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這篇文章給大家分享的是有關Python怎么實現遺傳算法的內容。小編覺得挺實用的，因此分享給大家做個參考，一起跟隨小編過來看看吧。
遺傳算法求解正方形拼圖游戲
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from PIL import Image, ImageDraw
import os
import gc
import random as r
import minpy.numpy as np
class Color(object):
'''
定義顏色的類，這個類包含r,g,b,a表示顏色屬性
'''
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):
'''
生成隨機數，判斷是否需要變異
'''
return True if rate > r.random() else False
class Triangle(object):
'''
定義三角形的類
屬性：
ax,ay,bx,by,cx,cy：表示每個三角形三個頂點的坐標
color : 表示三角形的顏色
img_t : 三角形繪制成的圖，用于合成圖片
方法：
mutate_from(self, parent): 從父代三角形變異
draw_it(self, size=(256, 256)): 繪制三角形
'''
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):
'''
定義每一張圖片的類
屬性：
mutate_rate : 變異概率
size : 圖片大小
target_pixels: 目標圖片像素值
方法：
add_triangles(self, num=1) : 在圖片類中生成num個三角形
mutate_from_parent(self, parent): 從父代圖片對象進行變異
calc_match_rate(self) : 計算環境適應度
draw_it(self, i) : 保存圖片
'''
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))
# 與下方代碼功能相同，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())] # 分解為RGBA四通道
for i in range(3): # 對RGB通道三個矩陣分別與目標圖片相應通道作差取平方加和評估相似度
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
# 聲明全局變量
img = Image.open(TARGET).resize((256, 256)).convert('RGBA')
size = (256, 256)
Canvas.target_pixels = [np.array(x) for x in list(img.split())]
# 生成一系列的圖片作為父本，選擇其中最好的一個進行遺傳
parentList = []
for i in range(20):
print('正在生成第%d個初代個體' % (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()
# 進入遺傳算法的循環
i = 0
while i < 30000:
childList = []
# 每一代從父代中變異出10個個體
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]
# 選擇其中適應度最好的一個個體
del childList
gc.collect()
parent.calc_match_rate()
if i % LOOP == 0:
print ('%10d parent rate %11d \t child1 rate %11d' % (i, parent.match_rate, child.match_rate))
parent = parent if parent.match_rate < child.match_rate else child
# 如果子代比父代更適應環境，那么子代成為新的父代
# 否則保持原樣
child = None
if i % LOOP == 0:
# 每隔LOOP代保存一次圖片
parent.draw_it(i)
#print(parent.match_rate)
#print ('%10d parent rate %11d \t child1 rate %11d' % (i, parent.match_rate, child.match_rate))
i += 1
'''
定義全局變量，獲取待處理的圖片名
'''
NAME = input('請輸入原圖片文件名：')
LOOP = 100
PREFIX = NAME.split('/')[-1].split('.')[0] # 取文件名
PATH = os.path.abspath('.') # 取當前路徑
PATH = os.path.join(PATH,'results')
TARGET = NAME # 源圖片文件名
TRIANGLE_NUM = 256 # 三角形個數
if __name__ == '__main__':
#print('開始進行遺傳算法')
main()
代碼是在遺傳算法求解三角形火狐拼圖改進而來，遺傳算法求解正方形拼圖游戲只需隨機生成一個坐標和一個常數值（作為正方形的邊長），通過正方形的性質，可以寫出正方形其他三個點的坐標，確定了四個點的坐標之后，進行遺傳和變異。
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