import numpy as np
from random import shuffle
[docs]class SinglePoint_Xover:
"""Simple single point crossover and mutation.
Parameters
----------
ProC : float, optional
[description], by default 1
DisC : float, optional
[description], by default 30
"""
def __init__(self, ProC: float = 1, DisC: float = 30):
"""[summary]
"""
self.ProC = ProC
self.DisC = DisC
[docs] def crossover(self, ind_0, ind_1):
"""
Single point crossover.
Args:
ind_0: individual_0
ind_1: individual_1
Ret:
new_0, new_1: the individuals generatd after crossover.
"""
assert(len(ind_0) == len(ind_1))
point = np.random.randint(len(ind_0))
# new_0, new_1 = np.zeros(len(ind_0)), np.zeros(len(ind_0))
new_0 = np.hstack((ind_0[:point], ind_1[point:]))
new_1 = np.hstack((ind_1[:point], ind_0[point:]))
assert(len(new_0) == len(ind_0))
return new_0, new_1
[docs] def mutation(self, indi):
"""
Simple mutation.
Arg:
indi: individual to mutation.
"""
point = np.random.randint(len(indi))
indi[point] = 1 - indi[point]
return indi
[docs] def do(self, pop: np.ndarray, mating_pop_ids: list = None) -> np.ndarray:
"""Consecutive members of mating_pop_ids are crossed over
in pairs. Example: if mating_pop_ids = [0, 2, 3, 6, 5] then the individuals
are crossover as: [0, 2], [3, 6], [5, 0]. Note: if the number of elements
is odd, the last individual is crossed over with the first one.
Parameters
----------
pop : np.ndarray
Array of all individuals
mating_pop_ids : list, optional
Indices of population members to mate, by default None, which shuffles and
mates whole population
Returns
-------
np.ndarray
The offspring produced as a result of crossover.
"""
pop_size, num_var = pop.shape
if mating_pop_ids is None:
shuffled_ids = list(range(pop_size))
shuffle(shuffled_ids)
else:
shuffled_ids = mating_pop_ids
mating_pop = pop[shuffled_ids]
mate_size = len(shuffled_ids)
if len(shuffled_ids) % 2 == 1:
# Maybe it should be pop_size-1?
mating_pop = np.vstack((mating_pop, mating_pop[0]))
mate_size = mate_size + 1
# The rest closely follows the matlab code.
offspring = np.zeros_like(mating_pop) # empty_like() more efficient?
for i in range(0, mate_size, 2):
offspring[i], offspring[i + 1] = self.crossover(mating_pop[i],mating_pop[i+1])
offspring = offspring.round()
return offspring
[docs]class SinglePoint_Mutation:
"""Simple single point crossover and mutation.
Parameters
----------
ProC : float, optional
[description], by default 1
DisC : float, optional
[description], by default 30
"""
def __init__(self, ProC: float = 1, DisC: float = 30):
"""[summary]
"""
self.ProC = ProC
self.DisC = DisC
[docs] def mutation(self, indi):
"""
Simple mutation.
Arg:
indi: individual to mutation.
"""
point = np.random.randint(len(indi))
indi[point] = 1 - indi[point]
return indi
[docs] def do(self, offspring: np.ndarray):
"""Consecutive members of mating_pop_ids are crossed over
in pairs. Example: if mating_pop_ids = [0, 2, 3, 6, 5] then the individuals
are crossover as: [0, 2], [3, 6], [5, 0]. Note: if the number of elements
is odd, the last individual is crossed over with the first one.
Parameters
----------
pop : np.ndarray
Array of all individuals
mating_pop_ids : list, optional
Indices of population members to mate, by default None, which shuffles and
mates whole population
Returns
-------
np.ndarray
The offspring produced as a result of crossover.
"""
offspring_size, num_var = offspring.shape
k = np.random.randint(0,num_var, offspring_size)
for i in range(offspring_size):
offspring[i,k[i]] = 1 - offspring[i,k[i]]
return offspring
