我试图重现这段代码：https://github.com/Code-Bullet/Smart-Dots-Genetic-Algorithm-Tutorial/tree/master/BestTutorialEver，但用的是Python，结果它不起作用，每代都持续变异最佳点，并且每代开始的点越来越少。以下是代码（我使用pygame进行图形处理）：

Brain类：

class Brain(object):    def __init__(self, size):        self.size = size        self.step = 0        self.directions = [[0.0, 0.0] for j in range(size)]        for i in range(len(self.directions)):            randomAngle = random.uniform(0, 2 * math.pi)            self.directions[i][0] = math.sin(randomAngle)            self.directions[i][1] = math.cos(randomAngle)    def mutate(self):        mutationRate = 1        for i in range(self.size):            rand = random.random()            if rand < mutationRate:                dirAngle = math.acos(self.directions[i][1]) * (1.0 + random.random())                self.directions[i][0] = math.sin(dirAngle)                self.directions[i][1] = math.cos(dirAngle)

Population类：

class Population(object):    def __init__(self, size, win):        self.bestDot = 0        self.fitnessSum = 0.0        self.win = win        self.size = size        self.dots = [Dot(win) for i in range(size)]    def show(self):        for i in range(self.size-1):            self.dots[i+1].show()        self.dots[0].show()    def updt(self):        for i in range(self.size):            self.dots[i].updt()    def calculateFitness(self):        for i in range(self.size):            self.dots[i].calculateFitness()    def allDotsDead(self):        for i in range(self.size):            if not self.dots[i].dead and not self.dots[i].reachGoal:                return False        return True    def naturalSelection(self):        newDots = [Dot(self.win) for i in range(self.size)]        self.setBestDot()        self.calculateFitnessSum()        newDots[0] = self.dots[self.bestDot].baby()        newDots[0].isBest = True        for i in range(self.size-1):            parent = self.selectParent()            newDots[i+1] = parent.baby()        print(newDots[1])        self.dots = newDots    def calculateFitnessSum(self):        self.fitnessSum = 0.0        for i in range(self.size):            self.fitnessSum += self.dots[i].fitness        print(self.fitnessSum)    def selectParent(self):        rand = random.uniform(0, self.fitnessSum)        runningSum = 0.0        for i in range(self.size):            runningSum += self.dots[i].fitness            if runningSum > rand:                return self.dots[i]        return None    def mutate(self):        for i in range(self.size):            if not self.dots[i].isBest:                self.dots[i].brain.mutate()    def setBestDot(self):        max = 0.0        maxIndex = 0        for i in range(len(self.dots)):            if self.dots[i].fitness > max:                max = self.dots[i].fitness                maxIndex = i        self.bestDot = maxIndex

Dot类：

WIDTH, HEIGHT = 720, 640GOAL = (WIDTH / 2, 50)class Dot(object):    def __init__(self, win):        self.win = win        self.fitness = 0        self.reachGoal = False        self.dead = False        self.brain = Brain(200)        self.pos = [WIDTH / 2, HEIGHT - 50]        self.vel = [0, 0]        self.acc = [0, 0]        self.isBest = False    def move(self):        if len(self.brain.directions) > self.brain.step:            self.acc = self.brain.directions[self.brain.step]            self.brain.step += 1        else:            self.dead = True        for i in range(len(self.vel)): self.vel[i] += self.acc[i]        if self.vel[0] >= 5: self.vel[0] = 5        if self.vel[1] >= 5: self.vel[1] = 5        for i in range(len(self.pos)): self.pos[i] += self.vel[i]    def show(self):        if self.isBest:            pygame.draw.circle(self.win, (0, 255, 0), self.pos, 4)        else:            pygame.draw.circle(self.win, (200, 100, 0), self.pos, 2)    def updt(self):        if not self.dead and not self.reachGoal:            self.move()            if self.pos[0] < 4 or self.pos[1] < 4 or self.pos[0] > WIDTH - 4 or self.pos[1] > HEIGHT - 4:                self.dead = True            elif math.hypot(self.pos[0] - GOAL[0], self.pos[1] - GOAL[1]) < 5:                self.reachGoal = True    def calculateFitness(self):        distToGoal = math.hypot(self.pos[0] - GOAL[0], self.pos[1] - GOAL[1])        self.fitness = 1.0 / 16.0 + 10000.0 / (distToGoal * distToGoal)    def baby(self):        baby = Dot(self.win)        baby.brain.directions = self.brain.directions        return baby

问题在于我指定了最佳点不应该变异，但它还是变异了或是变成了更差的点，此外，我不知道为什么但每代生成的点越来越少（或者点的脑完全相同甚至一点都没有变异），变异率是100%但每次运行时点的数量越来越少。以下是第一代和第五代的截图：https://i.sstatic.net/cGUJG.jpg

另外，如果有人有Python的遗传算法模型可以参考，那会很有帮助。

回答：

我没有尝试过你提到的项目。你可以尝试PyGAD，这是一个用于构建遗传算法和训练机器学习算法的Python 3库。它是开源的，你可以在GitHub上找到代码。

PyGAD使用简单，允许你轻松控制交叉、变异和父本选择操作。你还可以使用PyGAD控制遗传算法的许多参数。

PyGAD还支持用户定义的适应度函数，因此你可以将其适应于广泛的问题类型。

安装PyGAD后（pip install pygad），这里有一个简单的例子来开始，它试图找到满足以下方程的最佳W1、W2和W3值：

44 = 4xW_1 – 2xW_2 + 1.2xW_3

import pygadimport numpyfunction_inputs = [4,-2, 1.2]desired_output = 44def fitness_func(solution, solution_idx):    output = numpy.sum(solution*function_inputs)    fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)    return fitnessdef on_generation(ga_instance):    print(ga_instance.population)ga_instance = pygad.GA(num_generations=50,                       num_parents_mating=2,                       fitness_func=fitness_func,                       num_genes=3,                       sol_per_pop=5)ga_instance.run()ga_instance.plot_result()solution, solution_fitness, _ = ga_instance.best_solution()print("Parameters of the best solution : {solution}".format(solution=solution))print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))