cma.constraints_handler.PopulationEvaluator

class documentation

class PopulationEvaluator(object):

Constructor: PopulationEvaluator(objective, constraints, insert, offset_from_true_g)

evaluate and store f- and g-values of a population in attributes F and G.

If the g-function (constraints) has an insert method, x-values are "inserted" first.

If the constraints function has a true_g attribute (assigned during the call) and offset_from_true_g is True, population constraints values are corrected by an offset to guaranty that g >= 0 if true_g > 0. Named solutions are not offset.

Method	`__call__`	`kwargs` are named solutions resulting in
Method	`__init__`	Undocumented
Instance Variable	`constraints`	Undocumented
Instance Variable	`F`	Undocumented
Instance Variable	`G_all`	Undocumented
Instance Variable	`insert`	Undocumented
Instance Variable	`objective`	Undocumented
Instance Variable	`offset_from_true_g`	Undocumented
Instance Variable	`X`	Undocumented
Property	`feasibility_ratios`	or bias for equality constraints

def __call__(self, X, **kwargs): ¶

kwargs are named solutions resulting in

    self.name['x'] = kwargs[name]
    self.name['f'] = self.objective(kwargs[name])
    self.name['g'] = self.constraints(kwargs[name])

Store result in attributes `F` and `G`.

def __init__(self, objective, constraints, insert=True, offset_from_true_g=False): ¶

Undocumented

constraints = ¶

Undocumented

F = ¶

Undocumented

G_all = ¶

Undocumented

insert = ¶

Undocumented

objective = ¶

Undocumented

offset_from_true_g = ¶

Undocumented

X = ¶

Undocumented

@property
feasibility_ratios = ¶

or bias for equality constraints