metaheuristic_designer.strategies.classic.GA module

Genetic Algorithm strategy.

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

Bases: PopulationBasedStrategy

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:

• initializer (Initializer)

• mutation_op (Operator)

• crossover_op (Operator)

• parent_sel (ParentSelection)

• survivor_sel (SurvivorSelection)

• name (str)

• mutation_prob (float | SchedulableParameter)

• crossover_prob (float | SchedulableParameter)

• rng (Optional[RNGLike])

Methods

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.
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.

reset