import numpy as np
from copy import deepcopy
from random import choice
from random import shuffle
[docs]def mate(mating_pop, individuals: list, params):
"""Perform BioGP crossover functions. Produce two offsprings by swapping genetic
material of the two parents.
Standard crossover:
Swap two random subtrees between the parents.
Height-fair crossover:
Swap two random subtrees between the parents at the selected depth.
Parameters
----------
mating_pop : list
List of indices of individuals to mate. If None, choose from population
randomly.
Each entry should contain two indices, one for each parent.
individuals : list
List of all individuals.
params : dict
Parameters for evolution. If None, use defaults.
Returns
-------
offspring : list
The offsprings produced as a result of crossover.
"""
prob_crossover = params.get("prob_crossover", 0.9)
prob_standard = 0.5
prob_height_fair = prob_crossover - prob_standard
r = np.random.rand()
if mating_pop is None:
mating_pop = []
for i in range(len(individuals)):
mating_pop.append([i, np.random.randint(len(individuals))])
offspring = []
for mates in mating_pop:
offspring1 = deepcopy(individuals[mates[0]])
offspring2 = deepcopy(individuals[mates[1]])
# Height-fair xover
if r <= prob_height_fair:
depth = min(offspring1.total_depth, offspring2.total_depth)
rand_node1 = choice(offspring1.nodes_at_depth[depth])
rand_node2 = choice(offspring2.nodes_at_depth[depth])
tmp_value = rand_node1.value
tmp_roots = rand_node1.roots
rand_node1.value = rand_node2.value
rand_node1.roots = rand_node2.roots
rand_node2.value = tmp_value
rand_node2.roots = tmp_roots
offspring1.nodes = offspring1.get_sub_nodes()
offspring2.nodes = offspring2.get_sub_nodes()
# Standard xover
elif r <= prob_height_fair + prob_standard:
rand_node1 = choice(offspring1.nodes[1:]) # Exclude linear node
rand_node2 = choice(offspring2.nodes[1:])
tmp_value = rand_node1.value
tmp_roots = rand_node1.roots
rand_node1.value = rand_node2.value
rand_node1.roots = rand_node2.roots
rand_node2.value = tmp_value
rand_node2.roots = tmp_roots
offspring1.nodes = offspring1.get_sub_nodes()
offspring2.nodes = offspring2.get_sub_nodes()
else:
pass
offspring.extend((offspring1, offspring2))
return offspring
[docs]class BioGP_xover:
def __init__(
self, probability_crossover: float = 0.9, probability_standard: float = 0.5
):
self.probability_crossover: float = probability_crossover
if probability_standard > probability_crossover:
msg = (
f"Probability of standard crossover ({probability_standard}) should be "
f"less than probability of crossover ({probability_crossover})"
)
raise ValueError(msg)
self.probability_standard: float = probability_standard
[docs] def do(self, pop, mating_pop_ids):
prob_height_fair = self.probability_crossover - self.probability_standard
pop_size = pop.shape[0]
if mating_pop_ids is None:
shuffled_ids = list(range(pop_size))
shuffle(shuffled_ids)
else:
shuffled_ids = mating_pop_ids
if np.asarray(shuffled_ids).ndim == 1:
mating_pop = [
[shuffled_ids[x], shuffled_ids[x]]
for x in range(len(shuffled_ids))
if x % 2 == 0
]
elif np.asarray(shuffled_ids).ndim == 2:
mating_pop = shuffled_ids
offspring = []
r = np.random.rand()
for mates in mating_pop:
offspring1 = deepcopy(pop[mates[0]][0])
offspring2 = deepcopy(pop[mates[1]][0])
# Height-fair xover
if r <= prob_height_fair:
depth = min(offspring1.total_depth, offspring2.total_depth)
rand_node1 = choice(offspring1.nodes_at_depth[depth])
rand_node2 = choice(offspring2.nodes_at_depth[depth])
tmp_value = rand_node1.value
tmp_roots = rand_node1.roots
rand_node1.value = rand_node2.value
rand_node1.roots = rand_node2.roots
rand_node2.value = tmp_value
rand_node2.roots = tmp_roots
offspring1.nodes = offspring1.get_sub_nodes()
offspring2.nodes = offspring2.get_sub_nodes()
# Standard xover
elif r <= self.probability_crossover:
rand_node1 = choice(offspring1.nodes[1:]) # Exclude linear node
rand_node2 = choice(offspring2.nodes[1:])
tmp_value = rand_node1.value
tmp_roots = rand_node1.roots
rand_node1.value = rand_node2.value
rand_node1.roots = rand_node2.roots
rand_node2.value = tmp_value
rand_node2.roots = tmp_roots
offspring1.nodes = offspring1.get_sub_nodes()
offspring2.nodes = offspring2.get_sub_nodes()
else:
pass
offspring.extend((offspring1, offspring2))
return np.asarray(offspring).reshape(-1,1)
