def __init__(self, n_arguments: int, n_objectives: int, k: int):
assert k % (n_objectives - 1) == 0
assert k + 1 <= n_arguments
self._n_arguments = n_arguments
self._n_objectives = n_objectives
self._k = k
n = self._n_arguments
M = self._n_objectives
S = 2 * (np.arange(M) + 1)
A = np.ones(M - 1)
upper_bounds = 2 * (np.arange(n) + 1)
self.domain = np.zeros((n, 2))
self.domain[:, 1] = upper_bounds
shapes: List[shape_functions.BaseShapeFunction]
shapes = [shape_functions.ConcaveShapeFunction(M) for _ in range(M)]
transformations: List[List[
transformation_functions.BaseTransformations]]
transformations = [[] for _ in range(2)]
transformations[0] = [
transformation_functions.IdenticalTransformation()
for _ in range(k)
]
for _ in range(n - k):
transformations[0].append(
transformation_functions.LinearShiftTransformation(0.35))
def _input_converter(i: int, y: np.ndarray) -> np.ndarray:
indices = np.arange(i * k // (M - 1), (i + 1) * k // (M - 1))
return y[indices]
# transformations[1] = []
for i in range(M - 1):
transformations[1].append(
transformation_functions.NonSeparableReductionTransformation(
k // (M - 1), lambda y: _input_converter(i, y)))
transformations[1].append(
transformation_functions.NonSeparableReductionTransformation(
n - k,
lambda y: y[k:n],
))
# transformations = [transformations[0], transformations[1]]
self.wfg = BaseWFG(S, A, upper_bounds, shapes, transformations)
def __init__(self, n_arguments: int, n_objectives: int, k: int):
assert k % (n_objectives - 1) == 0
assert k + 1 <= n_arguments
self._n_arguments = n_arguments
self._n_objectives = n_objectives
self._k = k
n = self._n_arguments
M = self._n_objectives
S = 2 * (np.arange(M) + 1)
A = np.ones(M - 1)
upper_bounds = 2 * (np.arange(n) + 1)
self.domain = np.zeros((n, 2))
self.domain[:, 1] = upper_bounds
shapes: List[shape_functions.BaseShapeFunction]
shapes = [shape_functions.ConvexShapeFunction(M) for _ in range(M - 1)]
shapes.append(
shape_functions.MixedConvexOrConcaveShapeFunction(M, 1, 5))
transformations: List[List[
transformation_functions.BaseTransformations]]
transformations = [[] for _ in range(4)]
transformations[0] = [
transformation_functions.IdenticalTransformation()
for _ in range(k)
]
# transformations[0] = [lambda y: y for _ in range(k)]
for _ in range(n - k):
transformations[0].append(
transformation_functions.LinearShiftTransformation(0.35))
# transformations[1] = [lambda y: y for _ in range(k)]
transformations[1] = [
transformation_functions.IdenticalTransformation()
for _ in range(k)
]
for _ in range(n - k):
transformations[1].append(
transformation_functions.FlatRegionBiasTransformation(
0.8, 0.75, 0.85))
transformations[2] = [
transformation_functions.PolynomialBiasTransformation(0.02)
for _ in range(n)
]
def _input_converter(i: int, y: np.ndarray) -> np.ndarray:
indices = np.arange(i * k // (M - 1), (i + 1) * k // (M - 1))
return y[indices]
transformations[3] = [
transformation_functions.WeightedSumReductionTransformation(
2 * np.arange(i * k // (M - 1) + 1,
(i + 1) * k // (M - 1) + 1),
lambda y: _input_converter(i, y),
) for i in range(M - 1)
]
transformations[3].append(
transformation_functions.WeightedSumReductionTransformation(
2 * np.arange(k, n) + 1,
lambda y: y[k:n],
))
self.wfg = BaseWFG(S, A, upper_bounds, shapes, transformations)
def __init__(self, n_arguments: int, n_objectives: int, k: int):
assert k % (n_objectives - 1) == 0
assert k + 1 <= n_arguments
self._n_arguments = n_arguments
self._n_objectives = n_objectives
self._k = k
n = self._n_arguments
M = self._n_objectives
S = 2 * (np.arange(M) + 1)
A = np.ones(M - 1)
upper_bounds = 2 * (np.arange(n) + 1)
self.domain = np.zeros((n, 2))
self.domain[:, 1] = upper_bounds
shapes: List[shape_functions.BaseShapeFunction]
shapes = [shape_functions.ConcaveShapeFunction(M) for _ in range(M)]
def _input_converter0(i: int, y: np.ndarray) -> np.ndarray:
return y[i:n]
transformations: List[List[
transformation_functions.BaseTransformations]]
transformations = [[] for _ in range(3)]
transformations[0] = [
transformation_functions.ParameterDependentBiasTransformation(
np.ones(n - i),
lambda y: _input_converter0(i, y),
0.98 / 49.98,
0.02,
50,
i,
) for i in range(n - 1)
]
transformations[0].append(
transformation_functions.IdenticalTransformation())
transformations[1] = [
transformation_functions.DeceptiveShiftTransformation(
0.35, 0.001, 0.05) for _ in range(k)
]
for _ in range(n - k):
transformations[1].append(
transformation_functions.MultiModalShiftTransformation(
30, 95, 0.35))
def _input_converter(i: int, y: np.ndarray) -> np.ndarray:
indices = np.arange(i * k // (M - 1), (i + 1) * k // (M - 1))
return y[indices]
transformations[2] = []
for i in range(M - 1):
transformations[2].append(
transformation_functions.NonSeparableReductionTransformation(
k // (M - 1), lambda y: _input_converter(i, y)))
transformations[2].append(
transformation_functions.NonSeparableReductionTransformation(
n - k,
lambda y: y[k:n],
))
# transformations = [transformations[0], transformations[1], transformations[2]]
self.wfg = BaseWFG(S, A, upper_bounds, shapes, transformations)
def __init__(self, n_arguments: int, n_objectives: int, k: int):
assert k % (n_objectives - 1) == 0
assert k + 1 <= n_arguments // 2
assert (n_arguments - k) % 2 == 0
self._n_arguments = n_arguments
self._n_objectives = n_objectives
self._k = k
n = self._n_arguments
M = self._n_objectives
S = 2 * (np.arange(M) + 1)
A = np.zeros(M - 1)
A[0] = 1
upper_bounds = 2 * (np.arange(n) + 1)
self.domain = np.zeros((n, 2))
self.domain[:, 1] = upper_bounds
shapes: List[shape_functions.BaseShapeFunction]
shapes = [shape_functions.LinearShapeFunction(M) for _ in range(M)]
transformations: List[List[
transformation_functions.BaseTransformations]]
transformations = [[] for _ in range(3)]
transformations[0] = [
transformation_functions.IdenticalTransformation()
for _ in range(k)
]
for _ in range(n - k):
transformations[0].append(
transformation_functions.LinearShiftTransformation(0.35))
def _input_converter0(i: int, y: np.ndarray) -> np.ndarray:
indices = [k + 2 * (i + 1 - k) - 2, k + 2 * (i - k + 1) - 1]
return y[indices]
transformations[1] = [
transformation_functions.IdenticalTransformation()
for _ in range(k)
]
for i in range(k, n // 2):
transformations[1].append(
transformation_functions.NonSeparableReductionTransformation(
2, lambda y: _input_converter0(i, y)))
def _input_converter1(i: int, y: np.ndarray) -> np.ndarray:
indices = np.arange(i * k // (M - 1), (i + 1) * k // (M - 1))
return y[indices]
transformations[2] = [
transformation_functions.WeightedSumReductionTransformation(
np.ones(k // (M - 1)),
lambda y: _input_converter1(i, y),
) for i in range(M - 1)
]
transformations[2].append(
transformation_functions.WeightedSumReductionTransformation(
np.ones(n // 2 - k),
lambda y: y[k:n // 2],
))
# transformations = [transformations[0], transformations[1], transformations[2]]
self.wfg = BaseWFG(S, A, upper_bounds, shapes, transformations)
