最近我在研究遗传算法，并决定用Python编写自己的版本。我将在下面分享我所做的工作。

这些是我为驱动函数准备的一些辅助函数:

注意: 我认为这些函数是正确的，可以直接使用。

# Generates Random Populationdef 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 tuplesdef 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 offspringdef 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    returndef 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:

当我运行以下驱动函数并使用以下函数调用时:

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]

我在交叉函数中遇到了以下错误:

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) 

注意:

我也尝试使用np.concatenate函数，但在同一步骤时出现了以下错误:TypeError: only integer scalar arrays can be converted to a scalar index任何帮助将不胜感激!

回答：

我的评论变成了答案:

看起来你需要在每一代中对种群运行couples_selection()函数，然后对couples_selection()返回的配对运行get_offspring()函数，接着对get_offspring()返回的种群运行eval_pop()函数。然后，那一代的赢家将是eval_pop()返回列表中得分最高的个体。看起来eval_pop()应该按得分降序排列其返回列表，但似乎没有做到；否则，返回列表的[0]索引应该就是得分最高的个体，即赢家。

此外，如果你返回sorted_pop_with_score[0]作为绝对赢家，那么似乎你需要将每一代的赢家添加到某个列表中，然后在完成所有代之后对该列表运行eval_pop()，并将sorted_pop_with_score设置为该最终eval_pop()的结果。