Algorithms

sklearn_genetic.algorithms.eaMuCommaLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen, stats=None, halloffame=None, callbacks: Optional[Union[list, collections.abc.Callable]] = None, verbose=True)[source]

The base implementation is directly taken from: https://github.com/DEAP/deap/blob/master/deap/algorithms.py

This is the \((\mu~,~\lambda)\) evolutionary algorithm.

population: A list of individuals.

Population resulting of the iteration process.

toolbox: A Toolbox

Contains the evolution operators.

mu: int, default=None,

The number of individuals to select for the next generation.

lambda_: int, default=None

The number of children to produce at each generation.

cxpb: float, default=None

The probability that an offspring is produced by crossover.

mutpb: float, default=None

The probability that an offspring is produced by mutation.

ngen: int, default=None

The number of generation.

stats: A Statistics

Object that is updated inplace, optional.

halloffame: A HallOfFame

Object that will contain the best individuals, optional.

callbacks: list or Callable

One or a list of the callbacks methods available in the package.

verbose: bool, default=True

Whether or not to log the statistics.

Returns
pop: list

The final population.

log: Logbook

Statistics of the evolution.

n_gen: int

Number of generations used.

sklearn_genetic.algorithms.eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen, stats=None, halloffame=None, callbacks: Optional[Union[list, collections.abc.Callable]] = None, verbose=True)[source]

The base implementation is directly taken from: https://github.com/DEAP/deap/blob/master/deap/algorithms.py

This is the \((\mu + \lambda)\) evolutionary algorithm.

population: A list of individuals.

Population resulting of the iteration process.

toolbox: A Toolbox

Contains the evolution operators.

mu: int, default=None

The number of individuals to select for the next generation.

lambda_: int, default=None

The number of children to produce at each generation.

cxpb: float, default=None

The probability that an offspring is produced by crossover.

mutpb: float, default=None

The probability that an offspring is produced by mutation.

ngen: int, default=None

The number of generation.

stats: A Statistics

Object that is updated inplace, optional.

halloffame: A HallOfFame

Object that will contain the best individuals, optional.

callbacks: list or Callable

One or a list of the callbacks methods available in the package.

verbose: bool, default=True

Whether or not to log the statistics.

Returns
pop: list

The final population.

log: Logbook

Statistics of the evolution.

n_gen: int

Number of generations used.

sklearn_genetic.algorithms.eaSimple(population, toolbox, cxpb, mutpb, ngen, stats=None, halloffame=None, callbacks=None, verbose=True)[source]

The base implementation is directly taken from: https://github.com/DEAP/deap/blob/master/deap/algorithms.py

This algorithm reproduce the simplest evolutionary algorithm as presented in chapter 7 of Back2000.

population: A list of individuals.

Population resulting of the iteration process.

toolbox: A Toolbox

Contains the evolution operators.

cxpb: float, default=None

The probability of mating two individuals.

mutpb: float, default=None

The probability of mutating an individual.

ngen: int, default=None

The number of generation.

stats: A Statistics

Object that is updated inplace, optional.

halloffame: A HallOfFame

Object that will contain the best individuals, optional.

callbacks: list or callable

One or a list of the callbacks methods available in the package.

verbose: bool, default=True

Whether or not to log the statistics.

Returns
pop: list

The final population.

log: Logbook

Statistics of the evolution.

n_gen: int

Number of generations used.