python 遗传算法求函数极值的实现代码
作者:zzzzjh
今天小编就为大家分享一篇python 遗传算法求函数极值的实现代码,具有很好的参考价值,希望对大家有所帮助。一起跟随小编过来看看吧
废话不多说,大家直接看代码吧!
"""遗传算法实现求函数极大值—Zjh""" import numpy as np import random import matplotlib.pyplot as plt class Ga(): """求出二进制编码的长度""" def __init__(self): self.boundsbegin = -2 self.boundsend = 3 precision = 0.0001 # 运算精确度 self.Bitlength = int(np.log2((self.boundsend - self.boundsbegin)/precision))+1#%染色体长度 self.popsize = 50# 初始种群大小 self.Generationmax = 12# 最大进化代数 self.pcrossover = 0.90# 交叉概率 self.pmutation = 0.2# 变异概率 self.population=np.random.randint(0,2,size=(self.popsize,self.Bitlength)) """计算出适应度""" def fitness(self,population): Fitvalue=[] cumsump = [] for i in population: x=self.transform2to10(i)#二进制对应的十进制 xx=self.boundsbegin + x * (self.boundsend - self.boundsbegin) / (pow(2,self.Bitlength)-1) s=self.targetfun(xx) Fitvalue.append(s) fsum=sum(Fitvalue) everypopulation=[x/fsum for x in Fitvalue] cumsump.append(everypopulation[0]) everypopulation.remove(everypopulation[0]) for j in everypopulation: p=cumsump[-1]+j cumsump.append(p) return Fitvalue,cumsump """选择两个基因,准备交叉""" def select(self,cumsump): seln=[] for i in range(2): j = 1 r=np.random.uniform(0,1) prand =[x-r for x in cumsump] while prand[j] < 0: j = j + 1 seln.append(j) return seln """交叉""" def crossover(self, seln, pc): d=self.population[seln[1]].copy() f=self.population[seln[0]].copy() r=np.random.uniform() if r<pc: print('yes') c=np.random.randint(1,self.Bitlength-1) print(c) a=self.population[seln[1]][c:] b=self.population[seln[0]][c:] d[c:]=b f[c:]=a print(d) print(f) g=d h=f else: g=self.population[seln[1]] h=self.population[seln[0]] return g,h """变异操作""" def mutation(self,scnew,pmutation): r=np.random.uniform(0, 1) if r < pmutation: v=np.random.randint(0,self.Bitlength) scnew[v]=abs(scnew[v]-1) else: scnew=scnew return scnew """二进制转换为十进制""" def transform2to10(self,population): #x=population[-1] #最后一位的值 x=0 #n=len(population) n=self.Bitlength p=population.copy() p=p.tolist() p.reverse() for j in range(n): x=x+p[j]*pow(2,j) return x #返回十进制的数 """目标函数""" def targetfun(self,x): #y = x∗(np.sin(10∗(np.pi)∗x))+ 2 y=x*(np.sin(10*np.pi*x))+2 return y if __name__ == '__main__': Generationmax=12 gg=Ga() scnew=[] ymax=[] #print(gg.population) Fitvalue, cumsump=gg.fitness(gg.population) Generation = 1 while Generation < Generationmax +1: Fitvalue, cumsump = gg.fitness(gg.population) for j in range(0,gg.popsize,2): seln = gg.select( cumsump) #返回选中的2个个体的序号 scro = gg.crossover(seln, gg.pcrossover) #返回两条染色体 s1=gg.mutation(scro[0],gg.pmutation) s2=gg.mutation(scro[1],gg.pmutation) scnew.append(s1) scnew.append(s2) gg.population = scnew Fitvalue, cumsump = gg.fitness(gg.population) fmax=max(Fitvalue) d=Fitvalue.index(fmax) ymax.append(fmax) x = gg.transform2to10(gg.population[d]) xx = gg.boundsbegin + x * (gg.boundsend - gg.boundsbegin) / (pow(2, gg.Bitlength) - 1) Generation = Generation + 1 Bestpopulation = xx Targetmax = gg.targetfun(xx) print(xx) print(Targetmax) x=np.linspace(-2,3,30) y=x*(np.sin(10*np.pi*x))+2 plt.scatter(2.65,4.65,c='red') plt.xlim(0,5) plt.ylim(0,6) plt.plot(x,y) plt.annotate('local max', xy=(2.7,4.8), xytext=(3.6, 5.2),arrowprops=dict(facecolor='black', shrink=0.05)) plt.show()
一个函数求极值的仿真的作业,参考了别人的matlab代码,用python复现了一遍,加深印象!
以上这篇python 遗传算法求函数极值的实现代码就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持脚本之家。