问题描述
我创建了一种遗传算法，通过遵循此视频中的代码https://www.youtube.com/watch?v=nhT56blfRpE
不幸的是，我无法让几代部分工作，并且结果中有 0 代，即使它创建了一个在运行中每次都不同的解决方案。 我拥有的变量是名称、健康度和体重，它可以转换为节目名称、评分和剧集数。 至于代码的生成部分将无法工作，也不会考虑程序创建最佳解决方案所需的时间。
from random import choices,randint,randrange,random #allows for a random creations to be made
from typing import List,Callable,Tuple #allows to use list functions and callable
from collections import namedtuple #allows the use for named tuple
import time #allows for time to be used
from functools import partial #allows for partial to be used
Genome=List[int]
Population=List[Genome]
fitnessFunc=Callable[[Genome],int] #puts the fitness with the genome
PopulateFunc=Callable[[],Population]#gives out new solutions
SelectionFunc=Callable[[Population,fitnessFunc],Tuple[Genome,Genome]] #takes the population and fitness to make select for a new solution
CrossoverFunc=Callable[[Genome,Genome],Genome]]#takes two genomes and produces two genomes
MutationFunc=Callable[[Genome],Genome]#takes a genome and sometimes produces a new genome
PrinterFunc=Callable[[Population,int,None]
#puts the functions into perameters
Thing=namedtuple('Thing',['name','value','weight'])#gives a structure for the list below
things=[
Thing('Seishun Buta Yarou wa Bunny Girl Senpai no Yume wo Minai (TV)',8.38,13),Thing('Bakemonogatari',8.36,15),Thing('Kodomo no Jikan (TV)',6.82,12),Thing('Tsuki ga Kirei',8.18,Thing('Tokyo Ravens',7.53,24),Thing('Kono Subarashii Sekai ni Shukufuku wo!',8.15,10),Thing('Shingeki no Kyojin S4',9.2,Thing('Dr Stone S2',8.33,11),Thing('The Promised Neverland',8.11,Thing('Re:Zero',8.7,Thing('Toradora!',8.24,25),Thing('Sousei no Onmyouji',7.33,50),Thing('Rurouni Kenshin: Meiji Kenkaku Romantan',8.31,94),Thing('FullMetal Alchemist: brotherhood',64),Thing('Steins;Gate',9.11,Thing('Boku no Pico',4.31,3)
]
def generate_genome(length: int) -> Genome:
return choices([0,1],k=length)
#creates a random genome
##genome
def generate_population(size: int,genome_length: int) -> Population:
return [generate_genome(genome_length)for _ in range(size)]
#generates a list of genomes
##population
def fitness(genome: Genome,things: things,weight_limit: int) -> int:
if len(genome)!=len(things):#checks if the two are the same length
raise ValueError("genome and things must be of the same length")
weight=0
value=0
for i,thing in enumerate (things):
if genome[i]==1:
weight+=thing.weight
value+=thing.value #adds the weight values and fitness values
if weight>weight_limit: #checks if the genome is under the weight limit
return 0
return value
#fitness function
def selection_pair(population: Population,fitness_func: fitnessFunc) -> Population:
return choices(
population=population,weights=[fitness_func(genome) for genome in population],k=2 #draw twice (two parents)
)
#selection function
def single_point_crossover(a: Genome,b: Genome) -> Tuple[Genome,Genome]:
if len(a)!=len(b):
raise ValueError("Genomes a and b must be of same length")
#checks the size of the genomes that was selected
#the genomes have to be the same in length or it will not work
length=len(a)
if length<2:#checks the length as they would have to be at least 2 or else you are unable to cut them in half
return a,b
p=randint(1,length-1)#randomly takes parts of the two genomes
return a[0:p]+b[p:],b[0:p]+a[p:]#and then creates them
#crossover function
def mutation(genome: Genome,num: int=1,probability: float=0.5) -> Genome:
for _ in range(num):
index=randrange(len(genome))#if the number made is higher than the probability it is left alone
genome[index]=genome[index] if random() > probability else abs(genome[index]-1)#makes the absolute value so that it is either 1 or 0
#people hatee this one but why ^^
return genome
#mutation function
def run_evolution(
populate_func: PopulateFunc,fitness_func: fitnessFunc,#for these two it populates with variables
fitness_limit: int,#if the fitness limit is reached then the program is done
selection_func: SelectionFunc=selection_pair,crossover_func: CrossoverFunc=single_point_crossover,mutation_func: MutationFunc=mutation,# initialises the above three
generation_limit: int=100 #limits on how much is made
) -> Tuple[Population,int]:
population=populate_func()#populates with random genomes
for generations in range(generation_limit):
population=sorted(
population,key=lambda genome: fitness_func(genome),reverse=True
#sorts the fitness of the genomes so that the higher number is at the start
)
if fitness_func(population[0])>=fitness_limit:
break
#if the fitness limit is reached then the program ends (breaks)
next_generation=population[0:2]#gets the top two genomes for the next generation
for j in range(int(len(population)/2)-1):
parents=selection_func(population,fitness_func) #gets the top two to be made as parents
offspring_a,offspring_b=crossover_func(parents[0],parents[1]) #puts the genomes into seprate variables
offspring_a=mutation_func(offspring_a)
offspring_b=mutation_func(offspring_b)#creates a mutation for both genomes randomly
next_generation+=[offspring_a,offspring_b]
#creates the next genomes and puts them into population
population=next_generation
population=sorted(
population,reverse=True
#sorts the fitness of the genomes so that the higher number is at the start
)
return population,generations
#evolutionary main loop
def genome_to_things(genome: Genome,things: things) -> things:
result=[]
for i,thing in enumerate(things):
if genome[i]==1:
result+=[thing.name]
return result
#collects the best solutions so that it may be printied out for later
start=time.time()
population,generations=run_evolution(
populate_func=partial(
generate_population,size=10,genome_length=len(things)
),fitness_func=partial(
fitness,things=things,weight_limit=94
),fitness_limit=10,generation_limit=100
)
end=time.time()
#runs the program
print(f"number of generations: {generations}")
print(f"time: {end-start}s")
print(f"best solution: {genome_to_things(population[0],things)}")
#prints results
解决方法
这意味着你的 GA 在第一代初始 0 中直接找到了满足你的适应度极限的解。
如果我使用 fitness_limit=100 运行您的代码，我会得到以下输出。
number of generations: 99
time: 0.014637947082519531s
best solution: ['Seishun Buta Yarou wa Bunny Girl Senpai no Yume wo Minai (TV)','Bakemonogatari','Tsuki ga Kirei','Kono Subarashii Sekai ni Shukufuku wo!','Shingeki no Kyojin S4','Dr Stone S2','Re:Zero','Boku no Pico']