główny algorytm w funkcji
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magdabiadala
parent
3ff8b95999
commit
fc54101f4e
@ -1,18 +1,16 @@
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import random
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import random
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import math
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import math
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import statistics
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import time
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### prawdopodobieństwo mutacji
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### prawdopodobieństwo mutacji
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mutation_prob = 0.02
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mutation_prob = 0.03
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### ilość osobników w pokoleniu, powinna być parzysta
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### ilość osobników w pokoleniu, powinna być parzysta
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generation_size = 30
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generation_size = 20
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### liczba pokoleń
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### liczba pokoleń
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number_of_generations = 100
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number_of_generations = 30
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### liczba paczek
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### liczba paczek
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number_of_packages = 15
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number_of_packages = 45
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### liczba regałów
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### liczba regałów
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number_of_racks = 5
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number_of_racks = 70
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### jak bardzo promowane są osobniki wykorzystujące całą pojemność regału
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### jak bardzo promowane są osobniki wykorzystujące całą pojemność regału
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amount_of_promotion = 3
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amount_of_promotion = 3
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@ -95,55 +93,58 @@ def mutation(individual):
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individual[locus] = random.randint(0,number_of_racks-1)
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individual[locus] = random.randint(0,number_of_racks-1)
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return individual
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return individual
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def gen_alg(number_of_generations, generation_size, mutation_prob, amount_of_promotion):
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### WŁAŚCIWY ALGORYTM
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generation = first_gen()
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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])
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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)
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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])
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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])
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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])
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if specific_fitness > local_maximum:
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local_maximum = specific_fitness
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print('maximum 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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generation = descendants
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print('maximum globalne: ', global_maximum)
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### lista paczek, indeks to id paczki, wartość w liście to jej waga
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### lista paczek, indeks to id paczki, wartość w liście to jej waga
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packages = [random.randint(2,9) for i in range(number_of_packages)]
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packages = [random.randint(1,10) for i in range(number_of_packages)]
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### lista regałów, indeks to id regału, wartość w liście to jego pojemność
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### lista regałów, indeks to id regału, wartość w liście to jego pojemność
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racks = [random.randint(10,20) for i in range(number_of_racks)]
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racks = [random.randint(15,18) for i in range(number_of_racks)]
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# print(packages)
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# print(packages)
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# print(racks)
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# print(racks)
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gen_alg(number_of_generations, generation_size, mutation_prob, amount_of_promotion)
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### WŁAŚCIWY ALGORYTM
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generation = first_gen()
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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])
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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)
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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])
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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])
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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])
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if specific_fitness > local_maximum:
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local_maximum = specific_fitness
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print('maximum 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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generation = descendants
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print('maximum globalne: ', global_maximum)
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