Model

Model#

class pymoo.core.algorithm.Algorithm(termination=None, output=None, display=None, callback=None, archive=None, return_least_infeasible=False, save_history=False, verbose=False, seed=None, evaluator=None, **kwargs)[source]#

Attributes:

n_gen

Methods

advance
ask
finalize
has_next
infill
next
result
run
setup
tell

class pymoo.core.sampling.Sampling[source]#

This abstract class represents any sampling strategy that can be used to create an initial population or an initial search point.

Methods

class pymoo.core.selection.Selection(**kwargs)[source]#

This class is used to select parents for the mating or other evolutionary operators. Several strategies can be used to increase the selection pressure.

Methods

class pymoo.core.mutation.Mutation(prob=1.0, prob_var=None, **kwargs)[source]#

Methods

do
get_prob_var

class pymoo.core.crossover.Crossover(n_parents, n_offsprings, prob=0.9, **kwargs)[source]#

Methods

class pymoo.core.survival.Survival(filter_infeasible=True)[source]#

Methods

class pymoo.core.termination.Termination[source]#

Methods

update(algorithm)

Provide the termination criterion a current status of the algorithm to update the perc.

do_continue
has_terminated
terminate

update(algorithm)[source]#

Provide the termination criterion a current status of the algorithm to update the perc.

Parameters:

algorithmobject: The algorithm object which is used to determine whether a run has terminated.

class pymoo.core.indicator.Indicator(**kwargs)[source]#

Methods

__call__(F, *args, **kwargs)

Call self as a function.

class pymoo.core.population.Population(individuals=[])[source]#

Methods

apply
collect
create
empty
get
has
merge
new
set

class pymoo.core.individual.Individual(config: dict | None = None, **kwargs: Any)[source]#

Constructor for the Invididual class.

Parameters:

configdict, None: A dictionary of configuration metadata. If None, use a class-dependent default configuration.
kwargsAny: Additional keyword arguments containing data which is to be stored in the Individual.

Attributes:

CV: Get the constraint violation vector for an individual by either reading it from the cache or calculating it.
F: Get the objective function vector for an individual.
FEAS: Get whether an individual is feasible for each constraint.
G: Get the inequality constraint vector for an individual.
H: Get the equality constraint vector for an individual.
X: Get the decision vector for an individual.
cv: Convenience property.
dF: Get the objective function vector first derivatives for an individual.
dG: Get the inequality constraint(s) first derivatives for an individual.
dH: Get the equality constraint(s) first derivatives for an individual.
ddF: Get the objective function vector second derivatives for an individual.
ddG: Get the inequality constraint(s) second derivatives for an individual.
ddH: Get the equality constraint(s) second derivatives for an individual.
f: Convenience property.
feas: Convenience property.
feasible: Deprecated.
x: Convenience property.

Methods

`copy`([other, deep])	Copy an individual.
`default_config`()	Get default constraint violation configuration settings.
`duplicate`(key, new_key)	Duplicate a key to a new key.
`get`(*keys)	Get the values for one or more keys for an individual.
`has`(key)	Determine whether an individual has a provided key or not.
`new`()	Create a new instance of this class.
`reset`([data])	Reset the value of objective(s), inequality constraint(s), equality constraint(s), their first and second derivatives, the constraint violation, and the metadata to empty values.
`set`(key, value)	Set an individual's data or metadata based on a key and value.
`set_by_dict`(**kwargs)	Set an individual's data or metadata using values in a dictionary.

property CV: ndarray#

Get the constraint violation vector for an individual by either reading it from the cache or calculating it.

Returns:

outnp.ndarray: The constraint violation vector for an individual.

property F: ndarray#

Get the objective function vector for an individual.

Returns:

outnp.ndarray: The objective function vector for the individual.

property FEAS: ndarray#

Get whether an individual is feasible for each constraint.

Returns:

outnp.ndarray: An array containing whether each constraint is feasible for an individual.

property G: ndarray#

Get the inequality constraint vector for an individual.

Returns:

outnp.ndarray: The inequality constraint vector for the individual.

property H: ndarray#

Get the equality constraint vector for an individual.

Returns:

outnp.ndarray: The equality constraint vector for the individual.

property X: ndarray#

Get the decision vector for an individual.

Returns:

outnp.ndarray: The decision variable for the individual.

copy(other: Individual | None = None, deep: bool = True) → Individual[source]#

Copy an individual.

Parameters:

otherIndividual, None: The individual to copy. If None, assumed to be self.
deepbool: Whether to deep copy the individual.

Returns:

outIndividual: A copy of the individual.

property cv: float | None#

Convenience property. Get the first constraint violation value for an individual by either reading it from the cache or calculating it.

Returns:

outfloat, None: The constraint violation vector for an individual.

property dF: ndarray#

Get the objective function vector first derivatives for an individual.

Returns:

outnp.ndarray: The objective function vector first derivatives for the individual.

property dG: ndarray#

Get the inequality constraint(s) first derivatives for an individual.

Returns:

outnp.ndarray: The inequality constraint(s) first derivatives for the individual.

property dH: ndarray#

Get the equality constraint(s) first derivatives for an individual.

Returns:

outnp.ndarray: The equality constraint(s) first derivatives for the individual.

property ddF: ndarray#

Get the objective function vector second derivatives for an individual.

Returns:

outnp.ndarray: The objective function vector second derivatives for the individual.

property ddG: ndarray#

Get the inequality constraint(s) second derivatives for an individual.

Returns:

outnp.ndarray: The inequality constraint(s) second derivatives for the individual.

property ddH: ndarray#

Get the equality constraint(s) second derivatives for an individual.

Returns:

outnp.ndarray: The equality constraint(s) second derivatives for the individual.

default_config() → dict#

Get default constraint violation configuration settings.

Returns:

outdict: A dictionary of default constraint violation settings.

duplicate(key: str, new_key: str) → None[source]#

Duplicate a key to a new key.

Parameters:

keystr: Name of the key to duplicated.
new_keystr: Name of the key to which to duplicate the original key.

property f: float#

Convenience property. Get the first objective function value for an individual.

Returns:

outfloat: The first objective function value for the individual.

property feas: bool#

Convenience property. Get whether an individual is feasible for the first constraint.

Returns:

outbool: Whether an individual is feasible for the first constraint.

property feasible: ndarray#

Deprecated. Get whether an individual is feasible for each constraint.

Returns:

outnp.ndarray: An array containing whether each constraint is feasible for an individual.

get(*keys: str) → tuple | object[source]#

Get the values for one or more keys for an individual.

Parameters:

keysstr: Keys for which to get values.

Returns:

outtuple, object: If more than one key provided, return a tuple of retrieved values. If a single key provided, return the retrieved value.

has(key: str) → bool[source]#

Determine whether an individual has a provided key or not.

Parameters:

keystr: The key for which to test.

Returns:

outbool: Whether the Individual has the provided key.

new() → Individual[source]#

Create a new instance of this class.

Returns:

outIndividual: A new instance of an Individual.

reset(data: bool = True) → None[source]#

Reset the value of objective(s), inequality constraint(s), equality constraint(s), their first and second derivatives, the constraint violation, and the metadata to empty values.

Parameters:

databool: Whether to reset metadata associated with the Individiual.

set(key: str, value: object) → Individual[source]#

Set an individual’s data or metadata based on a key and value.

Parameters:

keystr: Key of the data for which to set.
valueobject: Value of the data for which to set.

Returns:

outIndividual: A reference to the Individual for which values were set.

set_by_dict(**kwargs: Any) → None[source]#

Set an individual’s data or metadata using values in a dictionary.

Parameters:

kwargsAny: Keyword arguments defining the data to set.

property x: ndarray#

Convenience property. Get the decision vector for an individual.

Returns:

outnp.ndarray: The decision variable for the individual.

class pymoo.core.result.Result[source]#

The resulting object of an optimization run.

Attributes:

cv
f
feas

Model

Contents

Model#