Modifiers

Modifiers transform test functions to create new optimization challenges. They allow you to test how your optimizer handles various real-world complications.

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Available Modifiers

Noise

Add stochastic noise to function evaluations. Test optimizer robustness to noisy objectives.

Noise

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Why Use Modifiers?

Real-world optimization problems are rarely clean:

• Noisy evaluations: Measurements have variance

• Shifted optima: The optimum may not be at zero

• Rotated spaces: Variables may be correlated

• Scaled outputs: Different magnitude ranges

Modifiers let you test these scenarios systematically.

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Using Modifiers

Modifiers wrap existing test functions:

from surfaces.test_functions.algebraic import SphereFunction
from surfaces.modifiers import GaussianNoise

# Create function with noise modifier
noisy_func = SphereFunction(
    n_dim=5,
    modifiers=[GaussianNoise(sigma=0.1)]
)

# Evaluate - now includes random noise
result = noisy_func({"x0": 0, "x1": 0, "x2": 0, "x3": 0, "x4": 0})

# Get true value without noise
true_result = noisy_func.pure({"x0": 0, "x1": 0, "x2": 0, "x3": 0, "x4": 0})

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Future Modifiers

Planned modifiers for future releases:

• Shift: Move the global optimum to a new location

• Rotation: Apply rotation matrix to input space

• Scaling: Scale input or output values

• Composition: Combine multiple functions

• Discretization: Convert continuous to discrete parameters

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