metaheuristic_designer.strategies.classic.random_search module

Random search strategy (baseline).

class RandomSearch(initializer, name='RandomSearch', rng=None, **kwargs)

Bases: PopulationBasedStrategy

Random search algorithm.

Each iteration replaces the current population with completely new random individuals (via a random.random operator). No perturbation of existing solutions occurs.

Parameters:

initializerInitializer

Population initializer.

namestr, optional

Display name (default "RandomSearch").

**kwargs

Forwarded to HillClimb.

Attributes:

params

Access parameter values by attribute-style lookup.

population_size

Gets the amount of individuals in the population.

Parameters:

initializer (Initializer)

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