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działający algorytm, do połączenia z resztą

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magdabiadala 2020-05-10 16:20:04 +02:00
parent 9a07af72f5
commit 3ff8b95999
1 changed files with 58 additions and 19 deletions
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genetic_algorithm.py
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@ -1,13 +1,16 @@
import random import random
import math
import statistics
import time
### prawdopodobieństwo mutacji ### prawdopodobieństwo mutacji
mutation_prob = 0.02 mutation_prob = 0.02
### ilość osobników w pokoleniu ### ilość osobników w pokoleniu, powinna być parzysta
generation_size = 4 generation_size = 30
### liczba pokoleń ### liczba pokoleń
number_of_generations = 4 number_of_generations = 100
### liczba paczek ### liczba paczek
number_of_packages = 10 number_of_packages = 15
### liczba regałów ### liczba regałów
number_of_racks = 5 number_of_racks = 5
### jak bardzo promowane są osobniki wykorzystujące całą pojemność regału ### jak bardzo promowane są osobniki wykorzystujące całą pojemność regału
@ -49,10 +52,10 @@ def roulette(generation):
for i in range(generation_size): for i in range(generation_size):
individual_fitness = evaluation(generation[i]) individual_fitness = evaluation(generation[i])
evaluations.append(individual_fitness) evaluations.append(individual_fitness)
# print("tablica niedopasowań: ", evaluations) # print("tablica dopasowań: ", evaluations)
minimum = min(evaluations) maximum = min(evaluations)
# dodaję tą 1 żeby nie wywalać najgorszego osobnika # dodaję tą 1 żeby nie wywalać najgorszego osobnika
normalized = [x+(-1*minimum)+1 for x in evaluations] normalized = [x+(-1*maximum)+1 for x in evaluations]
# print(normalized) # print(normalized)
sum_of_normalized = sum(normalized) sum_of_normalized = sum(normalized)
roulette_tab = [x/sum_of_normalized for x in normalized] roulette_tab = [x/sum_of_normalized for x in normalized]
@ -87,24 +90,60 @@ def crossover(individual1, individual2):
return new1, new2 return new1, new2
def mutation(individual): def mutation(individual):
print(individual) # print(individual)
locus = random.randint(0,number_of_packages-1) locus = random.randint(0,number_of_packages-1)
individual[locus] = random.randint(0,number_of_racks-1) individual[locus] = random.randint(0,number_of_racks-1)
return individual return individual
### lista paczek, indeks to id paczki, wartość w liście to jej waga ### lista paczek, indeks to id paczki, wartość w liście to jej waga
packages = [random.randint(2,10) for i in range(number_of_packages)] packages = [random.randint(2,9) for i in range(number_of_packages)]
### lista regałów, indeks to id regału, wartość w liście to jego pojemność ### lista regałów, indeks to id regału, wartość w liście to jego pojemność
racks = [random.randint(10,20) for i in range(number_of_racks)] racks = [random.randint(10,20) for i in range(number_of_racks)]
# print(packages) # print(packages)
# print(racks) # print(racks)
first_generation = first_gen()
# crossover(first_generation[0],first_generation[1]) ### WŁAŚCIWY ALGORYTM
# descendants = [] generation = first_gen()
# for i in range(0,generation_size,2): global_maximum = -math.inf
# pair = crossover(first_generation[i],first_generation[i+1])
# for each in pair: # pętla znajdująca najlepszy fitness w pierwszym pokoleniu
# descendants.append(each) for i in range(generation_size):
# print(descendants) evaluation_of_individual = evaluation(generation[i])
print("first gen: ",first_generation) if evaluation_of_individual > global_maximum:
roulette(first_generation) global_maximum = evaluation_of_individual
best_individual = generation[i].copy()
#właściwa pętla programu
for generation_index in range(number_of_generations):
print('pokolenie numer: ', generation_index)
# print(generation)
### RULETKA
survivors = roulette(generation)
# print('przetrwali: ',survivors)
### KRZYŻOWANIE
descendants = []
for individual in range(0,generation_size,2):
pair = crossover(survivors[individual],survivors[individual+1])
for each in pair:
descendants.append(each)
# print('potomkowie: ', descendants)
### MUTACJA
for individual in range(generation_size):
if random.random() <= mutation_prob:
mutation(descendants[individual])
# print('potomkowie po mutacji: ', descendants)
### NAJLEPSZE DOPASOWANIE
local_maximum = -math.inf
for each in range(generation_size):
specific_fitness = evaluation(descendants[each])
if specific_fitness > local_maximum:
local_maximum = specific_fitness
print('maximum w pokoleniu: ',local_maximum)
if local_maximum > global_maximum:
global_maximum = local_maximum
generation = descendants
print('maximum globalne: ', global_maximum)