saealib.Problem

class saealib.Problem(func, dim, n_obj, direction, lb=None, ub=None, comparator=None, constraints=None, *, eps_cv=1e-06, eps_obj=1e-06, handler=None, variables=None, space=None, evaluation_adapter=None)

ベースクラス: object

Definition of optimization problem.

パラメータ:

• func (Callable[..., Any])

• dim (int | None)

• n_obj (int)

• direction (np.ndarray)

• lb (list[float] | None)

• ub (list[float] | None)

• comparator (Comparator | None)

• constraints (list[InequalityConstraint] | None)

• eps_cv (float)

• eps_obj (float)

• handler (ConstraintHandler | None)

• variables (list[Variable] | None)

• space (SearchSpace | None)

• evaluation_adapter (EvaluationAdapter | None)

dim

Dimension of the design variables.

Type:

int

n_obj

Number of objectives.

Type:

int

direction

Optimization direction per objective. shape = (n_obj, ) Each element must be +1 (maximize) or -1 (minimize).

Type:

np.ndarray

lb

Lower bounds for design variables. shape = (dim, )

Type:

np.ndarray

ub

Upper bounds for design variables. shape = (dim, )

Type:

np.ndarray

variables

Per-dimension variable definitions.

Type:

list[Variable]

comparator

Comparator instance to compare solutions.

Type:

Comparator

eps_cv

Epsilon for constraint violation feasibility threshold. Only used to seed the default handler/comparator at construction time; mutating this attribute afterwards has no runtime effect. The actual running threshold is handler.feasibility_threshold, which the Runner syncs into comparator/pareto_archive every generation. Explicit handlers may therefore use a running threshold intentionally different from this value. This value remains the feasibility basis for Result and summary best_f; an explicit handler with a different running threshold can therefore make reported solutions disagree with the comparator's criterion.

Type:

float

eps_obj

Epsilon for objective value equality comparison.

Type:

float

func

Objective function to evaluate solutions.

Type:

callable -> float

constraints

List of inequality constraint definitions.

Type:

list[InequalityConstraint]

handler

Constraint-handling strategy used to aggregate violations and augment objectives.

Type:

ConstraintHandler

Methods

__init__	Initialize Problem instance.
evaluate	Evaluate the objective function at given solution x.
evaluate_batch	Evaluate the objective and constraint functions for a batch of solutions.
evaluate_constraints	Evaluate all constraint functions at x.
repair	Project x onto valid variable domains.

Method Details

Problem.__init__(func, dim, n_obj, direction, lb=None, ub=None, comparator=None, constraints=None, *, eps_cv=1e-06, eps_obj=1e-06, handler=None, variables=None, space=None, evaluation_adapter=None)

Initialize Problem instance.

パラメータ:

• func (callable -> float) -- Objective function to evaluate solutions.

• dim (int or None) -- Dimension of the design variables.

• n_obj (int) -- Number of objectives.

• direction (np.ndarray) -- Optimization direction per objective. shape = (n_obj, ) Each element must be +1 (maximize) or -1 (minimize).

• lb (list[float], optional) -- Lower bounds for design variables. length = dim. Required when variables is not provided.

• ub (list[float], optional) -- Upper bounds for design variables. length = dim. Required when variables is not provided.

• comparator (Comparator, optional) -- Comparator instance to use. If None, auto-selected based on n_obj: n_obj == 1 -> SingleObjectiveComparator, n_obj > 1 -> NSGA2Comparator.

• constraints (list[InequalityConstraint], optional) -- List of inequality constraint definitions. Default: empty list.

• eps_cv (float, optional) -- Epsilon for constraint violation feasibility threshold. Default: 1e-6.

• eps_obj (float, optional) -- Epsilon for objective value equality comparison. Default: 1e-6.

• handler (ConstraintHandler, optional) -- Constraint-handling strategy. If None, a StaticToleranceHandler (sum-of-violations, fixed eps_cv) is used, reproducing the default behavior.

• variables (list[Variable], optional) -- Per-dimension variable definitions. When provided, lb and ub are derived from the variable bounds. len(variables) must equal dim.

• space (SearchSpace, optional) -- Search space to use for non-vector representations. When omitted, a VectorSpace is constructed from variables or bounds.

• evaluation_adapter (EvaluationAdapter, optional) -- Adapter that transforms genome batches into evaluation payloads.

Problem.evaluate(x, g=None)

Evaluate the objective function at given solution x.

After computing the raw objective, handler.augment_objective is applied so that penalty-based or augmented-Lagrangian handlers can transform the objective using constraint information. The default StaticToleranceHandler leaves the objective unchanged.

パラメータ:

• x (np.ndarray) -- The solution to evaluate.

• g (np.ndarray, optional) -- Pre-computed raw constraint values g(x), shape = (n_constraints, ). When None, constraints are evaluated internally if any are defined. Pass this to avoid re-evaluating constraints when g is already available (e.g. from evaluate_constraints()).

戻り値:

The objective value(s) at solution x. shape = (n_obj, )

戻り値の型:

np.ndarray

Problem.evaluate_batch(x)

Evaluate the objective and constraint functions for a batch of solutions.

The default implementation returns None, meaning batch evaluation is not supported by this Problem -- callers should fall back to the row-by-row path (evaluate() / evaluate_constraints() applied one row at a time). Subclasses (or Problem instances constructed around a batch-capable func, e.g. a vectorized function, a GPU kernel, or an external library such as pymoo that can score many designs in one call) may override this method to evaluate all rows of x in a single call.

The returned values are raw: unlike evaluate(), the objective batch is not passed through handler.augment_objective, and unlike evaluate_constraints(), the constraint batch is not reduced via handler.compute_cv. Callers are expected to apply those transformations themselves afterward, per row.

パラメータ:

x (np.ndarray) -- Batch of solutions to evaluate. shape = (n, dim)

戻り値:

(f_batch, g_batch) if batch evaluation is supported, else None. f_batch : raw objective values. shape = (n, n_obj) g_batch : raw constraint values, in the same column order as self.constraints. shape = (n, n_constraints); (n, 0) when the problem has no constraints. Row order matches the order of rows in x.

戻り値の型:

tuple[np.ndarray, np.ndarray] or None

Problem.evaluate_constraints(x)

Evaluate all constraint functions at x.

パラメータ:

x (np.ndarray) -- The solution to evaluate. shape = (dim, )

戻り値:

• g (np.ndarray) -- Raw constraint values. shape = (n_constraints, ) Empty array when no constraints are defined.

• cv (float) -- Aggregate constraint violation as computed by handler.compute_cv. 0.0 when no constraints are defined.

戻り値の型:

tuple[ndarray, float]

Problem.repair(x)

Project x onto valid variable domains.

Rounds integer dimensions to the nearest integer and clips to bounds. Rounds categorical dimensions to the nearest valid index. Clips continuous dimensions to [lb, ub].

パラメータ:

x (np.ndarray) -- Design variable array. shape = (dim,) or (n, dim).

戻り値:

Repaired array, same shape as x.

戻り値の型:

np.ndarray