遗传算法_aforge遗传算法

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
DNA_SIZE = 24
POP_SIZE = 200
CROSSOVER_RATE = 0.8
MUTATION_RATE = 0.005
N_GENERATIONS = 50
X_BOUND = [-3, 3]
Y_BOUND = [-3, 3]
def F(x, y):
return 3*(1-x)**2*np.exp(-(x**2)-(y+1)**2)- 10*(x/5 - x**3 - y**5)*np.exp(-x**2-y**2)- 1/3**np.exp(-(x+1)**2 - y**2)
def plot_3d(ax):
X = np.linspace(*X_BOUND, 100)
Y = np.linspace(*Y_BOUND, 100)
X,Y = np.meshgrid(X, Y)
Z = F(X, Y)
ax.plot_surface(X,Y,Z,rstride=1,cstride=1,cmap=cm.coolwarm)
ax.set_zlim(-10,10)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.pause(3)
plt.show()
def get_fitness(pop): 
x,y = translateDNA(pop)
pred = F(x, y)
return (pred - np.min(pred)) + 1e-3 #减去最小的适应度是为了防止适应度出现负数，通过这一步fitness的范围为[0, np.max(pred)-np.min(pred)],最后在加上一个很小的数防止出现为0的适应度
def translateDNA(pop): #pop表示种群矩阵，一行表示一个二进制编码表示的DNA，矩阵的行数为种群数目
x_pop = pop[:,1::2]#奇数列表示X
y_pop = pop[:,::2] #偶数列表示y
#pop:(POP_SIZE,DNA_SIZE)*(DNA_SIZE,1) --> (POP_SIZE,1)
x = x_pop.dot(2**np.arange(DNA_SIZE)[::-1])/float(2**DNA_SIZE-1)*(X_BOUND[1]-X_BOUND[0])+X_BOUND[0]
y = y_pop.dot(2**np.arange(DNA_SIZE)[::-1])/float(2**DNA_SIZE-1)*(Y_BOUND[1]-Y_BOUND[0])+Y_BOUND[0]
return x,y
def crossover_and_mutation(pop, CROSSOVER_RATE = 0.8):
new_pop = []
for father in pop:		#遍历种群中的每一个个体，将该个体作为父亲
child = father		#孩子先得到父亲的全部基因（这里我把一串二进制串的那些0，1称为基因）
if np.random.rand() < CROSSOVER_RATE:			#产生子代时不是必然发生交叉，而是以一定的概率发生交叉
mother = pop[np.random.randint(POP_SIZE)]	#再种群中选择另一个个体，并将该个体作为母亲
cross_points = np.random.randint(low=0, high=DNA_SIZE*2)	#随机产生交叉的点
child[cross_points:] = mother[cross_points:]		#孩子得到位于交叉点后的母亲的基因
mutation(child)	#每个后代有一定的机率发生变异
new_pop.append(child)
return new_pop
def mutation(child, MUTATION_RATE=0.003):
if np.random.rand() < MUTATION_RATE: 				#以MUTATION_RATE的概率进行变异
mutate_point = np.random.randint(0, DNA_SIZE)	#随机产生一个实数，代表要变异基因的位置
child[mutate_point] = child[mutate_point]^1 	#将变异点的二进制为反转
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 print_info(pop):
fitness = get_fitness(pop)
max_fitness_index = np.argmax(fitness)
print("max_fitness:", fitness[max_fitness_index])
x,y = translateDNA(pop)
print("最优的基因型：", pop[max_fitness_index])
print("(x, y):", (x[max_fitness_index], y[max_fitness_index]))
if __name__ == "__main__":
fig = plt.figure()
ax = Axes3D(fig)	
plt.ion()#将画图模式改为交互模式，程序遇到plt.show不会暂停，而是继续执行
plot_3d(ax)
pop = np.random.randint(2, size=(POP_SIZE, DNA_SIZE*2)) #matrix (POP_SIZE, DNA_SIZE)
for _ in range(N_GENERATIONS):#迭代N代
x,y = translateDNA(pop)
if 'sca' in locals(): 
sca.remove()
sca = ax.scatter(x, y, F(x,y), c='black', marker='o');plt.show();plt.pause(0.1)
pop = np.array(crossover_and_mutation(pop, CROSSOVER_RATE))
#F_values = F(translateDNA(pop)[0], translateDNA(pop)[1])#x, y --> Z matrix
fitness = get_fitness(pop)
pop = select(pop, fitness) #选择生成新的种群
print_info(pop)
plt.ioff()
plot_3d(ax)