問題

Layer 1: Use

saealib.Problem	Definition of optimization problem.
saealib.PymooProblem	Adapter wrapping a pymoo Problem instance as a saealib Problem.
saealib.InequalityConstraint	A single inequality constraint: g(x) <= threshold.
saealib.EqualityConstraint	A single equality constraint: h(x) = 0, satisfied within a tolerance.
saealib.ConstraintHandler	Pluggable constraint-processing strategy.
saealib.StaticToleranceHandler	Default constraint handler reproducing the static-tolerance behavior.
saealib.EpsilonConstraintHandler	Dynamic ε-constraint handler.
saealib.GradientRepairHandler	Gradient-based constraint repair handler.

saealib.linear_epsilon_schedule(eps0, n_gen)

Return a linear ε schedule: eps0 at gen 0, 0.0 at gen n_gen.

パラメータ:

• eps0 (float) -- Initial ε value (gen 0).

• n_gen (int) -- Generation at which ε reaches 0.

戻り値:

schedule(gen) -> float

戻り値の型:

callable

saealib.exponential_epsilon_schedule(eps0, decay)

Return an exponential ε schedule: eps0 * decay**gen.

パラメータ:

• eps0 (float) -- Initial ε value (gen 0).

• decay (float) -- Multiplicative decay factor per generation (0 < decay < 1).

戻り値:

schedule(gen) -> float

戻り値の型:

callable

Problemはspaceを通じてSearchSpaceを受け取り、Genome表現と評価入力への変換を管理できます。 詳細はProblemとSearchSpaceを参照してください。