libcmaes::Parameters< TGenoPheno > Class Template Reference

Generic class for Evolution Strategy parameters. More...

#include <parameters.h>

Inheritance diagram for libcmaes::Parameters< TGenoPheno >:

Public Member Functions
	Parameters ()
	empty constructor.
	Parameters (const int &dim, const double *x0, const int &lambda=-1, const uint64_t &seed=0, const TGenoPheno &gp=GenoPheno< NoBoundStrategy >())
	constructor More...
void	set_x0 (const double &x0)
	sets initial objective function parameter values to x0 across all dimensions More...
void	set_x0 (const double *x0)
	sets initial objective function parameter values to array x0 More...
void	set_x0 (const dVec &x0)
	sets initial objective function parameter values from Eigen vector More...
void	set_x0 (const double &x0min, const double &x0max)
	sets bounds on initial objective function parameter values. Bounds are the same across all dimensions, and initial value is sampled uniformly within these bounds. More...
void	set_x0 (const double *x0min, const double *x0max)
	sets bounds on initial objective function parameter values. Initial value is sampled uniformly within these bounds. More...
void	set_x0 (const std::vector< double > &x0min, const std::vector< double > &x0max)
	sets bounds on initial objective function parameter values. Initial value is sampled uniformly within these bounds. More...
void	set_x0 (const dVec &x0min, const dVec &x0max)
	sets bounds on initial objective function parameter values. Initial value is sampled uniformly within these bounds. More...
dVec	get_x0min () const
	returns lower bound on x0 vector More...
dVec	get_x0max () const
	returns upper bound on x0 vector More...
void	set_fixed_p (const int &index, const double &value)
	freezes a parameter to a given value during optimization. More...
void	unset_fixed_p (const int &index)
	unfreezes a parameter. More...
void	set_max_iter (const int &maxiter)
	sets the maximum number of iterations allowed for the optimization. More...
int	get_max_iter () const
	returns maximum number of iterations More...
void	set_max_fevals (const int &fevals)
	sets the maximum budget of objective function calls allowed for the optimization. More...
int	get_max_fevals () const
	returns maximum budget of objective function calls More...
void	set_ftarget (const double &val)
	sets the objective function target value when known. More...
void	reset_ftarget ()
	resets the objective function target value to its inactive state.
double	get_ftarget () const
	returns objective function target value. More...
void	set_seed (const int &seed)
	sets random generator's seed, 0 is special value to generate random seed. More...
int	get_seed () const
	returns random generator's seed. More...
void	set_ftolerance (const double &v)
	sets function tolerance as stopping criteria for TolHistFun: monitors the difference in function value over iterations and stops optimization when below tolerance. More...
double	get_ftolerance () const
	returns function tolerance More...
void	set_xtolerance (const double &v)
	sets parameter tolerance as stopping criteria for TolX. More...
double	get_xtolerance () const
	returns parameter tolerance More...
int	lambda () const
	returns lambda, number of offsprings per generation More...
int	dim () const
	returns the problem's dimension More...
void	set_quiet (const bool &quiet)
	sets the quiet mode (no output from the library) for the optimization at hand More...
bool	quiet () const
	returns whether the quiet mode is on. More...
void	set_algo (const int &algo)
	sets the optimization algorithm. More...
int	get_algo () const
	returns which algorithm is set for the optimization at hand. More...
void	set_gp (const TGenoPheno &gp)
	sets the genotype/phenotype transform object. More...
TGenoPheno	get_gp () const
	returns the current genotype/phenotype transform object. More...
void	set_fplot (const std::string &fplot)
	sets the output filename (activates the output to file). More...
void	set_full_fplot (const bool &b)
	activates / deactivates the full output (for legacy plotting). More...
std::string	get_fplot () const
	returns the current output filename. More...
void	set_gradient (const bool &gradient)
	activates the gradient injection scheme. If no gradient function is defined, injects a numerical gradient solution instead More...
bool	get_gradient () const
	returns whether the gradient injection scheme is activated. More...
void	set_edm (const bool &edm)
	activates computation of expected distance to minimum when optimization has completed More...
bool	get_edm () const
	returns whether edm is activated. More...
void	set_mt_feval (const bool &mt)
	activate / deactivate the parallel evaluation of objective function More...
bool	get_mt_feval () const
	returns whether the parallel evaluation of objective function is activated More...
void	set_max_hist (const int &m)
	sets maximum history size, allows to keep memory requirements fixed. More...
void	set_maximize (const bool &maximize)
	active internal maximization scheme (simply returns -f instead of f) More...
bool	get_maximize () const
	returns whether the maximization mode is enabled More...
void	set_initial_fvalue (const bool &b)
	whether to compute initial objective function value (i.e. at x0) More...
void	set_uh (const bool &b)
	activates / deactivates uncertainty handling scheme. More...
bool	get_uh () const
	get uncertainty handling status. More...
void	set_tpa (const int &b)
	activates / deactivates two-point adaptation step-size mechanism More...
int	get_tpa () const
	get two-point adapation step-size mechanism status. More...

Protected Attributes
int	_dim
int	_lambda = -1
int	_max_iter = -1
int	_max_fevals = -1
bool	_quiet = true
std::string	_fplot = ""
bool	_full_fplot = false
dVec	_x0min
dVec	_x0max
double	_ftarget = -std::numeric_limits<double>::infinity()
double	_ftolerance = 1e-12
double	_xtol = 1e-12
uint64_t	_seed = 0
int	_algo = 0
bool	_with_gradient =false
bool	_with_edm =false
std::unordered_map< int, double >	_fixed_p
TGenoPheno	_gp
bool	_mt_feval = false
int	_max_hist = -1
bool	_maximize = false
bool	_initial_fvalue = false
bool	_uh = false
double	_rlambda
double	_epsuh = 1e-7
double	_thetauh = 0.2
double	_csuh = 1.0
double	_alphathuh = 1.0
int	_tpa = 1
double	_tpa_csigma = 0.3

Static Protected Attributes
static std::map< std::string, int >	_algos = {{"cmaes",0},{"ipop",1},{"bipop",2},{"acmaes",3},{"aipop",4},{"abipop",5},{"sepcmaes",6},{"sepipop",7},{"sepbipop",8},{"sepacmaes",9},{"sepipop",10},{"sepbipop",11},{"vdcma",12},{"vdipopcma",13},{"vdbipopcma",14}}

Friends
class	CMASolutions
template<class U , class V >
class	CMAStrategy
template<class U , class V , class W >
class	ESOStrategy
template<class U >
class	CMAStopCriteria
template<class U , class V >
class	IPOPCMAStrategy
template<class U , class V >
class	BIPOPCMAStrategy
class	CovarianceUpdate
class	ACovarianceUpdate
template<class U >
class	errstats
class	VDCMAUpdate
class	Candidate

Detailed Description

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>
class libcmaes::Parameters< TGenoPheno >

Generic class for Evolution Strategy parameters.

Constructor & Destructor Documentation

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

libcmaes::Parameters< TGenoPheno >::Parameters ( const int &  dim, const double *  x0, const int &  lambda = -1, const uint64_t &  seed = 0, const TGenoPheno &  gp = GenoPheno<NoBoundStrategy>()  )

inline

constructor

Parameters

dim	problem dimensions
x0	initial search point
lambda	number of offsprings sampled at each step
seed	initial random seed, useful for reproducing results (if unspecified, automatically generated from current time)
gp	genotype / phenotype object

Member Function Documentation

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::dim ( ) const

inline

returns the problem's dimension

Returns

dimensions

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::get_algo ( ) const

inline

returns which algorithm is set for the optimization at hand.

Returns

algorithm integer code

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::get_edm ( ) const

inline

returns whether edm is activated.

Returns

edm

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

std::string libcmaes::Parameters< TGenoPheno >::get_fplot ( ) const

inline

returns the current output filename.

Returns

output filename

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

double libcmaes::Parameters< TGenoPheno >::get_ftarget ( ) const

inline

returns objective function target value.

Returns

objective function target value

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

double libcmaes::Parameters< TGenoPheno >::get_ftolerance ( ) const

inline

returns function tolerance

Returns

function tolerance

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

TGenoPheno libcmaes::Parameters< TGenoPheno >::get_gp ( ) const

inline

returns the current genotype/phenotype transform object.

Returns

GenoPheno object

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::get_gradient ( ) const

inline

returns whether the gradient injection scheme is activated.

Returns

with gradient

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::get_max_fevals ( ) const

inline

returns maximum budget of objective function calls

Returns

max number of objective function evaluations

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::get_max_iter ( ) const

inline

returns maximum number of iterations

Returns

max number of iterations allowed

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::get_maximize ( ) const

inline

returns whether the maximization mode is enabled

Returns

true if maximizing

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::get_mt_feval ( ) const

inline

returns whether the parallel evaluation of objective function is activated

Returns

activation status

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::get_seed ( ) const

inline

returns random generator's seed.

Returns

integer seed

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::get_tpa ( ) const

inline

get two-point adapation step-size mechanism status.

Returns

two-point adaptation status.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::get_uh ( ) const

inline

get uncertainty handling status.

Returns

uncertainty handling status.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

dVec libcmaes::Parameters< TGenoPheno >::get_x0max ( ) const

inline

returns upper bound on x0 vector

Returns

upper bound on x0

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

dVec libcmaes::Parameters< TGenoPheno >::get_x0min ( ) const

inline

returns lower bound on x0 vector

Returns

lower bound on x0

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

double libcmaes::Parameters< TGenoPheno >::get_xtolerance ( ) const

inline

returns parameter tolerance

Returns

parameter tolerance

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::lambda ( ) const

inline

returns lambda, number of offsprings per generation

Returns

lambda

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::quiet ( ) const

inline

returns whether the quiet mode is on.

Returns

quiet mode

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_algo ( const int &  algo )

inline

sets the optimization algorithm.

Parameters

algo	from CMAES_DEFAULT, IPOP_CMAES, BIPOP_CMAES, aCMAES, aIPOP_CMAES, aBIPOP_CMAES, sepCMAES, sepIPOP_CMAES, sepBIPOP_CMAES, sepaCMAES, sepaIPOP_CMAES, sepaBIPOP_CMAES, VD_CMAES, VD_IPOP_CMAES, VD_BIPOP_CMAES

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_edm ( const bool &  edm )

inline

activates computation of expected distance to minimum when optimization has completed

Parameters

edm	true / false

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_fixed_p ( const int &  index, const double &  value  )

inline

freezes a parameter to a given value during optimization.

Parameters

index	dimension index of the parameter to be frozen
value	frozen value of the parameter

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_fplot ( const std::string &  fplot )

inline

sets the output filename (activates the output to file).

Parameters

fplot

filename

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_ftarget ( const double &  val )

inline

sets the objective function target value when known.

Parameters

val	objective function target value

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_ftolerance ( const double &  v )

inline

sets function tolerance as stopping criteria for TolHistFun: monitors the difference in function value over iterations and stops optimization when below tolerance.

Parameters

v	value of the function tolerance.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_full_fplot ( const bool &  b )

inline

activates / deactivates the full output (for legacy plotting).

Parameters

b	whether to activate / deactivate

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_gp ( const TGenoPheno &  gp )

inline

sets the genotype/phenotype transform object.

Parameters

gp	GenoPheno object

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_gradient ( const bool &  gradient )

inline

activates the gradient injection scheme. If no gradient function is defined, injects a numerical gradient solution instead

Parameters

gradient

true/false

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_initial_fvalue ( const bool &  b )

inline

whether to compute initial objective function value (i.e. at x0)

Parameters

b	activates / deactivates

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_max_fevals ( const int &  fevals )

inline

sets the maximum budget of objective function calls allowed for the optimization.

Parameters

fevals

maximum number of objective function evaluations

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_max_hist ( const int &  m )

inline

sets maximum history size, allows to keep memory requirements fixed.

Parameters

m	number of steps of candidate history that are kept into memory (for stopping criteria equalfunvals mostly).

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_max_iter ( const int &  maxiter )

inline

sets the maximum number of iterations allowed for the optimization.

Parameters

maxiter

maximum number of allowed iterations

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_maximize ( const bool &  maximize )

inline

active internal maximization scheme (simply returns -f instead of f)

Parameters

maximize

whether to maximize instead of minimizing

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_mt_feval ( const bool &  mt )

inline

activate / deactivate the parallel evaluation of objective function

Parameters

mt	true for activated, false otherwise

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_quiet ( const bool &  quiet )

inline

sets the quiet mode (no output from the library) for the optimization at hand

Parameters

quiet

true / false

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_seed ( const int &  seed )

inline

sets random generator's seed, 0 is special value to generate random seed.

Parameters

seed	integer seed

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_tpa ( const int &  b )

inline

activates / deactivates two-point adaptation step-size mechanism

Parameters

b	0: no, 1: auto, 2: yes

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_uh ( const bool &  b )

inline

activates / deactivates uncertainty handling scheme.

Parameters

b	activates / deactivates

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_x0 ( const double &  x0 )

inline

sets initial objective function parameter values to x0 across all dimensions

Parameters

x0	initial value

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_x0 ( const double *  x0 )

inline

sets initial objective function parameter values to array x0

Parameters

x0	array of initial parameter values

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_x0 ( const dVec &  x0 )

inline

sets initial objective function parameter values from Eigen vector

Parameters

x0	Eigen vector of initial parameter values

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_x0 ( const double &  x0min, const double &  x0max  )

inline

sets bounds on initial objective function parameter values. Bounds are the same across all dimensions, and initial value is sampled uniformly within these bounds.

Parameters

x0min	lower bound
x0max	upper bound

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_x0 ( const double *  x0min, const double *  x0max  )

inline

sets bounds on initial objective function parameter values. Initial value is sampled uniformly within these bounds.

Parameters

x0min	vector of initial lower bounds.
x0max	vector of initial upper bounds.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_x0 ( const std::vector< double > &  x0min, const std::vector< double > &  x0max  )

inline

sets bounds on initial objective function parameter values. Initial value is sampled uniformly within these bounds.

Parameters

x0min	vector of initial lower bounds.
x0max	vector of initial upper bounds.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_x0 ( const dVec &  x0min, const dVec &  x0max  )

inline

sets bounds on initial objective function parameter values. Initial value is sampled uniformly within these bounds.

Parameters

x0min	vector of initial lower bounds.
x0max	vector of initial upper bounds.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::set_xtolerance ( const double &  v )

inline

sets parameter tolerance as stopping criteria for TolX.

Parameters

v	value of the parameter tolerance.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

void libcmaes::Parameters< TGenoPheno >::unset_fixed_p ( const int &  index )

inline

unfreezes a parameter.

Parameters

index

dimenion index of the parameter to unfreeze

Member Data Documentation

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::_algo = 0

protected

selected algorithm.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

std::map< std::string, int > libcmaes::Parameters< TGenoPheno >::_algos = {{"cmaes",0},{"ipop",1},{"bipop",2},{"acmaes",3},{"aipop",4},{"abipop",5},{"sepcmaes",6},{"sepipop",7},{"sepbipop",8},{"sepacmaes",9},{"sepipop",10},{"sepbipop",11},{"vdcma",12},{"vdipopcma",13},{"vdbipopcma",14}}

staticprotected

of the form { {"cmaes",0}, {"ipop",1}, ...}

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

double libcmaes::Parameters< TGenoPheno >::_alphathuh = 1.0

protected

factor of increasing the population spread.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

double libcmaes::Parameters< TGenoPheno >::_csuh = 1.0

protected

learning rate for averaging the uncertainty measurement.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::_dim

protected

function space dimensions.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

double libcmaes::Parameters< TGenoPheno >::_epsuh = 1e-7

protected

mutation strength for the reevaluation.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

std::unordered_map<int,double> libcmaes::Parameters< TGenoPheno >::_fixed_p

protected

fixed parameters and values.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

std::string libcmaes::Parameters< TGenoPheno >::_fplot = ""

protected

plotting file, if specified.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

double libcmaes::Parameters< TGenoPheno >::_ftarget = -std::numeric_limits<double>::infinity()

protected

optional objective function target value.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

double libcmaes::Parameters< TGenoPheno >::_ftolerance = 1e-12

protected

tolerance of the best function values during the last 10+(30*dim/lambda) steps (TolHistFun).

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::_full_fplot = false

protected

whether to write to file full legacy data output.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

TGenoPheno libcmaes::Parameters< TGenoPheno >::_gp

protected

genotype / phenotype object.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::_initial_fvalue = false

protected

whether to compute initial objective function value (not required).

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::_lambda = -1

protected

number of offsprings.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::_max_fevals = -1

protected

max budget as number of function evaluations.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::_max_hist = -1

protected

max size of the history, keeps memory requirements fixed.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::_max_iter = -1

protected

max iterations.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::_maximize = false

protected

convenience option of maximizing -f instead of minimizing f.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::_mt_feval = false

protected

whether to force multithreaded (i.e. parallel) function evaluations.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::_quiet = true

protected

quiet all outputs.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

double libcmaes::Parameters< TGenoPheno >::_rlambda

protected

fraction of solutions to be reevaluated.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

uint64_t libcmaes::Parameters< TGenoPheno >::_seed = 0

protected

seed for random generator.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

double libcmaes::Parameters< TGenoPheno >::_thetauh = 0.2

protected

control parameter for the acceptance threshold for the measured rank-change value.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

int libcmaes::Parameters< TGenoPheno >::_tpa = 1

protected

whether to activate two-point adaptation, 0: no (forced), 1: auto, 2: yes (forced)

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::_uh = false

protected

whether to activate uncertainty handling.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::_with_edm =false

protected

whether to compute expected distance to minimum when optimization has completed.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::Parameters< TGenoPheno >::_with_gradient =false

protected

whether to use injected gradient.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

dVec libcmaes::Parameters< TGenoPheno >::_x0max

protected

initial mean vector max bound value for all components.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

dVec libcmaes::Parameters< TGenoPheno >::_x0min

protected

initial mean vector min bound value for all components.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

double libcmaes::Parameters< TGenoPheno >::_xtol = 1e-12

protected

tolerance on parameters error.

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

• src/parameters.h
• src/cmaparameters.h