def __init__(self, num_variables=10, peaks=None, **kwargs):
"""Constructor.
Parameters
----------
num_variables : int, optional
The search space dimension.
peaks : sequence of Peak
Previously prepared peaks. If None, a few peaks are generated
randomly.
kwargs
Arbitrary keyword arguments, passed through to the constructor
of the super class.
"""
self.min_bounds = [0.0] * num_variables
self.max_bounds = [1.0] * num_variables
TestProblem.__init__(self,
self.objective_function,
num_objectives=1,
**kwargs)
self.num_variables = num_variables
self.peaks = peaks
if peaks is None:
self.peaks = self.rand_uniform_peaks(num_variables=num_variables)
self.is_deterministic = True
def __init__(self, num_variables=10, peaks=None, **kwargs):
"""Constructor.
Parameters
----------
num_variables : int, optional
The search space dimension.
peaks : sequence of Peak
Previously prepared peaks. If None, a few peaks are generated
randomly.
kwargs
Arbitrary keyword arguments, passed through to the constructor
of the super class.
"""
self.min_bounds = [0.0] * num_variables
self.max_bounds = [1.0] * num_variables
TestProblem.__init__(self,
self.objective_function,
num_objectives=1,
**kwargs)
self.num_variables = num_variables
self.peaks = peaks
if peaks is None:
self.peaks = self.rand_uniform_peaks(num_variables=num_variables)
self.is_deterministic = True
def __init__(self, num_variables=30, phenome_preprocessor=None, **kwargs):
"""Constructor.
Parameters
----------
num_variables : int, optional
The search space dimension.
phenome_preprocessor : callable, optional
A callable potentially applying transformations or checks to
the phenome. Modifications should only be applied to a copy
of the input. The (modified) phenome must be returned.
Default behavior is to do no processing.
kwargs
Arbitrary keyword arguments, passed through to the constructor
of the super class.
"""
preprocessor = BinaryChecker(num_variables, phenome_preprocessor)
TestProblem.__init__(self,
one_max,
num_objectives=1,
phenome_preprocessor=preprocessor,
**kwargs)
self.num_variables = num_variables
self.is_deterministic = True
self.do_maximize = True
def __init__(self, num_variables,fid,iid=1, phenome_preprocessor=None, **kwargs):
self.is_deterministic = True
self.do_maximize = False
self.num_variables = num_variables
self.min_bounds = [-5] * num_variables
self.max_bounds = [5] * num_variables
bounds = (self.min_bounds, self.max_bounds)
self.fitness, self.best = bn.instantiate(fid,iid)
preprocessor = BoundConstraintsChecker(bounds, phenome_preprocessor)
TestProblem.__init__(self, self.objective_function,
phenome_preprocessor=preprocessor,
**kwargs)
def __init__(self,
num_variables,
fid,
iid=1,
phenome_preprocessor=None,
**kwargs):
self.is_deterministic = True
self.do_maximize = False
self.num_variables = num_variables
self.min_bounds = [-5] * num_variables
self.max_bounds = [5] * num_variables
bounds = (self.min_bounds, self.max_bounds)
self.fitness, self.best = bn.instantiate(fid, iid)
preprocessor = BoundConstraintsChecker(bounds, phenome_preprocessor)
TestProblem.__init__(self,
self.objective_function,
phenome_preprocessor=preprocessor,
**kwargs)
