pycellga.selection package

Submodules

class pycellga.selection.roulette_wheel_selection.RouletteWheelSelection(pop_list: List[Individual] = [], c: int = 0)[source]

Bases: SelectionOperator

RouletteWheelSelection performs a roulette wheel selection on a population of individuals to select parent individuals for crossover.

Parameters:

pop_list (list of Individual) – The population of individuals to select from.
c (int) – The index of the individual to start selection from.

__init__(pop_list: List[Individual] = [], c: int = 0)[source]

Initialize the RouletteWheelSelection object.

Parameters:

pop_list (list of Individual) – The population of individuals to select from.
c (int) – The index of the individual to start selection from.

get_parents() → List[Individual][source]

Perform the roulette wheel selection to get parent individuals.

class pycellga.selection.selection_operator.SelectionOperator[source]

Bases: object

class pycellga.selection.tournament_selection.TournamentSelection(pop_list: List[Individual] = [], c: int = 0, K: int = 2)[source]

Bases: SelectionOperator

TournamentSelection performs a tournament selection on a population of individuals to select parent individuals for crossover.

Parameters:

pop_list (list of Individual) – The population of individuals to select from.
c (int) – The index of the individual to start selection from.
K (int) – The number of individuals to be chosen at random from neighbors.

__init__(pop_list: List[Individual] = [], c: int = 0, K: int = 2)[source]

Initialize the TournamentSelection object.

Parameters:

pop_list (list of Individual) – The population of individuals to select from.
c (int) – The index of the individual to start selection from.
K (int) – The number of individuals to be chosen at random from neighbors.

get_parents() → List[Individual][source]

Perform the tournament selection to get parent individuals.