selections

thefittest.utils.selections.proportional_selection(fitness: ndarray[Any, dtype[float64]], rank: ndarray[Any, dtype[float64]], tour_size: int, quantity: int) → ndarray[Any, dtype[int64]]

Perform proportional selection to choose individuals based on their fitness.

Parameters:

fitnessNDArray[np.float64]

A 1D array containing the fitness values of individuals.

rankNDArray[np.float64]

A 1D array containing the rank values of individuals.

tour_sizeint

The size of the tournament selection.

quantityint

The number of individuals to choose.

Returns:

NDArray[np.int64]

A 1D array of selected individuals.

Examples

>>> from thefittest.utils.selections import proportional_selection
>>> import numpy as np
>>>
>>> # Example
>>> fitness_values = np.array([0.8, 0.5, 0.9, 0.3], dtype = np.float64)
>>> rank_values = np.array([3, 2, 4, 1], dtype = np.float64)
>>> tournament_size = 2
>>> num_selected = 2
>>> selected_individuals = proportional_selection(fitness_values, rank_values, tournament_size, num_selected)
>>> print("Selected Individuals:", selected_individuals)
Selected Individuals: ...

thefittest.utils.selections.rank_selection(fitness: ndarray[Any, dtype[float64]], rank: ndarray[Any, dtype[float64]], tour_size: int, quantity: int) → ndarray[Any, dtype[int64]]

Perform rank-based selection to choose individuals based on their rank values.

Parameters:

fitnessNDArray[np.float64]

A 1D array containing the fitness values of individuals.

rankNDArray[np.float64]

A 1D array containing the rank values of individuals. The higher the rank, the better the individual.

tour_sizeint

The size of the tournament selection (not used in rank-based selection).

quantityint

The number of individuals to choose.

Returns:

NDArray[np.int64]

A 1D array of selected individuals based on rank.

Notes

Rank-based selection assigns a probability of selection to each individual based on their rank. Individuals with higher ranks have higher probabilities of being selected.

Examples

>>> from thefittest.utils.selections import rank_selection
>>> import numpy as np
>>>
>>> # Example
>>> fitness_values = np.array([0.8, 0.5, 0.9, 0.3], dtype=np.float64)
>>> rank_values = np.array([3, 2, 4, 1], dtype=np.float64)
>>> tournament_size = 2
>>> num_selected = 2
>>> selected_individuals = rank_selection(fitness_values, rank_values, tournament_size, num_selected)
>>> print("Selected Individuals:", selected_individuals)
Selected Individuals: ...

thefittest.utils.selections.tournament_selection(fitness: ndarray[Any, dtype[float64]], rank: ndarray[Any, dtype[float64]], tour_size: int, quantity: int) → ndarray[Any, dtype[int64]]

Perform tournament selection to choose individuals based on their fitness values.

Parameters:

fitnessNDArray[np.float64]

A 1D array containing the fitness values of individuals.

rankNDArray[np.float64]

A 1D array containing the rank values of individuals (not used in tournament selection).

tour_sizeint

The size of each tournament. Individuals compete in groups of this size, and the winner is selected.

quantityint

The number of individuals to choose.

Returns:

NDArray[np.int64]

A 1D array of selected individuals based on tournament selection.

Notes

Tournament selection involves randomly selecting individuals in groups (tournaments) and choosing the individual with the highest fitness in each tournament. This process is repeated to fill the desired quantity.

Examples

>>> from thefittest.utils.selections import tournament_selection
>>> import numpy as np
>>>
>>> # Example
>>> fitness_values = np.array([0.8, 0.5, 0.9, 0.3], dtype=np.float64)
>>> rank_values = np.array([3, 2, 4, 1], dtype=np.float64)
>>> tournament_size = 2
>>> num_selected = 2
>>> selected_individuals = tournament_selection(fitness_values, rank_values, tournament_size, num_selected)
>>> print("Selected Individuals:", selected_individuals)
Selected Individuals: ...