def test_elementwise_crossover():
problem = get_problem('zdt1')
sampling = FloatRandomSampling()
pop = sampling.do(problem, n_samples=3)
crossover = ElementwiseCrossover(SBX(20))
off = crossover.do(problem, pop[0], pop[1])
assert len(off) == 2
crossover = ElementwiseCrossover(DEX())
off = crossover.do(problem, pop[0], pop[1], pop[2])
assert len(off) == 1
def __init__(self,
ref_dirs,
pop_size=None,
sampling=FloatRandomSampling(),
selection=TournamentSelection(func_comp=comp_by_cv_then_random),
crossover=SimulatedBinaryCrossover(eta=30, prob=1.0),
mutation=PolynomialMutation(eta=20, prob=None),
eliminate_duplicates=True,
n_offsprings=None,
display=MultiObjectiveDisplay(),
**kwargs):
"""
Parameters
----------
ref_dirs : {ref_dirs}
pop_size : int (default = None)
By default the population size is set to None which means that it will be equal to the number of reference
line. However, if desired this can be overwritten by providing a positive number.
sampling : {sampling}
selection : {selection}
crossover : {crossover}
mutation : {mutation}
eliminate_duplicates : {eliminate_duplicates}
n_offsprings : {n_offsprings}
"""
self.ref_dirs = ref_dirs
# in case of R-NSGA-3 they will be None - otherwise this will be executed
if self.ref_dirs is not None:
if pop_size is None:
pop_size = len(self.ref_dirs)
if pop_size < len(self.ref_dirs):
print(
f"WARNING: pop_size={pop_size} is less than the number of reference directions ref_dirs={len(self.ref_dirs)}.\n"
"This might cause unwanted behavior of the algorithm. \n"
"Please make sure pop_size is equal or larger than the number of reference directions. ")
if 'survival' in kwargs:
survival = kwargs['survival']
del kwargs['survival']
else:
survival = ReferenceDirectionSurvival(ref_dirs)
super().__init__(pop_size=pop_size,
sampling=sampling,
selection=selection,
crossover=crossover,
mutation=mutation,
survival=survival,
eliminate_duplicates=eliminate_duplicates,
n_offsprings=n_offsprings,
display=display,
advance_after_initial_infill=True,
**kwargs)
def __init__(self,
ref_dirs,
pop_size=None,
sampling=FloatRandomSampling(),
selection=NeighborhoodSelection(),
crossover=DEX(prob=0.9, CR=0.5, variant='bin'),
mutation=PM(eta=20),
display=MultiObjectiveDisplay(),
**kwargs):
# set reference directions and pop_size
self.ref_dirs = ref_dirs
if self.ref_dirs is not None:
if pop_size is None:
pop_size = len(self.ref_dirs)
super().__init__(pop_size=pop_size,
sampling=sampling,
selection=selection,
crossover=crossover,
mutation=mutation,
eliminate_duplicates=False,
display=display,
**kwargs)
self.RA = None
self.norm = None
self.archive = None
def __init__(self,
n_points_per_iteration=100,
sampling=FloatRandomSampling(),
display=SingleObjectiveDisplay(),
**kwargs):
super().__init__(display=display, **kwargs)
self.n_points_per_iteration = n_points_per_iteration
self.sampling = sampling
def __init__(self,
n_elites=200,
n_offsprings=700,
n_mutants=100,
bias=0.7,
sampling=FloatRandomSampling(),
survival=None,
display=SingleObjectiveDisplay(),
eliminate_duplicates=False,
**kwargs
):
"""
Parameters
----------
n_elites : int
Number of elite individuals
n_offsprings : int
Number of offsprings to be generated through mating of an elite and a non-elite individual
n_mutants : int
Number of mutations to be introduced each generation
bias : float
Bias of an offspring inheriting the allele of its elite parent
eliminate_duplicates : bool or class
The duplicate elimination is more important if a decoding is used. The duplicate check has to be
performed on the decoded variable and not on the real values. Therefore, we recommend passing
a DuplicateElimination object.
If eliminate_duplicates is simply set to `True`, then duplicates are filtered out whenever the
objective values are equal.
"""
if survival is None:
survival = EliteSurvival(n_elites, eliminate_duplicates=eliminate_duplicates)
super().__init__(pop_size=n_elites + n_offsprings + n_mutants,
n_offsprings=n_offsprings,
sampling=sampling,
selection=EliteBiasedSelection(),
crossover=BinomialCrossover(bias, prob=1.0),
mutation=NoMutation(),
survival=survival,
display=display,
eliminate_duplicates=True,
advance_after_initial_infill=True,
**kwargs)
self.n_elites = n_elites
self.n_mutants = n_mutants
self.bias = bias
self.default_termination = SingleObjectiveDefaultTermination()
def __init__(self,
ref_dirs,
alpha=2.0,
adapt_freq=0.1,
pop_size=None,
sampling=FloatRandomSampling(),
selection=RandomSelection(),
crossover=SimulatedBinaryCrossover(eta=30, prob=1.0),
mutation=PolynomialMutation(eta=20, prob=None),
eliminate_duplicates=True,
n_offsprings=None,
display=MultiObjectiveDisplay(),
**kwargs):
"""
Parameters
----------
ref_dirs : {ref_dirs}
adapt_freq : float
Defines the ratio of generation when the reference directions are updated.
pop_size : int (default = None)
By default the population size is set to None which means that it will be equal to the number of reference
line. However, if desired this can be overwritten by providing a positive number.
sampling : {sampling}
selection : {selection}
crossover : {crossover}
mutation : {mutation}
eliminate_duplicates : {eliminate_duplicates}
n_offsprings : {n_offsprings}
"""
# set reference directions and pop_size
self.ref_dirs = ref_dirs
if self.ref_dirs is not None:
if pop_size is None:
pop_size = len(self.ref_dirs)
# the fraction of n_max_gen when the the reference directions are adapted
self.adapt_freq = adapt_freq
# you can override the survival if necessary
survival = kwargs.pop("survival", None)
if survival is None:
survival = APDSurvival(ref_dirs, alpha=alpha)
super().__init__(pop_size=pop_size,
sampling=sampling,
selection=selection,
crossover=crossover,
mutation=mutation,
survival=survival,
eliminate_duplicates=eliminate_duplicates,
n_offsprings=n_offsprings,
display=display,
**kwargs)
def _infill(self):
pop = self.pop
# actually do the mating given the elite selection and biased crossover
off = self.mating.do(self.problem, pop, n_offsprings=self.n_offsprings, algorithm=self)
# create the mutants randomly to fill the population with
mutants = FloatRandomSampling().do(self.problem, self.n_mutants, algorithm=self)
# evaluate all the new solutions
return Population.merge(off, mutants)
def __init__(self,
n_offsprings=200,
pop_size=None,
rule=1.0 / 7.0,
phi=1.0,
gamma=0.85,
sampling=FloatRandomSampling(),
survival=FitnessSurvival(),
display=SingleObjectiveDisplay(),
**kwargs):
"""
Evolutionary Strategy (ES)
Parameters
----------
n_offsprings : int
The number of individuals created in each iteration.
pop_size : int
The number of individuals which are surviving from the offspring population (non-elitist)
rule : float
The rule (ratio) of individuals surviving. This automatically either calculated `n_offsprings` or `pop_size`.
phi : float
Expected rate of convergence (usually 1.0).
gamma : float
If not `None`, some individuals are created using the differentials with this as a length scale.
sampling : object
The sampling method for creating the initial population.
"""
if pop_size is None and n_offsprings is not None:
pop_size = int(np.math.ceil(n_offsprings * rule))
elif n_offsprings is None and pop_size is not None:
n_offsprings = int(np.math.fllor(n_offsprings / rule))
assert pop_size is not None and n_offsprings is not None, "You have to at least provivde pop_size of n_offsprings."
assert n_offsprings >= 2 * pop_size, "The number of offsprings should be at least double the population size."
super().__init__(pop_size=pop_size,
n_offsprings=n_offsprings,
sampling=sampling,
survival=survival,
display=display,
advance_after_initial_infill=True,
**kwargs)
self.default_termination = SingleObjectiveDefaultTermination()
self.phi = phi
self.gamma = gamma
self.tau, self.taup, self.sigma_max = None, None, None
def __init__(
self,
ref_dirs,
sampling=FloatRandomSampling(),
selection=RestrictedMating(func_comp=comp_by_cv_dom_then_random),
crossover=SimulatedBinaryCrossover(n_offsprings=1,
eta=30,
prob=1.0),
mutation=PolynomialMutation(eta=20, prob=None),
eliminate_duplicates=True,
display=MultiObjectiveDisplay(),
**kwargs):
"""
CTAEA
Parameters
----------
ref_dirs : {ref_dirs}
sampling : {sampling}
selection : {selection}
crossover : {crossover}
mutation : {mutation}
eliminate_duplicates : {eliminate_duplicates}
"""
self.ref_dirs = ref_dirs
pop_size = len(ref_dirs)
if 'survival' in kwargs:
survival = kwargs['survival']
del kwargs['survival']
else:
survival = CADASurvival(ref_dirs)
# Initialize diversity archives
self.da = None
super().__init__(pop_size=pop_size,
sampling=sampling,
selection=selection,
crossover=crossover,
mutation=mutation,
survival=survival,
eliminate_duplicates=eliminate_duplicates,
n_offsprings=pop_size,
display=display,
**kwargs)
def __init__(self,
ref_dirs,
n_neighbors=20,
decomposition=Tchebicheff2(),
prob_neighbor_mating=0.9,
sampling=FloatRandomSampling(),
crossover=SimulatedBinaryCrossover(prob=1.0, eta=20),
mutation=PolynomialMutation(prob=None, eta=20),
display=MultiObjectiveDisplay(),
**kwargs):
"""
Parameters
----------
ref_dirs
n_neighbors
decomposition
prob_neighbor_mating
display
kwargs
"""
self.ref_dirs = ref_dirs
self.pc_capacity = len(ref_dirs)
self.pc_pop = Population.new()
self.npc_pop = Population.new()
self.n_neighbors = min(len(ref_dirs), n_neighbors)
self.prob_neighbor_mating = prob_neighbor_mating
self.decomp = decomposition
# initialise the neighborhood of subproblems based on the distances of weight vectors
self.neighbors = np.argsort(cdist(self.ref_dirs, self.ref_dirs),
axis=1,
kind='quicksort')[:, :self.n_neighbors]
self.selection = NeighborhoodSelection(self.neighbors,
prob=prob_neighbor_mating)
super().__init__(pop_size=len(ref_dirs),
sampling=sampling,
crossover=crossover,
mutation=mutation,
eliminate_duplicates=DefaultDuplicateElimination(),
display=display,
advance_after_initialization=False,
**kwargs)
def __init__(self,
ref_dirs,
n_neighbors=20,
decomposition='auto',
prob_neighbor_mating=0.9,
sampling=FloatRandomSampling(),
crossover=SimulatedBinaryCrossover(prob=1.0, eta=20),
mutation=PolynomialMutation(prob=None, eta=20),
display=MultiObjectiveDisplay(),
**kwargs):
"""
Parameters
----------
ref_dirs
n_neighbors
decomposition
prob_neighbor_mating
display
kwargs
"""
self.ref_dirs = ref_dirs
self.n_neighbors = min(len(ref_dirs), n_neighbors)
self.prob_neighbor_mating = prob_neighbor_mating
self.decomp = decomposition
# neighbours includes the entry by itself intentionally for the survival method
self.neighbors = np.argsort(cdist(self.ref_dirs, self.ref_dirs),
axis=1,
kind='quicksort')[:, :self.n_neighbors]
self.selection = NeighborhoodSelection(self.neighbors,
prob=prob_neighbor_mating)
super().__init__(pop_size=len(ref_dirs),
sampling=sampling,
crossover=crossover,
mutation=mutation,
eliminate_duplicates=NoDuplicateElimination(),
display=display,
advance_after_initialization=False,
**kwargs)
# the mating is just performed once here - population does not need to be filled up
self.mating.n_max_iterations = 1
def __init__(self,
pop_size=100,
sampling=FloatRandomSampling(),
selection=TournamentSelection(func_comp=binary_tournament),
crossover=SBX(eta=15, prob=0.9),
mutation=PM(prob=None, eta=20),
eliminate_duplicates=True,
n_offsprings=None,
display=MultiObjectiveDisplay(),
**kwargs):
"""
Adapted from:
Panichella, A. (2019). An adaptive evolutionary algorithm based on non-euclidean geometry for many-objective
optimization. GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference, July, 595–603.
https://doi.org/10.1145/3321707.3321839
Parameters
----------
pop_size : {pop_size}
sampling : {sampling}
selection : {selection}
crossover : {crossover}
mutation : {mutation}
eliminate_duplicates : {eliminate_duplicates}
n_offsprings : {n_offsprings}
"""
super().__init__(pop_size=pop_size,
sampling=sampling,
selection=selection,
crossover=crossover,
mutation=mutation,
survival=AGEMOEASurvival(),
eliminate_duplicates=eliminate_duplicates,
n_offsprings=n_offsprings,
display=display,
advance_after_initial_infill=True,
**kwargs)
self.default_termination = MultiObjectiveDefaultTermination()
self.tournament_type = 'comp_by_rank_and_crowding'
def __init__(self,
pop_size=100,
sampling=FloatRandomSampling(),
selection=TournamentSelection(func_comp=binary_tournament),
crossover=SimulatedBinaryCrossover(eta=15, prob=0.9),
mutation=PolynomialMutation(prob=None, eta=20),
survival=RankAndCrowdingSurvival(),
eliminate_duplicates=True,
n_offsprings=None,
display=MultiObjectiveDisplay(),
**kwargs):
"""
Parameters
----------
pop_size : {pop_size}
sampling : {sampling}
selection : {selection}
crossover : {crossover}
mutation : {mutation}
eliminate_duplicates : {eliminate_duplicates}
n_offsprings : {n_offsprings}
"""
super().__init__(pop_size=pop_size,
sampling=sampling,
selection=selection,
crossover=crossover,
mutation=mutation,
survival=survival,
eliminate_duplicates=eliminate_duplicates,
n_offsprings=n_offsprings,
display=display,
advance_after_initial_infill=True,
**kwargs)
self.default_termination = MultiObjectiveDefaultTermination()
self.tournament_type = 'comp_by_dom_and_crowding'
def __init__(
self,
pop_size=100,
sampling=FloatRandomSampling(),
selection=TournamentSelection(func_comp=comp_by_cv_and_fitness),
crossover=SimulatedBinaryCrossover(prob=0.9, eta=3),
mutation=PolynomialMutation(prob=None, eta=5),
survival=FitnessSurvival(),
eliminate_duplicates=True,
n_offsprings=None,
display=SingleObjectiveDisplay(),
**kwargs):
"""
Parameters
----------
pop_size : {pop_size}
sampling : {sampling}
selection : {selection}
crossover : {crossover}
mutation : {mutation}
eliminate_duplicates : {eliminate_duplicates}
n_offsprings : {n_offsprings}
"""
super().__init__(pop_size=pop_size,
sampling=sampling,
selection=selection,
crossover=crossover,
mutation=mutation,
survival=survival,
eliminate_duplicates=eliminate_duplicates,
n_offsprings=n_offsprings,
display=display,
**kwargs)
self.default_termination = SingleObjectiveDefaultTermination()
def __init__(self,
pop_size=100,
sampling=FloatRandomSampling(),
selection=DES("rand"),
crossover=DEX(CR=0.7),
mutation=PM(eta=20),
survival=NichingSurvival(0.1, 0.01, 5, 4),
eliminate_duplicates=True,
n_offsprings=None,
display=SingleObjectiveDisplay(),
**kwargs):
super().__init__(pop_size=pop_size,
sampling=sampling,
selection=selection,
crossover=crossover,
mutation=mutation,
survival=survival,
eliminate_duplicates=eliminate_duplicates,
n_offsprings=n_offsprings,
display=display,
**kwargs)
self.default_termination = SingleObjectiveDefaultTermination()
def __init__(
self,
ref_points,
pop_per_ref_point,
mu=0.05,
sampling=FloatRandomSampling(),
selection=TournamentSelection(func_comp=comp_by_cv_then_random),
crossover=SimulatedBinaryCrossover(eta=30, prob=1.0),
mutation=PolynomialMutation(eta=20, prob=None),
eliminate_duplicates=True,
n_offsprings=None,
**kwargs):
"""
Parameters
----------
ref_points : {ref_points}
pop_per_ref_point : int
Size of the population used for each reference point.
mu : float
Defines the scaling of the reference lines used during survival selection. Increasing mu will result
having solutions with a larger spread.
Other Parameters
-------
n_offsprings : {n_offsprings}
sampling : {sampling}
selection : {selection}
crossover : {crossover}
mutation : {mutation}
eliminate_duplicates : {eliminate_duplicates}
"""
# number of objectives the reference lines have
n_obj = ref_points.shape[1]
# add the aspiration point lines
aspiration_ref_dirs = UniformReferenceDirectionFactory(
n_dim=n_obj, n_points=pop_per_ref_point).do()
survival = AspirationPointSurvival(ref_points,
aspiration_ref_dirs,
mu=mu)
pop_size = ref_points.shape[0] * aspiration_ref_dirs.shape[
0] + aspiration_ref_dirs.shape[1]
ref_dirs = None
super().__init__(ref_dirs,
pop_size=pop_size,
sampling=sampling,
selection=selection,
crossover=crossover,
mutation=mutation,
survival=survival,
eliminate_duplicates=eliminate_duplicates,
n_offsprings=n_offsprings,
**kwargs)
def __init__(self,
ref_dirs,
n_neighbors=20,
decomposition=Tchebicheff(),
prob_neighbor_mating=0.9,
rate_update_weight=0.05,
rate_evol=0.8,
wag=20,
archive_size_multiplier=1.5,
use_new_ref_dirs_initialization=True,
display=MultiObjectiveDisplay(),
**kwargs):
"""
MOEAD-AWA Algorithm.
Parameters
----------
ref_dirs
n_neighbors
decomposition
prob_neighbor_mating
rate_update_weight
rate_evol
wag
archive_size_multiplier
use_new_ref_dirs_initialization
display
kwargs
"""
self.n_neighbors = n_neighbors
self.prob_neighbor_mating = prob_neighbor_mating
self.decomp = decomposition
self.rate_update_weight = rate_update_weight
self.rate_evol = rate_evol
self.wag = wag
self.EP = None
self.nEP = np.ceil(len(ref_dirs) * archive_size_multiplier)
set_if_none(kwargs, 'pop_size', len(ref_dirs))
set_if_none(kwargs, 'sampling', FloatRandomSampling())
set_if_none(kwargs, 'crossover', SBX(prob=1.0, eta=20))
set_if_none(kwargs, 'mutation', PM(prob=None, eta=20))
set_if_none(kwargs, 'survival', None)
set_if_none(kwargs, 'selection', None)
super().__init__(display=display, **kwargs)
# initialized when problem is known
self.ref_dirs = ref_dirs
if use_new_ref_dirs_initialization:
self.ref_dirs = 1.0 / (self.ref_dirs + 1e-6)
self.ref_dirs = self.ref_dirs / np.sum(self.ref_dirs, axis=1)[:,
None]
if self.ref_dirs.shape[0] < self.n_neighbors:
print("Setting number of neighbours to population size: %s" %
self.ref_dirs.shape[0])
self.n_neighbors = self.ref_dirs.shape[0]
self.nds = NonDominatedSorting()
# compute neighbors of reference directions using euclidean distance
self._update_neighbors()