创建遗传算法

问题描述

我正在尝试重新创建此代码： https://github.com/Code-Bullet/Smart-Dots-Genetic-Algorithm-Tutorial/tree/master/BestTutorialEver ，但在 python 中，它不起作用，它不断改变最好的点，每一代都以较少的点开始。 这是代码（我使用 pygame 作为图形）：

大脑课：

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)

人口等级：

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

点类：

WIDTH,HEIGHT = 720,640
GOAL = (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,(200,100,2)

    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

问题是我指定最好的点不会变异，但它会变异或变成最坏的点，而且，我不知道为什么但在每个世代中都会产生更少的点（或点的大脑完全相同，不会变异甚至一点都没有），突变率是 100%，但在每次运行中，点越来越少。 这里是第一代和第五代的截图：https://imgur.com/a/675Jxit

另外，如果有人在 python 中有一些遗传算法作为模型，它会有所帮助。

解决方法

我没有尝试你提到的项目。您可以尝试 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，您可以在 GitHub 上找到代码。

它使用简单，可以让您以简单的方式控制交叉、变异和父选择运算符。您还可以使用 PyGAD 控制遗传算法的许多参数。

PyGAD 还与用户定义的适应度函数配合使用，因此您可以使其适应各种问题。

安装 PyGAD (pip install pygad) 后，下面是一个简单的入门示例，尝试找到满足以下等式的 W1、W2 和 W3 的最佳值：

44 = 4xW_1 - 2xW_2 + 1.2xW_3

import pygad
import numpy

function_inputs = [4,-2,1.2]
desired_output = 44

def fitness_func(solution,solution_idx):
    output = numpy.sum(solution*function_inputs)
    fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)

    return fitness

def 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))