def __init__(self, var_type, ref_points=None, epsilon=0.001, weights=None, **kwargs): self.ref_dirs = np.array(ref_points) self.epsilon = epsilon self.weights = weights set_default_if_none(var_type, kwargs) set_if_none(kwargs, 'survival', None) super().__init__(**kwargs)
def __init__(self, var_type="real", n_neighbors=15, **kwargs): #set_if_none(kwargs, "crossover", DifferentialEvolutionCrossover()) set_default_if_none(var_type, kwargs) super().__init__(**kwargs) self.n_neighbors = n_neighbors # initialized when problem is known self.weights = None self.neighbours = None self.ideal_point = None
def __init__(self, variant="DE/rand/1/exp", CR=0.1, F=0.75, **kwargs): set_default_if_none("real", kwargs) super().__init__(**kwargs) self.selection = RandomSelection() self.crossover = DifferentialEvolutionCrossover(weight=F) _, self.var_selection, self.var_n, self.var_mutation, = variant.split( "/") self.mutation = DifferentialEvolutionMutation(self.var_mutation, CR) self.func_display_attrs = disp_single_objective
def __init__(self, var_type, ref_points=None, mu=0.1, ref_pop_size=None, method='uniform', p=None, **kwargs): """ Parameters ---------- var_type : string Variable type which must be real in this case ref_points : numpy.array Reference points to be focused on during the evolutionary computation. mu : double The shrink factor ref_pop_size : int If the structured reference lines should be based off of a different population size than the actual population size. Default value is pop size. p : double If the structured reference directions should be based off of p gaps specify a p value, otherwise reference directions will be based on the population size. ref_sampling_method : string Reference direction generation method. Currently only 'uniform' or 'random'. """ self.ref_points = ref_points self.ref_dirs = None self.mu = mu self.method = method set_default_if_none(var_type, kwargs) set_if_none(kwargs, 'survival', None) self.ref_pop_size = ref_pop_size self.p = p super().__init__(**kwargs)
def __init__(self, var_type, ref_points=None, **kwargs): self.ref_points = ref_points self.ref_dirs = None set_default_if_none(var_type, kwargs) set_if_none(kwargs, 'survival', None) super().__init__(**kwargs)
def __init__(self, var_type, **kwargs): set_default_if_none(var_type, kwargs) set_if_none(kwargs, 'selection', TournamentSelection(f_comp=comp_by_rank_and_crowding)) set_if_none(kwargs, 'survival', RankAndCrowdingSurvival()) super().__init__(**kwargs)
def __init__(self, **kwargs): set_default_if_none("real", kwargs) super().__init__(**kwargs) self.crossover = DifferentialEvolutionCrossover(prob=0.5, weight=0.75, variant="DE/rand/1", method="binomial")
def __init__(self, var_type, **kwargs): set_if_none(kwargs, 'survival', FitnessSurvival()) set_default_if_none(var_type, kwargs) super().__init__(**kwargs)
def __init__(self, var_type, **kwargs): set_default_if_none(var_type, **kwargs) super().__init__(**kwargs)