utilities.fast_non_dominated_sorting
Module Contents
Functions

Returns true if x dominates y. 

Finds the nondominated front from a population of solutions. 

Conduct fast nondominated sorting on a population of solutions. 

Conduct fast nondominated sorting on a population of solutions. 
 utilities.fast_non_dominated_sorting.dominates(x: numpy.ndarray, y: numpy.ndarray) bool [source]
Returns true if x dominates y.
 Parameters:
x (np.ndarray) – First solution. Should be a 1D array of numerics.
y (np.ndarray) – Second solution. Should be the same shape as x.
 Returns:
True if x dominates y, false otherwise.
 Return type:
bool
 utilities.fast_non_dominated_sorting.non_dominated(data: numpy.ndarray) numpy.ndarray [source]
Finds the nondominated front from a population of solutions.
 Parameters:
data (np.ndarray) – 2D array of solutions, with each row being a single solution.
 Returns:
 Boolean array of same length as number of solutions (rows). The value is
true if corresponding solution is nondominated. False otherwise
 Return type:
np.ndarray
 utilities.fast_non_dominated_sorting.fast_non_dominated_sort(data: numpy.ndarray) numpy.ndarray [source]
Conduct fast nondominated sorting on a population of solutions.
 Parameters:
data (np.ndarray) – 2D array of solutions, with each row being a single solution.
 Returns:
 n x f boolean array. n is the number of solutions, f is the number of fronts.
The value of an array element is true if the corresponding solution id (column) belongs in the corresponding front (row).
 Return type:
np.ndarray
 utilities.fast_non_dominated_sorting.fast_non_dominated_sort_indices(data: numpy.ndarray) List[numpy.ndarray] [source]
Conduct fast nondominated sorting on a population of solutions.
This function returns identical results as fast_non_dominated_sort, but in a different format. This function returns an array of solution indices for each front, packed in a list.
 Parameters:
data (np.ndarray) – 2D array of solutions, with each row being a single solution.
 Returns:
 A list with f elements where f is the number of fronts in the data,
arranged in ascending order. Each element is a numpy array of the indices of solutions belonging to the corresponding front.
 Return type:
List[np.ndarray]