metaheuristic_designer.strategies.classic.GA#

class GA(initializer, mutation_op, crossover_op, parent_sel, survivor_sel, name='GA', mutation_prob=0.1, crossover_prob=0.9, rng=None, **kwargs)[source]#

Genetic Algorithm.

Combines crossover (applied with probability crossover_prob) and mutation (applied per individual with probability mutation_prob) via a BranchOperator. The population size is constant.

Parameters:
initializerInitializer

Population initializer.

mutation_opOperator

Mutation operator (will be applied probabilistically).

crossover_opOperator

Crossover operator (applied pairwise).

parent_selParentSelection

Parent selection method.

survivor_selSurvivorSelection

Survivor selection method.

namestr, optional

Display name (default "GA").

mutation_probfloat or SchedulableParameter, optional

Individual-level probability of mutation (default 0.1).

crossover_probfloat or SchedulableParameter, optional

Pair-level probability of crossover (default 0.9). If the crossover operator supports it, this value is injected via update_kwargs.

rngRNGLike, optional

Random number generator.

**kwargs

Forwarded to StaticPopulation.

Attributes:
params

Access parameter values by attribute-style lookup.

population_size

Gets the amount of individuals in the population.

Parameters:

Methods

reset
__init__(initializer, mutation_op, crossover_op, parent_sel, survivor_sel, name='GA', mutation_prob=0.1, crossover_prob=0.9, rng=None, **kwargs)[source]#
Parameters:

Methods

__init__(initializer, mutation_op, ...[, ...])

extra_report()

Hook called at the end of the optimization (intended for subclasses).

extra_step_info()

Hook called after each generation (intended for subclasses).

gather_parameters()

Collect the current parameters from all sub-components.

get_params()

Return a copy of the current parameter dictionary.

get_state()

Gets the current state of the search strategy as a dictionary.

initialize(objfunc)

Initializes the optimization search strategy.

reset(objfunc)

step(prev_population, objfunc)

Performs a single iteration of the algorithm on a given population.

store_kwargs([progress])

Store keyword arguments and evaluate them at the given progress.

update(progress)

Advances the state of the search by one iteration.

update_kwargs([progress])

Add or replace parameters and immediately evaluate them.