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Added decorator for repetitive code and modified for multiple children per crossover

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This commit is contained in:
SimpleArt
2020-12-29 22:53:20 -05:00
parent 49e20ad20d
commit 0c59c429eb
1 changed files with 63 additions and 53 deletions
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116
src/crossover/crossover_methods.py
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@ -39,11 +39,39 @@ def _check_weight(individual_method):
return new_method
@function_info
def _gene_by_gene(individual_method):
"""Perform crossover by making a single new chromosome
by combining each gene by gene.
"""
def new_method(ga, parent_1, parent_2, *, weight = individual_method.__kwdefaults__.get('weight', None)):
# Without any weight
if weight is None:
yield (
individual_method(ga, value_1, value_2)
for value_1, value_2
in zip(parent_1.gene_value_iter, parent_2.gene_value_iter)
)
# With a weight
else:
yield (
individual_method(ga, value_1, value_2, weight = weight)
for value_1, value_2
in zip(parent_1.gene_value_iter, parent_2.gene_value_iter)
)
return new_method
class Crossover_Methods:
# Allowing access to decorators when importing class
_append_to_next_population = _append_to_next_population
_check_weight = _check_weight
_check_weight = _check_weight
_gene_by_gene = _gene_by_gene
class Population:
@ -57,10 +85,10 @@ class Crossover_Methods:
The first parent is paired with the last parent.
"""
for index in range(len(mating_pool)): # for each parent in the mating pool
yield ga.crossover_individual_impl( # apply crossover to
mating_pool[index], # the parent and
mating_pool[index-1], # the previous parent
for index in range(len(mating_pool)): # for each parent in the mating pool
yield from ga.crossover_individual_impl( # apply crossover to
mating_pool[index], # the parent and
mating_pool[index-1], # the previous parent
)
@ -70,10 +98,10 @@ class Crossover_Methods:
Every parent is paired with a random parent.
"""
for parent in mating_pool: # for each parent in the mating pool
yield ga.crossover_individual_impl( # apply crossover to
parent, # the parent and
random.choice(mating_pool), # a random parent
for parent in mating_pool: # for each parent in the mating pool
yield from ga.crossover_individual_impl( # apply crossover to
parent, # the parent and
random.choice(mating_pool), # a random parent
)
@ -90,11 +118,8 @@ class Crossover_Methods:
# Weighted random integer from 0 to minimum parent length - 1
swap_index = int(ga.weighted_random(weight) * minimum_parent_length)
# Randomly choose which parent's genes are selected first.
if random.choice([True, False]):
return parent_1[:swap_index] + parent_2[swap_index:]
else:
return parent_2[:-swap_index] + parent_1[-swap_index:]
yield parent_1[:swap_index] + parent_2[swap_index:]
yield parent_2[:swap_index] + parent_1[swap_index:]
@_check_weight
@ -104,67 +129,52 @@ class Crossover_Methods:
@_check_weight
def uniform(ga, parent_1, parent_2, *, weight = 0.5):
@_gene_by_gene
def uniform(ga, value_1, value_2, *, weight = 0.5):
"""Cross two parents by swapping all genes randomly."""
for gene_pair in zip(parent_1, parent_2):
yield random.choices(gene_pair, cum_weights = [weight, 1])[0]
return random.choices(gene_pair, cum_weights = [weight, 1])[0]
class Arithmetic:
"""Crossover methods for numerical genes."""
def average(ga, parent_1, parent_2, *, weight = 0.5):
@_gene_by_gene
def average(ga, value_1, value_2, *, weight = 0.5):
"""Cross two parents by taking the average of the genes."""
values_1 = parent_1.gene_value_iter
values_2 = parent_2.gene_value_iter
average_value = weight*value_1 + (1-weight)*value_2
for value_1, value_2 in zip(values_1, values_2):
if type(value_1) == type(value_2) == int:
average_value = randround(value)
value = weight*value_1 + (1-weight)*value_2
if type(value_1) == type(value_2) == int:
value = randround(value)
yield value
return average_value
def extrapolate(ga, parent_1, parent_2, *, weight = 0.5):
@_gene_by_gene
def extrapolate(ga, value_1, value_2, *, weight = 0.5):
"""Cross two parents by extrapolating towards the first parent.
May result in gene values outside the expected domain.
"""
values_1 = parent_1.gene_value_iter
values_2 = parent_2.gene_value_iter
extrapolated_value = weight*value_1 + (1-weight)*value_2
for value_1, value_2 in zip(values_1, values_2):
if type(value_1) == type(value_2) == int:
extrapolated_value = randround(value)
value = (2-weight)*value_1 + (weight-1)*value_2
if type(value_1) == type(value_2) == int:
value = randround(value)
yield value
return extrapolated_value
@_check_weight
def random(ga, parent_1, parent_2, *, weight = 0.5):
@_gene_by_gene
def random(ga, value_1, value_2, *, weight = 0.5):
"""Cross two parents by taking a random integer or float value between each of the genes."""
values_1 = parent_1.gene_value_iter
values_2 = parent_2.gene_value_iter
value = value_1 + ga.weighted_random(weight) * (value_2-value_1)
for value_1, value_2 in zip(values_1, values_2):
if type(value_1) == type(value_2) == int:
value = randround(value)
# Weighted random value between value 1 and value 2
value = value_1 + ga.weighted_random(weight) * (value_2-value_1)
if type(value_1) == type(value_2) == int:
value = randround(value)
yield value
yield value
class Permutation:
@ -212,7 +222,7 @@ class Crossover_Methods:
gene_list_1[input_index] = gene_list_2.pop(-1)
input_index += 1
return gene_list_1
yield gene_list_1
@_check_weight
@ -221,7 +231,7 @@ class Crossover_Methods:
and then filling in the rest of the genes from the second parent,
preserving the ordering of genes wherever possible.
NOTE: Needs to be fixed."""
NOTE: Needs to be fixed, since genes are not hashable..."""
# Too small to cross
if len(parent_1) < 2:
@ -272,4 +282,4 @@ class Crossover_Methods:
gene_list_1[input_index] = gene_list_2.pop(-1)
input_index += 1
return gene_list_1
yield gene_list_1