def __init__(self,
pop_size=100,
sampling=LatinHypercubeSampling(iterations=100,
criterion="maxmin"),
variant="DE/rand/1/bin",
CR=0.5,
F=0.3,
dither="vector",
jitter=False,
**kwargs):
"""
Parameters
----------
pop_size : {pop_size}
sampling : {sampling}
variant : {{DE/(rand|best)/1/(bin/exp)}}
The different variants of DE to be used. DE/x/y/z where x how to select individuals to be pertubed,
y the number of difference vector to be used and z the crossover type. One of the most common variant
is DE/rand/1/bin.
F : float
The weight to be used during the crossover.
CR : float
The probability the individual exchanges variable values from the donor vector.
dither : {{'no', 'scalar', 'vector'}}
One strategy to introduce adaptive weights (F) during one run. The option allows
the same dither to be used in one iteration ('scalar') or a different one for
each individual ('vector).
jitter : bool
Another strategy for adaptive weights (F). Here, only a very small value is added or
subtracted to the weight used for the crossover for each individual.
"""
_, self.var_selection, self.var_n, self.var_mutation, = variant.split(
"/")
if self.var_mutation == "exp":
mutation = ExponentialCrossover(CR)
elif self.var_mutation == "bin":
mutation = UniformCrossover(CR)
super().__init__(pop_size=pop_size,
sampling=sampling,
selection=RandomSelection(),
crossover=DifferentialEvolutionCrossover(
weight=F, dither=dither, jitter=jitter),
mutation=mutation,
survival=None,
**kwargs)
self.func_display_attrs = disp_single_objective
def __init__(self,
variant="DE/rand/1/bin",
CR=0.5,
F=0.3,
dither="vector",
jitter=False,
selection=None,
crossover=None,
mutation=None,
**kwargs):
_, sel, n_diff, mut, = variant.split("/")
self.variant = sel
self.n_diffs = int(n_diff)
if "-to-" in self.variant:
self.n_diffs += 1
if selection is None:
selection = DESelection(sel)
if mutation is None:
if mut == "exp":
mutation = ExponentialCrossover(CR)
elif mut == "bin":
mutation = BiasedCrossover(CR)
if crossover is None:
crossover = DifferentialEvolutionCrossover(n_diffs=self.n_diffs,
weight=F,
dither=dither,
jitter=jitter)
super().__init__(selection, crossover, mutation, **kwargs)
def __init__(self,
variant,
CR,
F,
dither,
jitter,
**kwargs):
_, self.var_selection, self.var_n, self.var_mutation, = variant.split("/")
set_if_none(kwargs, 'pop_size', 200)
set_if_none(kwargs, 'sampling', LatinHypercubeSampling(criterion="maxmin", iterations=100))
set_if_none(kwargs, 'crossover', DifferentialEvolutionCrossover(weight=F, dither=dither, jitter=jitter))
set_if_none(kwargs, 'selection', RandomSelection())
if self.var_mutation == "exp":
set_if_none(kwargs, 'mutation', ExponentialCrossover(CR))
elif self.var_mutation == "bin":
set_if_none(kwargs, 'mutation', UniformCrossover(CR))
set_if_none(kwargs, 'survival', None)
super().__init__(**kwargs)
self.func_display_attrs = disp_single_objective