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Using ga.weighted_random method

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This commit is contained in:
SimpleArt
2020-12-29 19:34:35 -05:00
parent 88161bd114
commit 9ad3f100ec
1 changed files with 4 additions and 23 deletions
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27
src/crossover/crossover_methods.py
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@ -87,18 +87,8 @@ class Crossover_Methods:
minimum_parent_length = min(len(parent_1), len(parent_2))
# Equally weighted indexes
if weight == 0.5:
swap_index = random.randrange(minimum_parent_length)
# Use weighted random index.
else:
weight_conversion = 2*weight if (weight < 0.5) else 0.5 / (1-weight)
rand_num = random.random()
swap_index = int(
minimum_parent_length *
(1-(1-rand_num)**weight_conversion)**(1/weight_conversion)
)
# 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]):
@ -168,17 +158,8 @@ class Crossover_Methods:
for value_1, value_2 in zip(values_1, values_2):
# Use equally weighted values.
if weight == 0.5:
value = random.uniform(value_1, value_2)
# Use weighted random value, which gives values closer
# to value_1 if weight < 0.5 or values closer to value_2
# if weight > 0.5.
else:
t = 2*weight if (weight < 0.5) else 0.5 / (1-weight)
x = random.random()
value = value_1 + (value_2-value_1) * (1-(1-x)**t)**(1/t)
# 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)