我正在尝试实现一种遗传锦标赛选择算法，其中人口的平均适应度上升，但我的平均适应度没有改变。
如果有人可以查看我的代码并就我做错了什么提出建议，我将不胜感激。你可以在这里找到代码：https ://github.com/Mithycal/tournament-selection-algorithm
寻找适合度的代码：
for i in range(len(individuals)):
chosen = individuals[i]
fitness_scores.append(sum(([a * b for a, b in zip(equation_inputs, chosen)])))
我已经查看了您的代码。在这一点上，tournamentSize 是每个组的大小，对吗？
for k in range(tournamentSize):
randchoice = random.sample(list(individuals), 1)[0] #update individual list so values are different??!
randvalue = individuals.get(randchoice)
random_individuals.append(randvalue)
loc = list(individuals).index(randchoice)
random_fitness_scores.append(fitness_scores[loc])
print("\n", randchoice, "participates in the tournament")
print("\n")
如果我没记错的话，你想把你的 poblation 分成 N 组个人，然后你想只保留每组中最好的（或 n 个最好的）。
我建议您将人口代表更改为：
individuals = [[random.randint(-4,4) for _ in range(number_of_genes)] for i in pop ] # list
因此，您可以执行以下操作： score() -> 重新调整个人适应度的自定义函数
choosen_individuals = []
#go throw individual jumping tournamentSize each time
for k in range(0,len(individuals),tournamentSize):
tournament_individuals = individuals[k:k+tournamentSize] # current group
sorted_group = sorted( [ (score(individual),index) for index,individual in enumerate(tournament_individuals)],reverse = True)
#sorted_group contains a list of tuples (score,individual_position)
choosen_individuals.append(tournament_individuals[sorted_group[1]]) # saves the best
我要给你一个我实现的基因： https ://github.com/anigmo97/MUIARFID/blob/master/CUATRIMESTRE_1A/TIA/PROYECTO/algoritmo_gen%C3%A9tico/geneticos.py
我希望它有所帮助。
现在您的个人（重命名为人口）是一个氏族列表。你的人口是一个带有键（int）和整数值列表的字典。如果您考虑一下，基本上您使用的是字典，因为它是一个列表。我建议您更改人口的表示形式，例如：
{0：[2，-3]，1：[-1，-1]}
至
[[2,-3],[-1,-1]]
改变
individuals = { i : [random.randint(-4,4) for _ in range(number_of_genes)] for i in pop }
population = []
for i in range(population_size):
population.append([random.randint(-4,4) for _ in range(number_of_genes)])
您有每个基因的权重列表，因此我们有一个名为“权重”的列表，长度 = number_of_genes。（个人长度相同）。
使用新的表示形式，您的评分可能类似于：
def score_individual(individual):
return sum(([a * b for a, b in zip(weights, individual)]))
def fitness_calc(population):
fitness_scores = [] #local variable
for individual in population:
chosen = list(individuals.values())[i]
fitness_scores.append(score_individual(individual))
return fitness_scores
def sort_population_by_fitness(population):
return sorted(population,key=lambda i:score_individual(i),reverse=True)
from random import randint,shuffle
def generate_random_weights(num_weights):
return [randint(-200,200) for x in range(num_weights)]
def generate_population(number_of_gens):
population = []
for i in range(population_size):
population.append([randint(-4, 4) for _ in range(number_of_gens)])
return population
def score_individual(individual):
return sum(([a * b for a, b in zip(weights, individual)]))
def fitness_calc(population):
fitness_scores = [] #local variable
for individual in population:
fitness_scores.append(score_individual(individual))
return fitness_scores
def sort_population_by_fitness(population):
return sorted(population,key=lambda i:score_individual(i),reverse=True)
def calculate_population_score_avg(population):
scores = [score_individual(i) for i in population]
return sum(scores)/len(scores)
def make_tournament_selection(population,group_size):
shuffle(population)
choosen_individuals = []
#go throw individual jumping tournamentSize each time
for k in range(0, len(population), group_size):
tournament_individuals = population[k:k + group_size] # current group
sorted_group = sort_population_by_fitness(tournament_individuals)
choosen_individuals.append(sorted_group[0])
print("---->BEST INDIVIDUAL OF THE GROUP {}".format(score_individual(sorted_group[0])))
return choosen_individuals
def make_n_best_selection(population,num_individuals_to_keep):
return sort_population_by_fitness(population)[:num_individuals_to_keep]
if __name__ =="__main__":
population_size = 20
number_of_gens = 10
weights = generate_random_weights(number_of_gens)
population = generate_population(number_of_gens)
num_generations = 10
group_size = 5
score_avgs_by_generation = []
for i in range(num_generations):
# make selection
#population = make_tournament_selection(population,group_size)
population = make_n_best_selection(population,5)
print("BEST SCORE IN GENERATION {} = {}".format(
i,score_individual(sort_population_by_fitness(population)[0]))
)
avg_score = calculate_population_score_avg(population)
score_avgs_by_generation.append(avg_score)
print("SCORE AVG IN GENERATION {} = {}\n\n".format(i, avg_score))
# make crossbreeding
# make mutations
# add random individuals to add new genetic load
population += generate_population(10)
几点说明：
我怀疑next_generation你忘了添加next_generation到你的全局列表中。事实上，这个函数没有做任何事情。
由于全局变量rerun永远不会更改为 False，因此run_generation您每代都构造一组新个体，而不是计算下一代。唯一rerun更新的form_generation地方rerun是局部变量，而不是全局变量，但我怀疑这与前一点是相同的错误。
此外，您应该检查两个父母是否不同。就像现在一样，一个人很可能会与自己交配，从而产生一个与其父母相同的孩子（除了偶尔的突变）。
最后，你真的应该尽量避免使用全局变量，或者至少尝试只将它们用于全局常量。它们使跟踪程序的流程变得非常困难，正如您所见，它们是各种错误的根源。