"""
Visualize Genetic Algorithm to find a maximum point in a function.
Visit my tutorial website for more: https://mofanpy.com/tutorials/
"""
import numpy as np
import matplotlib.pyplot as plt
DNA_SIZE = 10 # DNA length
POP_SIZE = 100 # population size
CROSS_RATE = 0.8 # mating probability (DNA crossover)
MUTATION_RATE = 0.003 # mutation probability
N_GENERATIONS = 200
X_BOUND = [0, 5] # x upper and lower bounds
def F(x): return np.sin(10*x)*x + np.cos(2*x)*x # to find the maximum of this function
# find non-zero fitness for selection
def get_fitness(pred): return pred + 1e-3 - np.min(pred)
# convert binary DNA to decimal and normalize it to a range(0, 5)
def translateDNA(pop): return pop.dot(2 ** np.arange(DNA_SIZE)[::-1]) / float(2**DNA_SIZE-1) * X_BOUND[1]
def select(pop, fitness): # nature selection wrt pop's fitness
idx = np.random.choice(np.arange(POP_SIZE), size=POP_SIZE, replace=True,
p=fitness/fitness.sum())
return pop[idx]
def crossover(parent, pop): # mating process (genes crossover)
if np.random.rand() < CROSS_RATE:
i_ = np.random.randint(0, POP_SIZE, size=1) # select another individual from pop
cross_points = np.random.randint(0, 2, size=DNA_SIZE).astype(np.bool) # choose crossover points
parent[cross_points] = pop[i_, cross_points] # mating and produce one child
return parent
def mutate(child):
for point in range(DNA_SIZE):
if np.random.rand() < MUTATION_RATE:
child[point] = 1 if child[point] == 0 else 0
return child
pop = np.random.randint(2, size=(POP_SIZE, DNA_SIZE)) # initialize the pop DNA
# 构成一个大小为100*10的矩阵，矩阵里的数全是0和1
plt.ion() # something about plotting
x = np.linspace(*X_BOUND, 200)
plt.plot(x, F(x))
for _ in range(N_GENERATIONS):
F_values = F(translateDNA(pop)) # compute function value by extracting DNA
# something about plotting
if 'sca' in globals(): sca.remove()
sca = plt.scatter(translateDNA(pop), F_values, s=200, lw=0, c='red', alpha=0.5); plt.pause(0.05)
# GA part (evolution)
fitness = get_fitness(F_values)
print("Most fitted DNA: ", pop[np.argmax(fitness), :])
pop = select(pop, fitness)
pop_copy = pop.copy()
for parent in pop:
child = crossover(parent, pop_copy)
child = mutate(child)
parent[:] = child # parent is replaced by its child
plt.ioff(); plt.show()
请问为什么在child = crossover(parent, pop_copy)，child = mutate(child)后pop变了，明明在变异里面没有用到pop呢
