Implements the RVEA algorithm. More...

#include <shark/Algorithms/DirectSearch/RVEA.h>

Inheritance diagram for shark::RVEA:

Public Member Functions
SHARK_EXPORT_SYMBOL	RVEA (random::rng_type &rng=random::globalRng)

std::string	name () const
	returns the name of the object More...

double	crossoverProbability () const

double &	crossoverProbability ()

double	nm () const

double &	nm ()

double	nc () const

double &	nc ()

double	alpha () const

double &	alpha ()

double	adaptationFrequency () const

double &	adaptationFrequency ()

std::size_t	mu () const
	Size of parent population and number of reference vectors. More...

std::size_t	numInitPoints () const
	Returns the number of points this method requires for initialisation. More...

std::size_t	approxMu () const

std::size_t &	approxMu ()

RealMatrix	referenceVectors () const

RealMatrix &	referenceVectors ()

RealMatrix	initialReferenceVectors () const

std::size_t	maxIterations () const

std::size_t &	maxIterations ()

bool	willAdaptReferenceVectors () const
	True if the reference vectors will be adapted. More...

template<typename Archive >
void	serialize (Archive &archive)

SHARK_EXPORT_SYMBOL void	init (ObjectiveFunctionType const &function, std::vector< SearchPointType > const &initialSearchPoints)
	Initialize the optimizer for the supplied objective function using a set of initialisation points. More...

SHARK_EXPORT_SYMBOL void	step (ObjectiveFunctionType const &function)
	Carry out one step of the optimizer for the supplied objective function. More...

Public Member Functions inherited from shark::AbstractMultiObjectiveOptimizer< RealVector >
SolutionType const &	solution () const
	Accesses the current approximation of the Pareto-set and -front, respectively. More...

Public Member Functions inherited from shark::AbstractOptimizer< RealVector, RealVector, std::vector< ResultSet< RealVector, RealVector > > >
const Features &	features () const

virtual void	updateFeatures ()

bool	requiresValue () const

bool	requiresFirstDerivative () const

bool	requiresSecondDerivative () const

bool	canSolveConstrained () const

bool	requiresClosestFeasible () const

virtual	~AbstractOptimizer ()

virtual void	init (ObjectiveFunctionType const &function)
	Initialize the optimizer for the supplied objective function. More...

Public Member Functions inherited from shark::INameable
virtual	~INameable ()

Public Member Functions inherited from shark::ISerializable
virtual	~ISerializable ()
	Virtual d'tor. More...

virtual void	read (InArchive &archive)
	Read the component from the supplied archive. More...

virtual void	write (OutArchive &archive) const
	Write the component to the supplied archive. More...

void	load (InArchive &archive, unsigned int version)
	Versioned loading of components, calls read(...). More...

void	save (OutArchive &archive, unsigned int version) const
	Versioned storing of components, calls write(...). More...

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Static Public Member Functions
static SHARK_EXPORT_SYMBOL std::size_t	suggestMu (std::size_t n, std::size_t const approx_mu)

Protected Types
typedef shark::Individual< RealVector, RealVector >	IndividualType

Protected Member Functions
SHARK_EXPORT_SYMBOL void	doInit (std::vector< SearchPointType > const &initialSearchPoints, std::vector< ResultType > const &functionValues, RealVector const &lowerBounds, RealVector const &upperBounds, std::size_t const approx_mu, double const nm, double const nc, double const crossover_prob, double const alph, double const fr, std::size_t const max_iterations, std::vector< Preference > const &referenceVectorsPreferences=std::vector< Preference >())

SHARK_EXPORT_SYMBOL std::vector< IndividualType >	generateOffspring () const

SHARK_EXPORT_SYMBOL void	updatePopulation (std::vector< IndividualType > const &offspringvec)

Protected Member Functions inherited from shark::AbstractOptimizer< RealVector, RealVector, std::vector< ResultSet< RealVector, RealVector > > >
void	checkFeatures (ObjectiveFunctionType const &objectiveFunction)
	Convenience function that checks whether the features of the supplied objective function match with the required features of the optimizer. More...

Protected Attributes
std::vector< IndividualType >	m_parents

Protected Attributes inherited from shark::AbstractMultiObjectiveOptimizer< RealVector >
SolutionType	m_best
	The current Pareto-set/-front. More...

Protected Attributes inherited from shark::AbstractOptimizer< RealVector, RealVector, std::vector< ResultSet< RealVector, RealVector > > >
Features	m_features

Additional Inherited Members
Public Types inherited from shark::AbstractMultiObjectiveOptimizer< RealVector >
typedef super::SearchPointType	SearchPointType

typedef super::SolutionType	SolutionType

typedef super::ObjectiveFunctionType	ObjectiveFunctionType

Public Types inherited from shark::AbstractOptimizer< RealVector, RealVector, std::vector< ResultSet< RealVector, RealVector > > >
enum	Feature
	Models features that the optimizer requires from the objective function. More...

typedef RealVector	SearchPointType

typedef RealVector	ResultType

typedef std::vector< ResultSet< RealVector, RealVector > >	SolutionType

typedef AbstractObjectiveFunction< RealVector, ResultType >	ObjectiveFunctionType

typedef TypedFlags< Feature >	Features

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Detailed Description

Implements the RVEA algorithm.

Implementation of the RVEA algorithm from the following paper: R. Cheng, Y. Jin, M. Olhofer, and B. Sendhoff, “A reference vector guided evolutionary algorithm for many-objective optimization,” IEEE Transactions on Evolutionary Computation, Vol 20, Issue 5, October 2016 http://dx.doi.org/10.1109/TEVC.2016.2519378

Definition at line 55 of file RVEA.h.

Member Typedef Documentation

◆ IndividualType

typedef shark::Individual<RealVector, RealVector> shark::RVEA::IndividualType

protected

Definition at line 192 of file RVEA.h.

Constructor & Destructor Documentation

◆ RVEA()

SHARK_EXPORT_SYMBOL shark::RVEA::RVEA ( random::rng_type & rng = random::globalRng )

Member Function Documentation

◆ adaptationFrequency() [1/2]

double shark::RVEA::adaptationFrequency ( ) const

inline

Definition at line 96 of file RVEA.h.

Referenced by willAdaptReferenceVectors().

◆ adaptationFrequency() [2/2]

double& shark::RVEA::adaptationFrequency ( )

inline

Definition at line 100 of file RVEA.h.

◆ alpha() [1/2]

double shark::RVEA::alpha ( ) const

inline

Definition at line 88 of file RVEA.h.

◆ alpha() [2/2]

double& shark::RVEA::alpha ( )

inline

Definition at line 92 of file RVEA.h.

◆ approxMu() [1/2]

std::size_t shark::RVEA::approxMu ( ) const

inline

Definition at line 121 of file RVEA.h.

◆ approxMu() [2/2]

std::size_t& shark::RVEA::approxMu ( )

inline

Definition at line 125 of file RVEA.h.

◆ crossoverProbability() [1/2]

double shark::RVEA::crossoverProbability ( ) const

inline

Definition at line 64 of file RVEA.h.

◆ crossoverProbability() [2/2]

double& shark::RVEA::crossoverProbability ( )

inline

Definition at line 68 of file RVEA.h.

◆ doInit()

SHARK_EXPORT_SYMBOL void shark::RVEA::doInit	(	std::vector< SearchPointType > const &	initialSearchPoints,
		std::vector< ResultType > const &	functionValues,
		RealVector const &	lowerBounds,
		RealVector const &	upperBounds,
		std::size_t const	approx_mu,
		double const	nm,
		double const	nc,
		double const	crossover_prob,
		double const	alph,
		double const	fr,
		std::size_t const	max_iterations,
		std::vector< Preference > const &	referenceVectorsPreferences = `std::vector< Preference >()`
	)

protected

◆ generateOffspring()

SHARK_EXPORT_SYMBOL std::vector<IndividualType> shark::RVEA::generateOffspring ( ) const

protected

◆ init()

SHARK_EXPORT_SYMBOL void shark::RVEA::init	(	ObjectiveFunctionType const &	function,
		std::vector< SearchPointType > const &	initPoints
	)

virtual

Initialize the optimizer for the supplied objective function using a set of initialisation points.

Most single objective algorithms only require a single point. However multi-objective algorithms need a set of initialisation points. The number of points required should be numInitPoints(). Otherwise the algorithm might use heuristics to generate additional points if needed.

Be aware that function.init() has to be called before calling this function!

Parameters

[in]	function	The objective function to initialize for.
[in]	initPoints	points used for initialisation. Should be at least numInitPoints().

Implements shark::AbstractOptimizer< RealVector, RealVector, std::vector< ResultSet< RealVector, RealVector > > >.

Referenced by serialize().

◆ initialReferenceVectors()

RealMatrix shark::RVEA::initialReferenceVectors ( ) const

inline

Definition at line 137 of file RVEA.h.

◆ maxIterations() [1/2]

std::size_t shark::RVEA::maxIterations ( ) const

inline

Definition at line 141 of file RVEA.h.

◆ maxIterations() [2/2]

std::size_t& shark::RVEA::maxIterations ( )

inline

Definition at line 145 of file RVEA.h.

◆ mu()

std::size_t shark::RVEA::mu ( ) const

inline

Size of parent population and number of reference vectors.

In the RVEA algorithm, the exact mu value is determined by the simplex-lattice design (Lattice.h), so the user cannot set it directly. Instead, one must set the approxMu() value, which will be used as a parameter in the lattice. If one wants to know the exact mu value, set approxMu() to RVEA::suggestMu(n, m) where n is the objective dimension and m is the approximate mu. Then the actual mu value will not be changed in the initialization.

Definition at line 113 of file RVEA.h.

◆ name()

std::string shark::RVEA::name ( ) const

inlinevirtual

returns the name of the object

Reimplemented from shark::INameable.

Definition at line 60 of file RVEA.h.

◆ nc() [1/2]

double shark::RVEA::nc ( ) const

inline

Definition at line 80 of file RVEA.h.

References shark::SimulatedBinaryCrossover< PointType >::m_nc.

◆ nc() [2/2]

double& shark::RVEA::nc ( )

inline

Definition at line 84 of file RVEA.h.

References shark::SimulatedBinaryCrossover< PointType >::m_nc.

◆ nm() [1/2]

double shark::RVEA::nm ( ) const

inline

Definition at line 72 of file RVEA.h.

References shark::PolynomialMutator::m_nm.

◆ nm() [2/2]

double& shark::RVEA::nm ( )

inline

Definition at line 76 of file RVEA.h.

References shark::PolynomialMutator::m_nm.

◆ numInitPoints()

std::size_t shark::RVEA::numInitPoints ( ) const

inlinevirtual

Returns the number of points this method requires for initialisation.

The number of points supplied is allowed to be smaller, however in this case the optimizer will resort to techniques generating additional points if needed.

Implements shark::AbstractOptimizer< RealVector, RealVector, std::vector< ResultSet< RealVector, RealVector > > >.

Definition at line 117 of file RVEA.h.

◆ referenceVectors() [1/2]

RealMatrix shark::RVEA::referenceVectors ( ) const

inline

Definition at line 129 of file RVEA.h.

◆ referenceVectors() [2/2]

RealMatrix& shark::RVEA::referenceVectors ( )

inline

Definition at line 133 of file RVEA.h.

◆ serialize()

template<typename Archive >

void shark::RVEA::serialize ( Archive & archive )

inline

Definition at line 163 of file RVEA.h.

References init(), shark::AbstractMultiObjectiveOptimizer< RealVector >::m_best, m_parents, S, SHARK_EXPORT_SYMBOL, step(), and suggestMu().

◆ step()

SHARK_EXPORT_SYMBOL void shark::RVEA::step ( ObjectiveFunctionType const & function )

virtual

Carry out one step of the optimizer for the supplied objective function.

Parameters

[in] function The objective function to initialize for.

Implements shark::AbstractOptimizer< RealVector, RealVector, std::vector< ResultSet< RealVector, RealVector > > >.

Referenced by serialize().

◆ suggestMu()

static SHARK_EXPORT_SYMBOL std::size_t shark::RVEA::suggestMu	(	std::size_t	n,
		std::size_t const	approx_mu
	)

static

Referenced by serialize().

◆ updatePopulation()

SHARK_EXPORT_SYMBOL void shark::RVEA::updatePopulation ( std::vector< IndividualType > const & offspringvec )

protected

◆ willAdaptReferenceVectors()

bool shark::RVEA::willAdaptReferenceVectors ( ) const

inline

True if the reference vectors will be adapted.

Returns true if the algorithm will adapt the unit reference vectors in the current iteration. This is controlled by the adaptationFreqency() parameter; a value of, e.g., 0.2 will make the algorithm readapt reference vectors every 20% of the iteration. Running the algorithm for 1000 iterations will then make it readapt on iteration 0, 200, 400, etc.

Definition at line 156 of file RVEA.h.

References adaptationFrequency().

Member Data Documentation

◆ m_parents

std::vector<IndividualType> shark::RVEA::m_parents

protected

Definition at line 213 of file RVEA.h.

Referenced by serialize().

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

include/shark/Algorithms/DirectSearch/RVEA.h

Public Member Functions

Static Public Member Functions

Protected Types

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ IndividualType

Constructor & Destructor Documentation

◆ RVEA()

Member Function Documentation

◆ adaptationFrequency() [1/2]

◆ adaptationFrequency() [2/2]

◆ alpha() [1/2]

◆ alpha() [2/2]

◆ approxMu() [1/2]

◆ approxMu() [2/2]

◆ crossoverProbability() [1/2]

◆ crossoverProbability() [2/2]

◆ doInit()

◆ generateOffspring()

◆ init()

◆ initialReferenceVectors()

◆ maxIterations() [1/2]

◆ maxIterations() [2/2]

◆ mu()

◆ name()

◆ nc() [1/2]

◆ nc() [2/2]

◆ nm() [1/2]

◆ nm() [2/2]

◆ numInitPoints()

◆ referenceVectors() [1/2]

◆ referenceVectors() [2/2]

◆ serialize()

◆ step()

◆ suggestMu()

◆ updatePopulation()

◆ willAdaptReferenceVectors()

Member Data Documentation

◆ m_parents