python 遗传算法 python 怎样实现遗传算法

#-*- coding:utf-8 -*-

import random
import math
from operator import itemgetter

class Gene:
  '''
  This is a class to represent individual(Gene) in GA algorithom
  each object of this class have two attribute: data, size
  '''
  def __init__(self,**data):
    self.__dict__.update(data)    
    self.size = len(data['data'])#length of gene
                
    
class GA:
  '''
  This is a class of GA algorithm. 
  '''
  def __init__(self,parameter):
    '''
    Initialize the pop of GA algorithom and evaluate the pop by computing its' fitness value .
    The data structure of pop is composed of several individuals which has the form like that:
    
    {'Gene':a object of class Gene, 'fitness': 1.02(for example)}

    Representation of Gene is a list: [b s0 u0 sita0 s1 u1 sita1 s2 u2 sita2]
    
    '''
    #parameter = [CXPB, MUTPB, NGEN, popsize, low, up]
    self.parameter = parameter

    low = self.parameter[4]
    up = self.parameter[5]
    
    self.bound = []
    self.bound.append(low)
    self.bound.append(up)
    
    pop = []
    for i in range(self.parameter[3]):
      geneinfo = []
      for pos in range(len(low)):
        geneinfo.append(random.uniform(self.bound[0][pos], self.bound[1][pos]))#initialise popluation
        
      fitness = evaluate(geneinfo)#evaluate each chromosome
      pop.append({'Gene':Gene(data = geneinfo), 'fitness':fitness})#store the chromosome and its fitness
      
    self.pop = pop
    self.bestindividual = self.selectBest(self.pop)#store the best chromosome in the population
    
  def selectBest(self, pop):
    '''
    select the best individual from pop
    '''
    s_inds = sorted(pop, key = itemgetter("fitness"), reverse = False)
    return s_inds[0]
    
  def selection(self, individuals, k):
    '''
    select two individuals from pop
    '''
    s_inds = sorted(individuals, key = itemgetter("fitness"), reverse=True)#sort the pop by the reference of 1/fitness 
    sum_fits = sum(1/ind['fitness'] for ind in individuals) #sum up the 1/fitness of the whole pop
    
    chosen = []
    for i in xrange(k):
      u = random.random() * sum_fits#randomly produce a num in the range of [0, sum_fits]
      sum_ = 0
      for ind in s_inds:
        sum_ += 1/ind['fitness']#sum up the 1/fitness
        if sum_ > u:
          #when the sum of 1/fitness is bigger than u, choose the one, which means u is in the range of [sum(1,2,...,n-1),sum(1,2,...,n)] and is time to choose the one ,namely n-th individual in the pop
          chosen.append(ind)
          break
    
    return chosen  


  def crossoperate(self, offspring):
    '''
    cross operation
    '''
    dim = len(offspring[0]['Gene'].data)

    geninfo1 = offspring[0]['Gene'].data#Gene's data of first offspring chosen from the selected pop
    geninfo2 = offspring[1]['Gene'].data#Gene's data of second offspring chosen from the selected pop
    
    pos1 = random.randrange(1,dim)#select a position in the range from 0 to dim-1, 
    pos2 = random.randrange(1,dim)

    newoff = Gene(data = [])#offspring produced by cross operation
    temp = []
    for i in range(dim):
      if (i >= min(pos1,pos2) and i <= max(pos1,pos2)):
        temp.append(geninfo2[i])
        #the gene data of offspring produced by cross operation is from the second offspring in the range [min(pos1,pos2),max(pos1,pos2)]
      else:
        temp.append(geninfo1[i])
        #the gene data of offspring produced by cross operation is from the frist offspring in the range [min(pos1,pos2),max(pos1,pos2)]
    newoff.data = temp
    
    return newoff


  def mutation(self, crossoff, bound):
    '''
    mutation operation
    '''
    
    dim = len(crossoff.data)

    pos = random.randrange(1,dim)#chose a position in crossoff to perform mutation.

    crossoff.data[pos] = random.uniform(bound[0][pos],bound[1][pos])
    return crossoff
  
  def GA_main(self):
    '''
    main frame work of GA
    '''
    
    popsize = self.parameter[3]
    
    print("Start of evolution")
    
    # Begin the evolution
    for g in range(NGEN):
      
      print("-- Generation %i --" % g)   
           
      #Apply selection based on their converted fitness
      selectpop = self.selection(self.pop, popsize)  

      nextoff = []  
      while len(nextoff) != popsize:   
        # Apply crossover and mutation on the offspring      
                
        # Select two individuals
        offspring = [random.choice(selectpop) for i in xrange(2)]
        
        if random.random() < CXPB: # cross two individuals with probability CXPB
          crossoff = self.crossoperate(offspring)
          fit_crossoff = evaluate(self.xydata, crossoff.data)# Evaluate the individuals      
          
          if random.random() < MUTPB: # mutate an individual with probability MUTPB
            muteoff = self.mutation(crossoff,self.bound)
            fit_muteoff = evaluate(self.xydata, muteoff.data)# Evaluate the individuals
            nextoff.append({'Gene':muteoff,'fitness':fit_muteoff})
            
      # The population is entirely replaced by the offspring
      self.pop = nextoff
      
      # Gather all the fitnesses in one list and print the stats
      fits = [ind['fitness'] for ind in self.pop]
        
      length = len(self.pop)
      mean = sum(fits) / length
      sum2 = sum(x*x for x in fits)
      std = abs(sum2 / length - mean**2)**0.5
      best_ind = self.selectBest(self.pop)

      if best_ind['fitness'] < self.bestindividual['fitness']:
        self.bestindividual = best_ind

      print("Best individual found is %s, %s" % (self.bestindividual['Gene'].data,self.bestindividual['fitness']))
      print(" Min fitness of current pop: %s" % min(fits))
      print(" Max fitness of current pop: %s" % max(fits))
      print(" Avg fitness of current pop: %s" % mean)
      print(" Std of currrent pop: %s" % std)
    
    print("-- End of (successful) evolution --")  

if __name__ == "__main__":

  CXPB, MUTPB, NGEN, popsize = 0.8, 0.3, 50, 100#control parameters
  
  up = [64, 64, 64, 64, 64, 64, 64, 64, 64, 64]#upper range for variables
  low = [-64, -64, -64, -64, -64, -64, -64, -64, -64, -64]#lower range for variables
  parameter = [CXPB, MUTPB, NGEN, popsize, low, up]
  
  run = GA(parameter)
  run.GA_main()