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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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90
src/crossover/crossover_methods.py
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@ -39,11 +39,39 @@ def _check_weight(individual_method):
return new_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: class Crossover_Methods:
# Allowing access to decorators when importing class # Allowing access to decorators when importing class
_append_to_next_population = _append_to_next_population _append_to_next_population = _append_to_next_population
_check_weight = _check_weight _check_weight = _check_weight
_gene_by_gene = _gene_by_gene
class Population: class Population:
@ -58,7 +86,7 @@ class Crossover_Methods:
""" """
for index in range(len(mating_pool)): # for each parent in the mating pool for index in range(len(mating_pool)): # for each parent in the mating pool
yield ga.crossover_individual_impl( # apply crossover to yield from ga.crossover_individual_impl( # apply crossover to
mating_pool[index], # the parent and mating_pool[index], # the parent and
mating_pool[index-1], # the previous parent mating_pool[index-1], # the previous parent
) )
@ -71,7 +99,7 @@ class Crossover_Methods:
""" """
for parent in mating_pool: # for each parent in the mating pool for parent in mating_pool: # for each parent in the mating pool
yield ga.crossover_individual_impl( # apply crossover to yield from ga.crossover_individual_impl( # apply crossover to
parent, # the parent and parent, # the parent and
random.choice(mating_pool), # a random parent random.choice(mating_pool), # a random parent
) )
@ -90,11 +118,8 @@ class Crossover_Methods:
# Weighted random integer from 0 to minimum parent length - 1 # Weighted random integer from 0 to minimum parent length - 1
swap_index = int(ga.weighted_random(weight) * minimum_parent_length) swap_index = int(ga.weighted_random(weight) * minimum_parent_length)
# Randomly choose which parent's genes are selected first. yield parent_1[:swap_index] + parent_2[swap_index:]
if random.choice([True, False]): yield parent_2[:swap_index] + parent_1[swap_index:]
return parent_1[:swap_index] + parent_2[swap_index:]
else:
return parent_2[:-swap_index] + parent_1[-swap_index:]
@_check_weight @_check_weight
@ -104,61 +129,46 @@ class Crossover_Methods:
@_check_weight @_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.""" """Cross two parents by swapping all genes randomly."""
return random.choices(gene_pair, cum_weights = [weight, 1])[0]
for gene_pair in zip(parent_1, parent_2):
yield random.choices(gene_pair, cum_weights = [weight, 1])[0]
class Arithmetic: class Arithmetic:
"""Crossover methods for numerical genes.""" """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.""" """Cross two parents by taking the average of the genes."""
values_1 = parent_1.gene_value_iter average_value = weight*value_1 + (1-weight)*value_2
values_2 = parent_2.gene_value_iter
for value_1, value_2 in zip(values_1, values_2):
value = weight*value_1 + (1-weight)*value_2
if type(value_1) == type(value_2) == int: if type(value_1) == type(value_2) == int:
value = randround(value) average_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. """Cross two parents by extrapolating towards the first parent.
May result in gene values outside the expected domain. May result in gene values outside the expected domain.
""" """
values_1 = parent_1.gene_value_iter extrapolated_value = weight*value_1 + (1-weight)*value_2
values_2 = parent_2.gene_value_iter
for value_1, value_2 in zip(values_1, values_2):
value = (2-weight)*value_1 + (weight-1)*value_2
if type(value_1) == type(value_2) == int: if type(value_1) == type(value_2) == int:
value = randround(value) extrapolated_value = randround(value)
yield value return extrapolated_value
@_check_weight @_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.""" """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
for value_1, value_2 in zip(values_1, values_2):
# Weighted random value between value 1 and value 2
value = value_1 + ga.weighted_random(weight) * (value_2-value_1) value = value_1 + ga.weighted_random(weight) * (value_2-value_1)
if type(value_1) == type(value_2) == int: if type(value_1) == type(value_2) == int:
@ -212,7 +222,7 @@ class Crossover_Methods:
gene_list_1[input_index] = gene_list_2.pop(-1) gene_list_1[input_index] = gene_list_2.pop(-1)
input_index += 1 input_index += 1
return gene_list_1 yield gene_list_1
@_check_weight @_check_weight
@ -221,7 +231,7 @@ class Crossover_Methods:
and then filling in the rest of the genes from the second parent, and then filling in the rest of the genes from the second parent,
preserving the ordering of genes wherever possible. 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 # Too small to cross
if len(parent_1) < 2: if len(parent_1) < 2:
@ -272,4 +282,4 @@ class Crossover_Methods:
gene_list_1[input_index] = gene_list_2.pop(-1) gene_list_1[input_index] = gene_list_2.pop(-1)
input_index += 1 input_index += 1
return gene_list_1 yield gene_list_1