Exemple #1
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    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
Exemple #2
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    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
Exemple #3
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    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)
Exemple #5
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 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)