白云岛资源网 Design By www.pvray.com

ObjFunction.py

import math


def GrieFunc(vardim, x, bound):
 """
 Griewangk function
 """
 s1 = 0.
 s2 = 1.
 for i in range(1, vardim + 1):
  s1 = s1 + x[i - 1] ** 2
  s2 = s2 * math.cos(x[i - 1] / math.sqrt(i))
 y = (1. / 4000.) * s1 - s2 + 1
 y = 1. / (1. + y)
 return y


def RastFunc(vardim, x, bound):
 """
 Rastrigin function
 """
 s = 10 * 25
 for i in range(1, vardim + 1):
  s = s + x[i - 1] ** 2 - 10 * math.cos(2 * math.pi * x[i - 1])
 return s

GAIndividual.py

import numpy as np
import ObjFunction


class GAIndividual:

 '''
 individual of genetic algorithm
 '''

 def __init__(self, vardim, bound):
  '''
  vardim: dimension of variables
  bound: boundaries of variables
  '''
  self.vardim = vardim
  self.bound = bound
  self.fitness = 0.

 def generate(self):
  '''
  generate a random chromsome for genetic algorithm
  '''
  len = self.vardim
  rnd = np.random.random(size=len)
  self.chrom = np.zeros(len)
  for i in xrange(0, len):
   self.chrom[i] = self.bound[0, i] +     (self.bound[1, i] - self.bound[0, i]) * rnd[i]

 def calculateFitness(self):
  '''
  calculate the fitness of the chromsome
  '''
  self.fitness = ObjFunction.GrieFunc(
   self.vardim, self.chrom, self.bound)

GeneticAlgorithm.py

import numpy as np
from GAIndividual import GAIndividual
import random
import copy
import matplotlib.pyplot as plt


class GeneticAlgorithm:

 '''
 The class for genetic algorithm
 '''

 def __init__(self, sizepop, vardim, bound, MAXGEN, params):
  '''
  sizepop: population sizepop
  vardim: dimension of variables
  bound: boundaries of variables
  MAXGEN: termination condition
  param: algorithm required parameters, it is a list which is consisting of crossover rate, mutation rate, alpha
  '''
  self.sizepop = sizepop
  self.MAXGEN = MAXGEN
  self.vardim = vardim
  self.bound = bound
  self.population = []
  self.fitness = np.zeros((self.sizepop, 1))
  self.trace = np.zeros((self.MAXGEN, 2))
  self.params = params

 def initialize(self):
  '''
  initialize the population
  '''
  for i in xrange(0, self.sizepop):
   ind = GAIndividual(self.vardim, self.bound)
   ind.generate()
   self.population.append(ind)

 def evaluate(self):
  '''
  evaluation of the population fitnesses
  '''
  for i in xrange(0, self.sizepop):
   self.population[i].calculateFitness()
   self.fitness[i] = self.population[i].fitness

 def solve(self):
  '''
  evolution process of genetic algorithm
  '''
  self.t = 0
  self.initialize()
  self.evaluate()
  best = np.max(self.fitness)
  bestIndex = np.argmax(self.fitness)
  self.best = copy.deepcopy(self.population[bestIndex])
  self.avefitness = np.mean(self.fitness)
  self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness
  self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness
  print("Generation %d: optimal function value is: %f; average function value is %f" % (
   self.t, self.trace[self.t, 0], self.trace[self.t, 1]))
  while (self.t < self.MAXGEN - 1):
   self.t += 1
   self.selectionOperation()
   self.crossoverOperation()
   self.mutationOperation()
   self.evaluate()
   best = np.max(self.fitness)
   bestIndex = np.argmax(self.fitness)
   if best > self.best.fitness:
    self.best = copy.deepcopy(self.population[bestIndex])
   self.avefitness = np.mean(self.fitness)
   self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness
   self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness
   print("Generation %d: optimal function value is: %f; average function value is %f" % (
    self.t, self.trace[self.t, 0], self.trace[self.t, 1]))

  print("Optimal function value is: %f; " %
    self.trace[self.t, 0])
  print "Optimal solution is:"
  print self.best.chrom
  self.printResult()

 def selectionOperation(self):
  '''
  selection operation for Genetic Algorithm
  '''
  newpop = []
  totalFitness = np.sum(self.fitness)
  accuFitness = np.zeros((self.sizepop, 1))

  sum1 = 0.
  for i in xrange(0, self.sizepop):
   accuFitness[i] = sum1 + self.fitness[i] / totalFitness
   sum1 = accuFitness[i]

  for i in xrange(0, self.sizepop):
   r = random.random()
   idx = 0
   for j in xrange(0, self.sizepop - 1):
    if j == 0 and r < accuFitness[j]:
     idx = 0
     break
    elif r >= accuFitness[j] and r < accuFitness[j + 1]:
     idx = j + 1
     break
   newpop.append(self.population[idx])
  self.population = newpop

 def crossoverOperation(self):
  '''
  crossover operation for genetic algorithm
  '''
  newpop = []
  for i in xrange(0, self.sizepop, 2):
   idx1 = random.randint(0, self.sizepop - 1)
   idx2 = random.randint(0, self.sizepop - 1)
   while idx2 == idx1:
    idx2 = random.randint(0, self.sizepop - 1)
   newpop.append(copy.deepcopy(self.population[idx1]))
   newpop.append(copy.deepcopy(self.population[idx2]))
   r = random.random()
   if r < self.params[0]:
    crossPos = random.randint(1, self.vardim - 1)
    for j in xrange(crossPos, self.vardim):
     newpop[i].chrom[j] = newpop[i].chrom[
      j] * self.params[2] + (1 - self.params[2]) * newpop[i + 1].chrom[j]
     newpop[i + 1].chrom[j] = newpop[i + 1].chrom[j] * self.params[2] +       (1 - self.params[2]) * newpop[i].chrom[j]
  self.population = newpop

 def mutationOperation(self):
  '''
  mutation operation for genetic algorithm
  '''
  newpop = []
  for i in xrange(0, self.sizepop):
   newpop.append(copy.deepcopy(self.population[i]))
   r = random.random()
   if r < self.params[1]:
    mutatePos = random.randint(0, self.vardim - 1)
    theta = random.random()
    if theta > 0.5:
     newpop[i].chrom[mutatePos] = newpop[i].chrom[
      mutatePos] - (newpop[i].chrom[mutatePos] - self.bound[0, mutatePos]) * (1 - random.random() ** (1 - self.t / self.MAXGEN))
    else:
     newpop[i].chrom[mutatePos] = newpop[i].chrom[
      mutatePos] + (self.bound[1, mutatePos] - newpop[i].chrom[mutatePos]) * (1 - random.random() ** (1 - self.t / self.MAXGEN))
  self.population = newpop

 def printResult(self):
  '''
  plot the result of the genetic algorithm
  '''
  x = np.arange(0, self.MAXGEN)
  y1 = self.trace[:, 0]
  y2 = self.trace[:, 1]
  plt.plot(x, y1, 'r', label='optimal value')
  plt.plot(x, y2, 'g', label='average value')
  plt.xlabel("Iteration")
  plt.ylabel("function value")
  plt.title("Genetic algorithm for function optimization")
  plt.legend()
  plt.show()

运行程序:

 if __name__ == "__main__":
 
  bound = np.tile([[-600], [600]], 25)
  ga = GA(60, 25, bound, 1000, [0.9, 0.1, 0.5])
  ga.solve()

作者:Alex Yu
出处:http://www.cnblogs.com/biaoyu/

以上就是python实现简单遗传算法的详细内容,更多关于python 遗传算法的资料请关注其它相关文章!

白云岛资源网 Design By www.pvray.com
广告合作:本站广告合作请联系QQ:858582 申请时备注:广告合作(否则不回)
免责声明:本站资源来自互联网收集,仅供用于学习和交流,请遵循相关法律法规,本站一切资源不代表本站立场,如有侵权、后门、不妥请联系本站删除!
白云岛资源网 Design By www.pvray.com

稳了!魔兽国服回归的3条重磅消息!官宣时间再确认!

昨天有一位朋友在大神群里分享,自己亚服账号被封号之后居然弹出了国服的封号信息对话框。

这里面让他访问的是一个国服的战网网址,com.cn和后面的zh都非常明白地表明这就是国服战网。

而他在复制这个网址并且进行登录之后,确实是网易的网址,也就是我们熟悉的停服之后国服发布的暴雪游戏产品运营到期开放退款的说明。这是一件比较奇怪的事情,因为以前都没有出现这样的情况,现在突然提示跳转到国服战网的网址,是不是说明了简体中文客户端已经开始进行更新了呢?