Noisy meta-problem. More...

#include <noisy.h>

Inheritance diagram for pagmo::problem::noisy:

Public Types
enum	noise_type { NORMAL = 0, UNIFORM = 1 }
	Distribution type of the noise. More...

Public Types inherited from pagmo::problem::base
typedef decision_vector::size_type	size_type
	Problem's size type: the same as pagmo::decision_vector's size type.

typedef fitness_vector::size_type	f_size_type
	Fitness' size type: the same as pagmo::fitness_vector's size type.

typedef constraint_vector::size_type	c_size_type
	Constraints' size type: the same as pagmo::constraint_vector's size type.

Public Member Functions
	noisy (const base &=ackley(1), unsigned int trials=1, const double param_first=0.0, const double param_second=0.1, noise_type=NORMAL, unsigned int seed=0u)

	noisy (const noisy &)
	Copy Constructor. Performs a deep copy.

base_ptr	clone () const
	Clone method.

std::string	get_name () const
	Get problem's name. More...

void	set_noise_param (double, double)

double	get_param_first () const

double	get_param_second () const

Public Member Functions inherited from pagmo::problem::base_stochastic
	base_stochastic (int, unsigned int=0u)
	Constructor from global dimension and random seed. More...

	base_stochastic (int, int, int, int, int, const double &, unsigned int)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base_stochastic (int, int, int, int, int, const std::vector< double > &, unsigned int)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance vector. More...

unsigned int	get_seed () const
	Gets the pseudo random generator seed. More...

void	set_seed (unsigned int) const
	Sets the pseudo random generator seed. More...

Public Member Functions inherited from pagmo::problem::base
	base (int, int=0, int=1, int=0, int=0, const double &=0)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base (int, int, int, int, int, const std::vector< double > &)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base (const double &, const double &, int, int=0, int=1, int=0, int=0, const double &=0)
	Constructor from values for lower and upper bounds, global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base (const decision_vector &, const decision_vector &, int=0, int=1, int=0, int=0, const double &=0)
	Constructor from upper/lower bounds, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

template<std::size_t N>
	base (const double(&v1)[N], const double(&v2)[N], int ni=0, int nf=1, int nc=0, int nic=0, const double &c_tol=0)
	Constructor from raw arrays, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

template<class Iterator1 , class Iterator2 >
	base (Iterator1 start1, Iterator1 end1, Iterator2 start2, Iterator2 end2, int ni=0, int nf=1, int nc=0, int nic=0, const double &c_tol=0)
	Constructor from iterators, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

virtual	~base ()
	Trivial destructor. More...

std::string	human_readable () const
	Return human readable representation of the problem. More...

bool	operator== (const base &) const
	Equality operator. More...

bool	operator!= (const base &) const
	Inequality operator. More...

bool	is_compatible (const base &) const
	Compatibility operator. More...

bool	compare_x (const decision_vector &, const decision_vector &) const
	Compare decision vectors. More...

bool	verify_x (const decision_vector &) const
	Verify compatibility of decision vector x with problem. More...

bool	compare_fc (const fitness_vector &, const constraint_vector &, const fitness_vector &, const constraint_vector &) const
	Simultaneous fitness-constraint comparison. More...

virtual void	pre_evolution (population &) const
	Pre-evolution hook. More...

virtual void	post_evolution (population &) const
	Post-evolution hook. More...

virtual void	set_sparsity (int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
	Sets the sparsity pattern of the gradient. More...

const decision_vector &	get_lb () const
	Lower bounds getter. More...

const decision_vector &	get_ub () const
	Upper bounds getter. More...

void	set_bounds (const decision_vector &, const decision_vector &)
	Bounds setter from pagmo::decision_vector. More...

template<class Iterator1 , class Iterator2 >
void	set_bounds (Iterator1 start1, Iterator1 end1, Iterator2 start2, Iterator2 end2)
	Bounds setter from iterators. More...

template<std::size_t N>
void	set_bounds (const double(&v1)[N], const double(&v2)[N])
	Bounds setter from raw arrays. More...

void	set_bounds (const double &, const double &)
	Set bounds to specified values. More...

void	set_bounds (int, const double &, const double &)
	Set bounds to specified values. More...

void	set_lb (const decision_vector &)
	Set lower bounds from pagmo::decision_vector. More...

void	set_lb (int, const double &)
	Set specific lower bound to value. More...

void	set_lb (const double &)
	Set all lower bounds to value. More...

template<class Iterator >
void	set_lb (Iterator start, Iterator end)
	Lower bounds setter from iterators. More...

template<std::size_t N>
void	set_lb (const double(&v)[N])
	Lower bounds setter from raw array. More...

void	set_ub (const decision_vector &)
	Set upper bounds from pagmo::decision_vector. More...

void	set_ub (int, const double &)
	Set specific upper bound to value. More...

void	set_ub (const double &)
	Set all upper bounds to value. More...

template<class Iterator >
void	set_ub (Iterator start, Iterator end)
	Upper bounds setter from iterators. More...

template<std::size_t N>
void	set_ub (const double(&v)[N])
	Upper bounds setter from raw array. More...

unsigned int	get_fevals () const
	Return number of function evaluations. More...

unsigned int	get_cevals () const
	Return number of constraints function evaluations. More...

size_type	get_dimension () const
	Return global dimension. More...

size_type	get_i_dimension () const
	Return integer dimension. More...

f_size_type	get_f_dimension () const
	Return fitness dimension. More...

c_size_type	get_c_dimension () const
	Return global constraints dimension. More...

c_size_type	get_ic_dimension () const
	Return inequality constraints dimension. More...

const std::vector< double > &	get_c_tol () const
	Return constraints tolerance. More...

double	get_diameter () const
	Get the diameter of the problem. More...

constraint_vector	compute_constraints (const decision_vector &) const
	Compute constraints and return constraint vector. More...

void	compute_constraints (constraint_vector &, const decision_vector &) const
	Compute constraints and write them into contraint vector. More...

bool	compare_constraints (const constraint_vector &, const constraint_vector &) const
	Compare constraint vectors. More...

bool	test_constraint (const constraint_vector &, const c_size_type &) const
	Test i-th constraint of c (using tolerance information). More...

bool	feasibility_x (const decision_vector &) const
	Test feasibility of decision vector. More...

bool	feasibility_c (const constraint_vector &) const
	Test feasibility of constraint vector. More...

fitness_vector	objfun (const decision_vector &) const
	Return fitness of pagmo::decision_vector. More...

void	objfun (fitness_vector &, const decision_vector &) const
	Write fitness of pagmo::decision_vector into pagmo::fitness_vector. More...

bool	compare_fitness (const fitness_vector &, const fitness_vector &) const
	Compare fitness vectors. More...

void	reset_caches () const
	Reset internal caches. More...

const std::vector< constraint_vector > &	get_best_c (void) const
	Get the best known constraint vector. More...

const std::vector< decision_vector > &	get_best_x (void) const
	Get the best known decision vector. More...

const std::vector< fitness_vector > &	get_best_f (void) const
	Get the best known fitness vector. More...

void	set_best_x (const std::vector< decision_vector > &)
	Sets the best known decision vectors. More...

Protected Member Functions
std::string	human_readable_extra () const
	Extra human readable info for the problem. More...

void	objfun_impl (fitness_vector &, const decision_vector &) const

void	compute_constraints_impl (constraint_vector &, const decision_vector &) const

Protected Member Functions inherited from pagmo::problem::base
virtual bool	equality_operator_extra (const base &) const
	Extra requirements for equality. More...

virtual bool	compare_fc_impl (const fitness_vector &, const constraint_vector &, const fitness_vector &, const constraint_vector &) const
	Implementation of simultaneous fitness-constraint comparison. More...

void	estimate_sparsity (const decision_vector &, int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
	Heuristics to estimate the sparsity pattern of the problem. More...

void	estimate_sparsity (int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
	Heuristics to estimate the sparsity pattern of the problem. More...

virtual bool	compare_constraints_impl (const constraint_vector &, const constraint_vector &) const
	Implementation of constraint vector comparison. More...

virtual bool	compare_fitness_impl (const fitness_vector &, const fitness_vector &) const
	Implementation of fitness vectors comparison. More...

Friends
class	boost::serialization::access

Additional Inherited Members
Static Public Attributes inherited from pagmo::problem::base
static const std::size_t	cache_capacity = 5
	Capacity of the internal caches.

Protected Attributes inherited from pagmo::problem::base_stochastic
rng_double	m_drng
	Random number generator for double-precision floating point values.

rng_uint32	m_urng
	Random number generator for unsigned integer values.

unsigned int	m_seed
	Seed of the random number generator.

Detailed Description

Noisy meta-problem.

A meta-problem that transforms a problem into its stochastic version by injecting noises to the fitness vector (and constraint vector) with either uniform or gaussian distribution. The resulting value is then averaged upon m_trials as to rbe able to reproduce a typical set-up in Evolutionary Robotics, but aso in stochastic optimization

NOTE: for m_trials->infinity one recovers a deterministic problem, but the objective function computation soon becomes very expensive. The trade-off is to keep m_trials small, while being able to get good convergence.

Author: Yung-Siang Liau (liauy.nosp@m.s@gm.nosp@m.ail.c.nosp@m.om); Dario Izzo (dario.nosp@m..izz.nosp@m.o@gma.nosp@m.il.c.nosp@m.om)

Definition at line 55 of file noisy.h.

Member Enumeration Documentation

enum pagmo::problem::noisy::noise_type

Distribution type of the noise.

Enumerator
NORMAL	Normal distribution.
UNIFORM	Uniform distribution.

Definition at line 59 of file noisy.h.

Constructor & Destructor Documentation

pagmo::problem::noisy::noisy	(	const base &	p = `ackley(1)`,
		unsigned int	trials = `1`,
		const double	param_first = `0.0`,
		const double	param_second = `0.1`,
		noise_type	distribution = `NORMAL`,
		unsigned int	seed = `0u`
	)

Construct by specifying a problem to be transformed and the noise distribution, controlled by a flag and two params. Currently two types of noise distribution are supported, namely the normally distributed noise (NORMAL) and uniformly distributed noise (UNIFORM).

Parameters

[in]	p	pagmo::problem::base to be noisy
[in]	trials	number of samples to average upon
[in]	param_first	Mean of the Gaussian noise / Lower bound of the uniform noise
[in]	param_second	Standard deviation of the Gaussian noise / Upper bound of the uniform noise
[in]	distribution	Two types of distributions for the noise are currently supported: NORMAL or UNIFORM
[in]	seed	seed for the underlying rng

See also: problem::base_stochastic constructors.

Definition at line 54 of file noisy.cpp.

Member Function Documentation

void pagmo::problem::noisy::compute_constraints_impl	(	constraint_vector &	c,
		const decision_vector &	x
	)		const

protectedvirtual

Implementation of the constraints computation. Add noises to the computed constraint vector.

Reimplemented from pagmo::problem::base.

Definition at line 150 of file noisy.cpp.

std::string pagmo::problem::noisy::get_name ( ) const

virtual

Get problem's name.

Default implementation will return the problem's mangled C++ name.

Returns: name of the problem.

Reimplemented from pagmo::problem::base.

Definition at line 194 of file noisy.cpp.

double pagmo::problem::noisy::get_param_first ( ) const

Returns the first parameter. Interpretation depends on the noise specified, see constructor.

Definition at line 114 of file noisy.cpp.

double pagmo::problem::noisy::get_param_second ( ) const

Return the second parameter. Interpretation depends on the noise specified, see constructor.

Definition at line 123 of file noisy.cpp.

std::string pagmo::problem::noisy::human_readable_extra ( ) const

protectedvirtual

Extra human readable info for the problem.

Will return a formatted string containing the translation vector

Reimplemented from pagmo::problem::base.

Definition at line 203 of file noisy.cpp.

void pagmo::problem::noisy::objfun_impl	(	fitness_vector &	f,
		const decision_vector &	x
	)		const

protectedvirtual

Implementation of the objective function. Add noises to the computed fitness vector.

Implements pagmo::problem::base.

Definition at line 130 of file noisy.cpp.

void pagmo::problem::noisy::set_noise_param	(	double	param_first,
		double	param_second
	)

Configure parameters for the noise distribution

param[in] param_first Mean of the Gaussian noise / Lower bound of the uniform noise param[in] param_second Standard deviation of the Gaussian noise / Upper bound of the uniform noise

Definition at line 101 of file noisy.cpp.

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

Public Types

Public Member Functions

Protected Member Functions

Friends

Additional Inherited Members

Detailed Description

Member Enumeration Documentation

Constructor & Destructor Documentation

Member Function Documentation