paulin/EasyGA
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Cleaned up repetitive code and sorted the population before mutating

Browse Source
This commit is contained in:
SimpleArt
2020-12-29 19:35:26 -05:00
parent 9ad3f100ec
commit 57756833dc
1 changed files with 19 additions and 39 deletions
Download Patch File Download Diff File Expand all files Collapse all files

58
src/EasyGA.py
View File

@ -1,5 +1,5 @@
def function_info(decorator):
"""Recovers the name and doc-string for decorators."""
"""Recovers the name and doc-string for decorators throughout EasyGA for documentation purposes."""
def new_decorator(method):
@ -15,7 +15,6 @@ def function_info(decorator):
return new_decorator
# Import math for square root (ga.dist()) and ceil (crossover methods)
import math
@ -92,6 +91,7 @@ class GA(Attributes):
self.crossover_population_impl()
self.survivor_selection_impl()
self.population.update()
self.sort_by_best_fitness()
self.mutation_population_impl()
# Update and sort fitnesses
@ -154,41 +154,22 @@ class GA(Attributes):
best_chromosome = self.population[0]
tol = lambda i: self.dist(best_chromosome, self.population[i])
# Weighted averaging
average = lambda x, y: self.adapt_probability_rate * x + (1-self.adapt_probability_rate) * y
# Too few converged: cross more and mutate less
if tol(amount_converged//2) > tol(amount_converged//4)*2:
self.selection_probability = sum((
self.adapt_probability_rate * self.max_selection_probability,
(1-self.adapt_probability_rate) * self.selection_probability
))
self.chromosome_mutation_rate = sum((
self.adapt_probability_rate * self.min_chromosome_mutation_rate,
(1-self.adapt_probability_rate) * self.chromosome_mutation_rate
))
self.gene_mutation_rate = sum((
self.adapt_probability_rate * self.min_gene_mutation_rate,
(1-self.adapt_probability_rate) * self.gene_mutation_rate
))
self.selection_probability = average(self.max_selection_probability , self.selection_probability)
self.chromosome_mutation_rate = average(self.min_chromosome_mutation_rate, self.chromosome_mutation_rate)
self.gene_mutation_rate = average(self.min_gene_mutation_rate , self.gene_mutation_rate)
# Too many converged: cross less and mutate more
else:
self.selection_probability = sum((
self.adapt_probability_rate * self.min_selection_probability,
(1-self.adapt_probability_rate) * self.selection_probability
))
self.chromosome_mutation_rate = sum((
self.adapt_probability_rate * self.max_chromosome_mutation_rate,
(1-self.adapt_probability_rate) * self.chromosome_mutation_rate
))
self.gene_mutation_rate = sum((
self.adapt_probability_rate * self.max_gene_mutation_rate,
(1-self.adapt_probability_rate) * self.gene_mutation_rate
))
self.selection_probability = average(self.min_selection_probability , self.selection_probability)
self.chromosome_mutation_rate = average(self.max_chromosome_mutation_rate, self.chromosome_mutation_rate)
self.gene_mutation_rate = average(self.max_gene_mutation_rate , self.gene_mutation_rate)
def adapt_population(self):
@ -315,19 +296,18 @@ class GA(Attributes):
if chromosome_list is None:
chromosome_list = self.population
# Reversed sort if max fitness should be first
reverse = (self.target_fitness_type == 'max')
# Sort by fitness, assuming None should be moved to the end of the list
key = lambda chromosome: chromosome.fitness if chromosome.fitness is not None else float('inf') * (+1, -1)[int(reverse)]
if in_place:
chromosome_list.sort( # list to be sorted
key = lambda chromosome: chromosome.fitness, # by fitness
reverse = (self.target_fitness_type == 'max') # ordered by fitness type
)
chromosome_list.sort(key = key, reverse = reverse)
return chromosome_list
else:
return sorted(
chromosome_list, # list to be sorted
key = lambda chromosome: chromosome.fitness, # by fitness
reverse = (self.target_fitness_type == 'max') # ordered by fitness type
)
return sorted(chromosome_list, key = key, reverse = reverse)
def get_chromosome_fitness(self, index):