基于Python实现遗传算法拟合数字图像（源码+图片）.rar

''' 遗传算法拟合数字图像 ''' from PIL import Image import numpy as np ##计算适应度=-方差 def CalcFitness(genes, target): s = np.square(genes.astype(np.int32) - target.astype(np.int32)).sum(axis=-1).sum(axis=-1) #print('适应度', -s) index = np.argsort(s) # 适应度排序的序号数组 #print(index) return -s, index # 选择 # 保留适应度最高的fiti[0] def selection(fiti): ''' 联赛选择 :param fiti: 适应度排序的序号数组 :return: 选中的序号 ''' def s1(n, m=2): ''' 从n个选择一个，m选择压，返回选择的序号 ''' return np.random.randint(0, n, m).min() s = np.size(fiti) r = np.empty_like(fiti) for i in range(s): ri = fiti[s1(s)] r[i] = ri #print('选择', r) return r def AcrChrom(PG, sel): ''' 交叉 :param PG: :param sel: :return: ''' def acr(c1, c2, v1, v2): # 交叉 # 0.9概率 # 返回深复制 (x, y) = c1.shape ra=np.random.rand() if ra < 0.3: x1 = np.random.randint(0, x) x2 = np.random.randint(0, x) if x1 > x2: x1, x2 = x2, x1 v1[:x1, :] = c2[:x1, :] v1[x1:x2, :] = c1[x1:x2, :] v1[x2:, :] = c2[x2:, :] v2[:x1, :] = c1[:x1, :] v2[x1:x2, :] = c2[x1:x2, :] v2[x2:, :] = c1[x2:, :] elif 0.3<ra<0.6: x1 = np.random.randint(0, y) x2 = np.random.randint(0, y) if x1 > x2: x1, x2 = x2, x1 v1[:, :x1] = c2[:, :x1] v1[:, x1:x2] = c1[:, x1:x2] v1[:, x2:] = c2[:, x2:] v2[:, :x1] = c1[:, :x1] v2[:, x1:x2] = c2[:, x1:x2] v2[:, x2:] = c1[:, x2:] elif 0.6<ra<0.9: x1 = np.random.randint(0, x) x2 = np.random.randint(0, x) if x1 > x2: x1, x2 = x2, x1 y1 = np.random.randint(0, y) y2 = np.random.randint(0, y) v1 = c1 v2 = c2 v1[x1:x2,y1:y2]=c2[x1:x2,y1:y2] v2[x1:x2, y1:y2] = c1[x1:x2, y1:y2] else: v1 = c1 v2 = c2 s = np.size(sel) PG2 = np.empty_like(PG) i = 2 while i < s: acr(PG[sel[i]], PG[sel[i+1]], PG2[i], PG2[i + 1]) i += 2 return PG2 def mutation(PG,p):#p为变异率 n, a, b = PG.shape n=n-2 m=n*a*b mu = m*p sigma = mu*(1-p) ra= sigma * np.random.randn() + mu ra=int(ra) # print(ra) if ra<=0 : ra=1 elif ra>=m: ra=m for i in range(ra): PG[np.random.randint(2, n+2)][np.random.randint(0, a)][np.random.randint(0, b)] = np.random.randint(0, 256) return ra #### G = 0 FES = 0 N = 100 # 每代个体数 偶数 Gmax = 10000 # 最大迭代次数 rate=0.05 #初始变异率 ##读取图像 im = np.array(Image.open('0_0.bmp')) # 随机初始化 (a, b) = im.shape shape3d = (N, a, b) PG = np.random.randint(0, 256, shape3d, dtype=im.dtype) rate0=rate fitn = [] for i in range(Gmax+1): fitG, fiti = CalcFitness(PG, im) bestfit = PG[fiti[0]].copy() u = (np.mean(fitG), fitG[fiti[0]]) # fitn.append(u) sel = selection(fiti) PG = AcrChrom(PG, sel) ra= mutation(PG,rate) # rate= (1 - (1.0 * i / Gmax)) * rate0 # rate = (1 - (1.0 * i / Gmax))**2 * rate0 rate = (1 - (10.0 / Gmax)) * rate PG[0]=bestfit PG[1] = bestfit if i % 50 == 0 : print('代数 ', i, '变异数', ra, '适应度(平均，最高)', u) img=Image.fromarray(bestfit) img.save('./png/0_{}.png'.format(i)) print('结束') #print('适应度变化', fitn)