def __init__(self,
method,
with_bounds=False,
with_constr=False,
require_jac=False,
use_bounds=True,
use_constr=True,
estm_gradients=True,
disp=False,
show_warnings=False,
**kwargs):
super().__init__(display=SingleObjectiveDisplay(), **kwargs)
self.method, self.with_bounds, self.with_constr, self.require_jac = method, with_bounds, with_constr, require_jac
self.show_warnings = show_warnings
self.use_bounds = use_bounds
self.use_constr = use_constr
self.estm_gradients = estm_gradients
self.options = {
'maxiter':
int(1e8), # because of C code interfacing this can not be inf
'disp': disp
}
def __init__(self,
eps=1e-2,
penalty=0.1,
display=SingleObjectiveDisplay(),
**kwargs):
super().__init__(display=display, **kwargs)
self.eps = eps
self.penalty = penalty
def __init__(self,
n_points_per_iteration=100,
sampling=LatinHypercubeSampling(),
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,
eps=1e-2,
penalty=0.1,
n_max_candidates=10,
display=SingleObjectiveDisplay(),
**kwargs):
super().__init__(display=display, **kwargs)
self.eps = eps
self.penalty = penalty
self.n_max_candidates = n_max_candidates
def __init__(self,
pop_size=100,
sampling=LatinHypercubeSampling(),
variant="DE/rand/1/bin",
CR=0.5,
F=0.3,
dither="vector",
jitter=False,
display=SingleObjectiveDisplay(),
**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.
"""
mating = DifferentialEvolutionMating(variant=variant,
CR=CR,
F=F,
dither=dither,
jitter=jitter)
super().__init__(pop_size=pop_size,
sampling=sampling,
mating=mating,
survival=None,
display=display,
**kwargs)
self.default_termination = SingleObjectiveDefaultTermination()
def __init__(self,
pop_size=200,
n_parallel=10,
sampling=LatinHypercubeSampling(),
display=SingleObjectiveDisplay(),
repair=None,
individual=Individual(),
**kwargs):
"""
Parameters
----------
pop_size : {pop_size}
sampling : {sampling}
selection : {selection}
crossover : {crossover}
mutation : {mutation}
eliminate_duplicates : {eliminate_duplicates}
n_offsprings : {n_offsprings}
"""
super().__init__(display=display, **kwargs)
self.initialization = Initialization(sampling,
individual=individual,
repair=repair)
self.pop_size = pop_size
self.n_parallel = n_parallel
self.each_pop_size = pop_size // n_parallel
self.solvers = None
self.niches = []
def cmaes(problem, x):
solver = CMAES(x0=x, tolfun=1e-11, tolx=1e-3, restarts=0)
solver.initialize(problem)
solver.next()
return solver
def nelder_mead(problem, x):
solver = NelderMead(X=x)
solver.initialize(problem)
solver._initialize()
solver.n_gen = 1
solver.next()
return solver
self.func_create_solver = nelder_mead
self.default_termination = SingleObjectiveDefaultTermination()
def __init__(self,
eps=1e-2,
penalty=0.1,
n_max_candidates=10,
n_max_archive=400,
archive_reduct=0.66,
display=SingleObjectiveDisplay(),
**kwargs):
super().__init__(display=display, **kwargs)
self.eps = eps
self.penalty = penalty
self.n_max_candidates = n_max_candidates
self.n_max_archive = n_max_archive
self.archive_reduct = archive_reduct
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,
n_elites=20,
n_offsprings=70,
n_mutants=10,
bias=0.7,
sampling=FloatRandomSampling(),
survival=None,
display=SingleObjectiveDisplay(),
eliminate_duplicates=False,
**kwargs):
"""
Parameters
----------
n_elites : int
Population size
n_offsprings : int
Fraction of elite items into each population
n_mutants : int
Fraction of mutants introduced at each generation into the population
bias : float
Probability that an offspring inherits the allele of its elite parent
"""
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=BiasedCrossover(bias, prob=1.0),
mutation=NoMutation(),
survival=survival,
display=display,
eliminate_duplicates=True,
**kwargs)
self.n_elites = n_elites
self.n_mutants = n_mutants
self.bias = bias
self.default_termination = SingleObjectiveToleranceBasedTermination()
def __init__(self,
func_params=adaptive_params,
display=SingleObjectiveDisplay(),
**kwargs):
"""
Parameters
----------
X : np.array or Population
The initial point where the search should be based on or the complete initial simplex (number of
dimensions plus 1 points). The population objective can be already evaluated with objective
space values. If a numpy array is provided it is evaluated by the algorithm.
By default it is None which means the search starts at a random point.
func_params : func
A function that returns the parameters alpha, beta, gamma, delta for the search. By default:
>>> def adaptive_params(problem):
... n = problem.n_var
...
... alpha = 1
... beta = 1 + 2 / n
... gamma = 0.75 - 1 / (2 * n)
... delta = 1 - 1 / n
... return alpha, beta, gamma, delta
It can be overwritten if necessary.
criterion_local_restart : Termination
Provide a termination object which decides whether a local restart should be performed or not.
"""
super().__init__(display=display, **kwargs)
# the function to return the parameter
self.func_params = func_params
# the attributes for the simplex operations
self.alpha, self.beta, self.gamma, self.delta = None, None, None, None
# the scaling used for the initial simplex
self.simplex_scaling = None
# the termination used for nelder and mead if nothing else provided
self.default_termination = NelderAndMeadTermination()
def __init__(self,
x0=None,
sampling=LatinHypercubeSampling(),
display=SingleObjectiveDisplay(),
n_sample_points="auto",
n_max_sample_points=20,
**kwargs):
super().__init__(display=display, **kwargs)
self.sampling = sampling
self.n_sample_points = n_sample_points
self.n_max_sample_points = n_max_sample_points
self.x0 = x0
self.default_termination = SingleObjectiveSpaceToleranceTermination(
n_last=5, tol=1e-8)
self.is_local_initialized = False
def __init__(self,
X,
dX=None,
objective=0,
display=SingleObjectiveDisplay(),
**kwargs) -> None:
super().__init__(display=display, **kwargs)
self.objective = objective
self.n_restarts = 0
self.default_termination = SingleObjectiveDefaultTermination()
self.X, self.dX = X, dX
self.F, self.CV = None, None
if self.X.ndim == 1:
self.X = np.atleast_2d(X)
def __init__(self,
pop_size=25,
n_offsprings=None,
sampling=LHS(),
termination=SingleObjectiveDefaultTermination(),
display=SingleObjectiveDisplay(),
beta=1.5,
alpha=0.01,
pa=0.1,
**kwargs):
"""
Parameters
----------
sampling : {sampling}
termination : {termination}
pop_size : int
The number of nests to be used
beta : float
The input parameter of the Mantegna's Algorithm to simulate
sampling on Levy Distribution
alpha : float
The step size scaling factor and is usually 0.01.
pa : float
The switch probability, pa fraction of the nests will be abandoned on every iteration
"""
mating = kwargs.get("mating")
if mating is None:
mating = LevyFlights(alpha, beta)
super().__init__(pop_size=pop_size,
n_offsprings=n_offsprings,
sampling=sampling,
mating=mating,
termination=termination,
display=display,
**kwargs)
self.pa = pa
def __init__(self,
pop_size=100,
sampling=FloatRandomSampling(),
selection=RandomSelection(),
crossover=SimulatedBinaryCrossover(prob=0.9, eta=3),
mutation=PolynomialMutation(prob=None, eta=5),
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=NichingSurvival(),
eliminate_duplicates=eliminate_duplicates,
n_offsprings=n_offsprings,
display=display,
**kwargs)
# self.mating = NeighborBiasedMating(selection,
# crossover,
# mutation,
# repair=self.mating.repair,
# eliminate_duplicates=self.mating.eliminate_duplicates,
# n_max_iterations=self.mating.n_max_iterations)
self.default_termination = SingleObjectiveDefaultTermination()
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,
X=None,
func_params=adaptive_params,
n_max_local_restarts=0,
criterion_local_restart=NelderAndMeadTermination(xtol=1e-2, ftol=1e-2),
display=SingleObjectiveDisplay(),
**kwargs):
"""
Parameters
----------
X : np.array or Population
The initial point where the search should be based on or the complete initial simplex (number of
dimensions plus 1 points). The population objective can be already evaluated with objective
space values. If a numpy array is provided it is evaluated by the algorithm.
By default it is None which means the search starts at a random point.
func_params : func
A function that returns the parameters alpha, beta, gamma, delta for the search. By default:
>>> def adaptive_params(problem):
... n = problem.n_var
...
... alpha = 1
... beta = 1 + 2 / n
... gamma = 0.75 - 1 / (2 * n)
... delta = 1 - 1 / n
... return alpha, beta, gamma, delta
It can be overwritten if necessary.
n_max_local_restarts : int
This algorithm employs local restarts by starting with a new initial simplex. This can be turned of
by setting it to 0.
criterion_local_restart : Termination
Provide a termination object which decides whether a local restart should be performed or not.
"""
super().__init__(display=display, **kwargs)
# the function to return the parameter
self.func_params = func_params
# the attributes for the simplex operations
self.alpha, self.beta, self.gamma, self.delta = None, None, None, None
# the scaling used for the initial simplex
self.simplex_scaling = None
# the initial point to be used to build the simplex
self.x0 = X
self.opt = None
self.default_termination = NelderAndMeadTermination(xtol=1e-6, ftol=1e-6, n_max_iter=1e6, n_max_evals=1e6)
# everything needed for local restarts
self.n_max_local_restarts = n_max_local_restarts
self.criterion_local_restart = criterion_local_restart
# internal variables to keep track of restarts
self.restarts_disabled = False
self.restart_history = []
def __init__(self,
pop_size=100,
n_offsprings=None,
sampling=LHS(),
variant="DE/best/1/bin",
CR=0.5,
F=None,
dither="vector",
jitter=False,
mutation=NoMutation(),
survival=None,
display=SingleObjectiveDisplay(),
**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 F 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 F used for the crossover for each individual.
"""
# parse the information from the string
_, sel, n_diff, mut, = variant.split("/")
n_diffs = int(n_diff)
if "-to-" in variant:
n_diffs += 1
selection = DES(sel)
crossover = DEX(prob=1.0,
n_diffs=n_diffs,
F=F,
CR=CR,
variant=mut,
dither=dither,
jitter=jitter)
super().__init__(pop_size=pop_size,
n_offsprings=n_offsprings,
sampling=sampling,
selection=selection,
crossover=crossover,
mutation=mutation,
survival=survival,
display=display,
**kwargs)
self.default_termination = SingleObjectiveDefaultTermination()
