176 lines
7.1 KiB
Python
Executable File
176 lines
7.1 KiB
Python
Executable File
import random
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import math
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### prawdopodobieństwo mutacji
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mutation_prob = 0.03
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### ilość osobników w pokoleniu, powinna być parzysta
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# generation_size = 40
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### liczba pokoleń
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# number_of_generations = 30
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### jak bardzo promowane są osobniki wykorzystujące całą pojemność regału
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amount_of_promotion = 5
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def first_gen(number_of_packages, number_of_racks, generation_size):
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first_generation = []
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for individual in range(generation_size):
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individual = []
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for pack in range(number_of_packages):
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r = random.randint(0,number_of_racks-1)
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individual.append(r)
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first_generation.append(individual)
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return first_generation
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# def evaluation(individual, packages, racks, number_of_packages, number_of_racks, tree_predictor):
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# # im większy fitness tym lepszy osobnik
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# # print("regały: ",racks)
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# rest_of_capacity = [rack.capacity for rack in racks]
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# # print("początkowa pojemność: ",rest_of_capacity)
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# for i in range(number_of_packages):
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# can_place = tree_predictor.check_if_can_place(packages[i], racks[i])
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# if not can_place:
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# rest_of_capacity[individual[i]] -= packages[i].size * 5
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# else:
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# rest_of_capacity[individual[i]] -= packages[i].size
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# # print("pozostała pojemność: ",rest_of_capacity)
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# # pdb.set_trace()
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# fitness = 0
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# for i in range(number_of_racks):
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# # jak regał jest przepełniony, zmniejsza fitness osobnika
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# if rest_of_capacity[i] < 0:
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# fitness += rest_of_capacity[i]
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# # delikane promowanie osobników wykorzystujących regały w pełni
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# elif rest_of_capacity[i] == 0:
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# fitness += 2
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# else:
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# fitness += 1
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# return fitness
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def evaluation(individual, packages, racks, number_of_packages, number_of_racks, tree_predictor):
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# im większy fitness tym lepszy osobnik
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# print("regały: ",racks)
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rest_of_capacity = [rack.capacity for rack in racks]
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# print("początkowa pojemność: ",rest_of_capacity)
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bad_placed = 0
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for i in range(number_of_packages):
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rest_of_capacity[individual[i]] -= packages[i].size
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can_place = tree_predictor.check_if_can_place(packages[i], racks[i])
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if not can_place:
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bad_placed +=1
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# print("pozostała pojemność: ",rest_of_capacity)
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# pdb.set_trace()
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fitness = 0
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for i in range(number_of_racks):
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# jak regał jest przepełniony, zmniejsza fitness osobnika
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if rest_of_capacity[i] < 0:
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fitness += rest_of_capacity[i]
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# delikane promowanie osobników wykorzystujących regały w pełni
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elif rest_of_capacity[i] == 0:
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fitness += amount_of_promotion
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fitness -= 5*bad_placed
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return fitness
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def roulette(generation, packages, generation_size ,racks, number_of_packages, number_of_racks, tree_predictor):
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# print('pokolenie: ', generation)
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evaluations = []
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for i in range(generation_size):
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individual_fitness = evaluation(generation[i], packages, racks, number_of_packages, number_of_racks, tree_predictor)
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evaluations.append(individual_fitness)
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# print("tablica dopasowań: ", evaluations)
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maximum = min(evaluations)
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# dodaję tą 1 żeby nie wywalać najgorszego osobnika
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normalized = [x+(-1*maximum)+1 for x in evaluations]
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# print(normalized)
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sum_of_normalized = sum(normalized)
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roulette_tab = [x/sum_of_normalized for x in normalized]
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# print(roulette_tab)
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for i in range(1,generation_size-1):
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roulette_tab[i] += roulette_tab[i-1]
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# wpisuję 1 ręcznie, bo czasem liczby nie sumowały się idealnie do 1
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#(niedokładność komputera)
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roulette_tab[generation_size-1] = 1
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# print("ruletka: ", roulette_tab)
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survivors = []
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for individual in range(generation_size):
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random_number = random.random()
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interval_number = 0
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while random_number > roulette_tab[interval_number]:
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interval_number += 1
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survivors.append(generation[interval_number])
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# print('przetrwali: ',survivors)
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return survivors
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def crossover(individual1, individual2, number_of_packages):
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cut = random.randint(1,number_of_packages-1)
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new1 = individual1[:cut]
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new2 = individual2[:cut]
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new1 = new1 + individual2[cut:]
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new2 = new2 + individual1[cut:]
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# print(individual1)
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# print(individual2)
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# print(new1)
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# print(new2)
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# print(cut)
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return new1, new2
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def mutation(individual, number_of_packages, number_of_racks):
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# print(individual)
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locus = random.randint(0,number_of_packages-1)
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individual[locus] = random.randint(0,number_of_racks-1)
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return individual
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def gen_alg(packages, racks, number_of_generations, generation_size, mutation_prob, amount_of_promotion, tree_predictor):
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number_of_packages = len(packages)
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number_of_racks = len(racks)
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### WŁAŚCIWY ALGORYTM
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generation = first_gen(number_of_packages, number_of_racks, generation_size)
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global_maximum = -math.inf
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# pętla znajdująca najlepszy fitness w pierwszym pokoleniu
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for i in range(generation_size):
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evaluation_of_individual = evaluation(generation[i], packages, racks, number_of_packages, number_of_racks, tree_predictor)
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if evaluation_of_individual > global_maximum:
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global_maximum = evaluation_of_individual
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best_individual = generation[i].copy()
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#właściwa pętla programu
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for generation_index in range(number_of_generations):
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# print('pokolenie numer: ', generation_index)
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# print(generation)
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### RULETKA
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survivors = roulette(generation, packages, generation_size, racks, number_of_packages, number_of_racks, tree_predictor)
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# print('przetrwali: ',survivors)
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### KRZYŻOWANIE
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descendants = []
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for individual in range(0,generation_size,2):
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pair = crossover(survivors[individual],survivors[individual+1], number_of_packages)
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for each in pair:
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descendants.append(each)
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# print('potomkowie: ', descendants)
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### MUTACJA
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for individual in range(generation_size):
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if random.random() <= mutation_prob:
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mutation(descendants[individual], number_of_packages, number_of_racks)
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# print('potomkowie po mutacji: ', descendants)
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### NAJLEPSZE DOPASOWANIE
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local_maximum = -math.inf
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for each in range(generation_size):
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specific_fitness = evaluation(descendants[each], packages, racks, number_of_packages, number_of_racks, tree_predictor)
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if specific_fitness > local_maximum:
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local_maximum = specific_fitness
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generation_best_individual = descendants[each].copy()
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print('maksimum w pokoleniu: ',local_maximum)
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if local_maximum > global_maximum:
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global_maximum = local_maximum
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best_individual = generation_best_individual.copy()
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generation = descendants
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print('maksimum globalne: ', global_maximum)
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# print("jeśli maksimum globalne wynosi 0, każda paczka ma swój regał")
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print("najlepsze dopasowanie: ", best_individual)
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