Class to define an Abstract Evolutionary Algorithm. This is the base skeleton for future extensions. More...

#include <AbstractEA.h>

Inheritance diagram for AbstractEA< S >:

Public Member Functions
	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 void	run ()=0
	Main method of the EA. Runs the algorithm but must be implemented in the subclasses.

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...

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...

virtual json	to_json () const
	Generates and returns the JSON representation of the EA. More...

virtual Front< S >	getResults () const =0
	Returns a Front object with all the solutions of the evolutionary process. This method must be implemented in the subclasses. 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...

virtual void	setProblem (shared_ptr< Problem< S >> prob)
	Set the new problem to solve. Uses a shared_ptr pointer which updates the reference counter. Does no take the ownership. More...

virtual void	setProblem (Problem< S > *prob)
	Set the new problem to solve. Uses a raw pointer and takes the ownership of the object. 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 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 ()=0
	Initialises the evolutionary progress. This method must be implemented in the subclasses and should perform all necessary steps before the main loop.

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 ()=0
	Method which finishes the evolutionary progress. This method must be implemented in the subclasses and it should perform thinks like computing the execution time, etc.

virtual bool	isStoppingConditionReached ()=0
	Check whether the number of performed evaluations has reached the maximum allowed. Must be implemented in the subclasses to adapt special cases. More...

virtual void	createInitialPopulation ()=0
	Creates the initial population of individuals. Must be implemented in the subclasses to adapt special cases.

virtual void	evaluatePopulation (vector< S > &pop)=0
	Evaluates the entire population of solutions. This is a virtual method that must be implemented in the subclasses to adapt special cases.

virtual void	updateEvolution (vector< S > &pop)

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

Protected Attributes
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 AbstractEA< S >

Class to define an Abstract Evolutionary Algorithm. This is the base skeleton for future extensions.

Template Parameters

S	Type of individuals

Constructor & Destructor Documentation

◆ AbstractEA() [1/3]

template<class S >

AbstractEA< S >::AbstractEA

Construct a new Abstract EA object with default parameter values.

maxEvaluations is set to 100000 evaluations.
populationSize is set to 100 individuals of type S.

Template Parameters

S

◆ AbstractEA() [2/3]

template<class S >

AbstractEA< S >::AbstractEA	(	const int &	maxEvals,
		const int &	popsize
	)

AbstractEA constructor which initialises the parameters maxEvalautions and populationSize.

Template Parameters

S

Parameters

maxEvals	Maximum number of evaluations to perform
popsize	Number of individuals in the population

◆ AbstractEA() [3/3]

template<class S >

AbstractEA< S >::AbstractEA	(	unique_ptr< PopulationEvaluator< S >>	eval,
		const int &	maxEvals,
		const int &	popsize
	)

AbstractEA constructor which initialises the parameters maxEvalautions, the populationSize and the evaluator approach.

Template Parameters

S

Parameters

eval	Evaluator approach (Sequential, parallel, OMP, etc)
maxEvals	Maximum number of evaluations to perform
popsize	Number of individuals in the population

Member Function Documentation

◆ getElapsedTime()

template<class S >

double AbstractEA< S >::getElapsedTime ( ) const

inline

Get the elapsed time of the evolutionary process.

Returns: double

◆ getEvaluator()

template<class S >

PopulationEvaluator<S>* AbstractEA< S >::getEvaluator ( ) const

inline

Gets a pointer to the population evaluator system.

Returns: PopulationEvaluator*

◆ getEvolution()

template<class S >

Evolution< S > AbstractEA< S >::getEvolution

virtual

Get the Evolution data.

Returns the data of the evolution process. Includes the best fitness obtained at each evaluated checkpoint.

Returns: Evolution

Template Parameters

S

Returns: ~~Evolution~~

◆ getID()

template<class S >

virtual string AbstractEA< S >::getID ( ) const

pure virtual

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.

Returns: string

Implemented in Heuristic< IntIntSolution >, Heuristic< BoolFloatSolution >, Heuristic< IntFloatSolution >, EIG< IP, IS, OP, OS >, SteadyGA< S >, SimulatedAnnealing< S >, ParallelGeneticAlgorithm< S >, GenerationalGA< S >, FIGA< S >, NSGA2< S >, Heuristic< S >, tsp_heuristics::TwoOpt, tsp_heuristics::NearestNeighbour, tsp_heuristics::Greedy, MPW, MiW, MinKnap, MaP, ExpKnap, Default, Combo, WorstFit, NextFit, FirstFit, and BestFit.

◆ getMaxEvaluations()

template<class S >

int AbstractEA< S >::getMaxEvaluations ( ) const

inline

Get the maximum number of evaluations to perform.

Returns: int

◆ getName()

template<class S >

virtual string AbstractEA< 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

Implemented in Heuristic< IntIntSolution >, Heuristic< BoolFloatSolution >, Heuristic< IntFloatSolution >, MapElites< IP, IS, OP, OS >, EIG< IP, IS, OP, OS >, SteadyGA< S >, SimulatedAnnealing< S >, ParallelGeneticAlgorithm< S >, GenerationalGA< S >, FIGA< S >, NSGA2< S >, Heuristic< S >, AbstractGA< S >, tsp_heuristics::TwoOpt, tsp_heuristics::NearestNeighbour, tsp_heuristics::Greedy, MPW, MiW, MinKnap, MaP, ExpKnap, Default, Combo, WorstFit, NextFit, FirstFit, and BestFit.

◆ getPerformedEvaluations()

template<class S >

int AbstractEA< S >::getPerformedEvaluations ( ) const

inline

Get the performed evaluations.

Returns: int

◆ getPopulation()

template<class S >

const vector<S>& AbstractEA< S >::getPopulation ( ) const

inline

Gets a reference of the population.

Returns: const vector&

◆ getPopulationSize()

template<class S >

int AbstractEA< S >::getPopulationSize ( ) const

inline

Get the population size.

Returns: int

◆ getPrintingInterval()

template<class S >

int AbstractEA< S >::getPrintingInterval ( ) const

inline

Get the interval of checkpoints.

Returns: int

◆ getProblem()

template<class S >

const Problem<S>* AbstractEA< S >::getProblem ( ) const

inline

Gets a pointer to the problem which is being solved.

Returns: const Problem*

◆ getResults()

template<class S >

virtual Front<S> AbstractEA< S >::getResults ( ) const

pure virtual

Returns a Front object with all the solutions of the evolutionary process. This method must be implemented in the subclasses.

Returns: Front

Implemented in Heuristic< IntIntSolution >, Heuristic< BoolFloatSolution >, Heuristic< IntFloatSolution >, MapElites< IP, IS, OP, OS >, EIG< IP, IS, OP, OS >, tsp_heuristics::TwoOpt, tsp_heuristics::NearestNeighbour, tsp_heuristics::Greedy, SimulatedAnnealing< S >, MinKnap, ExpKnap, Default, Combo, WorstFit, NextFit, FirstFit, BestFit, Heuristic< S >, AbstractGA< S >, and NSGA2< S >.

◆ isStoppingConditionReached()

template<class S >

virtual bool AbstractEA< S >::isStoppingConditionReached ( )

protectedpure virtual

Check whether the number of performed evaluations has reached the maximum allowed. Must be implemented in the subclasses to adapt special cases.

Returns: true if the maximum number of evaluations have been performed; false otherwise

Implemented in EIG< IP, IS, OP, OS >, Heuristic< S >, Heuristic< IntIntSolution >, Heuristic< BoolFloatSolution >, Heuristic< IntFloatSolution >, SimulatedAnnealing< S >, and AbstractGA< S >.

◆ setEvaluator()

template<class S >

void AbstractEA< S >::setEvaluator ( unique_ptr< PopulationEvaluator< S >> eval )

inline

Updates the population evaluator system with the unique_ptr. The method takes the ownership of the object behind.

Parameters

eval

◆ setMaxEvaluations()

template<class S >

void AbstractEA< S >::setMaxEvaluations ( int maxEval )

inline

Set a new maximum number of evaluations to perform.

Parameters

maxEval

◆ setPerformedEvaluations()

template<class S >

void AbstractEA< S >::setPerformedEvaluations ( int pEvals )

inline

Set the Performed Evaluations. Useful for the updateProgress method.

Parameters

pEvals

◆ setPopulation()

template<class S >

void AbstractEA< S >::setPopulation ( const vector< S > & pop )

Set the Population.

Set the population of the EA.

Parameters

pop
pop	New population to the EA.

◆ setPopulationSize()

template<class S >

void AbstractEA< S >::setPopulationSize ( int pSize )

Setter to update the population size.

Template Parameters

S

Parameters

pSize New size of the population

◆ setProblem() [1/2]

template<class S >

virtual void AbstractEA< S >::setProblem ( Problem< S > * prob )

inlinevirtual

Set the new problem to solve. Uses a raw pointer and takes the ownership of the object.

Parameters

prob

Reimplemented in AbstractGA< S >, SimulatedAnnealing< S >, and NSGA2< S >.

◆ setProblem() [2/2]

template<class S >

virtual void AbstractEA< S >::setProblem ( shared_ptr< Problem< S >> prob )

inlinevirtual

Set the new problem to solve. Uses a shared_ptr pointer which updates the reference counter. Does no take the ownership.

Parameters

prob

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

◆ to_json()

template<class S >

json AbstractEA< S >::to_json

virtual

Generates and returns the JSON representation of the EA.

Template Parameters

S

Returns: json

Reimplemented in MapElites< IP, IS, OP, OS >, EIG< IP, IS, OP, OS >, AbstractGA< S >, ParallelGeneticAlgorithm< S >, Heuristic< S >, Heuristic< IntIntSolution >, Heuristic< BoolFloatSolution >, and Heuristic< IntFloatSolution >.

◆ updateEvolution() [1/2]

template<class S >

void AbstractEA< S >::updateEvolution	(	const int &	checkpoint,
		vector< S > &	solutions
	)

protectedvirtual

Updates the evolution results of the algorithm for the given checkpoint

Template Parameters

Problem
S

Parameters

checkpoint	Checkpoint in the evolution process
solutions	solutions Population of solutions in the checkpoin to collect data from

Reimplemented in Heuristic< S >.

◆ updateEvolution() [2/2]

template<class S >

void AbstractEA< S >::updateEvolution ( vector< S > & solutions )

protectedvirtual

Updates the evolution results of the algorithm for the given checkpoint

Template Parameters

Problem
S

Parameters

solutions Population of solutions in the checkpoin to collect data from

Reimplemented in Heuristic< S >.

Member Data Documentation

◆ DEFAULT_EVALUATIONS_LIMIT

template<class S >

const int AbstractEA< S >::DEFAULT_EVALUATIONS_LIMIT = 100000

static

Default evaluation limit equal to 100000 evaluations.

Template Parameters

S

◆ DEFAULT_POPULATION_SIZE

template<class S >

const int AbstractEA< S >::DEFAULT_POPULATION_SIZE = 100

static

Default population size equal to 100 individuals.

Template Parameters

S

◆ maxEvaluations

template<class S >

int AbstractEA< S >::maxEvaluations

protected

Number of evaluations to perform by the EA

◆ populationSize

template<class S >

int AbstractEA< S >::populationSize

protected

Number of individuals in the population

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

include/dignea/core/AbstractEA.h

Public Member Functions

Static Public Attributes

Protected Member Functions

Protected Attributes

Detailed Description

template<class S> class AbstractEA< S >

Constructor & Destructor Documentation

◆ AbstractEA() [1/3]

◆ AbstractEA() [2/3]

◆ AbstractEA() [3/3]

Member Function Documentation

◆ getElapsedTime()

◆ getEvaluator()

◆ getEvolution()

◆ getID()

◆ getMaxEvaluations()

◆ getName()

◆ getPerformedEvaluations()

◆ getPopulation()

◆ getPopulationSize()

◆ getPrintingInterval()

◆ getProblem()

◆ getResults()

◆ isStoppingConditionReached()

◆ setEvaluator()

◆ setMaxEvaluations()

◆ setPerformedEvaluations()

◆ setPopulation()

◆ setPopulationSize()

◆ setProblem() [1/2]

◆ setProblem() [2/2]

◆ to_json()

◆ updateEvolution() [1/2]

◆ updateEvolution() [2/2]

Member Data Documentation

◆ DEFAULT_EVALUATIONS_LIMIT

◆ DEFAULT_POPULATION_SIZE

◆ maxEvaluations

◆ populationSize

template<class S>
class AbstractEA< S >