遗传编程pyeasyGA和Zelle图形在Python上

概述我想制作一个简单的程序来提高我对这类编程的了解.我发现了一个非常有用的库,pyeasyGA,并且我尝试使用graphics.py创建一个简单的程序,从随机生成的“pass”序列创建一个收敛到一个点的序列.这就是它的工作原理：def create_individual(data): a = [(randint(0,5),randint(0,5)) fo

我想制作一个简单的程序来提高我对这类编程的了解.
我发现了一个非常有用的库,pyeasyGA,并且我尝试使用graphics.py创建一个简单的程序,从随机生成的“pass”序列创建一个收敛到一个点的序列.

这就是它的工作原理：

def create_indivIDual(data):    a = [(randint(0,5),randint(0,5)) for n in range(len(data))]    print(a)    return a

此函数创建一系列传递,因为graphics.py库允许您通过为对象移动它想要移动它来移动对象.那是我的“个人”.

为了计算健身,我使用了这个：

def fitness(indivIDual,data):    totX=0    totY=0    for elem in indivIDual:        totX+=elem[0]        totY+=elem[1]    tot = (totX,totY)    return distEuclIDea(arrivo,tot)def distEuclIDea(p1,p2):    x1 = p1[0]    y1 = p1[1]    x2 = p2[0]    y2 = p2[1]    return ((x2-x1)**2+(y2-y1)**2)**(1/2)

此功能计算距所需到达点的距离.

在这些过程之后,程序会产生很多代,并且会使个体具有最低的适应度,但它不起作用.

它没有发展.每个传递序列似乎都是随机生成的.

有谁可以帮助我吗？

Here’s the full code

编辑：

该计划似乎有效.唯一的问题是几代人.

最佳答案我发现你的健身功能最难理解.而不是平均角落或找到中心,它将角落相加然后找到距离.什么是几何解释？

此外,您的代码是指ga.logGenerations,它不是当前pyeasyga 0.3.1版本的一部分.

以下是我对你的要求的近似.如果它没有标记,那么请用示例和/或图表来扩充您的解释：

from time import sleepfrom random import randintfrom itertools import cyclefrom graphics import *from pyeasyga import pyeasygaNUMBER_OF_RECTANGLES = 4  # make one more than what you want to seeNUMBER_OF_POINTS = 2arrivo = (90,90)colori = ["red","green","blue","cyan","magenta","yellow"]X,Y = 0,1def distEuclIDea(p1,p2):    x1,y1 = p1    x2,y2 = p2    return ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5def create_indivIDual(colors):    color = next(colors)    while color in rectangles and rectangles[color] is None:  # skip over deleted rectangle        color = next(colors)    if color in rectangles:        rectangle = rectangles[color]        p1,p2 = rectangle.getP1(),rectangle.getP2()        points = [[p1.getX(),p1.getY()],[p2.getX(),p2.getY()]]    else:        points = [[randint(0,20),20)] for _ in range(NUMBER_OF_POINTS)]        rectangle = Rectangle(*[Point(x,y) for x,y in points])        rectangle.setoutline(color)        rectangle.draw(win)        rectangles[color] = rectangle    return [color,points]def fitness(indivIDual,colors):    _,points = indivIDual    rectangle = Rectangle(*[Point(x,y in points])    center = rectangle.getCenter()    return distEuclIDea(arrivo,(center.getX(),center.getY()))def mutate(indivIDual):    _,points = indivIDual    mutate_index = randint(0,NUMBER_OF_POINTS - 1)    points[mutate_index][X] += randint(-1,1)    points[mutate_index][Y] += randint(-1,1)def is_point_insIDe_rectangle(point,rectangle):    p1,rectangle.getP2()    return min(p1.getX(),p2.getX()) < point.getX() < max(p1.getX(),p2.getX()) and \        min(p1.getY(),p2.getY()) < point.getY() < max(p1.getY(),p2.getY())win = GraphWin("genetic Graphics",500,500)win.setCoords(0,100,100)rectangles = {}color_generator = cycle(colori[0:NUMBER_OF_RECTANGLES])arrivoC = Circle(Point(*arrivo),1)arrivoC.setFill("orange")arrivoC.draw(win)number_of_rectangles = NUMBER_OF_RECTANGLESwhile True:    ga = pyeasyga.geneticAlgorithm(color_generator,\        elitism=False,\        maximise_fitness=False,\        crossover_probability=0.0,\        population_size=number_of_rectangles)    ga.create_indivIDual = create_indivIDual    ga.fitness_function = fitness    ga.mutate_function = mutate    ga.run()    for member in ga.last_generation():        my_fitness,(my_color,my_points) = member        if rectangles[my_color] is None:            continue  # skip over deleted rectangle        rectangle = Rectangle(*[Point(x,y in my_points])        rectangle.setoutline(my_color)        rectangle.draw(win)        rectangles[my_color] = rectangle        if is_point_insIDe_rectangle(arrivoC.getCenter(),rectangle):            rectangles[my_color] = None  # delete finished rectangle            number_of_rectangles -= 1    if number_of_rectangles < 2:        break    sleep(0.1)for value in rectangles.values():    if value is not None:        value.undraw()  # delete unfinished rectanglewin.getMouse()win.close()

以上是粗略的代码(例如,它并不总是保持通用域点和矩形独立于graphics.py点和矩形.)但它应该给你一些实验：

它在窗口的左下角创建矩形,遗传算法在右上角向目标突变,当它们到达目标时丢弃矩形.

我的代码的一部分复杂性是pyeasyga没有提供一个功能钩子来可视化每一代发生的事情.更好的方法可能是将pyeasyga子类化为添加这样的钩子以简化代码的逻辑.

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