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gen-math.py
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gen-math.py
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#bin/python
#code by : MIYANDI279
#Team : @bengkulucyberteam
#TOOLS :MATHGEN
##################################################
no recode ya bangsat,mandul 7 turunan kalau recode
##################################################
import numpy as np
import random
import matplotlib.pyplot as plt
print(";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;")
print(";;;;;;code by miyandi279 ;;;;;;")
print(";;;;;;team BENGKULU CYBER TEAM ;;")
print(";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;")
def hitung_fitness(x):
#y = 1000 * (x[0] - 2 * x[1])**2 + (1 - x[0])**2
y = 1 / (x[0]**2 + x[1]**2 + 0.001)
y = round(y, 3)
return y
def dekode_biner_to_desimal(krom):
idx_awal = 0
idx_akhir = jum_gen_per_var
desimal = []
for i in range(jum_var):
k = krom[idx_awal:idx_akhir]
temp1 = 0
temp2 = 0
for j in range(jum_gen_per_var):
temp1 = temp1 + (k[j] * 2**(-(j+1)))
temp2 = temp2 + 2**(-(j+1))
temp_desimal = bts_bawah + ((bts_atas - bts_bawah)/temp2) * temp1
desimal.append(round(temp_desimal, 3))
idx_awal = idx_akhir
idx_akhir = idx_akhir + jum_gen_per_var
return desimal
def roulette_wheel(krom, fitness):
# menskalakan nilai fitness dengan linear fitness ranking
LFR = linear_fitness_ranking(fitness)
# membuat proporsi nilai fitness tiap kromosom
kumulatif_fitness = 0
acak = random.uniform(0,1)
idx_induk = 0
for i in range(uk_pop):
kumulatif_fitness = kumulatif_fitness + (LFR[i] / sum(LFR))
if (kumulatif_fitness > acak):
idx_induk = i
break
return idx_induk
def linear_fitness_ranking(fitness):
sort_fitness = sorted(fitness)
max_fitness = np.argmax(sort_fitness)
min_fitness = np.argmin(sort_fitness)
LFR = []
for i in range(uk_pop):
LFR.append(max_fitness - (max_fitness - min_fitness) * (i-1) / (uk_pop-1))
return LFR
def crossover_1_titik(krom1, krom2):
# konversi array ke list agar bisa diconcate
krom1 = list(krom1)
krom2 = list(krom2)
# tentukan titik potong
titik = int(np.fix(np.random.rand() * jum_gen) + 1)
# tukar gen
anak1 = krom1[0:titik] + krom2[titik:]
anak2 = krom2[0:titik] + krom1[titik:]
return anak1, anak2
def crossover_n_titik(krom1, krom2, jum_titik_potong=1):
# konversi array ke list agar bisa diconcate
krom1 = list(krom1)
krom2 = list(krom2)
# tentukan titik potong
batas = 0
titik = []
pembagi = int(np.fix(jum_gen / jum_titik_potong))
for i in range(jum_titik_potong):
acak = int(np.fix(np.random.rand() * pembagi+1))
batas = batas + acak
titik.append(batas)
titik.append(jum_gen)
# tukar gen
anak1 = []
anak2 = []
idx = 0
for i in range(len(titik)):
# tukar gen ketika i genap (agar pertukaran selang-seling antara ganjil dan genap)
if (i % 2 == 0):
anak1 = anak1 + krom2[idx:titik[i]]
anak2 = anak2 + krom1[idx:titik[i]]
else:
anak1 = anak1 + krom1[idx:titik[i]]
anak2 = anak2 + krom2[idx:titik[i]]
idx = titik[i]
return anak1, anak2
def crossover_uniform(krom1, krom2):
pola = np.round(np.random.rand(jum_gen))
anak1 = krom1
anak2 = krom2
for i in range(jum_gen):
if (pola[i] == 1):
# tukar gen
anak1[i], anak2[i] = anak2[i], anak1[i]
return anak1, anak2
def mutasi_biner(krom):
acak = random.uniform(0,1)
for j in range(jum_gen):
if (acak <= pm):
krom[j] = 1 - krom[j]
return krom
# Inisialisasi parameter GA
uk_pop = 50
max_generasi = 100
bts_bawah = -5.12
bts_atas = 5.12
jum_var = 2
jum_gen_per_var = 14
jum_gen = jum_var * jum_gen_per_var
pc = 0.8
pm = 0.1
best_kromosom = []
best_fitness = 0
best_genotipe = []
list_best_fitness = []
max_fitness = 1000
# Inisialisasi populasi biner
kromosom = np.round(np.random.rand(uk_pop, jum_gen))
#------------------------------------------------------
# Proses evolusi kromosom
#------------------------------------------------------
generasi = 0
while (generasi < max_generasi and best_fitness < max_fitness):
# dekode kromosom dan evaluasi fitness
desimal = []
fitness = []
for j in range(uk_pop):
desimal.append(dekode_biner_to_desimal(kromosom[j]))
fitness.append(hitung_fitness(desimal[j]))
if (generasi == 0):
best_fitness = np.max(fitness)
else:
if (best_fitness < np.max(fitness)):
best_fitness = np.max(fitness)
idx_best_kromosom = np.argmax(fitness)
best_kromosom = kromosom[idx_best_kromosom]
best_genotipe = desimal[idx_best_kromosom]
list_best_fitness.append(best_fitness)
# tampilkan informasi tiap generasi
print("Generasi ke-" + str(generasi) + " ==> " + str(best_genotipe) + " = " + str(best_fitness))
# elitisme
kromosom_anak = []
if (uk_pop % 2 == 0):
kromosom_anak.append(best_kromosom)
kromosom_anak.append(best_kromosom)
iterasi_seleksi = 2
else:
kromosom_anak.append(best_kromosom)
iterasi_seleksi = 1
# seleksi induk/ orang tua
idx_induk = []
for iterasi_seleksi in range(uk_pop):
idx_induk.append(roulette_wheel(kromosom, fitness))
random.shuffle(idx_induk)
# crossover kromosom
jum_pasangan_induk = int(len(idx_induk) / 2)
for i in range(jum_pasangan_induk):
induk1 = kromosom[idx_induk[i]]
induk2 = kromosom[idx_induk[i+1]]
acak = random.uniform(0,1)
if (acak <= pc):
anak1, anak2 = crossover_1_titik(induk1, induk2)
#anak1, anak2 = crossover_n_titik(induk1, induk2, jum_titik_potong=3)
#anak1, anak2 = crossover_uniform(induk1, induk2)
kromosom_anak.append(anak1)
kromosom_anak.append(anak2)
else:
kromosom_anak.append(induk1)
kromosom_anak.append(induk2)
i += 2
# mutasi kromosom
for i in range(uk_pop):
kromosom_anak[i] = mutasi_biner(kromosom_anak[i])
# generational replacement
kromosom = kromosom_anak
generasi += 1
# tampilkan hasil optimasi
print(str(best_genotipe) + " = " + str(best_fitness) + " (" + str(idx_best_kromosom) + ")")
print("Best Kromosom = " + str(best_kromosom))
plt.title("Grafik Evolusi Algoritma Genetika")
plt.plot(list_best_fitness)
plt.show(block=False)
plt.waitforbuttonpress()