metaheuristic_designer.strategies.single_solution_strategy module#

Single solution abstract strategy

class SingleSolutionStrategy(initializer, operator=None, survivor_sel=None, name='HillClimb', rng=None, **kwargs)[source]#

No parent selection method exists, we only have one solution at each given time

Parameters:

initializerInitializer: Population initializer (typically creates a single individual).
operatorOperator, optional: Perturbation operator. Defaults to NullOperator.
survivor_selSurvivorSelection, optional: Survivor selection method; defaults to "hill_climb".
namestr, optional: Display name (default "HillClimb").
rngRNGLike, optional: Random number generator.
**kwargs: Forwarded to SearchStrategy.

Attributes:

Parameters:

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

step(prev_population, objfunc)[source]#

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

Return type:

Population

Parameters:

populationPopulation: Population of solutions in which to perform the operators.

Returns:

Population: Next population after performing all the steps in the iteration.

Parameters: