paulin/EasyGA
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Cleaned up some parts and optimized a little

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SimpleArt
2020-12-05 23:41:10 -05:00
parent 15e17f0b84
commit a6530fb15e
1 changed files with 20 additions and 15 deletions
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35
src/EasyGA.py
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@ -1,5 +1,8 @@
# Import square root function for ga.adapt() # Import math for square root (ga.dist()) and ceil (crossover methods)
from math import sqrt import math
# Import random for many methods
import random
# Import all the data structure prebuilt modules # Import all the data structure prebuilt modules
from structure import Population as create_population from structure import Population as create_population
@ -189,17 +192,12 @@ class GA(Attributes):
# First non-zero tolerance after amount_converged/4 # First non-zero tolerance after amount_converged/4
for i in range(amount_converged//4, len(self.population)): for i in range(amount_converged//4, len(self.population)):
if tol(i) > 0: if (tol_i := tol(i)) > 0:
break break
# First significantly different tolerance # First significantly different tolerance
for j in range(i, len(self.population)): for j in range(i, len(self.population)):
if tol(j) > 2*tol(i): if (tol_j := tol(j)) > 2*tol_i:
break
# Second significantly different tolerance
for k in range(j, len(self.population)):
if tol(k) > 2*tol(j):
break break
# Strongly cross the best chromosome with the worst chromosomes # Strongly cross the best chromosome with the worst chromosomes
@ -208,11 +206,12 @@ class GA(Attributes):
# Strongly cross with the best chromosome # Strongly cross with the best chromosome
# May reject negative weight # May reject negative weight
try: try:
tol_n = tol(n)
self.population[n] = self.crossover_individual_impl( self.population[n] = self.crossover_individual_impl(
self, self,
self.population[n], self.population[n],
best_chromosome, best_chromosome,
min(0.25, (2*tol(n) - tol(j)) / tol(n)) min(0.25, (2*tol_n - tol_j) / tol_n)
) )
# If negative weights can't be used, # If negative weights can't be used,
@ -221,17 +220,22 @@ class GA(Attributes):
self.population[n] = self.crossover_individual_impl( self.population[n] = self.crossover_individual_impl(
self, self,
self.population[n], self.population[n],
self.population[k], self.population[j],
0.75 0.75
) )
# Update fitnesses # Update fitnesses
self.population[n].fitness = self.fitness_function_impl(self.population[n]) self.population[n].fitness = self.fitness_function_impl(self.population[n])
if self.target_fitness_type == 'max' and self.population[n].fitness > best_chromosome.fitness: # Update best chromosome
best_chromosome = self.population[n] if self.target_fitness_type == 'max':
cond = (self.population[n].fitness > best_chromosome.fitness)
elif self.target_fitness_type == 'min' and self.population[n].fitness < best_chromosome.fitness: if self.target_fitness_type == 'min':
cond = (self.population[n].fitness < best_chromosome.fitness)
if cond:
tol_j = tol(j)
best_chromosome = self.population[n] best_chromosome = self.population[n]
self.population.sort_by_best_fitness(self) self.population.sort_by_best_fitness(self)
@ -269,10 +273,11 @@ class GA(Attributes):
""" """
if in_place: if in_place:
return chromosome_list.sort( # list to be sorted chromosome_list.sort( # list to be sorted
key = lambda chromosome: chromosome.fitness, # by fitness key = lambda chromosome: chromosome.fitness, # by fitness
reverse = (self.target_fitness_type == 'max') # ordered by fitness type reverse = (self.target_fitness_type == 'max') # ordered by fitness type
) )
return chromosome_list
else: else:
return sorted( return sorted(