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GeneticAlgorithme.py
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GeneticAlgorithme.py
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from testParameters import *
import copy
import itertools
import numpy as np
from collections import namedtuple
import time
Item = namedtuple("Item", ['id', 'size'])
Candidate = namedtuple("Candidate", ['items', 'fitness'])
#------------BIN-------------
def cost(bins):
return len(bins)
class Bin(object):
count = itertools.count()
def __init__(self, capacity):
self.id = next(Bin.count)
self.capacity = capacity
self.free_space = capacity
self.items = []
self.used_space = 0
def add_item(self, item):
self.items.append(item)
self.free_space -= item.size
self.used_space += item.size
def remove_item(self, item_index):
item_to_remove = self.items[item_index]
del self.items[item_index]
self.free_space += item_to_remove.size
self.used_space -= item_to_remove.size
def fits(self, item):
return self.free_space >= item.size
def __str__(self):
items = [str(it) for it in self.items]
items_string = '[' + ' '.join(items) + ']'
return "Bin n° " + str(self.id) + " containing the " + \
str(len(self.items)) + " following items : " + items_string + \
" with " + str(self.free_space) + " free space."
def __copy__(self):
new_bin = Bin(self.capacity)
new_bin.free_space = self.free_space
new_bin.used_space = self.used_space
new_bin.items = self.items[:]
return new_bin
#-----------GA HEUR-----------
def nextfit(items, current_bins, capacity):
bins = [copy.copy(b) for b in current_bins]
if not bins:
bin = Bin(capacity)
bins.append(bin)
for item in items:
if item.size > capacity:
continue
if bin.fits(item):
bin.add_item(item)
else:
bin = Bin(capacity)
bin.add_item(item)
bins.append(bin)
return bins
def bestfit(items, current_bins, capacity):
bins = [copy.copy(b) for b in current_bins]
if not bins:
bins = [Bin(capacity)]
for item in items:
if item.size > capacity:
continue
possible_bins = [bin for bin in bins if bin.fits(item)]
if not possible_bins:
bin = Bin(capacity)
bin.add_item(item)
bins.append(bin)
else:
index, free_space = min(enumerate(possible_bins), key=lambda it: it[1].free_space)
possible_bins[index].add_item(item)
return bins
def firstfit(items, current_bins, capacity):
bins = [copy.copy(b) for b in current_bins]
if not bins:
bins = [Bin(capacity)]
for item in items:
if item.size > capacity:
continue
first_bin = next((bin for bin in bins if bin.free_space >= item.size), None)
if first_bin is None:
bin = Bin(capacity)
bin.add_item(item)
bins.append(bin)
else:
first_bin.add_item(item)
return bins
def worstfit(items, current_bins, capacity):
bins = [copy.copy(b) for b in current_bins]
if not bins:
bins = [Bin(capacity)]
for item in items:
if item.size > capacity:
continue
possible_bins = [bin for bin in bins if bin.fits(item)]
if not possible_bins:
bin = Bin(capacity)
bin.add_item(item)
bins.append(bin)
else:
index, free_space = max(enumerate(possible_bins), key=lambda it: it[1].free_space)
possible_bins[index].add_item(item)
return bins
#-----------GA------------
# step 01 : Create initial Population
def population_generator(items, capacity, population_size, greedy_solver):
candidate = Candidate(items[:], fitness(items, capacity, greedy_solver))
population = [candidate]
new_items = items[:]
for i in range(population_size - 1):
np.random.shuffle(new_items)
candidate = Candidate(new_items[:], fitness(new_items, capacity, greedy_solver))
if candidate not in population:
population.append(candidate)
return population
# step 02 : calculate fitness for each candidate of this population
def fitness(candidate, capacity, greedy_solver):
if greedy_solver == 'FF':
return firstfit(candidate,[], capacity)
elif greedy_solver == 'BF':
return bestfit(candidate,[], capacity)
return nextfit(candidate,[], capacity)
# step 03 : selection
# Selection par tournoi
def tournament_selection(population, tournament_selection_probability, k):
candidates = [population[(np.random.randint(0, len(population) - 1))]]
while len(candidates) < k:
new_indiv = population[(np.random.randint(0, len(population) - 1))]
if new_indiv not in candidates:
candidates.append(new_indiv)
ind = int(np.random.geometric(tournament_selection_probability, 1))
while ind >= k:
ind = int(np.random.geometric(tournament_selection_probability, 1))
return candidates[ind]
# sélection par roue de roulette.
def roulette_wheel_selection(population):
max = sum([len(e.fitness) for e in population])
pick = np.random.uniform(0, max)
current = max
for item in population:
current -= len(item.fitness)
if current < pick:
return item
# Selection par rang
def rank_selection(population):
length = len(population)
rank_sum = length * (length + 1) / 2
pick = np.random.uniform(0, rank_sum)
current = 0
i = length
for item in population:
current += i
if current > pick:
return item
i -= 1
# Stochastic Universal Sampling (SUS)
def SUS(population, n):
selected = []
pointers = []
max = sum([len(e.fitness) for e in population])
distance = max / n
start = np.random.uniform(0, distance)
for i in range(n):
pointers.append(start + i * distance)
for pointer in pointers:
current = 0
for item in population:
current += len(item.fitness)
if current > pointer:
selected.append(item)
return selected
# step 04 : crossover
def crossover(parent1, parent2):
taken = [False] * len(parent1)
child = []
i = 0
while i < len(parent1):
element = parent1[i]
if not taken[element.id]:
child.append(element)
taken[element.id] = True
element = parent2[i]
if not taken[element.id]:
child.append(element)
taken[element.id] = True
i += 1
return child
# step 05 : mutation
def mutation(member, capacity, greedy_solver):
member_items = member.items
a = np.random.randint(0, len(member_items) - 1)
b = np.random.randint(0, len(member_items) - 1)
while a == b:
b = np.random.randint(0, len(member_items) - 1)
c = member_items[a]
member_items[a] = member_items[b]
member_items[b] = c
member = Candidate(member_items, fitness(member_items, capacity, greedy_solver))
return member
#################### GENETIC ALGORITHM ################################
def genetic_algorithm(weights, capacity, population_size, generations, k, tournament_selection_probability, crossover_probability, mutation_probability, greedy_solver, allow_duplicate_parents, selection_method):
items = [Item]
items = [ Item(i,weights[i]) for i in range(len(weights))]
population = population_generator(items, capacity, population_size, greedy_solver)
best_solution = fitness(items, capacity, greedy_solver)
i = 0
while i < generations:
new_generation = []
best_child = best_solution
for j in range(population_size):
if selection_method == 'SUS':
first_parent = SUS(population, 1)[0].items
second_parent = SUS(population, 1)[0].items
if not allow_duplicate_parents:
while first_parent == second_parent:
second_parent = SUS(population, 1)[0].items
elif selection_method == 'TS':
first_parent = tournament_selection(population, tournament_selection_probability, k).items
second_parent = tournament_selection(population, tournament_selection_probability, k).items
if not allow_duplicate_parents:
while first_parent == second_parent:
second_parent = tournament_selection(population, tournament_selection_probability, k).items
elif selection_method == 'RW':
first_parent = roulette_wheel_selection(population).items
second_parent = roulette_wheel_selection(population).items
if not allow_duplicate_parents:
while first_parent == second_parent:
second_parent = roulette_wheel_selection(population).items
elif selection_method == 'RS':
first_parent = rank_selection(population).items
second_parent = rank_selection(population).items
if not allow_duplicate_parents:
while first_parent == second_parent:
second_parent = rank_selection(population).items
else:
return
child = crossover(first_parent, second_parent)
child = Candidate(child[:], fitness(child, capacity, greedy_solver))
prob = np.random.rand()
if prob <= mutation_probability:
child = mutation(child, capacity, greedy_solver)
if len(child.fitness) < len(best_child):
best_child = child.fitness
new_generation.append(child)
if len(best_child) < len(best_solution):
best_solution = best_child
population = [Candidate(p.items[:], p.fitness) for p in new_generation]
population.sort(key=lambda candidate: len(candidate.fitness), reverse=True)
i += 1
return len(best_solution), [[item.size for item in bin.items] for bin in best_solution]
def AG(benchmarkFileName):
with open(benchmarkFileName, "r") as ifile:
items = []
for i in range(nb_objets):
line = ifile.readline().strip()
if line:
items.append(int(line))
start_time = time.time()
best_length, solution = genetic_algorithm(items, bin_size, 50, 50, 2, 0.7, 0.3, 0.4, 'FF', False, 'TS')
end_time = time.time()
execution_time = end_time - start_time
return execution_time,best_length,solution
# Pour le test en ce fichier
# AG()