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Population.next_population and better comments

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This commit is contained in:
SimpleArt
2020-10-15 00:22:58 -04:00
parent 543b295e52
commit 5df19df643
6 changed files with 160 additions and 59 deletions
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73
src/crossover/crossover_methods.py
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@ -12,7 +12,15 @@ class Crossover_Methods:
"""
mating_pool = ga.population.get_mating_pool()
return ga.make_population([ga.crossover_individual_impl(ga, mating_pool[index], mating_pool[index-1]) for index in range(len(mating_pool))])
for index in range(len(mating_pool)): # for each parent in the mating pool
ga.population.add_child( # add a child
ga.crossover_individual_impl( # by crossing
ga, #
mating_pool[index], # the parent and
mating_pool[index-1] # the previous parent
)
)
def random_selection(ga):
@ -21,7 +29,15 @@ class Crossover_Methods:
"""
mating_pool = ga.population.get_mating_pool()
return ga.make_population([ga.crossover_individual_impl(ga, parent, random.choice(mating_pool)) for parent in mating_pool])
for parent in mating_pool: # for each parent in the mating pool
ga.population.add_child( # add a child
ga.crossover_individual_impl( # by crossing
ga, #
parent, # the parent and
random.choice(mating_pool) # a random parent
)
)
class Individual:
@ -41,18 +57,27 @@ class Crossover_Methods:
def uniform(ga, parent_one, parent_two):
"""Cross two parents by swapping all genes randomly"""
return ga.make_chromosome([
random.choice([parent_one.get_gene(i), parent_two.get_gene(i)])
for i in range(parent_one.size())])
return ga.make_chromosome([ # Make a new chromosome
random.choice([ # by selecting random genes from
parent_one.get_gene(i), # each parent
parent_two.get_gene(i) #
]) #
for i in range(parent_one.size())]) # for each gene
class Arithmetic:
"""Crossover methods for numerical genes"""
def int_random(ga, parent_one, parent_two):
"""Cross two parents by taking a random integer value between each of the genes"""
return ga.make_chromosome([
ga.make_gene(random.randint(*sorted([parent_one.get_gene(i).get_value(), parent_two.get_gene(i).get_value()])))
for i in range(parent_one.size())])
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene( # filled with new genes
random.randint(*sorted([ # by choosing random integers between
parent_one.get_gene(i).get_value(), # the parents' genes
parent_two.get_gene(i).get_value() #
]))) #
for i in range(parent_one.size())]) # for each gene
def int_weighted(ga, parent_one, parent_two):
@ -60,16 +85,26 @@ class Crossover_Methods:
# the percentage of genes taken from the first gene
weight = 0.25
return ga.make_chromosome([
ga.make_gene(int(weight*parent_one.get_gene(i).get_value()+(1-weight)*parent_two.get_gene(i).get_value()))
for i in range(parent_one.size())])
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene(int( # filled with new integer genes
weight*parent_one.get_gene(i).get_value()+ # with weight% from parent one and
(1-weight)*parent_two.get_gene(i).get_value() # (100-weight)% from parent two
)) #
for i in range(parent_one.size())]) # for each gene
def float_random(ga, parent_one, parent_two):
"""Cross two parents by taking a random numeric value between each of the genes"""
return ga.make_chromosome([
ga.make_gene(random.uniform(parent_one.get_gene(i).get_value(), parent_two.get_gene(i).get_value()))
for i in range(parent_one.size())])
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene( # filled with new genes
random.uniform( # by taking a random float between
parent_one.get_gene(i).get_value(), # the parents' genes
parent_two.get_gene(i).get_value() #
) #
) #
for i in range(parent_one.size())]) # for each gene
def float_weighted(ga, parent_one, parent_two):
@ -77,6 +112,10 @@ class Crossover_Methods:
# the percentage of genes taken from the first gene
weight = 0.25
return ga.make_chromosome([
ga.make_gene(weight*parent_one.get_gene(i).get_value()+(1-weight)*parent_two.get_gene(i).get_value())
for i in range(parent_one.size())])
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene( # filled with new float genes
weight*parent_one.get_gene(i).get_value()+ # with weight% from parent one and
(1-weight)*parent_two.get_gene(i).get_value() # (100-weight)% from parent two
) #
for i in range(parent_one.size())]) # for each gene