from typing import Tuple
import torch
from evox.utils import minimum_int
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def DE_differential_sum(
diff_padding_num: int, num_diff_vectors: torch.Tensor, index: torch.Tensor, population: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Computes the difference vectors' sum in differential evolution.
:param diff_padding_num: The number of padding difference vectors.
:param num_diff_vectors: The number of difference vectors used in mutation.
:param index: The index of current individual.
:param population: The population tensor.
:return: The difference sum and the index of first difference vector.
"""
device = population.device
pop_size = population.size(0)
if num_diff_vectors.ndim == 0:
num_diff_vectors = num_diff_vectors.unsqueeze(0)
select_len = num_diff_vectors.unsqueeze(1) * 2 + 1
rand_indices = torch.randint(0, pop_size, (pop_size, diff_padding_num), device=device)
rand_indices = torch.where(rand_indices == index.unsqueeze(1), pop_size - 1, rand_indices)
pop_permute = population[rand_indices]
mask = torch.arange(diff_padding_num, device=device).unsqueeze(0) < select_len
pop_permute_padding = torch.where(mask.unsqueeze(2), pop_permute, 0)
diff_vectors = pop_permute_padding[:, 1:]
difference_sum = diff_vectors[:, 0::2].sum(dim=1) - diff_vectors[:, 1::2].sum(dim=1)
return difference_sum, rand_indices[:, 0]
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def DE_binary_crossover(mutation_vector: torch.Tensor, current_vector: torch.Tensor, CR: torch.Tensor):
"""
Performs binary crossover in differential evolution.
:param mutation_vector: The mutated vector for each individual in the population.
:param current_vector: The current vector for each individual in the population.
:param CR: The crossover probability for each individual.
:return: The trial vector after crossover for each individual.
"""
device = mutation_vector.device
pop_size, dim = mutation_vector.size()
if CR.ndim == 1:
CR = CR.unsqueeze(1)
mask = torch.randn(pop_size, dim, device=device) < CR
rind = torch.randint(0, dim, (pop_size,), device=device).unsqueeze(1)
jind = torch.arange(dim, device=device).unsqueeze(0) == rind
trial_vector = torch.where(torch.logical_or(mask, jind), mutation_vector, current_vector)
return trial_vector
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def DE_exponential_crossover(mutation_vector: torch.Tensor, current_vector: torch.Tensor, CR: torch.Tensor):
"""
Performs exponential crossover in differential evolution.
:param mutation_vector: The mutated vector for each individual in the population.
:param current_vector: The current vector for each individual in the population.
:param CR: The crossover probability for each individual.
:return: The trial vector after crossover for each individual.
"""
device = mutation_vector.device
pop_size, dim = mutation_vector.size()
nn = torch.randint(0, dim, (pop_size,), device=device)
# Geometric distribution random ll
float_tiny = 1.1754943508222875e-38
ll = torch.rand(pop_size, device=device).clamp(min=float_tiny)
ll = (ll.log() / (-CR.log1p())).floor().to(dtype=nn.dtype)
mask = torch.arange(dim, device=device).unsqueeze(0) < (minimum_int(ll, dim) - 1).unsqueeze(1)
mask = torch.gather(torch.tile(mask, (1, 2)), 1, nn.unsqueeze(1) + torch.arange(dim, device=device))
trial_vector = torch.where(mask, mutation_vector, current_vector)
return trial_vector
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def DE_arithmetic_recombination(mutation_vector: torch.Tensor, current_vector: torch.Tensor, K: torch.Tensor):
"""
Performs arithmetic recombination in differential evolution.
:param mutation_vector: The mutated vector for each individual in the population.
:param current_vector: The current vector for each individual in the population.
:param K: The coefficient for each individual.
:return: The trial vector after recombination for each individual.
"""
if K.ndim == 1:
K = K.unsqueeze(1)
trial_vector = current_vector + K * (mutation_vector - current_vector)
return trial_vector
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