def calculate_probability_of_crossover(self, individual,
crossover_pressure):
avg = uls.calculate_media_solution(self.population)
if individual.fitness >= avg:
best_fitness = self.best_solution.fitness
return crossover_pressure * (best_fitness - individual.fitness) / (
best_fitness - avg)
return self.p_c
def calculate_probability_of_crossover(self, p1, p2, crossover_pressure):
if p1.fitness < p2.fitness:
parent_fitness = p2.fitness
else:
parent_fitness = p1.fitness
avg = uls.calculate_media_solution(self.population)
if parent_fitness >= avg:
best_fitness = self.best_solution.fitness
return crossover_pressure*(best_fitness - parent_fitness)/(best_fitness - avg)
return self.p_c
def search(self, n_iterations=0, report=False, log=False):
offsprings = []
while len(offsprings) < len(self.population):
off1, off2 = p1, p2 = self.selection(self.population,
self._random_state,
self.pressure)
if self._random_state.uniform() < self.p_c:
self._crossover(p1, p2)
if self._random_state.uniform() < self.p_m:
off1 = self._mutation(off1)
off2 = self._mutation(off2)
if not (hasattr(off1, 'fitness') and hasattr(off2, 'fitness')):
self.problem_instance.evaluate(off1)
self.problem_instance.evaluate(off2)
#self.select_the_offpring(p1, p2, off1, off2, offsprings)
offsprings.extend([off1, off2])
while len(offsprings) > len(self.population):
offsprings.pop()
#print(np.std([offspring.fitness for offspring in offsprings]))
if np.std([offspring.fitness for offspring in offsprings]) < 0.02:
self.selection = uls.parametrized_tournament_selection(0.2)
else:
self.selection = uls.parametrize_roulette_wheel_w_pressure(0.2)
self.population = np.asarray(self.sort_populations(offsprings))
self.best_solution = self._get_elite(self.population)
if report:
self._verbose_reporter_inner(self.best_solution, n_iterations)
if self.flag:
if self.best_solution.fitness - uls.calculate_media_solution(
self.population) < 0.02:
self.selection = uls.parametrize_rank_selection(self.pressure)
else:
self.selection = uls.parametrize_roulette_wheel_w_pressure(
self.pressure)
ga1.initialize()
islands.append(ga1)
best_solution = ga1.best_solution
for iteration in range(n_gen):
ga1.search(iteration, False, False)
for i in range(len(islands)):
if best_solution.fitness < islands[i].best_solution.fitness:
algorithm = islands[i]
best_solution = islands[i].best_solution
print("\n")
print(">>>>>>>>>>>>>>>>>INTERATION: ", iteration)
print(">>>>>>>>>>>>>>>>>BEST_SOLUTION: ", best_solution.id)
print("G1: ", [
ga1.best_solution.fitness,
round(uls.calculate_media_solution(ga1.population), 2),
len(ga1.population)
],
sep='\t')
print(">>>>>>>>>>>>>>>>>FITNESS>>>>>>>>>>>>>>>>>>>>: ",
str(round(best_solution.fitness, 2)))
print("\n")
best_solution.print_()
print("Training fitness of the best solution: %.2f" % best_solution.fitness)
print("Validation fitness of the best solution: %.2f" % best_solution.fitness)
#++++++++++++++++++++++++++
# TEST
#++++++++++++++++++++++++++
def determinine_pressure(self):
return (self.pressure * self._determine_average_reproductive_guys()
) / (uls.calculate_media_solution(self.population))
def determinine_pressure(self):
print("Média: ", self._determine_average_reproductive_guys())
print("Denominador: ", uls.calculate_media_solution(self.population))
return (self.presure * self._determine_average_reproductive_guys()) / (
uls.calculate_media_solution(self.population))
save_solutions = []
save_solutions.append(ga1.best_solution)
for iteration in range(n_gen):
ga1.search(100, False, False)
ga2.search(100, False, False)
ga3.search(100, False, False)
ga4.search(100, False, False)
for i in range(len(islands)):
if best_solution.fitness < islands[i].best_solution.fitness:
algorithm = islands[i]
best_solution = islands[i].best_solution
print('\n')
print(">>>>>>>>>>>>>>>>>INTERATION: ", iteration)
print(">>>>>>>>>>>>>>>>>FITNESS>>>>>>>>>>>>>>>>>>>>: ", str(ga1.best_solution.fitness))
print(">>>>>>>>>>>>>>>>>G1 - MEAN: ", uls.calculate_media_solution(ga1.population))
print("\n")
print(">>>>>>>>>>>>>>>>>FITNESS>>>>>>>>>>>>>>>>>>>>: ", str(ga2.best_solution.fitness))
print(">>>>>>>>>>>>>>>>>G2 - MEAN: ", uls.calculate_media_solution(ga2.population))
print("\n")
print(">>>>>>>>>>>>>>>>>FITNESS>>>>>>>>>>>>>>>>>>>>: ", str(ga3.best_solution.fitness))
print(">>>>>>>>>>>>>>>>>G3 - MEAN: ", uls.calculate_media_solution(ga3.population))
print("\n")
print(">>>>>>>>>>>>>>>>>FITNESS>>>>>>>>>>>>>>>>>>>>: ", str(ga4.best_solution.fitness))
print(">>>>>>>>>>>>>>>>>G4 - MEAN: ", uls.calculate_media_solution(ga4.population))
best_solution.print_()
print("Training fitness of the best solution: %.2f" % best_solution.fitness)
print("Validation fitness of the best solution: %.2f" % best_solution.validation_fitness)
migration3 = copy.deepcopy(ga3.population[:3])
migration4 = copy.deepcopy(ga4.population[:3])
migration5 = copy.deepcopy(ga5.population[:3])
ga1.population[:3] = migration2
ga2.population[:3] = migration3
ga3.population[:3] = migration4
ga4.population[:3] = migration5
ga5.population[:3] = migration1
print('\n')
print(">>>>>>>>>>>>>>>>>INTERATION: ", iteration)
print(">>>>>>>>>>>>>>>>>FITNESS>>>>>>>>>>>>>>>>>>>>: ",
str(ga1.best_solution.fitness))
print(">>>>>>>>>>>>>>>>>G1 - MEAN: ",
uls.calculate_media_solution(ga1.population))
print(">>>>>>>>>>>>>>>>>FITNESS>>>>>>>>>>>>>>>>>>>>: ",
str(ga2.best_solution.fitness))
print(">>>>>>>>>>>>>>>>>G2 - MEAN: ",
uls.calculate_media_solution(ga2.population))
print(">>>>>>>>>>>>>>>>>FITNESS>>>>>>>>>>>>>>>>>>>>: ",
str(ga3.best_solution.fitness))
print(">>>>>>>>>>>>>>>>>G3 - MEAN: ",
uls.calculate_media_solution(ga3.population))
print(">>>>>>>>>>>>>>>>>FITNESS>>>>>>>>>>>>>>>>>>>>: ",
str(ga4.best_solution.fitness))
print(">>>>>>>>>>>>>>>>>G4 - MEAN: ",
uls.calculate_media_solution(ga4.population))
print(">>>>>>>>>>>>>>>>>FITNESS>>>>>>>>>>>>>>>>>>>>: ",
str(ga5.best_solution.fitness))
print(">>>>>>>>>>>>>>>>>G5 - MEAN: ",
import os
import datetime
import logging
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import utils as uls
from problems.ANNOP import ANNOP
from ANN.ANN import ANN, softmax, sigmoid
from algorithms.genetic_algorithm_Elitism import GeneticAlgorithm
# setup logger
file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "LogFiles/" + (str(datetime.datetime.now().date()) + "-" + str(datetime.datetime.now().hour) + \
"_" + str(datetime.datetime.now().minute) + "_log.csv"))
logging.basicConfig(filename=file_path,
level=logging.DEBUG,
format='%(name)s,%(message)s')
#++++++++++++++++++++++++++
# THE DATA
# restrictions:
# - MNIST digits (8*8)
# - 33% for testing
# - flattened input images
#++++++++++++++++++++++++++
# import data
digits = datasets.load_digits()
flat_images = np.array([image.flatten() for image in digits.images])
print(flat_images.shape)
print(digits.target_names)
