Python实现遗传算法：基于面向对象与字典管理的完整代码解析

#染色体的类
class Chrom:
  chrom = []
  fitness = 0
  def showChrom(self):
    print(self.chrom)
  def showFitness(self):
    print(self.fitness)

#基础参数
N = 200 #种群内个体数目
mut = 0.2 #突变概率
acr = 0.2 #交叉概率

pop = {} #存储染色体的字典
for i in range(N):
  pop['chrom'+str(i)] = Chrom()
chromNodes = 2 #染色体节点数（变量个数）
iterNum = 10000 #迭代次数
chromRange = [[0, 10], [0, 10]] #染色体范围
aveFitnessList = [] #平均适应度
bestFitnessList = [] #最优适应度

#初始染色体
pop = Genetic.initialize(pop, chromNodes, chromRange)
pop = Fitness.calFitness(pop) #计算适应度
bestChrom = Genetic.findBest(pop) #寻找最优染色体
bestFitnessList.append(bestChrom[1]) #将当前最优适应度压入列表中
aveFitnessList.append(Genetic.calAveFitness(pop, N)) #计算并存储平均适应度

#开始迭代
for t in range(iterNum):
  #染色体突变
  pop = Genetic.mutChrom(pop, mut, chromNodes, bestChrom, chromRange)
  #染色体交换
  pop = Genetic.acrChrom(pop, acr, chromNodes)
  #寻找最优
  nowBestChrom = Genetic.findBest(pop)
  #比较前一个时间的最优和现在的最优
  bestChrom = Genetic.compareChrom(nowBestChrom, bestChrom)
  #寻找与替换最劣
  worseChrom = Genetic.findWorse(pop)
  pop[worseChrom[0]].chrom = pop[bestChrom[0]].chrom.copy()
  pop[worseChrom[0]].fitness = pop[bestChrom[0]].fitness
  #存储最优与平均
  bestFitnessList.append(bestChrom[1])
  aveFitnessList.append(Genetic.calAveFitness(pop, N))

plt.figure(1)
plt.plot(x, aveFitnessList)
plt.plot(x, bestFitnessList)
plt.show()

import Genetic
import Fitness
import matplotlib.pyplot as plt
import numpy as np

import random

#寻找最优染色体
def findBest(pop):
  best = ['1', 0.0000001]
  for i in pop:
    if best[1] < pop[i].fitness:
      best = [i, pop[i].fitness]
  return best

#寻找最劣染色体
def findWorse(pop):
  worse = ['1', 999999]
  for i in pop:
    if worse[1] > pop[i].fitness:
      worse = [i, pop[i].fitness]
  return worse

#赋初始值
def initialize(pop, chromNodes, chromRange):
  for i in pop:
    chromList = []
    for j in range(chromNodes):
      chromList.append(random.uniform(chromRange[j][0], chromRange[j][1]+1))
    pop[i].chrom = chromList.copy()
  return pop

#计算平均适应度
def calAveFitness(pop, N):
  sumFitness = 0
  for i in pop:
    sumFitness = sumFitness + pop[i].fitness
  aveFitness = sumFitness / N
  return aveFitness

#进行突变
def mutChrom(pop, mut, chromNodes, bestChrom, chromRange):
  for i in pop:
    #如果随机数小于变异概率（即可以变异)
    if mut > random.random():
      mutNode = random.randrange(0,chromNodes)
      mutRange = random.random() * (1-pop[i].fitness/bestChrom[1])**2
      pop[i].chrom[mutNode] = pop[i].chrom[mutNode] * (1+mutRange)
      #判断变异后的范围是否在要求范围内
      pop[i].chrom[mutNode] = inRange(pop[i].chrom[mutNode], chromRange[mutNode])
  return pop

#检验便宜范围是否在要求范围内
def inRange(mutNode, chromRange):
  if chromRange[0] < mutNode < chromRange[1]:
    return mutNode
  elif mutNode-chromRange[0] > mutNode-chromRange[1]:
    return chromRange[1]
  else:
    return chromRange[0]

#进行交叉
def acrChrom(pop, acr, chromNodes):
  for i in pop:
    for j in pop:
      if acr > random.random():
        acrNode = random.randrange(0, chromNodes)
        #两个染色体节点进行交换
        pop[i].chrom[acrNode], pop[j].chrom[acrNode] = pop[j].chrom[acrNode], pop[i].chrom[acrNode]
  return pop

#进行比较
def compareChrom(nowbestChrom, bestChrom):
  if bestChrom[1] > nowbestChrom[1]:
    return bestChrom
  else:
    return nowbestChrom

import math

def calFitness(pop):
  
  for i in pop:
    #计算每个染色体的适应度
    pop[i].fitness = funcFitness(pop[i].chrom)

  return pop

def funcFitness(chrom):
  #适应度函数
  fitness = math.sin(chrom[0])+math.cos(chrom[1])+0.1*(chrom[0]+chrom[1])