evox.problems.numerical.basic

Module Contents

Classes

ShiftAffineNumericalProblem	A numerical problem with a shift and affine transformations to the input points.
Ackley	The Ackley function whose minimum is x = [0, …, 0]
Griewank	The Griewank function whose minimum is x = [0, …, 0]
Rastrigin	The Rastrigin function whose minimum is x = [0, …, 0]
Rosenbrock	The Rosenbrock function whose minimum is x = [1, …, 1]
Schwefel	The Schwefel function whose minimum is x = [420.9687, …, 420.9687]
Sphere	The sphere function whose minimum is x = [0, …, 0]
Ellipsoid	The Ellipsoid function whose minimum is x = [0, …, 0]
Zakharov	The Zakharov function whose minimum is x = [0, …, 0]
Levy	The Levy function whose minimum is x = [1, …, 1]

Functions

ackley_func
griewank_func
rastrigin_func
rosenbrock_func
schwefel_func
sphere_func
ellipsoid_func
zakharov_func
levy_func

API

class evox.problems.numerical.basic.ShiftAffineNumericalProblem(shift: torch.Tensor | None = None, affine: torch.Tensor | None = None)

Bases: evox.core.Problem

A numerical problem with a shift and affine transformations to the input points.

Initialization

Initialize the ShiftAffineNumericalProblem.

Parameters:

• shift – The shift vector. Defaults to None. None represents no shift.

• affine – The affine transformation matrix. Defaults to None. None represents no affine transformation.

evaluate(pop: torch.Tensor) → torch.Tensor

Evaluate the given population by shifting and applying an affine transformation to the input points first, and then evaluating the points with the actual function.

Parameters:

pop – The population of points to evaluate.

Returns:

The evaluated fitness of the population.

class evox.problems.numerical.basic.Ackley(a: float = 20.0, b: float = 0.2, c: float = 2 * torch.pi, **kwargs)

Bases: evox.problems.numerical.basic.ShiftAffineNumericalProblem

The Ackley function whose minimum is x = [0, …, 0]

Initialization

Initialize the Ackley function with the given parameters.

Parameters:

• a – The parameter \(a\) in the equation. Defaults to 20.0.

• b – The parameter \(b\) in the equation. Defaults to 0.2.

• c – The parameter \(c\) in the equation. Defaults to 2 * pi.

• **kwargs – The keyword arguments (shift and affine) to pass to the superclass ShiftAffineNumericalProblem.

_true_evaluate(x: torch.Tensor) → torch.Tensor

class evox.problems.numerical.basic.Griewank(**kwargs)

Bases: evox.problems.numerical.basic.ShiftAffineNumericalProblem

The Griewank function whose minimum is x = [0, …, 0]

Initialization

Initialize the Griewank function with the given parameters.

Parameters:

**kwargs – The keyword arguments (shift and affine) to pass to the superclass ShiftAffineNumericalProblem.

_true_evaluate(x: torch.Tensor) → torch.Tensor

class evox.problems.numerical.basic.Rastrigin(**kwargs)

Bases: evox.problems.numerical.basic.ShiftAffineNumericalProblem

The Rastrigin function whose minimum is x = [0, …, 0]

Initialization

Initialize the Griewank function with the given parameters.

Parameters:

**kwargs – The keyword arguments (shift and affine) to pass to the superclass ShiftAffineNumericalProblem.

_true_evaluate(x: torch.Tensor) → torch.Tensor

class evox.problems.numerical.basic.Rosenbrock(**kwargs)

Bases: evox.problems.numerical.basic.ShiftAffineNumericalProblem

The Rosenbrock function whose minimum is x = [1, …, 1]

Initialization

Initialize the Griewank function with the given parameters.

Parameters:

**kwargs – The keyword arguments (shift and affine) to pass to the superclass ShiftAffineNumericalProblem.

_true_evaluate(x: torch.Tensor) → torch.Tensor

class evox.problems.numerical.basic.Schwefel(**kwargs)

Bases: evox.problems.numerical.basic.ShiftAffineNumericalProblem

The Schwefel function whose minimum is x = [420.9687, …, 420.9687]

Initialization

Initialize the Griewank function with the given parameters.

Parameters:

**kwargs – The keyword arguments (shift and affine) to pass to the superclass ShiftAffineNumericalProblem.

_true_evaluate(x: torch.Tensor) → torch.Tensor

class evox.problems.numerical.basic.Sphere(**kwargs)

Bases: evox.problems.numerical.basic.ShiftAffineNumericalProblem

The sphere function whose minimum is x = [0, …, 0]

Initialization

Initialize the Griewank function with the given parameters.

Parameters:

**kwargs – The keyword arguments (shift and affine) to pass to the superclass ShiftAffineNumericalProblem.

_true_evaluate(x: torch.Tensor) → torch.Tensor

class evox.problems.numerical.basic.Ellipsoid(**kwargs)

Bases: evox.problems.numerical.basic.ShiftAffineNumericalProblem

The Ellipsoid function whose minimum is x = [0, …, 0]

Initialization

Initialize the Ellipsoid function with the given parameters.

Parameters:

**kwargs – The keyword arguments (shift and affine) to pass to the superclass ShiftAffineNumericalProblem.

_true_evaluate(x: torch.Tensor) → torch.Tensor

class evox.problems.numerical.basic.Zakharov(**kwargs)

Bases: evox.problems.numerical.basic.ShiftAffineNumericalProblem

The Zakharov function whose minimum is x = [0, …, 0]

Initialization

Initialize the Zakharov function with the given parameters.

Parameters:

**kwargs – The keyword arguments (shift and affine) to pass to the superclass ShiftAffineNumericalProblem.

_true_evaluate(x: torch.Tensor) → torch.Tensor

class evox.problems.numerical.basic.Levy(**kwargs)

Bases: evox.problems.numerical.basic.ShiftAffineNumericalProblem

The Levy function whose minimum is x = [1, …, 1]

Initialization

Initialize the Levy function with the given parameters.

Parameters:

**kwargs – The keyword arguments (shift and affine) to pass to the superclass ShiftAffineNumericalProblem.

_true_evaluate(x: torch.Tensor) → torch.Tensor

evox.problems.numerical.basic.ackley_func(a: float, b: float, c: float, x: torch.Tensor) → torch.Tensor

evox.problems.numerical.basic.griewank_func(x: torch.Tensor) → torch.Tensor

evox.problems.numerical.basic.rastrigin_func(x: torch.Tensor) → torch.Tensor

evox.problems.numerical.basic.rosenbrock_func(x)

evox.problems.numerical.basic.schwefel_func(x)

evox.problems.numerical.basic.sphere_func(x)

evox.problems.numerical.basic.ellipsoid_func(x: torch.Tensor)

evox.problems.numerical.basic.zakharov_func(x: torch.Tensor) → torch.Tensor

evox.problems.numerical.basic.levy_func(x: torch.Tensor) → torch.Tensor