def set_params(self,
population: "Population",
population_size: int = None,
lattice_resolution: int = None,
interact: bool = False,
a_priori_preference: bool = False,
generations_per_iteration: int = 10,
iterations: int = 10,
plotting: bool = True,
logging: bool = False,
logfile=None,
**kwargs):
lattice_resolution_options = {
"2": 49,
"3": 13,
"4": 7,
"5": 5,
"6": 4,
"7": 3,
"8": 3,
"9": 3,
"10": 3,
}
if population.problem.num_of_objectives < 11:
lattice_resolution = lattice_resolution_options[str(
population.problem.num_of_objectives)]
else:
lattice_resolution = 3
reference_vectors = ReferenceVectors(
lattice_resolution, population.problem.num_of_objectives)
nsga3params = {
"population_size": reference_vectors.number_of_vectors,
"lattice_resolution": lattice_resolution,
"interact": interact,
"a_priori": a_priori_preference,
"generations": generations_per_iteration,
"iterations": iterations,
"ploton": plotting,
"logging": logging,
"logfile": logfile,
"current_iteration_gen_count": 0,
"current_iteration_count": 0,
"current_total_gen_count": 0,
"total_generations": iterations * generations_per_iteration,
"reference_vectors": reference_vectors,
"extreme_points": None,
}
nsga3params.update(kwargs)
return nsga3params
def set_params(
self,
population: "Population",
generations_per_iteration: int = 10,
iterations: int = 10,
Alpha: float = 2,
ref_point: list = None,
old_point: list = None,
**kwargs
):
"""Set up the parameters. Save in RVEA.params. Note, this should be
changed to align with the current structure.
Parameters
----------
population : Population
Population object
Alpha : float
The alpha parameter of APD selection.
plotting : bool
Useless really.
Returns
-------
"""
ref_vectors = ReferenceVectors(
number_of_objectives=population.problem.num_of_objectives,
creation_type="Sparse_Focused",
ref_point=old_point,
)
if ref_point is None:
ref_point = ref_vectors.values[0]
rveaparams = {
"reference_vectors": ref_vectors,
"population_size": ref_vectors.number_of_vectors,
"generations": generations_per_iteration,
"iterations": iterations,
"Alpha": Alpha,
"current_iteration_gen_count": 0,
"current_iteration_count": 0,
"current_total_gen_count": 0,
"total_generations": iterations * generations_per_iteration,
"ref_point": ref_point,
}
rveaparams.update(kwargs)
return rveaparams
def set_params(
self,
population: "Population",
population_size: int = None,
lattice_resolution: int = None,
interact: bool = False,
a_priori_preference: bool = False,
generations_per_iteration: int = 10,
iterations: int = 10,
Alpha: float = 2,
plotting: bool = True,
):
"""Set up the parameters. Save in RVEA.params. Note, this should be
changed to align with the current structure.
Parameters
----------
population : Population
Population object
population_size : int
Population Size
lattice_resolution : int
Lattice resolution
interact : bool
bool to enable or disable interaction. Enabled if True
a_priori_preference : bool
similar to interact
generations_per_iteration : int
Number of generations per iteration.
iterations : int
Total Number of iterations.
Alpha : float
The alpha parameter of APD selection.
plotting : bool
Useless really.
Returns
-------
"""
lattice_resolution_options = {
"2": 49,
"3": 13,
"4": 7,
"5": 5,
"6": 4,
"7": 3,
"8": 3,
"9": 3,
"10": 3,
}
if population.problem.num_of_objectives < 11:
lattice_resolution = lattice_resolution_options[str(
population.problem.num_of_objectives)]
else:
lattice_resolution = 3
rveaparams = {
"population_size":
population_size,
"lattice_resolution":
lattice_resolution,
"interact":
interact,
"a_priori":
a_priori_preference,
"generations":
generations_per_iteration,
"iterations":
iterations,
"Alpha":
Alpha,
"ploton":
plotting,
"current_iteration_gen_count":
0,
"current_iteration_count":
0,
"reference_vectors":
ReferenceVectors(lattice_resolution,
population.problem.num_of_objectives),
}
return rveaparams