问题描述
我一直在研究以下代码，以使用遗传算法最大化多项式，但是它陷入了一个较低端，并且变异函数似乎不起作用。这是代码：
# genetic Algorithm to optimize polynomial
import numpy as np
import tabulate
import random
def fitness_score(arr):
f=[]
integers=[]
for x in arr:
s=np.sum(2**np.arange(len(x))*x)#binary to decimal
integers.append(s)
f.append(2*s-(s*s/16))
return f,integers
def takefifth(elem):
return elem[5]
def deterministic_sampling(fitness,population,population_size):
g=[]
sorted_population=[]
for i in range(population_size):
normalize=population_size*(fitness[i]/sum(fitness))
x=np.append(population[i],[normalize])
sorted_population.append(x)
for j in range(int(normalize)):
g.append(list(population[i][0:5]))
sorted_population=sorted(sorted_population,key=takefifth)
if(len(g)!=population_size):
for i in range(population_size):
if(sorted_population[i][5]<1):
g.append(list(sorted_population[i][0:5].astype(int)))
if(len(g)==population_size):
break
return g,sorted_population
def crossover(initial_population,population_size):
c=[]
new_population=[]
mates=initial_population[:]
np.random.shuffle(mates)
for i in range(population_size):
r = random.randint(0,5)
c.append(r)
if(i%2==0):
temp=initial_population[i][0:r]
temp.extend(list(mates[i][r:]))
else:
temp=mates[i][0:r]
temp.extend(list(initial_population[i][r:]))
new_population.append(temp)
header = ['Initial Population','Mates','Crossover site','New Population']
print(tabulate.tabulate([[initial_population[i][::-1],mates[i][::-1],c[i],new_population[i][::-1]] for i in range(population_size)],headers=header,tablefmt='grid'))
return new_population
def mutation(population,populationsize):
r= random.randint(0,4)
i= random.randint(0,3)
print(r,i)
if(population[i][r]==0):
population[i][r]==1
else:
population[i][r]==0
return population
def genetic():
population_size=4
#generating initial population of 32 chromosomes(integers)
chromosomes=np.arange(32)
#converting to binary
chromosomes_bin= (((chromosomes[:,None] & (1 << np.arange(5))))>0).astype(int)
print("Chromosomes\n",chromosomes_bin)
#selecting initial population(replace=False to avoid repititions)
random_rows=np.random.choice(chromosomes_bin.shape[0],population_size,replace=False)
initial_population=chromosomes_bin[random_rows,:]
#calculate fitness scores of initial population
fitness_scores,integers=fitness_score(initial_population)
#deterministic sampling for parent selection
#chromosomes with low fitness discarded
#chromosomes with high fitness repeated
parents,sorted_population=deterministic_sampling(fitness_scores,initial_population,population_size)
header = ['Population','Integers','fitness score','Parents for next generation']
print(tabulate.tabulate([[initial_population[i][::-1],integers[i],fitness_scores[i],parents[i][::-1]] for i in range(population_size)],tablefmt='grid'))
def sublists_equal(a,b):
return all(l for l in b if l in a)
while(max(fitness_scores)!=16):
initial_population=parents[:]
#generate new population using simple crossover method
new_population=crossover(initial_population,population_size)
#calculate fitness score
fitness_scores,integers=fitness_score(new_population)
#deterministic sampling for parent selection
#chromosomes with low fitness discarded
#chromosomes with high fitness repeated
parents,new_population,population_size)
parents=mutation(parents,population_size)
header = ['Population','Parents for next generation']
print(tabulate.tabulate([[new_population[i][::-1],tablefmt='grid'))
genetic()
Chromosomes
[[0 0 0 0 0]
[1 0 0 0 0]
[0 1 0 0 0]
[1 1 0 0 0]
[0 0 1 0 0]
[1 0 1 0 0]
[0 1 1 0 0]
[1 1 1 0 0]
[0 0 0 1 0]
[1 0 0 1 0]
[0 1 0 1 0]
[1 1 0 1 0]
[0 0 1 1 0]
[1 0 1 1 0]
[0 1 1 1 0]
[1 1 1 1 0]
[0 0 0 0 1]
[1 0 0 0 1]
[0 1 0 0 1]
[1 1 0 0 1]
[0 0 1 0 1]
[1 0 1 0 1]
[0 1 1 0 1]
[1 1 1 0 1]
[0 0 0 1 1]
[1 0 0 1 1]
[0 1 0 1 1]
[1 1 0 1 1]
[0 0 1 1 1]
[1 0 1 1 1]
[0 1 1 1 1]
[1 1 1 1 1]]
+--------------+------------+-----------------+-------------------------------+
| Population | Integers | fitness score | Parents for next generation |
+==============+============+=================+===============================+
| [0 1 1 0 1] | 13 | 15.4375 | [0,1,1] |
+--------------+------------+-----------------+-------------------------------+
| [1 0 0 1 0] | 18 | 15.75 | [1,0] |
+--------------+------------+-----------------+-------------------------------+
| [0 0 0 0 1] | 1 | 1.9375 | [1,0] |
+--------------+------------+-----------------+-------------------------------+
| [1 0 1 1 0] | 22 | 13.75 | [0,1] |
+--------------+------------+-----------------+-------------------------------+
+----------------------+-----------------+------------------+------------------+
| Initial Population | Mates | Crossover site | New Population |
+======================+=================+==================+==================+
| [0,1] | [0,1] | 4 | [0,1] |
+----------------------+-----------------+------------------+------------------+
| [1,0] | [1,0] | 3 | [1,0] |
+----------------------+-----------------+------------------+------------------+
| [1,0] | [0,1] | 0 | [0,1] |
+----------------------+-----------------+------------------+------------------+
| [0,1] | [1,0] | 1 | [0,0] |
+----------------------+-----------------+------------------+------------------+
1 1
+-----------------+------------+-----------------+-------------------------------+
| Population | Integers | fitness score | Parents for next generation |
+=================+============+=================+===============================+
| [0,1] | 13 | 15.4375 | [0,1] |
+-----------------+------------+-----------------+-------------------------------+
| [1,0] | 22 | 13.75 | [1,0] |
+-----------------+------------+-----------------+-------------------------------+
| [0,1] |
+-----------------+------------+-----------------+-------------------------------+
| [0,0] | 0 | 0 | [0,0] |
+-----------------+------------+-----------------+-------------------------------+
+----------------------+-----------------+------------------+------------------+
| Initial Population | Mates | Crossover site | New Population |
+======================+=================+==================+==================+
| [0,1] | 3 | [0,1] | 5 | [0,0] | 2 | [1,0] | 2 | [0,0] |
+----------------------+-----------------+------------------+------------------+
4 1
+-----------------+------------+-----------------+-------------------------------+
| Population | Integers | fitness score | Parents for next generation |
+=================+============+=================+===============================+
| [0,1] | 21 | 14.4375 | [1,1] | 2 | [0,1] |
+----------------------+-----------------+------------------+------------------+
4 3
+-----------------+------------+-----------------+-------------------------------+
| Population | Integers | fitness score | Parents for next generation |
+=================+============+=================+===============================+
| [0,1] | 1 | 1.9375 | [0,1] |
+-----------------+------------+-----------------+-------------------------------+
+----------------------+-----------------+------------------+------------------+
| Initial Population | Mates | Crossover site | New Population |
+======================+=================+==================+==================+
| [0,1] | 1 | [0,1] |
+----------------------+-----------------+------------------+------------------+
4 1
+-----------------+------------+-----------------+-------------------------------+
| Population | Integers | fitness score | Parents for next generation |
+=================+============+=================+===============================+
| [0,1] |
+----------------------+-----------------+------------------+------------------+
有人可以帮我吗？我要去哪里错了？
解决方法
耦合点...
您的代码不太可读。您有很多1个字符的命名变量，很难解释，并且某些构造非常奇怪，并且可能更清楚地表达，尤其是在您的deterministic_sampling函数中。
也就是说，在您的突变功能中，您有一个致命的错字。您没有按照期望重新分配1/0。更改：
population[i][r]==1
到
population[i][r]=1
，并且对于更改为零的行也是如此。