Python怎么实现遗传算法

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（一）问题

遗传算法求解正方形拼图游戏

（二）代码

#!/usr/bin/env python# -*- coding: utf-8 -*- from PIL import Image, ImageDrawimport osimport gcimport random as rimport minpy.numpy as np class Color(object):    '''    定义颜色的类，这个类包含r,g,b,a表示颜色属性    '''    def __init__(self):        self.r = r.randint(0, 255)        self.g = r.randint(0, 255)        self.b = r.randint(0, 255)        self.a = r.randint(95, 115)  def mutate_or_not(rate):    '''    生成随机数，判断是否需要变异    '''    return True if rate > r.random() else False  class Triangle(object):    '''    定义三角形的类    属性：            ax,ay,bx,by,cx,cy：表示每个三角形三个顶点的坐标            color                       : 表示三角形的颜色            img_t                    : 三角形绘制成的图，用于合成图片    方法：            mutate_from(self, parent):      从父代三角形变异            draw_it(self, size=(256, 256)): 绘制三角形    '''      max_mutate_rate = 0.08    mid_mutate_rate = 0.3    min_mutate_rate = 0.8      def __init__(self, size=(255, 255)):        t = r.randint(0, size[0])        self.ax = r.randint(0, size[0])        self.ay = r.randint(0, size[1])        self.bx = self.ax+t        self.by = self.ay        self.cx = self.ax+t        self.cy = self.ay-t        self.dx = self.ax        self.dy = self.ay-t        self.color = Color()        self.img_t = None      def mutate_from(self, parent):        if mutate_or_not(self.max_mutate_rate):            t = r.randint(0, 255)            self.ax = r.randint(0, 255)            self.ay = r.randint(0, 255)            self.bx = self.ax + t            self.by = self.ay            self.dx = self.ax            self.dy = self.ay - t            self.cx = self.ax + t            self.cy = self.ay - t        if mutate_or_not(self.mid_mutate_rate):            t = min(max(0, parent.ax + r.randint(-15, 15)), 255)            self.ax = min(max(0, parent.ax + r.randint(-15, 15)), 255)            self.ay = min(max(0, parent.ay + r.randint(-15, 15)), 255)            self.bx = self.ax + t            self.by = self.ay            self.dx = self.ax            self.dy = self.ay - t            self.cx = self.ax + t            self.cy = self.ay - t        if mutate_or_not(self.min_mutate_rate):            t = min(max(0, parent.ax + r.randint(-3, 3)), 255)            self.ax = min(max(0, parent.ax + r.randint(-3, 3)), 255)            self.ay = min(max(0, parent.ay + r.randint(-3, 3)), 255)            self.bx = self.ax + t            self.by = self.ay            self.dx = self.ax            self.dy = self.ay - t            self.cx = self.ax + t            self.cy = self.ay - t                  # color        if mutate_or_not(self.max_mutate_rate):            self.color.r = r.randint(0, 255)        if mutate_or_not(self.mid_mutate_rate):            self.color.r = min(max(0, parent.color.r + r.randint(-30, 30)), 255)        if mutate_or_not(self.min_mutate_rate):            self.color.r = min(max(0, parent.color.r + r.randint(-10, 10)), 255)         if mutate_or_not(self.max_mutate_rate):            self.color.g = r.randint(0, 255)        if mutate_or_not(self.mid_mutate_rate):            self.color.g = min(max(0, parent.color.g + r.randint(-30, 30)), 255)        if mutate_or_not(self.min_mutate_rate):            self.color.g = min(max(0, parent.color.g + r.randint(-10, 10)), 255)         if mutate_or_not(self.max_mutate_rate):            self.color.b = r.randint(0, 255)        if mutate_or_not(self.mid_mutate_rate):            self.color.b = min(max(0, parent.color.b + r.randint(-30, 30)), 255)        if mutate_or_not(self.min_mutate_rate):            self.color.b = min(max(0, parent.color.b + r.randint(-10, 10)), 255)        # alpha        if mutate_or_not(self.mid_mutate_rate):            self.color.a = r.randint(95, 115)        # if mutate_or_not(self.mid_mutate_rate):        #     self.color.a = min(max(0, parent.color.a + r.randint(-30, 30)), 255)        # if mutate_or_not(self.min_mutate_rate):        #     self.color.a = min(max(0, parent.color.a + r.randint(-10, 10)), 255)      def draw_it(self, size=(256, 256)):        self.img_t = Image.new('RGBA', size)        draw = ImageDraw.Draw(self.img_t)        draw.polygon([(self.ax, self.ay),                      (self.bx, self.by),                      (self.cx, self.cy),                      (self.dx, self.dy)],                     fill=(self.color.r, self.color.g, self.color.b, self.color.a))        return self.img_t  class Canvas(object):    '''    定义每一张图片的类    属性：            mutate_rate      : 变异概率            size             : 图片大小            target_pixels: 目标图片像素值    方法：            add_triangles(self, num=1)      : 在图片类中生成num个三角形            mutate_from_parent(self, parent): 从父代图片对象进行变异            calc_match_rate(self)                   : 计算环境适应度            draw_it(self, i)                           : 保存图片    '''      mutate_rate = 0.01    size = (256, 256)    target_pixels = []      def __init__(self):        self.triangles = []        self.match_rate = 0        self.img = None      def add_triangles(self, num=1):        for i in range(0, num):            triangle = Triangle()            self.triangles.append(triangle)      def mutate_from_parent(self, parent):        flag = False        for triangle in parent.triangles:            t = triangle            if mutate_or_not(self.mutate_rate):                flag = True                a = Triangle()                a.mutate_from(t)                self.triangles.append(a)                continue            self.triangles.append(t)        if not flag:            self.triangles.pop()            t = parent.triangles[r.randint(0, len(parent.triangles) - 1)]            a = Triangle()            a.mutate_from(t)            self.triangles.append(a)      def calc_match_rate(self):        if self.match_rate > 0:            return self.match_rate        self.match_rate = 0        self.img = Image.new('RGBA', self.size)        draw = ImageDraw.Draw(self.img)        draw.polygon([(0, 0), (0, 255), (255, 255), (255, 0)], fill=(255, 255, 255, 255))        for triangle in self.triangles:            self.img = Image.alpha_composite(self.img, triangle.img_t or triangle.draw_it(self.size))            # 与下方代码功能相同，此版本便于理解但效率低        # pixels = [self.img.getpixel((x, y)) for x in range(0, self.size[0], 2) for y in range(0, self.size[1], 2)]        # for i in range(0, min(len(pixels), len(self.target_pixels))):        #     delta_red   = pixels[i][0] - self.target_pixels[i][0]        #     delta_green = pixels[i][1] - self.target_pixels[i][1]        #     delta_blue  = pixels[i][2] - self.target_pixels[i][2]        #     self.match_rate += delta_red   * delta_red   + \        #                        delta_green * delta_green + \        #                        delta_blue  * delta_blue        arrs = [np.array(x) for x in list(self.img.split())]    # 分解为RGBA四通道        for i in range(3):                                      # 对RGB通道三个矩阵分别与目标图片相应通道作差取平方加和评估相似度            self.match_rate += np.sum(np.square(arrs[i]-self.target_pixels[i]))[0]     def draw_it(self, i):        #self.img.save(os.path.join(PATH, "%s_%d_%d_%d.png" % (PREFIX, len(self.triangles), i, self.match_rate)))        self.img.save(os.path.join(PATH, "%d.png" % (i)))  def main():        global LOOP, PREFIX, PATH, TARGET, TRIANGLE_NUM        # 声明全局变量        img = Image.open(TARGET).resize((256, 256)).convert('RGBA')        size = (256, 256)        Canvas.target_pixels = [np.array(x) for x in list(img.split())]        # 生成一系列的图片作为父本，选择其中最好的一个进行遗传        parentList = []        for i in range(20):            print('正在生成第%d个初代个体' % (i))            parentList.append(Canvas())            parentList[i].add_triangles(TRIANGLE_NUM)            parentList[i].calc_match_rate()        parent = sorted(parentList, key=lambda x: x.match_rate)[0]        del parentList        gc.collect()        # 进入遗传算法的循环        i = 0        while i < 30000:            childList = []            # 每一代从父代中变异出10个个体            for j in range(10):                childList.append(Canvas())                childList[j].mutate_from_parent(parent)                childList[j].calc_match_rate()            child = sorted(childList, key=lambda x: x.match_rate)[0]            # 选择其中适应度最好的一个个体            del childList            gc.collect()            parent.calc_match_rate()            if i % LOOP == 0:                print ('d parent rate d \t child1 rate d' % (i, parent.match_rate, child.match_rate))            parent = parent if parent.match_rate < child.match_rate else child            # 如果子代比父代更适应环境，那么子代成为新的父代            # 否则保持原样            child = None            if i % LOOP == 0:                # 每隔LOOP代保存一次图片                parent.draw_it(i)                #print(parent.match_rate)                #print ('d parent rate d \t child1 rate d' % (i, parent.match_rate, child.match_rate))            i += 1  '''定义全局变量，获取待处理的图片名'''NAME = input('请输入原图片文件名：')LOOP = 100PREFIX = NAME.split('/')[-1].split('.')[0]  # 取文件名PATH = os.path.abspath('.')  # 取当前路径PATH = os.path.join(PATH,'results')TARGET = NAME  # 源图片文件名TRIANGLE_NUM = 256  # 三角形个数 if __name__ == '__main__':    #print('开始进行遗传算法')    main()

（三）运行结果

（四）结果描述

代码是在遗传算法求解三角形火狐拼图改进而来，遗传算法求解正方形拼图游戏只需随机生成一个坐标和一个常数值（作为正方形的边长），通过正方形的性质，可以写出正方形其他三个点的坐标，确定了四个点的坐标之后，进行遗传和变异。

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