python遗传算法 遗传算法python版

#-*- 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()