def plotit():
plt.rcParams['font.family'] = 'SimHei'
plt.rcParams['axes.unicode_minus'] = False
inicoding = generateit()[3]
inidecoding = ga.decodeit(inicoding, boudary[0], brange)
initfittness = ga.fitnessevaluations(inidecoding)
decoding = generateit()[1][-1]
fitnessvalue = generateit()[2][-1]
fitness = generateit()[4]
x = np.linspace(boudary[0], boudary[1], 1000)
fits = x + 10*np.sin(5*x) + 7*np.cos(4*x)
plt.suptitle("简单遗传算法")
plt.subplot(121)
ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.plot(x, fits) # 适应度曲线
plt.scatter(inidecoding, initfittness, c='blue') # 初始编码
plt.scatter(decoding, fitnessvalue, c='red') # 进化后的编码
plt.xlabel("编码")
plt.ylabel("适应度值")
plt.title("适应度曲线")
plt.subplots_adjust(wspace=0.5)
plt.subplot(122)
ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.plot(range(g), [j for j in fitness]) # 适应度值变化曲线
plt.xlabel("进化次数")
plt.ylabel("适应度值")
plt.title("适应度值变化曲线")
plt.savefig("简单遗传算法")
def multiply(coding):
#
# 繁衍一代
# 传入参数,上一代编码coding
# 返回,新一代编码mutationcoding,新一代解码值decoding,新一代适应度值fitnessvalues
#
inipol = coding
decoding = ga.decodeit(inipol, boudary[0], brange)
fitnessvalues = ga.fitnessevaluations(decoding)
copycoding = ga.copyoperator(inipol, fitnessvalues)
crossovercoding = ga.crossoveroprator(copycoding, crossoverpossibility)
mutationcoding = ga.mutationoprator(crossovercoding, mutationpossibility, boudary[0], brange)
return mutationcoding, decoding, fitnessvalues