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Added weighted crossover and then some

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- Added weighted methods. (default setting does not change).
- Removed unnecessary exception catching. Letting exceptions flow through instead, and let the user decide how to handle them.
- Removed list conversion.
- Removed parent sorting for extrapolate.
- Simplified variable names.
This commit is contained in:
SimpleArt
2020-11-30 14:19:14 -05:00
parent 8e0437bf4e
commit cd19725a5a
1 changed files with 56 additions and 93 deletions
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149
src/crossover/crossover_methods.py
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@ -7,51 +7,10 @@ def append_to_next_population(population_method):
return lambda ga:\ return lambda ga:\
ga.population.append_children( ga.population.append_children(
list(population_method(ga, ga.population.mating_pool)) population_method(ga, ga.population.mating_pool)
) )
def weak_check_exceptions(population_method):
"""Checks if the first and last chromosomes can be crossed."""
def new_method(ga, mating_pool):
# check if any genes are an Exception from
# crossing just the first and last parents.
for gene in ga.crossover_individual_impl(ga, mating_pool[0], mating_pool[-1]):
if isinstance(gene.value, Exception):
raise gene.value
# continue if no Exceptions found.
else:
return population_method(ga, ga.population.mating_pool)
return new_method
def strong_check_exceptions(population_method):
"""Checks if every pair of selected chromosomes can be crossed.
Warning: Very slow, consider comparing the types of genes
allowed to the method used beforehand instead.
"""
def new_method(ga, mating_pool):
next_population = list(population_method(ga, ga.population.mating_pool))
# check if any genes are an Exception.
for chromosome in next_population:
for gene in chromosome:
if isinstance(gene.value, Exception):
raise gene.value
# continue if no Exceptions found.
else:
return next_population
return new_method
def genes_to_chromosome(individual_method): def genes_to_chromosome(individual_method):
"""Converts a collection of genes into a chromosome. """Converts a collection of genes into a chromosome.
Note: Will recreate the gene list if given gene list. Note: Will recreate the gene list if given gene list.
@ -59,9 +18,9 @@ def genes_to_chromosome(individual_method):
and use yield for efficiency. and use yield for efficiency.
""" """
return lambda ga, parent_1, parent_2:\ return lambda ga, parent_1, parent_2, weight:\
ga.make_chromosome( ga.make_chromosome(
list(individual_method(ga, parent_1, parent_2)) individual_method(ga, parent_1, parent_2, weight)
) )
@ -70,11 +29,11 @@ def values_to_genes(individual_method):
Returns a generator of genes to avoid storing a new list. Returns a generator of genes to avoid storing a new list.
""" """
return lambda ga, parent_1, parent_2:\ return lambda ga, parent_1, parent_2, weight:\
( (
ga.make_gene(value) ga.make_gene(value)
for value for value
in individual_method(ga, parent_1, parent_2) in individual_method(ga, parent_1, parent_2, weight)
) )
@ -82,8 +41,6 @@ class Crossover_Methods:
# Private method decorators, see above. # Private method decorators, see above.
_append_to_next_population = append_to_next_population _append_to_next_population = append_to_next_population
_weak_check_exceptions = weak_check_exceptions
_strong_check_exceptions = strong_check_exceptions
_genes_to_chromosome = genes_to_chromosome _genes_to_chromosome = genes_to_chromosome
_values_to_genes = values_to_genes _values_to_genes = values_to_genes
@ -93,7 +50,6 @@ class Crossover_Methods:
@append_to_next_population @append_to_next_population
@weak_check_exceptions
def sequential_selection(ga, mating_pool): def sequential_selection(ga, mating_pool):
"""Select sequential pairs from the mating pool. """Select sequential pairs from the mating pool.
Every parent is paired with the previous parent. Every parent is paired with the previous parent.
@ -104,12 +60,12 @@ class Crossover_Methods:
yield ga.crossover_individual_impl( # apply crossover to yield ga.crossover_individual_impl( # apply crossover to
ga, # ga, #
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
0.5 # with equal weight
) )
@append_to_next_population @append_to_next_population
@weak_check_exceptions
def random_selection(ga, mating_pool): def random_selection(ga, mating_pool):
"""Select random pairs from the mating pool. """Select random pairs from the mating pool.
Every parent is paired with a random parent. Every parent is paired with a random parent.
@ -119,7 +75,8 @@ class Crossover_Methods:
yield ga.crossover_individual_impl( # apply crossover to yield ga.crossover_individual_impl( # apply crossover to
ga, # ga, #
parent, # the parent and parent, # the parent and
random.choice(mating_pool) # a random parent random.choice(mating_pool), # a random parent
0.5 # with equal weight
) )
@ -128,25 +85,36 @@ class Crossover_Methods:
@genes_to_chromosome @genes_to_chromosome
def single_point(ga, parent_1, parent_2): def single_point(ga, parent_1, parent_2, weight = 0.5):
"""Cross two parents by swapping genes at one random point.""" """Cross two parents by swapping genes at one random point."""
swap_index = random.randrange(len(parent_1)) N = min(len(parent_1), len(parent_2))
if weight == 0.5:
swap_index = random.randrange(N)
else:
weights = [
weight*n + (1-weight)*(N-n)
for n
in range(N)
]
swap_index = random.choices(range(N), weights)[0]
return parent_1[:swap_index] + parent_2[swap_index:] return parent_1[:swap_index] + parent_2[swap_index:]
@genes_to_chromosome @genes_to_chromosome
def multi_point(ga, parent_1, parent_2): def multi_point(ga, parent_1, parent_2, weight = 0.5):
"""Cross two parents by swapping genes at multiple points.""" """Cross two parents by swapping genes at multiple points."""
pass pass
@genes_to_chromosome @genes_to_chromosome
def uniform(ga, parent_1, parent_2): def uniform(ga, parent_1, parent_2, weight = 0.5):
"""Cross two parents by swapping all genes randomly.""" """Cross two parents by swapping all genes randomly."""
for gene_pair in zip(parent_1, parent_2): for gene_pair in zip(parent_1, parent_2):
yield random.choice(gene_pair) yield random.choice(gene_pair, [weight, 1-weight])
class Arithmetic: class Arithmetic:
@ -154,61 +122,56 @@ class Crossover_Methods:
@genes_to_chromosome @genes_to_chromosome
@values_to_genes @values_to_genes
def random(ga, parent_1, parent_2): def random(ga, parent_1, parent_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."""
value_iter_1 = parent_1.gene_value_iter values_1 = parent_1.gene_value_iter
value_iter_2 = parent_2.gene_value_iter values_2 = parent_2.gene_value_iter
for value_1, value_2 in zip(values_1, values_2):
value = weight*values_1 + (1-weight)*random.uniform(value_1, value_2)
for value_1, value_2 in zip(value_iter_1, value_iter_2):
if type(value_1) == type(value_2) == int: if type(value_1) == type(value_2) == int:
yield random.randint(*sorted([value_1, value_2])) value = round(value)
else:
try: yield value
yield random.uniform(value_1, value_2)
except:
yield ValueError("Unhandled gene type found. Use integer or float genes.")
@genes_to_chromosome @genes_to_chromosome
@values_to_genes @values_to_genes
def average(ga, parent_1, parent_2): def average(ga, parent_1, parent_2, weight = 0.5):
"""Cross two parents by taking the average of the genes.""" """Cross two parents by taking the average of the genes."""
value_iter_1 = parent_1.gene_value_iter values_1 = parent_1.gene_value_iter
value_iter_2 = parent_2.gene_value_iter 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
for value_1, value_2 in zip(value_iter_1, value_iter_2):
if type(value_1) == type(value_2) == int: if type(value_1) == type(value_2) == int:
yield (value_1+value_2)//2 value = round(value)
else:
try: yield value
yield (value_1+value_2)/2
except:
raise ValueError("Could not take the average of the gene values. Use integer or float genes.")
@genes_to_chromosome @genes_to_chromosome
@values_to_genes @values_to_genes
def extrapolate(ga, parent_1, parent_2): def extrapolate(ga, parent_1, parent_2, weight = 0.5):
"""Cross two parents by extrapolating towards the better 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.
""" """
# Swap so that parent 1 is the better parent. values_1 = parent_1.gene_value_iter
if ga.target_fitness_type == 'min' and parent_1.fitness > parent_2.fitness: values_2 = parent_2.gene_value_iter
parent_1, parent_2 = parent_2, parent_1
if ga.target_fitness_type == 'max' and parent_1.fitness < parent_2.fitness:
parent_1, parent_2 = parent_2, parent_1
value_iter_1 = parent_1.gene_value_iter for value_1, value_2 in zip(values_1, values_2):
value_iter_2 = parent_2.gene_value_iter
value = (2-weight)*value_1 + (weight-1)*value_2
for value_1, value_2 in zip(value_iter_1, value_iter_2):
if type(value_1) == type(value_2) == int: if type(value_1) == type(value_2) == int:
yield value_1 + (value_1-value_2)//4 value = round(value)
else:
try: yield value
yield value_1 + (value_1-value_2)/4
except:
raise ValueError("Could not take the average of the gene values. Use integer or float genes.")