试着做一个遗传算法

Question

我最近一直在学习遗传算法，我决定用Python制作自己的。我将在下面分享我所做的工作

以下是我在驱动程序功能中使用的一些帮助功能：

注意：我相信这些功能都很好，可以按原样使用。

# 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, ..., 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], 1, 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, mutp, ngenerations):
    pop = generate_random_population(npop, limits, 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,mutp, 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]

我在交叉函数中得到了这个错误：

ValueError                                Traceback (most recent call last)
<ipython-input-20-375adbb7b149> in <module>
----> 1 runGA(100, 5, list(zip(np.ones(5)*-2,np.ones(5)*2)), fopt13, 4, 0.4, 25)

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

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

我还尝试了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 任何帮助都将不胜感激

Answer 1

我的评论变成了答案：

因此，看起来您需要对每一代的总体运行couples_selection()，然后对从couples_selection()返回的夫妇运行get_offspring()，然后对从get_offspring()返回的总体运行eval_pop()。然后，这一代人的获胜者将是返回的eval_pop()列表中得分最高的个人。看起来eval_pop()应该按照分数的降序对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，然后在完成所有代后在该列表上运行eval_pop()，并将sorted_pop_with_score设置为最后eval_pop()的结果