#-*- 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()