问题描述
from random import *
def start(pp_length):#generate random population
population=[]
for i in range(pp_length):
temp=[]
for j in range(0,6):#random func
temp.append(uniform(-10,10))
population.append(temp)
return population#context
def fitness(population,parameters):#fitness calculator using (1/mse)*10000
lst=[]
for i in range(len(population)):
mse=0#context
for j in range(len(population[0])):
mse+=(population[i][j]-parameters[j])**2
lst.append((1/mse)*10000)
return lst#context
def selectparents(lst,pp_size):#selecting parents based on fitness scores. select 1 percent randomly
largest=lst[0]
sel = []#context
for i in range(0,int(pp_size*0.1)):
for j in range(0,len(lst)-1):
if lst[j] >= largest and j not in sel:
largest = lst[j]
sel.append(j)
for i in range(0,int(pp_size*0.01)):
sel.append(randint(0,len(lst)))
return sel#context
def thekid(pp_size,pp,parents):
child = []#context
for i in range(0,pp_size):
what1 = randint(0,len(parents))
what2 = randint(0,len(parents))
child.append(pp[parents[what1-1]][:3] + pp[parents[what2-1]][3:])
return child#context
def mutate(pp,mutation_rate):#mutate according to mutation rate
for i in range(0,int(len(pp)*mutation_rate)):
what1=randint(0,len(pp)-1)
what2=randint(0,len(pp[0])-1)
what3=randint(-100,100)
pp[what1][what2]=what3#context
return pp#context
pp_size=int(input('size : '))#input population size
mutation_rate=input('mutation rate(0~100) : ')#input mutation rate
mutation_rate=float(mutation_rate)/100#fix mutation rate
iteration=int(input('iteration : '))#input iteration
pp=start(pp_size)
parameters=[]
lst2=[]
for i in range(0,6):
tmp=int(input('input integer (-10~10): '))
parameters.append(tmp)
lst=[100,200,300,400,500,600,700]
changed=False
cnt=0
new=mutation_rate*1.02
mutation=mutation_rate
while cnt<iteration:
cnt+=1
lst = fitness(pp,parameters)
print(sum(lst)/len(lst)/len(pp[0]))
lst2.append(sum(lst)/len(lst)/len(pp[0]))
parents=selectparents(lst,pp_size)
child=thekid(pp_size,parents)
pp=child
pp=mutate(pp,mutation_rate)
try:
if (lst2[-2]-lst2[-1])<=1:
mutation_rate=new
changed=True
elif changed is True:
changed = False
mutation_rate=mutation
except IndexError:#context
pass
genetic algorithm: choose fittest 10% of population,1% of randomly selected individuals
error that occured: indexerror in thechild function
error code:
File "C:\Users\user\Downloads\genetic.py",line 65,in
File "C:\Users\user\Downloads\genetic.py",line 34,in thekid
child.append(pp[parents[what1-1]][:3] + pp[parents[what2-1]][3:])
IndexError: list index out of range
apparently,the function got wrong lists. Otherwise,there wouldn't be an indexerror
or is it possible that the population numbers changed when doing the reproduction sequence?
the website tells me to insert more context but I don't have any,so ignore what I wrote
below:
Just kidding. Please read the error message I pasted below.
It just says list index out of range at thekid function.
The method I am currently using first generates random population,and then evaluates them
using the fitness function. Instead of the roulette wheel method,It selects top 10% and another 1%,this time randomly. Then,it creates children and mutates a certain
Traceback (most recent call last):
File "C:\Users\user\Downloads\genetic.py",in <module>
child=thekid(pp_size,parents)
File "C:\Users\user\Downloads\genetic.py",in thekid
child.append(pp[parents[what1-1]][:3] + pp[parents[what2-1]][3:])
IndexError: list index out of range
解决方法
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