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Removed useless import statements

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This commit is contained in:
danielwilczak101
2020-10-12 23:13:02 -04:00
parent b8f1de9b52
commit 50e7166ea5
2 changed files with 8 additions and 16 deletions
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20
src/parent_selection/parent_selection_methods.py
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@ -1,8 +1,4 @@
import random
from initialization.chromosome_structure.chromosome import Chromosome as create_chromosome
from initialization.gene_structure.gene import Gene as create_gene
from initialization.population_structure.population import Population
from initialization.chromosome_structure.chromosome import Chromosome
class Parent_Selection:
@ -13,18 +9,18 @@ class Parent_Selection:
Will make tournaments of size tournament_size and choose the winner (best fitness) from the tournament and use it as a parent for the next generation
The total number of parents selected is determined by parent_ratio, an attribute to the GA object.
"""
tournament_size = int(ga.population.size()*ga.tournament_size_ratio)
if tournament_size < 5:
tournament_size = 5
# Probability used for determining if a chromosome should enter the mating pool.
selection_probability = ga.selection_probability
# Repeat tournaments until the mating pool is large enough.
while (len(ga.population.mating_pool) < ga.population.size()*ga.parent_ratio):
# Generate a random tournament group and sort by fitness.
tournament_group = ga.sort_by_best_fitness([random.choice(ga.population.get_all_chromosomes()) for n in range(tournament_size)])
# For each chromosome, add it to the mating pool based on its rank in the tournament.
for index in range(tournament_size):
# Probability required is selection_probability * (1-selection_probability) ^ (tournament_size-index+1)
@ -45,19 +41,19 @@ class Parent_Selection:
Where the chromosomes are the numbers to be selected and the board size for
those numbers are directly proportional to the chromosome's current fitness. Where
the ball falls is a randomly generated number between 0 and 1"""
total_fitness = sum(ga.population.chromosome_list[i].get_fitness() for i in range(ga.population.size()))
rel_fitnesses = []
for chromosome in ga.population.chromosome_list:
if (total_fitness != 0):
rel_fitnesses.append(float(chromosome.fitness)/total_fitness)
probability = [sum(rel_fitnesses[:i+1]) for i in range(len(rel_fitnesses))]
while (len(ga.population.mating_pool) < ga.population.size()*ga.parent_ratio):
rand_number = random.random()
# Loop through the list of probabilities
for i in range(len(probability)):
# If the probability is greater than the random_number, then select that chromosome

4
src/survivor_selection/survivor_selection_methods.py
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@ -1,8 +1,4 @@
import random
from initialization.chromosome_structure.chromosome import Chromosome as create_chromosome
from initialization.gene_structure.gene import Gene as create_gene
from initialization.population_structure.population import Population
from initialization.chromosome_structure.chromosome import Chromosome
class Survivor_Selection:
"""Survivor selection determines which individuals should be brought to the next generation"""