deephyper.skopt.moo.MoAugmentedChebyshevFunction#
- class deephyper.skopt.moo.MoAugmentedChebyshevFunction(n_objectives: int = 1, weight=None, weight_sampling_periode: int = 1, utopia_point=None, random_state=None, alpha: float = 0.001)[source]#
Bases:
MoScalarFunction
This scalarizing function computes a sum of weighted infinity- and 1-norms of the individual objective values (after automatically scaling them in [0, 1]).
- Parameters:
n_objectives (int, optional) – Number of objective functions. Defaults to
1
.weight (float or 1-D array, optional) – Array of weights for each objective function. Defaults to
None
.weight_sampling_periode (int, optional) – Sampling periode for the weight vector. Defaults to
5
.utopia_point (float or 1-D array, optional) – Array of reference values for each objective function. Defaults to
None
.random_state (int, optional) – Random seed. Defaults to
None
.penalty (float, optional) – Value of weight given to 1-norm. Defaults to
0.001
.
Methods
Compute normalization constants based on the history of evaluated objective values.
Convert the input array (or scalar) into a scalar value.
update_weight
- normalize(yi)#
Compute normalization constants based on the history of evaluated objective values.
- Parameters:
yi (array) – Array of evaluated objective values.
- Raises:
ValueError – Raised if yi is not a list of scalars each of length _n_objectives.