#include <NSGA2.h>

Inheritance diagram for NSGA2< S >:

Public Member Functions
	NSGA2 ()
	Creates a RAW instance of a Generational GA algorithm. More...

string	getName () const override
	Get the Name. More...

string	getID () const override
	Get the ID of the algorithm. 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 multi-objective. More...

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

virtual Front< S >	getResults () const
	Creates the front of solution from the evolution. More...

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

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

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...

Protected Member Functions
void	initProgress () override
	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...

void	updateProgress () override
	Updates the performed evaluations to the population size on each call. More...

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

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

virtual vector< S >	createMating () override
	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 &) override
	Applies the genetic operator. This methods is invoked from createMating. More...

Protected Member Functions inherited from AbstractGA< S >
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...

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

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

Additional Inherited Members
Static Public Attributes inherited from AbstractGA< S >
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 Attributes inherited from AbstractGA< S >
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 NSGA2< S >

@brief Class to represents a Non-dominated Sorting Genetic Algorithm II.

NSGA: Non-dominated Sorting GA (Srinivas & Deb, 1995)

-> N. Srinivas, K. Deb: Multiobjetive Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation 2 (1995) 221-248.

NSGA-II: Non-dominated Sorting Genetic Algorithm II

The algorithm was proposed by Deb in 2000. It is based on:

A fast non-dominated sorting approach, based on dominance fronts
A crowding operator
A selection operator which creates a mating pool by cominning the parent and child populations and selecting the best with respect to fitness and spread

Template Parameters

S	Type of individual in the population

Constructor & Destructor Documentation

◆ NSGA2()

template<class S >

NSGA2< S >::NSGA2

Creates a RAW instance of a Generational GA algorithm.

Template Parameters

Problem
S

Member Function Documentation

◆ createInitialPopulation()

template<class S >

void NSGA2< S >::createInitialPopulation

overrideprotectedvirtual

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

Template Parameters

S

Reimplemented from AbstractGA< S >.

◆ createMating()

template<class S >

vector< S > NSGA2< S >::createMating

overrideprotectedvirtual

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 from AbstractGA< S >.

◆ evaluatePopulation()

template<class S >

void NSGA2< S >::evaluatePopulation ( vector< S > & pop )

overrideprotectedvirtual

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

Template Parameters

S

Parameters

pop

Reimplemented from AbstractGA< S >.

◆ getID()

template<class S >

string NSGA2< S >::getID ( ) const

inlineoverridevirtual

Get the ID of the algorithm.

Returns: string

Implements AbstractEA< S >.

◆ getName()

template<class S >

string NSGA2< S >::getName ( ) const

inlineoverridevirtual

Get the Name.

Returns: string

Implements AbstractGA< S >.

◆ getResults()

template<class S >

Front< S > NSGA2< S >::getResults

virtual

Creates the front of solution from the evolution.

Template Parameters

S

Returns: Front

Reimplemented from AbstractGA< S >.

◆ initProgress()

template<class S >

void NSGA2< S >::initProgress

overrideprotectedvirtual

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

Reimplemented from AbstractGA< S >.

◆ reproduction()

template<class S >

void NSGA2< S >::reproduction	(	S &	child1,
		S &	child2
	)

overrideprotectedvirtual

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

Template Parameters

S

Parameters

child1
child2

Reimplemented from AbstractGA< S >.

◆ run()

template<class S >

void NSGA2< 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 >.

◆ setProblem() [1/2]

template<class S >

void NSGA2< S >::setProblem ( Problem< S > * prob )

overridevirtual

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

Template Parameters

S

Parameters

prob

Reimplemented from AbstractEA< S >.

◆ setProblem() [2/2]

template<class S >

void NSGA2< S >::setProblem ( shared_ptr< Problem< S >> prob )

overridevirtual

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

Template Parameters

S

Parameters

prob

Reimplemented from AbstractEA< S >.

◆ updateProgress()

template<class S >

void NSGA2< S >::updateProgress

overrideprotectedvirtual

Updates the performed evaluations to the population size on each call.

Template Parameters

Problem
S

Implements AbstractGA< S >.

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

include/dignea/algorithms/multiobjective/NSGA2.h

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

template<class S> class NSGA2< S >

@brief Class to represents a Non-dominated Sorting Genetic Algorithm II.

NSGA: Non-dominated Sorting GA (Srinivas & Deb, 1995)

NSGA-II: Non-dominated Sorting Genetic Algorithm II

Constructor & Destructor Documentation

◆ NSGA2()

Member Function Documentation

◆ createInitialPopulation()

◆ createMating()

◆ evaluatePopulation()

◆ getID()

◆ getName()

◆ getResults()

◆ initProgress()

◆ reproduction()

◆ run()

◆ setProblem() [1/2]

◆ setProblem() [2/2]

◆ updateProgress()

template<class S>
class NSGA2< S >