getProblem () const
	Gets a pointer to the problem which is being solved. More...

PopulationEvaluator< S > *	getEvaluator () const
	Gets a pointer to the population evaluator system. More...

void	setEvaluator (unique_ptr< PopulationEvaluator< S >> eval)
	Updates the population evaluator system with the unique_ptr. The method takes the ownership of the object behind. More...

int	getPerformedEvaluations () const
	Get the performed evaluations. More...

void	setPerformedEvaluations (int pEvals)
	Set the Performed Evaluations. Useful for the updateProgress method. More...

void	setPopulation (const vector< S > &pop)
	Set the Population. More...

virtual Evolution< S >	getEvolution () const
	Get the Evolution data. More...

int	getPrintingInterval () const
	Get the interval of checkpoints. More...

Static Public Attributes
static double	DEFAULT_MUTATION_RATE = 0.05
	Default mutation rate for GAs set to 0.05. More...

static double	DEFAULT_CROSSOVER_RATE = 0.8
	Default crossover rate to GAs set to 0.8. More...

static int	DEFAULT_POPULATION_SIZE = 32
	Default population size to GAs set to 32 individuals. More...

Static Public Attributes inherited from AbstractEA< S >
static const int	DEFAULT_POPULATION_SIZE = 100
	Default population size equal to 100 individuals. More...

static const int	DEFAULT_EVALUATIONS_LIMIT = 100000
	Default evaluation limit equal to 100000 evaluations. More...

static const std::string	NAME = "Algorithm"

static const std::string	MAX_EVALUATIONS = "Max Evaluations"

static const std::string	POP_SIZE = "Population Size"

static const std::string	ELAPSED_TIME = "Elapsed Time"

static const std::string	EVALUATOR = "Evaluator"

static const int	EVOLUTION_SIZE = 10

Protected Member Functions
virtual void	initProgress ()
	Starts the evolutionary process. By default this methods sets the number of performed evaluations to the population size since it considers that all individuals have been evaluated in the createInitialPopulation method. More...

virtual void	updateProgress ()=0
	Method which updates the evolutionary progress. This method must be implemented in the subclasses and it should perform thinks like updating the number of performed evaluations, etc.

virtual void	finishProgress ()
	Finishes the evolutionary process by computing the elapsed time. More...

virtual bool	isStoppingConditionReached ()
	Checks whether the evolutionary process has reached the maximum limit. More...

virtual void	createInitialPopulation ()
	Creates the initial population of individuals before starting the evolutionary process. More...

virtual void	evaluatePopulation (vector< S > &pop)
	Evaluates the entire population of individuals using the problem evaluation function. More...

virtual vector< S >	createMating ()
	Generates the mating population of individuals to be evaluated. The individuals are selected and after that the genetic operators are applied here. More...

virtual void	reproduction (S &, S &)
	Applies the genetic operator. This methods is invoked from createMating. More...

Protected Member Functions inherited from AbstractEA< S >
virtual void	updateEvolution (vector< S > &pop)

virtual void	updateEvolution (const int &checkpoint, vector< S > &)

Protected Attributes
double	mutationRate

double	crossRate

unique_ptr< Mutation< S > >	mutation

unique_ptr< Crossover< S > >	crossover

unique_ptr< Selection< S > >	selection

unique_ptr< Replacement< S > >	replacement

Protected Attributes inherited from AbstractEA< S >
int	maxEvaluations

int	performedEvaluations

int	populationSize

vector< S >	population

shared_ptr< Problem< S > >	problem

unique_ptr< PopulationEvaluator< S > >	evaluator

Evolution< S >	evolution

AvgEvolution< S >	avgEvolution

std::chrono::system_clock::time_point	startTime

std::chrono::system_clock::time_point	endTime

double	elapsedTime

int	nextCheckpoint

int	evolutionInterval

Detailed Description

template<class S>
class AbstractGA< S >

Class to represent an Abstract Genetic Algorithm. Base skeleton is defined here, to extend in particular versions of GAs. This class extends AbstractEA. Use this class to create your own version of GAs.

Template Parameters

S	Type of individual in the population.

Constructor & Destructor Documentation

◆ AbstractGA()

template<class S >

AbstractGA< S >::AbstractGA

Creates a new AbstractGA which initialises all parameter to default. The operators will be set to null.

Template Parameters

S

Member Function Documentation

◆ createInitialPopulation()

template<class S >

void AbstractGA< S >::createInitialPopulation

protectedvirtual

Creates the initial population of individuals before starting the evolutionary process.

Template Parameters

S

Implements AbstractEA< S >.

Reimplemented in NSGA2< S >, and ParallelGeneticAlgorithm< S >.

◆ createMating()

template<class S >

vector< S > AbstractGA< S >::createMating

protectedvirtual

Generates the mating population of individuals to be evaluated. The individuals are selected and after that the genetic operators are applied here.

Template Parameters

S

Returns: vector

Reimplemented in SteadyGA< S >, and NSGA2< S >.

◆ evaluatePopulation()

template<class S >

void AbstractGA< S >::evaluatePopulation ( vector< S > & pop )

protectedvirtual

Evaluates the entire population of individuals using the problem evaluation function.

Template Parameters

S

Parameters

pop

Implements AbstractEA< S >.

Reimplemented in NSGA2< S >.

◆ finishProgress()

template<class S >

void AbstractGA< S >::finishProgress

protectedvirtual

Finishes the evolutionary process by computing the elapsed time.

Template Parameters

S

Implements AbstractEA< S >.

◆ getCrossover()

template<class S >

const Crossover< S > * AbstractGA< S >::getCrossover

Returns a raw pointer to the crossover operator.

Template Parameters

S

Returns: const Crossover*

◆ getCrossRate()

template<class S >

double AbstractGA< S >::getCrossRate

Gets the crossover rate.

Template Parameters

S

Returns: double

◆ getMutation()

template<class S >

const Mutation< S > * AbstractGA< S >::getMutation

Gets a pointer to the mutation operator.

Template Parameters

S

Returns: const Mutation*

◆ getMutationRate()

template<class S >

double AbstractGA< S >::getMutationRate

Gets the mutation rate.

Template Parameters

S

Returns: double

◆ getName()

template<class S >

virtual string AbstractGA< S >::getName ( ) const

pure virtual

Returns the name of the algorithm, this is used in the to_json method. Must be implemented in the subclasses.

Returns: string

Implements AbstractEA< S >.

Implemented in SteadyGA< S >, ParallelGeneticAlgorithm< S >, GenerationalGA< S >, FIGA< S >, and NSGA2< S >.

◆ getResults()

template<class S >

Front< S > AbstractGA< S >::getResults

virtual

Returns a front with the feasible non repeated individuals in the last population of the Genetic Algorithm. This is the final solution of the algorithm to the problem at hand.

Template Parameters

S

Returns: Front

Implements AbstractEA< S >.

Reimplemented in NSGA2< S >.

◆ getSelection()

template<class S >

const Selection< S > * AbstractGA< S >::getSelection

Returns a raw pointer of the selection operator.

Template Parameters

S

Returns: const Selection*

◆ initProgress()

template<class S >

void AbstractGA< S >::initProgress

protectedvirtual

Starts the evolutionary process. By default this methods sets the number of performed evaluations to the population size since it considers that all individuals have been evaluated in the createInitialPopulation method.

Template Parameters

S

Implements AbstractEA< S >.

Reimplemented in NSGA2< S >, and ParallelGeneticAlgorithm< S >.

◆ isStoppingConditionReached()

template<class S >

bool AbstractGA< S >::isStoppingConditionReached

protectedvirtual

Checks whether the evolutionary process has reached the maximum limit.

Template Parameters

S

Returns: true If the evolutionary has reached the maximum number of evaluations to perform; false otherwise

Implements AbstractEA< S >.

◆ reproduction()

template<class S >

void AbstractGA< S >::reproduction	(	S &	child1,
		S &	child2
	)

protectedvirtual

Applies the genetic operator. This methods is invoked from createMating.

Template Parameters

S

Parameters

child1
child2

Reimplemented in NSGA2< S >, and ParallelGeneticAlgorithm< S >.

◆ run()

template<class S >

void AbstractGA< S >::run

overridevirtual

Runs the evolutionary process. This is the main EA method. This methods is in charge of:

Creating the initial population.
While the stop condition is not met:
1. Mate the individuals.
2. Evaluate the population.
3. Perform the replacement.
4. Update the process.
5. Update the evolution metrics.

Template Parameters

S

Implements AbstractEA< S >.

◆ setCrossover()

template<class S >

void AbstractGA< S >::setCrossover ( unique_ptr< Crossover< S >> newCX )

Updates the crossover operator. Takes a unique_ptr pointing to the new Crossover operator and takes the ownership.

Template Parameters

S

Parameters

newCX

◆ setCrossRate()

template<class S >

void AbstractGA< S >::setCrossRate ( double crossRate )

Updates the crossover rate.

Template Parameters

S

Parameters

crossRate

◆ setMutation()

template<class S >

void AbstractGA< S >::setMutation ( unique_ptr< Mutation< S >> newMut )

Updates the mutation operator. Takes a unique_ptr pointing to the new Mutation operator and takes the ownership.

Template Parameters

S

Parameters

newMut

◆ setMutationRate()

template<class S >

void AbstractGA< S >::setMutationRate ( double mutationRate )

Updates the mutation rate.

Template Parameters

S

Parameters

mutationRate

◆ setProblem() [1/2]

template<class S >

void AbstractGA< S >::setProblem ( Problem< S > * prob )

overridevirtual

Uptades the problem to solve using a raw pointer. The problem must be single-objective.

Template Parameters

S

Parameters

prob

Reimplemented from AbstractEA< S >.

◆ setProblem() [2/2]

template<class S >

void AbstractGA< S >::setProblem ( shared_ptr< Problem< S >> prob )

overridevirtual

Uptades the problem to solve using a share_ptr. The problem must be single-objective.

Template Parameters

S

Parameters

prob

Reimplemented from AbstractEA< S >.

◆ setSelection()

template<class S >

void AbstractGA< S >::setSelection ( unique_ptr< Selection< S >> newSelection )

Updates the selection operator. Takes a unique_ptr pointing to the new Selection operator and takes the ownership.

Template Parameters

S

Parameters

newSelection

◆ to_json()

template<class S >

json AbstractGA< S >::to_json

overridevirtual

Creates and returns JSON object with the GA information.

Template Parameters

S

Returns: a json representation of the GA

Reimplemented from AbstractEA< S >.

Member Data Documentation

◆ crossover

template<class S >

unique_ptr<Crossover<S> > AbstractGA< S >::crossover

protected

Crossover operator

◆ crossRate

template<class S >

double AbstractGA< S >::crossRate

protected

Crossover rate to apply the crossover operator

◆ DEFAULT_CROSSOVER_RATE

template<class S >

double AbstractGA< S >::DEFAULT_CROSSOVER_RATE = 0.8

static

Default crossover rate to GAs set to 0.8.

Template Parameters

S

◆ DEFAULT_MUTATION_RATE

template<class S >

double AbstractGA< S >::DEFAULT_MUTATION_RATE = 0.05

static

Default mutation rate for GAs set to 0.05.

Template Parameters

S

◆ DEFAULT_POPULATION_SIZE

template<class S >

int AbstractGA< S >::DEFAULT_POPULATION_SIZE = 32

static

Default population size to GAs set to 32 individuals.

Template Parameters

S

◆ mutation

template<class S >

unique_ptr<Mutation<S> > AbstractGA< S >::mutation

protected

Mutation operator

◆ mutationRate

template<class S >

double AbstractGA< S >::mutationRate

protected

Mutation rate to apply the mutation operator

◆ replacement

template<class S >

unique_ptr<Replacement<S> > AbstractGA< S >::replacement

protected

Replacement operator

◆ selection

template<class S >

unique_ptr<Selection<S> > AbstractGA< S >::selection

protected

Selection operator

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

include/dignea/core/AbstractGA.h

Public Member Functions

Static Public Attributes

Protected Member Functions

Protected Attributes

Detailed Description

template<class S> class AbstractGA< S >

Constructor & Destructor Documentation

◆ AbstractGA()

Member Function Documentation

◆ createInitialPopulation()

◆ createMating()

◆ evaluatePopulation()

◆ finishProgress()

◆ getCrossover()

◆ getCrossRate()

◆ getMutation()

◆ getMutationRate()

◆ getName()

◆ getResults()

◆ getSelection()

◆ initProgress()

◆ isStoppingConditionReached()

◆ reproduction()

◆ run()

◆ setCrossover()

◆ setCrossRate()

◆ setMutation()

◆ setMutationRate()

◆ setProblem() [1/2]

◆ setProblem() [2/2]

◆ setSelection()

◆ to_json()

Member Data Documentation

◆ crossover

◆ crossRate

◆ DEFAULT_CROSSOVER_RATE

◆ DEFAULT_MUTATION_RATE

◆ DEFAULT_POPULATION_SIZE

◆ mutation

◆ mutationRate

◆ replacement

◆ selection

template<class S>
class AbstractGA< S >