A version of Dakota::Optimizer for instantiation of John Eddy's Genetic Algorithms (JEGA). More...

Inheritance diagram for JEGAOptimizer:

Classes
class	Driver
	A subclass of the JEGA front end driver that exposes the individual protected methods to execute the algorithm. More...

class	Evaluator
	An evaluator specialization that knows how to interact with Dakota. More...

class	EvaluatorCreator
	A specialization of the JEGA::FrontEnd::EvaluatorCreator that creates a new instance of a Evaluator. More...

Public Member Functions
virtual void	core_run ()
	Performs the iterations to determine the optimal set of solutions. More...

virtual bool	accepts_multiple_points () const
	Overridden to return true since JEGA algorithms can accept multiple initial points. More...

virtual bool	returns_multiple_points () const
	Overridden to return true since JEGA algorithms can return multiple final points. More...

virtual void	initial_points (const VariablesArray &pts)
	Overridden to assign the _initPts member variable to the passed in collection of Dakota::Variables. More...

virtual const VariablesArray &	initial_points () const
	Overridden to return the collection of initial points for the JEGA algorithm created and run by this JEGAOptimizer. More...

	JEGAOptimizer (ProblemDescDB &problem_db, Model &model)
	Constructs a JEGAOptimizer class object. More...

	~JEGAOptimizer ()
	Destructs a JEGAOptimizer.

Public Member Functions inherited from Optimizer
void	get_common_stopping_criteria (int &max_fn_evals, int &max_iters, double &conv_tol, double &min_var_chg, double &obj_target)

int	num_nonlin_ineq_constraints_found () const

template<typename AdapterT >
bool	get_variable_bounds_from_dakota (typename AdapterT::VecT &lower, typename AdapterT::VecT &upper)

template<typename VecT >
void	get_responses_from_dakota (const RealVector &dak_fn_vals, VecT &funs, VecT &cEqs, VecT &cIneqs)

Public Member Functions inherited from Minimizer
void	constraint_tolerance (Real constr_tol)
	set the method constraint tolerance (constraintTol)

Real	constraint_tolerance () const
	return the method constraint tolerance (constraintTol)

std::shared_ptr< TPLDataTransfer >	get_data_transfer_helper () const

bool	resize ()
	reinitializes iterator based on new variable size

Public Member Functions inherited from Iterator
	Iterator (std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	default constructor More...

	Iterator (ProblemDescDB &problem_db, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	standard envelope constructor, which constructs its own model(s) More...

	Iterator (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate envelope constructor which uses the ProblemDescDB but accepts a model from a higher level (meta-iterator) context, instead of constructing its own More...

	Iterator (const String &method_string, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate envelope constructor for instantiations by name without the ProblemDescDB More...

	Iterator (const Iterator &iterator)
	copy constructor More...

virtual	~Iterator ()
	destructor

Iterator	operator= (const Iterator &iterator)
	assignment operator

virtual void	derived_set_communicators (ParLevLIter pl_iter)
	derived class contributions to setting the communicators associated with this Iterator instance

virtual void	derived_free_communicators (ParLevLIter pl_iter)
	derived class contributions to freeing the communicators associated with this Iterator instance

virtual void	pre_run ()
	pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori More...

virtual void	pre_output ()
	write variables to file, following pre-run

virtual void	post_input ()
	read tabular data for post-run mode

virtual void	reset ()
	restore initial state for repeated sub-iterator executions

virtual void	nested_variable_mappings (const SizetArray &c_index1, const SizetArray &di_index1, const SizetArray &ds_index1, const SizetArray &dr_index1, const ShortArray &c_target2, const ShortArray &di_target2, const ShortArray &ds_target2, const ShortArray &dr_target2)
	set primaryA{CV,DIV,DRV}MapIndices, secondaryA{CV,DIV,DRV}MapTargets within derived Iterators; supports computation of higher-level sensitivities in nested contexts (e.g., derivatives of statistics w.r.t. inserted design variables)

virtual void	nested_response_mappings (const RealMatrix &primary_coeffs, const RealMatrix &secondary_coeffs)
	set primaryResponseCoefficients, secondaryResponseCoefficients within derived Iterators; Necessary for scalarization case in MLMC NonDMultilevelSampling to map scalarization in nested context

virtual void	initialize_iterator (int job_index)
	used by IteratorScheduler to set the starting data for a run

virtual void	pack_parameters_buffer (MPIPackBuffer &send_buffer, int job_index)
	used by IteratorScheduler to pack starting data for an iterator run

virtual void	unpack_parameters_buffer (MPIUnpackBuffer &recv_buffer, int job_index)
	used by IteratorScheduler to unpack starting data for an iterator run

virtual void	unpack_parameters_initialize (MPIUnpackBuffer &recv_buffer, int job_index)
	used by IteratorScheduler to unpack starting data and initialize an iterator run

virtual void	pack_results_buffer (MPIPackBuffer &send_buffer, int job_index)
	used by IteratorScheduler to pack results data from an iterator run

virtual void	unpack_results_buffer (MPIUnpackBuffer &recv_buffer, int job_index)
	used by IteratorScheduler to unpack results data from an iterator run

virtual void	update_local_results (int job_index)
	used by IteratorScheduler to update local results arrays

virtual const Variables &	variables_results () const
	return a single final iterator solution (variables)

virtual const Response &	response_results () const
	return a single final iterator solution (response)

virtual const VariablesArray &	variables_array_results ()
	return multiple final iterator solutions (variables). This should only be used if returns_multiple_points() returns true.

virtual const ResponseArray &	response_array_results ()
	return multiple final iterator solutions (response). This should only be used if returns_multiple_points() returns true.

virtual void	response_results_active_set (const ActiveSet &set)
	set the requested data for the final iterator response results

virtual const RealSymMatrix &	response_error_estimates () const
	return error estimates associated with the final iterator solution

virtual void	initial_point (const Variables &pt)
	sets the initial point for this iterator (user-functions mode for which Model updating is not used)

virtual void	initial_point (const RealVector &pt)
	sets the initial point (active continuous variables) for this iterator (user-functions mode for which Model updating is not used)

virtual void	variable_bounds (const RealVector &cv_lower_bnds, const RealVector &cv_upper_bnds)
	assign nonlinear inequality and equality constraint allowables for this iterator (user-functions mode for which Model updating is not used)

virtual void	linear_constraints (const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_lb, const RealVector &lin_ineq_ub, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_tgt)
	assign linear inequality and linear equality constraints for this iterator (user-functions mode for which Model updating is not used)

virtual void	nonlinear_constraints (const RealVector &nln_ineq_lb, const RealVector &nln_ineq_ub, const RealVector &nln_eq_tgt)
	assign nonlinear inequality and equality constraint allowables for this iterator (user-functions mode for which Model updating is not used)

virtual void	initialize_graphics (int iterator_server_id=1)
	initialize the 2D graphics window and the tabular graphics data More...

virtual void	check_sub_iterator_conflict ()
	detect any conflicts due to recursive use of the same Fortran solver More...

virtual unsigned short	uses_method () const
	return name of any enabling iterator used by this iterator

virtual void	method_recourse ()
	perform a method switch, if possible, due to a detected conflict

virtual const VariablesArray &	all_variables ()
	return the complete set of evaluated variables

virtual const RealMatrix &	all_samples ()
	return the complete set of evaluated samples

virtual const IntResponseMap &	all_responses () const
	return the complete set of computed responses

virtual size_t	num_samples () const
	get the current number of samples

virtual void	sampling_reset (size_t min_samples, bool all_data_flag, bool stats_flag)
	reset sampling iterator to use at least min_samples

virtual void	sampling_reference (size_t samples_ref)
	set reference number of samples, which is a lower bound during reset

virtual void	sampling_increment ()
	increment to next in sequence of refinement samples

virtual void	random_seed (int seed)
	set randomSeed, if present

virtual unsigned short	sampling_scheme () const
	return sampling name

virtual bool	compact_mode () const
	returns Analyzer::compactMode

virtual IntIntPair	estimate_partition_bounds ()
	estimate the minimum and maximum partition sizes that can be utilized by this Iterator

virtual void	declare_sources ()
	Declare sources to the evaluations database.

void	init_communicators (ParLevLIter pl_iter)
	initialize the communicators associated with this Iterator instance

void	set_communicators (ParLevLIter pl_iter)
	set the communicators associated with this Iterator instance

void	free_communicators (ParLevLIter pl_iter)
	free the communicators associated with this Iterator instance

void	resize_communicators (ParLevLIter pl_iter, bool reinit_comms)
	Resize the communicators. This is called from the letter's resize()

void	parallel_configuration_iterator (ParConfigLIter pc_iter)
	set methodPCIter

ParConfigLIter	parallel_configuration_iterator () const
	return methodPCIter

void	parallel_configuration_iterator_map (std::map< size_t, ParConfigLIter > pci_map)
	set methodPCIterMap

std::map< size_t, ParConfigLIter >	parallel_configuration_iterator_map () const
	return methodPCIterMap

void	run (ParLevLIter pl_iter)
	invoke set_communicators(pl_iter) prior to run()

void	run ()
	orchestrate initialize/pre/core/post/finalize phases More...

void	assign_rep (std::shared_ptr< Iterator > iterator_rep)
	replaces existing letter with a new one More...

void	iterated_model (const Model &model)
	set the iteratedModel (iterators and meta-iterators using a single model instance)

Model &	iterated_model ()
	return the iteratedModel (iterators & meta-iterators using a single model instance)

ProblemDescDB &	problem_description_db () const
	return the problem description database (probDescDB)

ParallelLibrary &	parallel_library () const
	return the parallel library (parallelLib)

void	method_name (unsigned short m_name)
	set the method name to an enumeration value

unsigned short	method_name () const
	return the method name via its native enumeration value

void	method_string (const String &m_str)
	set the method name by string

String	method_string () const
	return the method name by string

String	method_enum_to_string (unsigned short method_enum) const
	convert a method name enumeration value to a string

unsigned short	method_string_to_enum (const String &method_str) const
	convert a method name string to an enumeration value

String	submethod_enum_to_string (unsigned short submethod_enum) const
	convert a sub-method name enumeration value to a string

const String &	method_id () const
	return the method identifier (methodId)

int	maximum_evaluation_concurrency () const
	return the maximum evaluation concurrency supported by the iterator

void	maximum_evaluation_concurrency (int max_conc)
	set the maximum evaluation concurrency supported by the iterator

size_t	maximum_iterations () const
	return the maximum iterations for this iterator

void	maximum_iterations (size_t max_iter)
	set the maximum iterations for this iterator

void	convergence_tolerance (Real conv_tol)
	set the method convergence tolerance (convergenceTol)

Real	convergence_tolerance () const
	return the method convergence tolerance (convergenceTol)

void	output_level (short out_lev)
	set the method output level (outputLevel)

short	output_level () const
	return the method output level (outputLevel)

void	summary_output (bool summary_output_flag)
	Set summary output control; true enables evaluation/results summary.

size_t	num_final_solutions () const
	return the number of solutions to retain in best variables/response arrays

void	num_final_solutions (size_t num_final)
	set the number of solutions to retain in best variables/response arrays

void	active_set (const ActiveSet &set)
	set the default active set (for use with iterators that employ evaluate_parameter_sets())

const ActiveSet &	active_set () const
	return the default active set (used by iterators that employ evaluate_parameter_sets())

void	active_set_request_vector (const ShortArray &asv)
	return the default active set request vector (used by iterators that employ evaluate_parameter_sets())

const ShortArray &	active_set_request_vector () const
	return the default active set request vector (used by iterators that employ evaluate_parameter_sets())

void	active_set_request_values (short asv_val)
	return the default active set request vector (used by iterators that employ evaluate_parameter_sets())

void	sub_iterator_flag (bool si_flag)
	set subIteratorFlag (and update summaryOutputFlag if needed)

bool	is_null () const
	function to check iteratorRep (does this envelope contain a letter?)

std::shared_ptr< Iterator >	iterator_rep () const
	returns iteratorRep for access to derived class member functions that are not mapped to the top Iterator level

virtual void	eval_tag_prefix (const String &eval_id_str)
	set the hierarchical eval ID tag prefix More...

std::shared_ptr< TraitsBase >	traits () const
	returns methodTraits for access to derived class member functions that are not mapped to the top TraitsBase level

bool	top_level ()
	Return whether the iterator is the top level iterator.

void	top_level (bool tflag)
	Set the iterator's top level flag.

Protected Member Functions
void	LoadDakotaResponses (const JEGA::Utilities::Design &from, Variables &vars, Response &resp) const
	Loads the JEGA-style Design class into equivalent Dakota-style Variables and Response objects. More...

void	ReCreateTheParameterDatabase ()
	Destroys the current parameter database and creates a new empty one.

void	LoadTheParameterDatabase ()
	Reads information out of the known Dakota::ProblemDescDB and puts it into the current parameter database. More...

void	LoadAlgorithmConfig (JEGA::FrontEnd::AlgorithmConfig &aConfig)
	Completely initializes the supplied algorithm configuration. More...

void	LoadProblemConfig (JEGA::FrontEnd::ProblemConfig &pConfig)
	Completely initializes the supplied problem configuration. More...

void	LoadTheDesignVariables (JEGA::FrontEnd::ProblemConfig &pConfig)
	Adds DesignVariableInfo objects into the problem configuration object. More...

void	LoadTheObjectiveFunctions (JEGA::FrontEnd::ProblemConfig &pConfig)
	Adds ObjectiveFunctionInfo objects into the problem configuration object. More...

void	LoadTheConstraints (JEGA::FrontEnd::ProblemConfig &pConfig)
	Adds ConstraintInfo objects into the problem configuration object. More...

void	GetBestSolutions (const JEGA::Utilities::DesignOFSortSet &from, const JEGA::Algorithms::GeneticAlgorithm &theGA, std::multimap< RealRealPair, JEGA::Utilities::Design * > &designSortMap)
	Returns up to _numBest designs sorted by DAKOTA's fitness (L2 constraint violation, then utopia or objective), taking into account the algorithm type. The front of the returned map can be viewed as a single "best". More...

void	GetBestMOSolutions (const JEGA::Utilities::DesignOFSortSet &from, const JEGA::Algorithms::GeneticAlgorithm &theGA, std::multimap< RealRealPair, JEGA::Utilities::Design * > &designSortMap)
	Retreive the best Designs from a set of solutions assuming that they are generated by a multi objective algorithm. More...

void	GetBestSOSolutions (const JEGA::Utilities::DesignOFSortSet &from, const JEGA::Algorithms::GeneticAlgorithm &theGA, std::multimap< RealRealPair, JEGA::Utilities::Design * > &designSortMap)
	Retreive the best Designs from a set of solutions assuming that they are generated by a single objective algorithm. More...

JEGA::DoubleMatrix	ToDoubleMatrix (const VariablesArray &variables) const
	Converts the items in a VariablesArray into a DoubleMatrix whereby the items in the matrix are the design variables. More...

Protected Member Functions inherited from Optimizer
	Optimizer (std::shared_ptr< TraitsBase > traits)
	default constructor

	Optimizer (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits)
	alternate constructor; accepts a model

	Optimizer (unsigned short method_name, Model &model, std::shared_ptr< TraitsBase > traits)
	alternate constructor for "on the fly" instantiations

	Optimizer (unsigned short method_name, size_t num_cv, size_t num_div, size_t num_dsv, size_t num_drv, size_t num_lin_ineq, size_t num_lin_eq, size_t num_nln_ineq, size_t num_nln_eq, std::shared_ptr< TraitsBase > traits)
	alternate constructor for "on the fly" instantiations

	~Optimizer ()
	destructor

void	initialize_run ()

void	post_run (std::ostream &s)

void	finalize_run ()
	utility function to perform common operations following post_run(); deallocation and resetting of instance pointers More...

void	print_results (std::ostream &s, short results_state=FINAL_RESULTS)

void	configure_constraint_maps ()

int	configure_inequality_constraints (CONSTRAINT_TYPE ctype)

void	configure_equality_constraints (CONSTRAINT_TYPE ctype, size_t index_offset)

template<typename AdapterT >
void	get_linear_constraints_and_bounds (typename AdapterT::VecT &lin_ineq_lower_bnds, typename AdapterT::VecT &lin_ineq_upper_bnds, typename AdapterT::VecT &lin_eq_targets, typename AdapterT::MatT &lin_ineq_coeffs, typename AdapterT::MatT &lin_eq_coeffs)

Protected Member Functions inherited from Minimizer
	Minimizer (std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	default constructor

	Minimizer (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	standard constructor More...

	Minimizer (unsigned short method_name, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate constructor for "on the fly" instantiations

	Minimizer (unsigned short method_name, size_t num_lin_ineq, size_t num_lin_eq, size_t num_nln_ineq, size_t num_nln_eq, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate constructor for "on the fly" instantiations

	Minimizer (Model &model, size_t max_iter, size_t max_eval, Real conv_tol, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate constructor for "on the fly" instantiations

	~Minimizer ()
	destructor

void	update_from_model (const Model &model)
	set inherited data attributes based on extractions from incoming model

const Model &	algorithm_space_model () const

Model	original_model (unsigned short recasts_left=0) const
	Return a shallow copy of the original model this Iterator was originally passed, optionally leaving recasts_left on top of it.

void	data_transform_model ()
	Wrap iteratedModel in a RecastModel that subtracts provided observed data from the primary response functions (variables and secondary responses are unchanged) More...

void	scale_model ()
	Wrap iteratedModel in a RecastModel that performs variable and/or response scaling. More...

Real	objective (const RealVector &fn_vals, const BoolDeque &max_sense, const RealVector &primary_wts) const
	compute a composite objective value from one or more primary functions More...

Real	objective (const RealVector &fn_vals, size_t num_fns, const BoolDeque &max_sense, const RealVector &primary_wts) const
	compute a composite objective with specified number of source primary functions, instead of userPrimaryFns More...

void	objective_gradient (const RealVector &fn_vals, const RealMatrix &fn_grads, const BoolDeque &max_sense, const RealVector &primary_wts, RealVector &obj_grad) const
	compute the gradient of the composite objective function

void	objective_gradient (const RealVector &fn_vals, size_t num_fns, const RealMatrix &fn_grads, const BoolDeque &max_sense, const RealVector &primary_wts, RealVector &obj_grad) const
	compute the gradient of the composite objective function More...

void	objective_hessian (const RealVector &fn_vals, const RealMatrix &fn_grads, const RealSymMatrixArray &fn_hessians, const BoolDeque &max_sense, const RealVector &primary_wts, RealSymMatrix &obj_hess) const
	compute the Hessian of the composite objective function

void	objective_hessian (const RealVector &fn_vals, size_t num_fns, const RealMatrix &fn_grads, const RealSymMatrixArray &fn_hessians, const BoolDeque &max_sense, const RealVector &primary_wts, RealSymMatrix &obj_hess) const
	compute the Hessian of the composite objective function More...

virtual void	archive_best_results ()
	top-level archival method

void	archive_best_variables (const bool active_only=false) const
	archive best variables for the index'th final solution

void	archive_best_objective_functions () const
	archive the index'th set of objective functions

void	archive_best_constraints () const
	archive the index'th set of constraints

void	archive_best_residuals () const
	Archive residuals when calibration terms are used.

void	resize_best_vars_array (size_t newsize)
	Safely resize the best variables array to newsize taking into account the envelope-letter design pattern and any recasting. More...

void	resize_best_resp_array (size_t newsize)
	Safely resize the best response array to newsize taking into account the envelope-letter design pattern and any recasting. More...

bool	local_recast_retrieve (const Variables &vars, Response &response) const
	infers MOO/NLS solution from the solution of a single-objective optimizer and returns true if lookup succeeds More...

Protected Member Functions inherited from Iterator
	Iterator (BaseConstructor, ProblemDescDB &problem_db, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	constructor initializes the base class part of letter classes (BaseConstructor overloading avoids infinite recursion in the derived class constructors - Coplien, p. 139) More...

	Iterator (NoDBBaseConstructor, unsigned short method_name, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate constructor for base iterator classes constructed on the fly More...

	Iterator (NoDBBaseConstructor, unsigned short method_name, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate constructor for base iterator classes constructed on the fly More...

	Iterator (NoDBBaseConstructor, Model &model, size_t max_iter, size_t max_eval, Real conv_tol, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate envelope constructor for instantiations without ProblemDescDB More...

virtual void	derived_init_communicators (ParLevLIter pl_iter)
	derived class contributions to initializing the communicators associated with this Iterator instance

StrStrSizet	run_identifier () const
	get the unique run identifier based on method name, id, and number of executions

void	initialize_model_graphics (Model &model, int iterator_server_id)
	helper function that encapsulates initialization operations, modular on incoming Model instance More...

void	export_final_surrogates (Model &data_fit_surr_model)
	export final surrogates generated, e.g., GP in EGO and friends More...

Private Attributes
EvaluatorCreator *	_theEvalCreator
	A pointer to an EvaluatorCreator used to create the evaluator used by JEGA in Dakota (a JEGAEvaluator).

JEGA::Utilities::ParameterDatabase *	_theParamDB
	A pointer to the ParameterDatabase from which all parameters are retrieved by the created algorithms.

VariablesArray	_initPts
	An array of initial points to use as an initial population. More...

Additional Inherited Members
Static Public Member Functions inherited from Optimizer
static void	not_available (const std::string &package_name)
	Static helper function: third-party opt packages which are not available.

Static Public Member Functions inherited from Minimizer
static Real	sum_squared_residuals (size_t num_pri_fns, const RealVector &residuals, const RealVector &weights)
	return weighted sum of squared residuals

static void	print_residuals (size_t num_terms, const RealVector &best_terms, const RealVector &weights, size_t num_best, size_t best_index, std::ostream &s)
	print num_terms residuals and misfit for final results

static void	print_model_resp (size_t num_pri_fns, const RealVector &best_fns, size_t num_best, size_t best_index, std::ostream &s)
	print the original user model resp in the case of data transformations

static void	print_best_eval_ids (const String &interface_id, const Variables &best_vars, const ActiveSet &active_set, std::ostream &s)
	print best evaluation matching vars and set, or partial matches with matching variables only. More...

Static Protected Member Functions inherited from Iterator
static void	gnewton_set_recast (const Variables &recast_vars, const ActiveSet &recast_set, ActiveSet &sub_model_set)
	conversion of request vector values for the Gauss-Newton Hessian approximation More...

Protected Attributes inherited from Optimizer
size_t	numObjectiveFns
	number of objective functions (iterator view)

bool	localObjectiveRecast
	flag indicating whether local recasting to a single objective is used

Optimizer *	prevOptInstance
	pointer containing previous value of optimizerInstance

int	numNonlinearIneqConstraintsFound
	number of nonlinear ineq constraints actually used (based on conditional and bigRealBoundSize

std::vector< int >	constraintMapIndices
	map from Dakota constraint number to APPS constraint number

std::vector< double >	constraintMapMultipliers
	multipliers for constraint transformations

std::vector< double >	constraintMapOffsets
	offsets for constraint transformations

Protected Attributes inherited from Minimizer
size_t	numFunctions
	number of response functions

size_t	numContinuousVars
	number of active continuous vars

size_t	numDiscreteIntVars
	number of active discrete integer vars

size_t	numDiscreteStringVars
	number of active discrete string vars

size_t	numDiscreteRealVars
	number of active discrete real vars

Real	constraintTol
	optimizer/least squares constraint tolerance

Real	bigRealBoundSize
	cutoff value for inequality constraint and continuous variable bounds

int	bigIntBoundSize
	cutoff value for discrete variable bounds

size_t	numNonlinearIneqConstraints
	number of nonlinear inequality constraints

size_t	numNonlinearEqConstraints
	number of nonlinear equality constraints

size_t	numLinearIneqConstraints
	number of linear inequality constraints

size_t	numLinearEqConstraints
	number of linear equality constraints

size_t	numNonlinearConstraints
	total number of nonlinear constraints

size_t	numLinearConstraints
	total number of linear constraints

size_t	numConstraints
	total number of linear and nonlinear constraints

bool	optimizationFlag
	flag for use where optimization and NLS must be distinguished

size_t	numUserPrimaryFns
	number of objective functions or least squares terms in the inbound model; always initialize at Minimizer, even if overridden later

size_t	numIterPrimaryFns
	number of objective functions or least squares terms in iterator's view, after transformations; always initialize at Minimizer, even if overridden later

bool	boundConstraintFlag
	convenience flag for denoting the presence of user-specified bound constraints. Used for method selection and error checking.

bool	speculativeFlag
	flag for speculative gradient evaluations

bool	calibrationDataFlag
	flag indicating whether user-supplied calibration data is active

ExperimentData	expData
	Container for experimental data to which to calibrate model using least squares or other formulations which minimize SSE.

size_t	numExperiments
	number of experiments

size_t	numTotalCalibTerms
	number of total calibration terms (sum over experiments of number of experimental data per experiment, including field data)

Model	dataTransformModel
	Shallow copy of the data transformation model, when present (cached in case further wrapped by other transformations)

bool	scaleFlag
	whether Iterator-level scaling is active

Model	scalingModel
	Shallow copy of the scaling transformation model, when present (cached in case further wrapped by other transformations)

Minimizer *	prevMinInstance
	pointer containing previous value of minimizerInstance

bool	vendorNumericalGradFlag
	convenience flag for gradient_type == numerical && method_source == vendor

std::shared_ptr< TPLDataTransfer >	dataTransferHandler
	Emerging helper class for handling data transfers to/from Dakota and the underlying TPL.

Protected Attributes inherited from Iterator
ProblemDescDB &	probDescDB
	class member reference to the problem description database More...

ParallelLibrary &	parallelLib
	class member reference to the parallel library

ParConfigLIter	methodPCIter
	the active ParallelConfiguration used by this Iterator instance

Model	iteratedModel
	the model to be iterated (for iterators and meta-iterators employing a single model instance)

size_t	myModelLayers
	number of Models locally (in Iterator or derived classes) wrapped around the initially passed in Model

unsigned short	methodName
	name of the iterator (the user's method spec)

Real	convergenceTol
	iteration convergence tolerance

size_t	maxIterations
	maximum number of iterations for the method

size_t	maxFunctionEvals
	maximum number of fn evaluations for the method

int	maxEvalConcurrency
	maximum number of concurrent model evaluations More...

ActiveSet	activeSet
	the response data requirements on each function evaluation

size_t	numFinalSolutions
	number of solutions to retain in best variables/response arrays

VariablesArray	bestVariablesArray
	collection of N best solution variables found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone)

ResponseArray	bestResponseArray
	collection of N best solution responses found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone)

bool	subIteratorFlag
	flag indicating if this Iterator is a sub-iterator (NestedModel::subIterator or DataFitSurrModel::daceIterator)

short	outputLevel
	output verbosity level: {SILENT,QUIET,NORMAL,VERBOSE,DEBUG}_OUTPUT

bool	summaryOutputFlag
	flag for summary output (evaluation stats, final results); default true, but false for on-the-fly (helper) iterators and sub-iterator use cases

ResultsManager &	resultsDB
	reference to the global iterator results database

EvaluationStore &	evaluationsDB
	reference to the global evaluation database

EvaluationsDBState	evaluationsDBState
	State of evaluations DB for this iterator.

ResultsNames	resultsNames
	valid names for iterator results

std::shared_ptr< TraitsBase >	methodTraits
	pointer that retains shared ownership of a TraitsBase object, or child thereof

bool	topLevel
	Whether this is the top level iterator.

bool	exportSurrogate = false
	whether to export final surrogates

String	surrExportPrefix
	base filename for exported surrogates

unsigned short	surrExportFormat = NO_MODEL_FORMAT
	(bitwise) format(s) to export

Static Protected Attributes inherited from Optimizer
static Optimizer *	optimizerInstance
	pointer to Optimizer instance used in static member functions

Static Protected Attributes inherited from Minimizer
static Minimizer *	minimizerInstance
	pointer to Minimizer used in static member functions

Detailed Description

A version of Dakota::Optimizer for instantiation of John Eddy's Genetic Algorithms (JEGA).

This class encapsulates the necessary functionality for creating and properly initializing the JEGA algorithms (MOGA and SOGA).

Constructor & Destructor Documentation

◆ JEGAOptimizer()

JEGAOptimizer	(	ProblemDescDB &	problem_db,
		Model &	model
	)

Constructs a JEGAOptimizer class object.

This method does some of the initialization work for the algorithm. In particular, it initialized the JEGA core.

Parameters

problem_db	The Dakota::ProblemDescDB with information on how the algorithm controls should be set.
model	The Dakota::Model that will be used by this optimizer for problem information, etc.

References JEGAOptimizer::_theEvalCreator, ProblemDescDB::get_int(), ProblemDescDB::get_short(), Iterator::iteratedModel, JEGAOptimizer::LoadTheParameterDatabase(), Iterator::maxEvalConcurrency, Iterator::methodName, Iterator::numFinalSolutions, and Iterator::probDescDB.

Member Function Documentation

◆ LoadDakotaResponses()

void LoadDakotaResponses	(	const JEGA::Utilities::Design &	from,
		Dakota::Variables &	vars,
		Dakota::Response &	resp
	)		const

protected

Loads the JEGA-style Design class into equivalent Dakota-style Variables and Response objects.

This version is meant for the case where a Variables and a Response object exist and just need to be loaded.

Parameters

from	The JEGA Design class object from which to extract the variable and response information for Dakota.
vars	The Dakota::Variables object into which to load the design variable values of from.
resp	The Dakota::Response object into which to load the objective function and constraint values of from.

References Variables::continuous_variables(), Variables::discrete_int_variables(), Variables::discrete_real_variables(), Variables::discrete_string_variable(), Response::function_values(), Response::num_functions(), and Dakota::set_index_to_value().

◆ LoadTheParameterDatabase()

void LoadTheParameterDatabase ( )

protected

Reads information out of the known Dakota::ProblemDescDB and puts it into the current parameter database.

This should be called from the JEGAOptimizer constructor since it is the only time when the problem description database is certain to be configured to supply data for this optimizer.

Referenced by JEGAOptimizer::JEGAOptimizer().

◆ LoadAlgorithmConfig()

void LoadAlgorithmConfig ( JEGA::FrontEnd::AlgorithmConfig & aConfig )

protected

Completely initializes the supplied algorithm configuration.

This loads the supplied configuration object with appropriate data retrieved from the parameter database.

Parameters

aConfig The algorithm configuration object to load.

◆ LoadProblemConfig()

void LoadProblemConfig ( JEGA::FrontEnd::ProblemConfig & pConfig )

protected

Completely initializes the supplied problem configuration.

This loads the fresh configuration object using the LoadTheDesignVariables, LoadTheObjectiveFunctions, and LoadTheConstraints methods.

Parameters

pConfig The problem configuration object to load.

◆ LoadTheDesignVariables()

void LoadTheDesignVariables ( JEGA::FrontEnd::ProblemConfig & pConfig )

protected

Adds DesignVariableInfo objects into the problem configuration object.

This retrieves design variable information from the ParameterDatabase and creates DesignVariableInfo's from it.

Parameters

pConfig The problem configuration object to load.

◆ LoadTheObjectiveFunctions()

void LoadTheObjectiveFunctions ( JEGA::FrontEnd::ProblemConfig & pConfig )

protected

Adds ObjectiveFunctionInfo objects into the problem configuration object.

This retrieves objective function information from the ParameterDatabase and creates ObjectiveFunctionInfo's from it.

Parameters

pConfig The problem configuration object to load.

◆ LoadTheConstraints()

void LoadTheConstraints ( JEGA::FrontEnd::ProblemConfig & pConfig )

protected

Adds ConstraintInfo objects into the problem configuration object.

This retrieves constraint function information from the ParameterDatabase and creates ConstraintInfo's from it.

Parameters

pConfig The problem configuration object to load.

References Dakota::asstring(), and Dakota::copy_row_vector().

◆ GetBestSolutions()

void GetBestSolutions	(	const JEGA::Utilities::DesignOFSortSet &	from,
		const JEGA::Algorithms::GeneticAlgorithm &	theGA,
		std::multimap< RealRealPair, JEGA::Utilities::Design * > &	designSortMap
	)

protected

Returns up to _numBest designs sorted by DAKOTA's fitness (L2 constraint violation, then utopia or objective), taking into account the algorithm type. The front of the returned map can be viewed as a single "best".

Parameters

from	The full set of designs returned by the solver.
theGA	The GA used to generate this set; needed for its weights in the SO case, provided to both for consistency
designSortMap	Map of best solutions with key pair<constraintViolation, fitness>

eventually this functionality must be moved into a separate post-processing application for MO datasets.

◆ GetBestMOSolutions()

void GetBestMOSolutions	(	const JEGA::Utilities::DesignOFSortSet &	from,
		const JEGA::Algorithms::GeneticAlgorithm &	theGA,
		std::multimap< RealRealPair, JEGA::Utilities::Design * > &	designSortMap
	)

protected

Retreive the best Designs from a set of solutions assuming that they are generated by a multi objective algorithm.

eventually this functionality must be moved into a separate post-processing application for MO datasets.

◆ GetBestSOSolutions()

void GetBestSOSolutions	(	const JEGA::Utilities::DesignOFSortSet &	from,
		const JEGA::Algorithms::GeneticAlgorithm &	theGA,
		std::multimap< RealRealPair, JEGA::Utilities::Design * > &	designSortMap
	)

protected

Retreive the best Designs from a set of solutions assuming that they are generated by a single objective algorithm.

eventually this functionality must be moved into a separate post-processing application for MO datasets.

References Dakota::abort_handler().

◆ ToDoubleMatrix()

JEGA::DoubleMatrix ToDoubleMatrix ( const VariablesArray & variables ) const

protected

Converts the items in a VariablesArray into a DoubleMatrix whereby the items in the matrix are the design variables.

The matrix will not contain responses but when being used by Dakota, this doesn't matter. JEGA will attempt to re-evaluate these points but Dakota will recognize that they do not require re-evaluation and thus it will be a cheap operation.

Parameters

variables The array of DakotaVariables objects to use as the contents of the returned matrix.

Returns: The matrix created using the supplied VariablesArray.

◆ core_run()

void core_run ( )

virtual

Performs the iterations to determine the optimal set of solutions.

Override of pure virtual method in Optimizer base class.

The extraction of parameter values actually occurs in this method when the JEGA::FrontEnd::Driver::ExecuteAlgorithm is called. Also the loading of the problem and algorithm configurations occurs in this method. That way, if it is called more than once and the algorithm or problem has changed, it will be accounted for.

Reimplemented from Iterator.

References JEGAOptimizer::Driver::DestroyAlgorithm(), JEGAOptimizer::Driver::ExtractAllData(), and JEGAOptimizer::Driver::PerformIterations().

◆ accepts_multiple_points()

bool accepts_multiple_points ( ) const

virtual

Overridden to return true since JEGA algorithms can accept multiple initial points.

Returns: true, always.

Reimplemented from Iterator.

◆ returns_multiple_points()

bool returns_multiple_points ( ) const

virtual

Overridden to return true since JEGA algorithms can return multiple final points.

Returns: true, always.

Reimplemented from Iterator.

◆ initial_points() [1/2]

void initial_points ( const VariablesArray & pts )

virtual

Overridden to assign the _initPts member variable to the passed in collection of Dakota::Variables.

Parameters

pts	The array of initial points for the JEGA algorithm created and run by this JEGAOptimizer.

Reimplemented from Iterator.

◆ initial_points() [2/2]

const VariablesArray & initial_points ( ) const

virtual

Overridden to return the collection of initial points for the JEGA algorithm created and run by this JEGAOptimizer.

Returns: The collection of initial points for the JEGA algorithm created and run by this JEGAOptimizer.

Reimplemented from Iterator.

Member Data Documentation

◆ _initPts

VariablesArray _initPts

private

An array of initial points to use as an initial population.

This member is here to help support the use of JEGA algorithms in Dakota strategies. If this array is populated, then whatever initializer is specified will be ignored and the DoubleMatrix initializer will be used instead on a matrix created from the data in this array.

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

Classes

Public Member Functions

Protected Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ JEGAOptimizer()

Member Function Documentation

◆ LoadDakotaResponses()

◆ LoadTheParameterDatabase()

◆ LoadAlgorithmConfig()

◆ LoadProblemConfig()

◆ LoadTheDesignVariables()

◆ LoadTheObjectiveFunctions()

◆ LoadTheConstraints()

◆ GetBestSolutions()

◆ GetBestMOSolutions()

◆ GetBestSOSolutions()

◆ ToDoubleMatrix()

◆ core_run()

◆ accepts_multiple_points()

◆ returns_multiple_points()

◆ initial_points() [1/2]

◆ initial_points() [2/2]

Member Data Documentation

◆ _initPts