jiaruifeng / genetic-algorithm-with-python Goto Github PK

def fitness(population,aimFunction):
    value=[]
    for i in range(len(population)):
        value.append(aimFunction(decode(population[i])))
        #weed out negative value
        if value[i]<8:
            value[i]=0
        else:
            value[i]-=8 # 因为最大值是13，所以把适应度都减个8(5也行)，这样优秀种群更突出一些，更容易被随机到
    return value

plt.figure()
plt.plot(x,y)
# np.argmax
max_y=max(y)
max_x=x[y.index(max(y))]
print((max_x,max_y))
plt.scatter(max_x,max_y,color='r')

plt.figure()
plt.plot(t)
plt.axhline(max(y), linewidth=1, color='r')
plt.axhline(max(t), linewidth=1, color='g') # 红线和绿线重叠表明达到了最优解
plt.show()