尝试制作遗传算法注意：我相信这些函数都很好，可以照原样使用问题：注意：

问题描述

我最近一直在研究遗传算法，我决定使用 Python 自己制作。我将在下面分享我所做的工作。

这些是我在驱动程序函数中使用的一些帮助函数：

注意：我相信这些函数都很好，可以照原样使用。

# Generates Random Population
def generate_random_population(npop,limits=list(zip(np.zeros(5),np.ones(5))),ngenes=5):
  
  def new_ind():
    return [random.uniform(limits[i][0],limits[i][1]) for i in range(ngenes)]

  return np.array([new_ind() for n in range(npop)])


# Function to evaluate all individuals and give them a score
# fopt1 only has a minimum (unimodal) at x = (0,...,0) in which fopt1 = 0.
def fopt1(ind):
  
    x0 = [ind[len(ind)-1]]
    xlast = [ind[0]]
    working_array = np.concatenate((x0,ind,xlast))
    res = 0

    for j in range(1,len(ind)+1):
        res += (2*working_array[j-1] + (working_array[j]**2)*working_array[j+1] - working_array[j+1])**2

    return res

# Receives a certain population of individuals and an evaluation function (usually called * fitness function *) and returns an ordered list of tuples
def eval_pop(pop,f):
  # Returns a list of tuples in descending order of the goodness of f. Shape of tuples are (individual,score),e.g.,([2.3,0.004,1,8.2,6],0.361).
  
    list = []
    
    for i in pop:
        j = (pop,f(pop))
        list.append(j)
    

    return list


# Function to produce a next generation of individuals is to select the pairs that will interbreed to have offspring
def couples_selection(ordered_pop,n_elitism):
    if len(ordered_pop) < 10:
        print("Error: population's size should be higher than 9")
        return
  
    len_a = int(len(ordered_pop)/10)
    len_b = len_a * 3
    len_c = len_a * 4

    a = np.ones(len_a) * 0.5 / len_a
    b = np.ones(len_b) * 0.3 / len_b
    c = np.ones(len_c) * 0.15 / len_c
    d = np.ones(len(ordered_pop) - len_a*8)
    d = d * 0.05 / len(d)

    prob = np.concatenate((a,b,c,d))
    indices = range(len(ordered_pop))
    selected_indices = [choice(indices,2,p=prob) for i in range(len(ordered_pop) - n_elitism)]
    couples = [[ordered_pop[i1],ordered_pop[i2]] for [i1,i2] in selected_indices]
    return np.array(couples)

def mutate(ind,limits):
    # print("Mutating individual ",ind)
    factor = 1 + (0.2 * choice([-1,1],1))
    gene_index = choice(range(len(ind)),1)[0]
    mutated_val = ind.item(gene_index) * factor

    if mutated_val < limits[gene_index][0]:
        mutated_val = limits[gene_index][0]
    elif mutated_val > limits[gene_index][1]:
        mutated_val = limits[gene_index][1]

    ind[gene_index] = mutated_val

    return

def crossover(couple):
    ancestor1 = couple[0]
    ancestor2 = couple[1]

    c1,c2 = ancestor1.copy(),ancestor2.copy()
    
    pt = randint(1,len(ancestor1)-2)
    # perform crossover
    c1 = ancestor1[:pt] + ancestor2[pt:]
    c2 = ancestor2[:pt] + ancestor1[pt:]

    return [c1,c2]
  

def get_offspring(couples,mutp,limits):

    children = [crossover(couple) for couple in couples]
    mutation_roulette = [choice([True,False],p=[mutp,1-mutp]) for _ in children]
    children_roulette = list(zip(children,mutation_roulette))

    for child in children_roulette:
        if child[1][0]:
            mutate(child[0],limits) 
            # print("Mutated: ",child[0])

    return np.array([child[0] for child in children_roulette])

问题：

当我使用以下函数调用运行以下驱动程序函数时：

runGA(100,5,list(zip(np.ones(5)*-2,np.ones(5)*2)),fopt13,4,0.4,25)

def runGA(npop,ngenes,limits,fitness,nelitism,ngenerations):
    pop = generate_random_population(npop,ngenes)
    sorted_pop_with_score = eval_pop(pop,fitness)
    new_pop = np.array([p[0] for p in sorted_pop_with_score])

    for g in range(ngenerations):

    # TO DO: Complete your GA!
    
        couples = couples_selection(new_pop,nelitism)
        popp = get_offspring(couples,limits)
        eval_pop_result = eval_pop(pop,fitness)
    
    
    # END OF TO DO
    
        print("Winner after generation",g,":",eval_pop_result[0])

    print("Absolute winner:")
    return sorted_pop_with_score[0]

我在交叉函数中收到此错误：

ValueError                                Traceback (most recent call last)
<ipython-input-20-375adbb7b149> in <module>
----> 1 runGA(100,25)

<ipython-input-12-6619b9c7d476> in runGA(npop,ngenerations)
      8     # TO DO: Complete your GA!
      9         couples = couples_selection(new_pop,nelitism)
---> 10         popp = get_offspring(couples,limits)
     11         eval_pop_result = eval_pop(pop,fitness)
     12 

<ipython-input-16-5e8ace236573> in get_offspring(couples,limits)
     34 def get_offspring(couples,limits):
     35 
---> 36     children = [crossover(couple) for couple in couples]
     37     mutation_roulette = [choice([True,1-mutp]) for _ in children]
     38     children_roulette = list(zip(children,mutation_roulette))

<ipython-input-16-5e8ace236573> in <listcomp>(.0)
     34 def get_offspring(couples,mutation_roulette))

<ipython-input-16-5e8ace236573> in crossover(couple)
     25     print(len(ancestor1))
     26     print(len(ancestor2))
---> 27     c1 = ancestor1[:pt] + ancestor2[pt:]
     28     c2 = ancestor2[:pt] + ancestor1[pt:]
     29 

ValueError: operands Could not be broadcast together with shapes (39,5) (61,5)

注意：

我也尝试了 np.concatenate 函数，但在同一步骤中出现以下错误： TypeError: only integer scalar arrays can be converted to a scalar index 任何帮助将不胜感激！

解决方法

我的评论变成了答案：

所以看起来你需要对每一代的总体运行 couples_selection()，然后对从 get_offspring() 返回的夫妇运行 couples_selection()，然后在 eval_pop() 上运行从 get_offspring() 返回的人口。然后，那一代的获胜者将是返回的 eval_pop() 列表中得分最高的个体。看起来 eval_pop() 应该按照分数的降序对其返回的列表进行排序，但似乎没有；否则，返回列表的 [0] 索引将是得分最高的索引，也就是获胜者。

此外，如果您将 sorted_pop_with_score[0] 作为绝对赢家返回，那么您似乎需要将每一代的赢家添加到某个列表中，然后在该列表上运行 eval_pop()您完成所有代，并将 sorted_pop_with_score 设置为最终 eval_pop() 的结果。

genetic-algorithm machine-learning python

尝试制作遗传算法 注意：我相信这些函数都很好，可以照原样使用问题：注意：

问题描述

这些是我在驱动程序函数中使用的一些帮助函数：

注意：我相信这些函数都很好，可以照原样使用。

问题：

注意：

解决方法

尝试制作遗传算法注意：我相信这些函数都很好，可以照原样使用问题：注意：